**Separation, Extraction and Purification of Natural Products from Plants**

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**Xingchu Gong**

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## **Contents**

Reprinted from: *Separations* **2021**, *8*, 185, doi:10.3390/separations8100185 .............. **139**

#### **Yanni Tai, Jingjing Pan, Haibin Qu and Xingchu Gong**

## *Article* **Optimization of Steam Distillation Process for Volatile Oils from** *Forsythia suspensa* **and** *Lonicera japonica* **According to the Concept of Quality by Design**

**Xinying Chen 1,2, Dongyun Guo 1, Xingchu Gong 2,3,\*, Na Wan 1,\* and Zhenfeng Wu 1,4**

**Abstract:** In this study, the process of steam distillation to collect volatile oils from *Forsythia suspensa* (*F. suspensa*) and *Lonicera japonica* (*L. japonica*) was optimized according to the concept of quality by design. First, the liquid/material ratio, distillation time, and collection temperature were identified as critical process parameters by a review of the literature and single-factor experiments. Then, a Box–Behnken design was used to study the quantitative relationship between the three process parameters, two raw material properties, and the yield of volatile oil. A mathematical model was established with an R2 value exceeding 0.90. Furthermore, the design space of the volatile oil yield was calculated by a probability-based method. The results of a verification experiment showed that the model was accurate and the design space was reliable. A total of 16 chemical constituents were identified in the volatile oil from mixtures of *F. suspensa* and *L. japonica*. The content of β-pinene was the highest (54.75%), and the composition was similar to that of the volatile oil of *F. suspensa*. The results showed that when *F. suspensa* and *L. japonica* were distilled together, the main contribution to the volatile oil was from *F. suspensa*. The volatile oil yield from the combination of *F. suspensa* and *L. japonica* was not higher than that from *L. japonica*.

**Keywords:** *Forsythia suspensa*; *Lonicera japonica*; volatile oil; steam distillation; design space

#### **1. Introduction**

*Forsythia suspensa* is the dry fruit of *Forsythia suspensa (Thunb.) Vahl*, and *Lonicera japonica* consists of the dry buds or flowers of *Lonicera japonica Thunb.* that bloom early in the spring [1]. Both have the medicinal functions of reducing fevers, detoxification, and dispersing wind-heat and are often used together as a pair in Chinese medicine [2–4]. The 2020 edition of the *Chinese Pharmacopoeia* lists 45 Chinese patent medicines that use *F. suspensa* and *L. japonica* in combination [1]. The volatile oils of *F. suspensa* and *L. japonica* are collected and used in the manufacture of four Chinese patent medicines, including Xiao'er Resuqing Koufuye, Xiao'er Resuqing Keli, Xiao'er Resuqing Tangjiang, and Jinchan Zhiyang Jiaonang [1]. At present, several studies have shown that the volatile oil of *F. suspensa* has antibacterial, antioxidant, antiviral, antipyretic, anti-inflammatory, antitumor, and other pharmacological effects [3,5,6]. The volatile oil of *L. japonica* also has pharmacological effects, such as heat clearing, detoxification, and antibacterial activity [2,4,7].

Steam distillation is a commonly used method to collect volatile oils [8–13], and it has the advantages of simple equipment, easy operation, low cost, and the use of safe solvents [14–16]. However, it is also a time-consuming process with a low volatile oil

**Citation:** Chen, X.; Guo, D.; Gong, X.; Wan, N.; Wu, Z. Optimization of Steam Distillation Process for Volatile Oils from *Forsythia suspensa* and *Lonicera japonica* According to the Concept of Quality by Design. *Separations* **2023**, *10*, 25. https:// doi.org/10.3390/separations10010025

Academic Editors: Daniele Naviglio and Carlo Bicchi

Received: 25 September 2022 Revised: 7 November 2022 Accepted: 9 November 2022 Published: 1 January 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

yield. The steam distillation process is used in the production of the abovementioned four Chinese patent medicines from the volatile oils collected from *F. suspensa* and *L. japonica* [1].

References from the literature concerning the distillation of the volatile oils of *F. suspensa* or *L. japonica* by steam distillation are listed in Table 1. Liu Yan et al. [17] and Wang Yan et al. [18] studied the three parameters of soaking time, distillation time, and material/liquid ratio and found that the influence of each parameter on the volatile oil yield was: material/liquid ratio > distillation time > soaking time. The volatile oil yields from *F. suspensa* and *L. japonica* were approximately 0.93 mL/100 g and 0.34 mL/100 g, respectively. Gu Ke et al. [19] and Li Jianjun et al. [20] studied the collection of volatile oil from *L. japonica* by steam distillation, and the volatile oil yields were about 0.15 g/100 g and 0.17 g/100 g. However, Tong Qiaozhen et al. [21] failed to distillate volatile oil from *L. japonica* by steam distillation. In these published works, the authors suggested that the volatile oil of *L. japonica* has a relatively high solubility in water, which may lead to the loss of *L. japonica* volatile oil in the steam distillation process. When co-distilled with *F. suspensa*, the volatile oil of *F. suspensa* may extract some *L. japonica* volatile oil from the aqueous phase, resulting in the collection of more *L. japonica* volatile oil. However, at present, there are no references in the literature addressing the steam distillation of volatile oil from mixtures of *F. suspensa* and *L. japonica*. Therefore, the authors tried to obtain the volatile oil by the co-distillation of *F. suspensa* and *L. japonica*.

**Table 1.** Literature on steam distillation.

In recent years, the concept of quality by design [22–25] has been used in the optimization of many processes related to Chinese medicines, such as distillation [26], precipitation [27], and column chromatography [28]. In this study, based on the concept of quality by design, the steam distillation process was optimized to distill the volatile oils of *F. suspensa* and *L. japonica*. One of the aims of this work was to find the optimized parameters of steam distillation for collecting a relatively stable amount of volatile oil, which would improve the batch-to-batch consistency of Chinese medicine quality. The critical process parameters were determined through a search of the literature and single-factor experiments. The critical raw material properties of *F. suspensa* were also determined. The quantitative relationship between the process parameters, raw material properties, and volatile oil yield was studied by a Box–Behnken design [29], and a mathematical model was established. The design space of the distillation process of volatile oil from *F. suspensa* and *L. japonica* was calculated by a probability-based method. The operation points inside and outside the design space were selected for verification. Finally, the chemical composition of the volatile oil of *F. suspensa* and the volatile oil of mixtures of *F. suspensa* and *L. japonica* were analyzed and compared.

#### **2. Materials and Methods**

#### *2.1. Materials and Reagents*

Methanol (chromatographically pure) was obtained from Merck, Germany. Acetonitrile (chromatographically pure) was also obtained from Merck, Germany. Ultrapure water was prepared by an ultrapure water preparation system (Milli-Q, Millipore, Germany). β-Pinene (batch number: C12071236, purity ≥ 98%) was purchased from Shanghai McLean Biochemical Technology Co., Ltd. The medicinal material numbers, origins, and suppliers of *F. suspensa* and *L. japonica* are shown in Table 2.

**Table 2.** Origin and batch information of medicinal materials.

#### *2.2. Steam Distillation*

The volatile oil in *F. suspensa* and *L. japonica* was collected by steam distillation. The experimental setup is shown in Figure 1. A low-temperature thermostated bath (THYD-1030 W, Ningbo Tianheng Instrument Factory) was used to lower the temperature of the collection part of the volatile oil extractor [30]. An electronic balance (CN-LQC60002, Kunshan Youkeweiter Electronic Technology Co., Ltd.) was used to weigh 50 g of *F. suspensa* and 50 g of *L. japonica* (Medicinal Material Number: JYH-2). The samples were placed in a 2000 mL flask. A certain amount of water was added, and the flask was shaken to fully wet the medicinal materials. The volatile oil extractor and the condenser pipe were connected. The condensed glycerol and condensed water were fed into the water-cooling jacket and the condenser pipe of the volatile oil extractor, respectively. The volatile oil extractor was filled with water from the top of the condenser until it overflowed into the flask. The electric heater (DZTW 2000 mL, Shaoxing Yuecheng Kechen Instrument and Equipment Co., Ltd.) was turned on, and it started slowly heating the water to boiling. After boiling began, the power of the electric heater was adjusted to 50 W. Starting with the first drop of condensed water dripping into the volatile oil extractor, heating was stopped after heating and refluxing for a certain period of time. After the volatile oil stood for 10 min, its volume was recorded. The volatile oil yield was calculated as shown in Formula (1).

Volatile oil yield (mL/100 g) = volatile oil volume (mL)/medicinal material (100 g) (1)

#### *2.3. Determination of the Volatile Oil Content in F. suspensa*

The thermostat was used to lower the temperature of the collection part of the volatile oil extractor [30]. An electronic balance was used to weigh 100 g of *F. suspensa*, which was then placed in a 2000 mL flask. A certain amount of water was added, and the flask was shaken to fully wet the medicinal materials. The volatile oil extractor and the condenser pipe were connected. The condensed glycerol and condensed water were fed into the water-cooling jacket and the condenser pipe of the volatile oil extractor, respectively. The volatile oil extractor was filled with water from the top of the condenser until it overflowed into the flask. The electric heater was turned on, and it slowly heated the water to boiling. After boiling, the power of the electric heater was adjusted to 50 W. Starting with the first drop of condensed water dripping into the volatile oil extractor, heating was stopped after heating and refluxing for a certain period of time. After the volatile oil stood for 10 min, its

volume was recorded. The volatile oil content (mL/100 g) in different batches of *F. suspensa* was calculated as shown in Formula (1).

**Figure 1.** Experimental device for steam distillation.

#### *2.4. Determination of β-Pinene Content in F. suspensa*

#### 2.4.1. Preparation of Reference Solution and Test Solution

Because the reference substance of β-pinene was a liquid, we precisely measured 83.40 mg of the β-pinene reference substance with a pipette. After that, it was diluted to 10 mL with methanol to obtain the storage reference solution with a concentration of 8340 μg/mL. Then, the storage reference solution was diluted 50, 100, 200, 400, 500, and 800 times with methanol, respectively, to prepare reference solutions with concentrations of 166.8, 83.40, 41.70, 20.85, 16.68, and 10.42 μg/mL.

One hundred microliters of the distilled *F. suspensa* volatile oil was precisely measured, placed in a 10 mL volumetric flask, diluted to volume with methanol, and shaken well to obtain the concentrated stock solution of the test product. A total of 100 μL of the concentrated stock solution of the test product was precisely measured, placed in a 5 mL volumetric flask, diluted to volume with methanol, and shaken well to obtain the test solution.

#### 2.4.2. Chromatographic Conditions

The β-Pinene content in the volatile oil was determined using liquid chromatography (Agilent 1100-DAD, Agilent, USA). The HPLC conditions were modified from the method described in the literature [31]. The details were as follows: Column—Agilent ZORBAX SB-C18, 4.6×250 mm (5-Micron). Mobile phase—0.4% phosphoric acid solution (A)/acetonitrile (B), gradient elution (0 min, 65% B; 0~5 min, 65~70% B; 5~25 min, 70% B; 25~30 min, 70~80% B). Post-run—15 min; injection volume—10 μL; flow rate—1 mL/min; column temperature—25 ◦C; detection wavelength—202 nm. The chromatograms of the reference solution and test solution are shown in Figure 2.

(**b**) Test solution

#### 2.4.3. Methodological Validation

Linear relationship investigation: The β-pinene reference substance was used to investigate the linear relationship. Reference solutions with concentrations of 166.8, 83.40, 41.70, 20.85, 16.68, and 10.42 μg/mL were prepared according to the method described in Section 2.4.1. A 0.22 μm microporous filter membrane was used for filtration, injection, and analysis according to the chromatographic conditions described in Section 2.4.2. Taking the concentration of the reference solution as the abscissa (X, μg/mL) and the peak area as the ordinate (Y, mAU), the linear regression equation and correlation coefficient were obtained.

Precision test: The volatile oil of *F. suspensa* was used to prepare the concentrated stock solution of the test product according to the method outlined in Section 2.4.1. One milliliter of the concentrated stock solution of the test product was accurately absorbed, placed in a 10 mL volumetric flask, diluted to volume with methanol, and shaken well to obtain the test solution. The test solution was filtered with a 0.22 μm microporous membrane. The samples were injected into the column 6 times continuously according to the chromatographic conditions provided in Section 2.4.2, and the RSD value of the peak area was calculated.

Repeatability test: Six replicates of the same batch of *F. suspensa* volatile oil were obtained. The concentrated stock solution of the test product was prepared according to the method in Section 2.4.1. One milliliter of the concentrated stock solution of the test product was accurately absorbed, placed in a 10 mL volumetric flask, diluted to volume with methanol, and shaken well to obtain the test solution. The test solution was filtered with a 0.22 μm microporous membrane. The samples were injected and analyzed according to the chromatographic conditions outlined in Section 2.4.2, and the RSD value of the concentration was calculated.

Stability test: The volatile oil of *F. suspensa* was used to prepare a concentrated stock solution of the test product according to the method described in Section 2.4.1. One milliliter of the concentrated stock solution of the test product was accurately measured, placed in a 10 mL volumetric flask, diluted to volume with methanol, and shaken well to obtain the test solution. The test solution was filtered with a 0.22 μm microporous membrane. The samples were injected and analyzed at 0, 3, 6, 9, 12, and 24 h according to the chromatographic conditions described in Section 2.4.2, and the relative standard deviation (RSD) value of the peak area was calculated.

Sample addition and recovery test: We took 9 aliquots of *F. suspensa* volatile oil from the same batch with a known content of β-pinene and prepared a concentrated stock solution of the test sample according to the method described in Section 2.4.1. One milliliter of the concentrated stock solution of the test sample was accurately absorbed, placed in a 20 mL volumetric flask, diluted to volume with methanol, and shaken well to obtain the test solution (33.2 μg/mL). The reference solution (41.7 μg/mL) was prepared according to the method outlined in Section 2.4.1. The test solution was divided into three groups: low, medium, and high. We precisely measured 1.25 mL of each group and placed it in a 5 mL volumetric flask. The ratios of the added amount of the reference substance to the content of β-pinene in the test solution were controlled at approximately 0.5:1.0, 1.0:1.0, and 1.5:1.0, and 0.5, 1.0, and 1.5 mL of the reference substance was added to the low, medium, and high groups, respectively. Each mixture was diluted to volume with methanol, shaken well, and filtered with a 0.22 μm microporous membrane. The samples were injected and analyzed according to the chromatographic conditions provided in Section 2.4.2, and the β-pinene content and sample recovery rate of each group were calculated.

#### 2.4.4. Determination of Content

An appropriate amount of volatile oil was taken from different batches of *F. suspensa*. The test solutions were prepared according to the method outlined in Section 2.4.1. Then, the test solutions were injected and analyzed according to the chromatographic conditions described in Section 2.4.2. The peak areas were recorded, and the β-pinene contents in different batches of *F. suspensa* were calculated.

#### *2.5. Optimization of Distillation Process Parameters*

Based on the literature search and single-factor experiments, it was believed that the critical process parameters affecting the volatile oil yield from *F. suspensa* and *L. japonica* by steam distillation were the amount of water, distillation time, and collection temperature, and the critical properties of the raw materials were the volatile oil content and the β-pinene content of *F. suspensa*.

A Box–Behnken design was adopted, and the water addition (X1), distillation time (X2), collection temperature (X3), volatile oil content (Z1), and β-pinene content (Z2) were used as the investigation factors. The volatile oil yield (Y) was used as the evaluation index to optimize the distillation process of volatile oil from *F. suspensa* and *L. japonica*. The factors and levels of the Box–Behnken design are shown in Table 3, and the results are shown in Table 4.

#### *2.6. Analysis of Chemical Constituents of Volatile Oil*

A combined total of 20 μL of volatile oil from *F. suspensa* and volatile oil from *F. suspensa* and *L. japonica* was accurately measured; then, the samples were supplemented with 1 mL of diethyl ether for dilution, filtered with a 0.22 μm microporous membrane, and placed into sample bottles. The chemical constituents in the volatile oil of *F. suspensa* and the volatile oil of *F. suspensa* and *L. japonica* were analyzed.

**Table 3.** The factors and levels of the Box–Behnken design.

**Table 4.** The results of the Box–Behnken design.

The chemical constituents of the volatile oils were analyzed with a gas chromatography– mass spectrometer [32,33] (GC–MS; Agilent 7890B-7000C, Agilent, USA). The GC–MS conditions were modified from the methods describe in the literature [34,35]. The conditions were as follows: chromatographic column—Agilent HP-5MS, 30 m × 0.25 mm × 0.25 μm; inlet temperature—250 ◦C; flow—1 mL/min constant flow (He); split ratio—10:1; heating program—40 ◦C for 4 min, 2 ◦C/min to 100 ◦C, hold for 10 min, 10 ◦C/min to 200 ◦C, and hold for 5 min; MS detector—detector temperature 250 ◦C and scan range *m*/*z* 30–550.

#### *2.7. Data Processing*

#### 2.7.1. Mathematical Model

The results of the Box–Behnken design were analyzed using Design-Expert 12.0.3 software (American Stat-Ease Company). Taking the volatile oil yield (Y) as the evaluation index, quadratic polynomial fitting was performed on the five factors of water addition (X1), distillation time (X2), collection temperature (X3), volatile oil content (Z1) and β-pinene content (Z2). The mathematical model is shown as Formula (2).

$$Y = a\_0 + \sum\_{i=1}^{n} b\_i X\_i + \sum\_{i=1}^{n} b\_{ii} X\_i^2 + \sum\_{i=1}^{n-1} \sum\_{j=i+1}^{n} b\_{ij} X\_i X\_j + \sum\_{k=1}^{m} d\_k Z\_k \tag{2}$$

where X is a process parameter; Z is a raw material property; Y is the evaluation index; superscripts n and m are the number of process parameters and raw material properties, respectively; a0 is the intercept; and b and d are the partial regression coefficients. The model was simplified using a stepwise regression method with *p* values of 0.1 for both inclusion and removal from the model.

#### 2.7.2. Design Space

The construction design space was calculated by the probability-based method using MATLAB R2018b (American Math Works Company). Under the conditions of fixed batches of medicinal materials, the effects of the changes in the three process parameters on the volatile oil yield were randomly simulated. The parameter combination with a lower limit of volatile oil yield of 0.60 mL and a probability of reaching the standard of no less than 0.80 was used as the design space. In the calculation, the steps of water addition, distillation time, and collection temperature were set to 0.04, 0.03, and 0.10, respectively. The number of simulations was 1000.

#### **3. Results**

#### *3.1. Single-Factor Experiments*

The results of the single-factor experiments are shown in Table 5. The results indicated that the effects of crushed particle size and soaking time on the volatile oil yield were not significant. The volatile oil yield decreased with increasing water addition, which may have been due to the increase in water improving the amount of volatile oil dissolved in water, which resulted in a decrease in the volatile oil yield.

**Table 5.** Single-factor experiments.

Table 6 shows the results of separately distilling volatile oils from *F. suspensa* and *L. japonica*. The results showed that no volatile oil was distilled from the two different batches of *L. japonica*, which may have been because the *L. japonica* used in this study contained little volatile oil. *F. suspensa* could be distilled to obtain volatile oil. Therefore, in a follow-up study, we will investigate whether the distillation of volatile oil from *L. japonica* can be promoted by the combined steam distillation of *F. suspensa* and *L. japonica*.

**Table 6.** Separate distillation of volatile oils from *F. suspensa* or *L. japonica*.

The results of the co-distillation of volatile oil from *F. suspensa* and *L. japonica* are shown in Table 7. The effects of water addition and collection temperature on the volatile oil yield were investigated. The results showed that both had a certain influence on the volatile oil yield.

#### *3.2. Methodological Validation*

Taking the concentration of the β-pinene reference solution as the abscissa (X, μg/mL) and the corresponding peak area as the ordinate (Y, mAU), linear regression was performed to obtain the linear regression equation: Y = 210.56X + 282.59. The linear range of β-pinene was 10.425–166.8 μg/mL, and the coefficient of determination R2 in this range was 0.9997, which confirmed a good linear relationship. The results of the precision test showed that the RSD of the peak area of β-pinene was 0.68%, indicating that the precision of the instrument was good. The results of the repeatability test showed that the RSD of the βpinene concentration was 0.84%, which proved that the method had good repeatability. The results of the stability test showed that the peak-area RSD of β-pinene was 1.84%, indicating that the test solution had good stability within 24 h. The results of the sample addition recovery test are shown in Table S1. The measured recovery rates were between 95 and 102%, the average recovery rate was 98.71%, and the RSD of the three levels was 1.89%, which met the measurement requirements, indicating that the method has good accuracy.

#### *3.3. Characterization of Raw Material Properties of Different Batches of F. suspensa*

The process parameters of the steam distillation method were fixed as follows: 100 g raw material, 1000 mL water addition, 5 ◦C collection temperature, and 6 h distillation time. The contents of volatile oil and β-pinene in different batches of *F. suspensa* were measured as shown in Table 8. The results showed that the raw material properties of different batches of *F. suspensa* were quite different. For example, the volatile oil content of LQ-4 was 1.394 mL/100 g, while the volatile oil content of LQ-5 was 1.969 mL/100 g. The β-pinene content of LQ-6 was 0.746 mg/100 g, while the β-pinene content of LQ-5 reached 1.139 mg/100 g.

#### *3.4. Optimization of Distillation Parameters of Volatile Oil*

#### 3.4.1. Data Processing and Model Fitting

The polynomial regression model obtained by modeling the data acquired from the experimental distillation of volatile oil from *F. suspensa* and *L. japonica* is shown in Formula (3).

$$\text{Y} = 0.2294 + 0.04977 \times 2 - 0.5199 \text{Z}\_1 + 1.325 \text{Z}\_2 - 0.000318 \times \text{X}\_1 \text{X}\_3 \tag{3}$$

The coefficient of determination of the model R<sup>2</sup> was 0.9022, indicating that the model fit well and could explain the changes in the experimental data. The variance analysis of each item in the model is shown in Table 9. The *p* value of the model was less than 0.0001, indicating that the model was extremely significant. In the model, X2 (*p* = 0.0067), Z1 (*p* = 0.0014), and Z2 (*p* = 0.0001) were all extremely significant items, indicating the influence of distillation time and raw material properties (volatile oil content, β-pinene content) on the volatile oil yield. X1X3 (*p* = 0.0139) was also a significant item, which indicated that the interaction of the two factors of water addition and collection temperature was significant for volatile oil yield.

**Table 9.** Regression coefficient and variance analysis of the model.

\* *p* < 0.05, \*\* *p* < 0.01.

#### 3.4.2. Contour Diagram

The contour maps of the volatile oil yield from *F. suspensa* and *L. japonica* are shown in Figures 3–5. The figures reflect the effect of water addition, distillation time, and collection temperature on the volatile oil yield under the conditions of fixed raw material properties. Under the conditions of a constant collection temperature with increasing distillation time, the volatile oil yield increased gradually. Under the conditions of a constant distillation time with increasing water addition and a decreasing collection temperature, or decreasing water addition and an increasing collection temperature, the volatile oil yield increased. Under the conditions of unchanging water addition with increasing distillation time, the volatile oil yield increased gradually.

#### 3.4.3. Design Space Calculation and Verification

The properties of the raw materials were fixed as LQ-6, and the design space calculated by the probability-based method is shown in Figures 6 and 7. The design space diagram shows that the distillation time had the greatest influence on the volatile oil yield.

A point inside the design space and a point outside the design space were selected for verification. The selection conditions of the verification points in the design space were as follows: the water addition was 10 mL/g, the distillation time was 6 h, and the collection temperature was 10 ◦C. The probability of reaching the standard was 0.976. Under these conditions, the volatile oil yield predicted by the model was 0.71 mL. According to the above conditions, three parallel experiments were carried out. The obtained volatile oil yields were 0.76 mL, 0.71 mL, and 0.74 mL, respectively. The average volatile oil yield was 0.74 mL, and the relative standard deviation was 2.52%.

The selection conditions of the verification point outside the design space were as follows: the water addition was 10 mL/g, the distillation time was 3 h, and the collection temperature was 10 ◦C. The probability of reaching the standard was 0.333. Under these conditions, the volatile oil yield predicted by the model was 0.56 mL, and the experimental results were 0.60 mL, 0.60 mL, and 0.58 mL, respectively. The average volatile oil yield was 0.59 mL, and the relative standard deviation was 1.15%. The measured values of the two verification points were relatively close to the model-predicted values, indicating that the mathematical model established according to the Box–Behnken design was accurate. The volatile oil yield at the verification point in the design space was higher than the preset standard, and the volatile oil yield at the verification point outside the design space was lower than the preset standard, indicating that the design space was reliable.

**Figure 3.** Volatile oil yield at a fixed collection temperature of 10 ◦C.

**Figure 4.** Volatile oil yield when the fixed distillation time was 4.5 h.

**Figure 5.** Volatile oil yield when the fixed water addition was 10 mL/g.

**Figure 6.** Three-dimensional design space diagram.

**Figure 7.** Two-dimensional design space diagram (water addition was fixed at 10 mL/g; " " is the verification point outside the design space; " " is the verification point inside the design space; and the color bar on the right is the probability of meeting the standard).

#### *3.5. Qualitative Analysis of Chemical Constituents of Volatile Oil*

The distilled volatile oil of *F. suspensa* and the volatile oil of mixtures of *F. suspensa* and *L. japonica* were analyzed by GC–MS. The total ion chromatogram is shown in Figure 8, and the mass spectrometry results are shown in Tables 10 and 11. Interactions between multiple components can also have an impact on pharmacological activity [36,37]. A total of 16 chemical components were identified in the volatile oil of *F. suspensa*, among which the relative content of β-pinene was the largest (53.71%). Some other components with relatively large contents were α-pinene (21.12%), terpinene-4-ol (5.97%), (+)-limonene (4.36%), and α-thujene (4.25%). A total of 16 chemical components were identified in the volatile oil of *F. suspensa* and *L. japonica*, among which the relative content of β-pinene was also the largest (54.75%). Some other components with relatively large contents were α-pinene (21.38%), terpinene-4-ol (4.98%), (+)-limonene (4.48%), and α-thujene (4.41%).

Tables 10 and 11 show that the chemical composition of the volatile oil of *F. suspensa* was basically the same as that of the mixtures of *F. suspensa* and *L. japonica*. This meant that when *F. suspensa* and *L. japonica* were distilled together, the main contribution of the volatile oil chemical components came from *F. suspensa,* while *L. japonica* basically did not contribute. The combination of the two medicinal materials did not increase the amount of volatile oil distilled from *L. japonica*.

The authors attempted to distill the volatile oil from *L. japonica* but failed to collect any volatile oil. Li Jianjun et al. [20] studied the collection of volatile oil from *L. japonica* by steam distillation, and a total of 79 chemical components were identified in the volatile oil, among which the contents of palmitic acid (46.42%) and linoleic acid (14.32%) were the highest.

**Figure 8.** Total ion chromatogram of the GC–MS analysis of volatile oils.

#### **Table 10.** *Cont.*

**Table 11.** The volatile oil of mixtures of *F. suspensa* and *L. japonica*.

#### **4. Conclusions**

In this study, the steam distillation process of volatile oil from *F. suspensa* and *L. japonica* was optimized according to the concept of quality by design. First, the liquid/material ratio, distillation time, and collection temperature were identified as critical process parameters by a search of the literature and single-factor experiments. In addition, this study further investigated the effect of different batches of *F. suspensa* on the distillation of volatile oil and determined that the critical raw material properties were the volatile oil content and βpinene content. HPLC was used to evaluate the different batches. The content of β-pinene in *F. suspensa* was measured, and related methodological verification work was performed. Then, a Box–Behnken design was used to study the quantitative relationship between three process parameters, two raw material properties, and the volatile oil yield. A mathematical model was established with R2 exceeding 0.90. Furthermore, the design space of the volatile yield was calculated by a probability-based method, and verification experiments were carried out. The measured values of two verification points were relatively close to the model-predicted values, indicating that the mathematical model established according to the Box–Behnken design was accurate. The volatile oil yield at the verification point in the design space was greater than the preset standard, and the volatile oil yield at the verification point outside the design space was lower than the preset standard, indicating that the design space was reliable. In this study, the chemical constituents of the volatile oil of *F. suspensa* and the volatile oil of *F. suspensa* and *L. japonica* were analyzed by GC–MS. A total of 16 chemical constituents were identified in the volatile oil of *F. suspensa*, among which the content of β-pinene was the largest (53.71%). A total of 16 chemical constituents were identified in the volatile oil of *F. suspensa* and *L. japonica*, among which the content of β-pinene was also the largest (54.75%), and the composition was similar to that of the volatile oil of *F. suspensa*. The results showed that when *F. suspensa* and *L. japonica* were distilled together, the main contributor to the volatile oil was *F. suspensa*. The contribution of *L. japonica* was small. The combination of *F. suspensa* and *L. japonica* did not increase the volatile oil yield from *L. japonica*.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/separations10010025/s1, Table S1: Sample Recovery Test Results.

**Author Contributions:** Conceptualization, X.G., N.W. and Z.W.; investigation, X.C. and D.G.; data curation, X.C. and X.G.; writing—original draft preparation, X.C., X.G. and N.W.; writing—review and editing, X.G., Z.W. and N.W.; supervision, N.W. and X.G.; funding acquisition, X.G., N.W. and Z.W. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the National Natural Science Foundation of China (82060720); the Open Project of the State Key Laboratory of Innovative Medicine and High-Efficiency and Energy-Saving Pharmaceutical Equipment at Jiangxi University of Chinese Medicine (grant number: GZSYS202001); the Provincial College Students' Innovation and Entrepreneurship Program (S202210412062); the Innovation Team and Talent Cultivation Program of the National Administration of Traditional Chinese Medicine (ZYYCXTD-D-202002); and the Jiangxi Traditional Chinese Medicine Research Program (2018A318).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** All data generated or analyzed during this study are included in the published article.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

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## *Article* **Development of a Quantitative Chromatographic Fingerprint Analysis Method for Sugar Components of Xiaochaihu Capsules Based on Quality by Design Concept**

**Jing Lan 1,2, Gelin Wu 3,\*, Linlin Wu 1,2, Haibin Qu 1,2, Ping Gong 3, Yongjian Xie 3, Peng Zhou <sup>3</sup> and Xingchu Gong 1,2,4,\***

**Abstract:** Background: Xiaochaihu capsule is composed of seven traditional Chinese medicines. The pharmacopoeia only focuses on the quantitative detection of baicalin, which cannot fully reflect the quality of the preparation. Some medium polar components were used to establish the fingerprint of Xiaochaihu capsule, but there was no report on the strong polar components. Methods: A high performance liquid chromatography-corona charged aerosol detection technology was used to establish a fingerprint analysis method for Xiaochaihu capsules following an analytical quality by design approach. Definitive screening designed experiments were used to optimize the method parameters. A stepwise regression method was used to build quantitative models. The method operable design region was calculated using the experimental error simulation method. Plackett–Burman designed experiments were carried out to test robustness. Results: The contents of four components were simultaneously determined. There were seven common peaks in the fingerprint. The common peak area accounted for 91.72%. Both fingerprint and quantitative analysis methods were validated as applicable in the methodology study. The quantitative fingerprint analysis method for sugar components can fill the gap in the detection of strong polar components in the existing methods. It provides a new technology for the comprehensive overall evaluation of Xiaochaihu capsule.

**Keywords:** quality by design; Xiaochaihu capsule; quantitative chromatographic fingerprint; design space; definitive screening design; Plackett–Burman

#### **1. Introduction**

In recent years, analytical quality by design (AQbD) has been successfully used in the development of analytical methods for drug discovery [1–7]. AQbD allows analytical methods to be adapted within the method operable design region (MODR). The changes in analytical parameters within MODR do not affect method validity, which meets the method objectives [8–10]. AQbD facilitates the development of robust, effective, and economical analytical methods for the entire product life cycle. This promotes flexibility in the regulatory process of analytical methods [11–13]. AQbD implementation steps include determining the analytical method objective profile, identifying critical method attributes (CMAs) and critical method parameters, establishing mathematical models and MODR, conducting method validation, implementing control strategies, etc. [14–16].

Xiaochaihu capsule is composed of *Bupleurum* root, *Scutellaria* root, *Glycorrhiza* root and rhizome, *Codonopsis* root, jujube fruit, fresh ginger rhizome, and *Pinellia* rhizome prepared with ginger and aluminum. It is prepared by decoction, percolation, concentration, drying,

**Citation:** Lan, J.; Wu, G.; Wu, L.; Qu, H.; Gong, P.; Xie, Y.; Zhou, P.; Gong, X. Development of a Quantitative Chromatographic Fingerprint Analysis Method for Sugar Components of Xiaochaihu Capsules Based on Quality by Design Concept. *Separations* **2023**, *10*, 13. https://doi.org/10.3390/ separations10010013

Academic Editors: Marcello Locatelli and Aleksandra Mišan

Received: 11 November 2022 Revised: 10 December 2022 Accepted: 23 December 2022 Published: 26 December 2022

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

mixing, granulation, and capsule filling. It can be used for the treatment of symptoms of exogenous diseases, such as bitter fullness in the chest, loss of appetite, irritability and vomiting, bitter mouth, and dry throat [17–19]. High performance liquid chromatography (HPLC) is used to determine the content of baicalin in Xiaochaihu capsules for quality control in the 2020 edition of the Chinese Pharmacopoeia Vol. 1. The qualitative identification of *Bupleurum* root, liquorice root, and *Glycyrrhiza* root and rhizome is also used as reference herbs [20]. Xiaochaihu capsules are made from seven medicinal materials; therefore, it is obvious that the quantitative determination of baicalin cannot fully reflect Xiaochaihu capsule quality.

Simultaneous quantitative determination of multi-indicator components is more often used in the 2020 edition of the Chinese Pharmacopoeia Vol. 1. It is a widely accepted and feasible method for testing the quality of traditional Chinese medicine (TCM) [21,22]. Fingerprinting is another effective method for the detection of TCM preparations. Similarity and other indicators are used to reflect the overall spectral or peak information of fingerprints [23]. Recently, fingerprint technology has been greatly developed [24–26]. Zhang Xue et al. established a quantitative fingerprint analysis method of the moderately polar components such as glycyrrhizin, baicalin, and chaihu saponin B1 in Xiaochaihu granules [27]. Liu Aoxue et al. also determined the moderately polar components of saponins in Xiaochaihu granules using quantitative analysis of multi-components by a single marker [28]. However, there is no quantitative fingerprint analysis of the sugar components of Xiaochaihu capsules [29].

Because sugar components from Chinese herbs can be easily extracted in the process of decocting with water, they are often the main components of Chinese patent medicines. The chemical composition of Xiaochaihu capsules can be reflected more comprehensively by the detection of sugar components. Stachyose is a functional oligosaccharide [30]. It is naturally found in the Lamiaceae herbs [31]. It can significantly promote the value of beneficial intestinal flora in humans [32]. *Scutellaria* root belongs to the Lamiaceae. *Scutellaria* root is regarded as the minister drug of Xiaochaihu capsules. This may explain the effects of Xiaochaihu capsules in treating loss of appetite, irritability, and vomiting. Ribitol is a reduction product of D-ribose. It is present in the *Bupleurum* root in its free state. It is a characteristic component of *Bupleurum* root. The detection of ribitol can reflect the presence of *Bupleurum* root. This can be used as a supplement to the existing quality control methods of the Pharmacopoeia.

As strongly polar components, sugar components are difficult to analyze with conventional reversed-phase columns. They usually show weak UV absorption. These reasons result in more difficulties in the analysis of sugar components than that of moderately polar components. Therefore, Amino columns and hydrophilic columns are often used to separate sugar components, such as the Prevail Carbohydrate ES (250 mm × 4.6 mm, 5 μm) of Garce Alltech [33], Asahipak NH2P-50 4E column (4.6 × 250 mm, 5 μm) of Shodex [34], and XBridge BEH Amide XP column (3 × 150 mm, 2.5 μm) of Waters [35].

Recently, several researchers have used charged aerosol detection (CAD) to detect sugar components [36–38]. The detection principle of CAD is as follows. The eluent is formed into particles by atomization. Compared to evaporative light-scattering detector, CAD has higher sensitivity, better reproducibility, and a wider linearity range [39]. Thus, CAD is expected to achieve a better separation with a lower detection limit in the quantitative analysis of sugar components of the Xiaochaihu capsule.

In this work, AQbD was used to establish a fingerprint analysis method for Xiaochaihu capsules. Parameters were determined. Definitive screening design (DSD) was used to investigate the relationships between CMAs and method parameters. A stepwise regression method was used to build quantitative models between CMAs and method parameters. The experimental error simulation method was used to calculate and verify the probabilitybased MODR. After optimizing the analysis conditions, the content determination components were identified, and the quantitative fingerprint was established. Finally, the durability of the analytical method was investigated.

#### **2. Materials and Reagents**

Acetonitrile was purchased from Merck (chromatographic purity, Darmstadt, Germany). Ultrapure water was prepared using a Milli-Q purification system (Millipore, Billerica, MA, USA). Ribitol (Lot No. 210916, HPLC > 99%), fructose (Lot No. 210519, HPLC > 99%), sucrose (Lot No. 210620, HPLC > 99%), stachyose (Lot No. 211026, HPLC > 99%), glucose (Lot No. 210917, HPLC > 99%), maltose (Lot No. 2110522, HPLC > 99%), and raffinose (Lot No. 210528, HPLC > 99%) were purchased from Shanghai Ronghe Pharmaceutical Technology Development Co., Ltd. (Shanghai, China).

There were 12 batches of Xiaochaihu capsule samples. The specific source merchant and lot number information are shown in Table S1.

#### **3. Methods**

#### *3.1. Sample Preparation*

3.1.1. Preparation of the Chemical Reference Solution

The four chemical reference substances were weighed precisely (AB204-N, Mettler Toledo, Zurich, Switzerland) and dissolved in 10 mL 60% acetonitrile solution. Four kinds of single-standard solution were pipetted into the same 50 mL volumetric flask. 60% acetonitrile solution was used for constant volume. The mixed reserve standard solution was diluted 5 times to obtain the mixed standard solution. The mixed standard solution was composed of 0.6512 mg/mL ribitol, 2.189 mg/mL fructose, 1.636 mg/mL sucrose, and 0.6767 mg/mL stachyose.

#### 3.1.2. Preparation of the Sample Solution

The contents of Xiaochaihu capsules were weighed precisely and dissolved in 25 mL 60% acetonitrile solution. After being ultrasonically heated (LMTD15, Lumiere Tech, Beijing, China) and centrifuged (Minispin, Eppendorf, Hamburg, Germany), the supernatant was separated from the solution to obtain the sample solution.

#### *3.2. HPLC Analysis*

All HPLC analyses were performed on a Dionex Ultimate 3000 system (Thermo Fisher, Waltham, MA, USA) equipped with an SRD-3600 degasser, an HPG-3400RS pump, a WPS-3000TRS autosampler, a TCC-3000RScolumn thermostat, a photodiode array detector, and a Corona VeoRS CAD. The evaporation temperature was set at 35 ◦C. Chromatographic separation was carried out on an Asahipak NH2P-50 4E column (4.6 × 250 mm, 5 μm). The column temperature was set at 30 ◦C. The injection volume was set at 10 μL. The mobile phase consisted of solvent A (water) and solvent B (acetonitrile). The gradient elution program was as follows: 0–10 min, 78–74% B; 10–28 min, 74–50% B; 28–33 min, 50% B. The flow rate was set at 0.6 mL/min.

#### *3.3. Experimental Design*

#### 3.3.1. DSD Experiment

Potential critical method parameters were identified with a fishbone diagram, as shown in Figure S1. In Figure 1, an improved AObD process was proposed based on the characteristics of traditional Chinese medicine. Based on the preliminary experiment results, the gradient, column temperature (*X*5), and flow rate (*X*6) were selected as potential critical method parameters for the experimental design. The other elution conditions are described in Section 3.2.

**Figure 1.** The AQbD process of this work.

As shown in Table 1, the mobile phase gradient was designed as 3 gradients involving 4 parameters (*X*1–*X*4).

**Table 1.** HPLC gradient conditions.

A DSD method was employed to determine the relationships between the factors (*X*1–*X*6) and the response variables. After preliminary experiments, the levels of the factors were defined. The coded and uncoded values of each factor are summarized in Table 2. The CMAs were the peak number (*Y*1), percentage of common peak (*Y*2), and retention time of the last peak (*Y*3). The center point was repeated 3 times. There were 2 additional dummy factors. The total number of experiments was 20. The specific experimental conditions are shown in Table 3.

**Table 2.** Factors and levels of DSD.

**Table 3.** Experimental conditions and results of DSD.

3.3.2. Data Processing and Model Validation

The quantitative model between each CMA and the method parameters was developed using Equation (1). The model was simplified with the stepwise backward method (α = 0.1) using Minitab software (v19, Minitab, State College, PA, USA).

$$Y = a\_0 + \sum\_{i=1}^{6} a\_i X\_i + \sum\_{i=1}^{6} a\_{ii} X\_i^2 + \sum\_{i=1}^{5} \sum\_{j=i+1}^{6} a\_{ij} X\_i X\_j \tag{1}$$

where *a*<sup>0</sup> is the constant; *ai*, *aii* and *aij* are the regression coefficients of the primary, secondary and interaction terms, respectively; *X* refers to a method parameter; and *Y* is a CMA.

The experimental error simulation method was used to calculate MODR [40]. MAT-LAB software (R2017b, MathWorks, Natick, MA, USA) was used for program calculations. All parameters were calculated in the form of coded values. The calculation steps of *X*<sup>1</sup> to *X*<sup>5</sup> were set at 1. The calculation step of *X*<sup>6</sup> was set at 0.1. The number of simulations was 500. After obtaining the prediction results with different combinations of method parameters, the corresponding probability values were calculated statistically. The MODR was obtained with the lowest acceptable probability of 0.8.

#### 3.3.3. Plackett–Burman Designed Experiment

In the robustness test, Plackett–Burman designed experiments were carried out to investigate the variation in the response variables with slight changes in analytical conditions to ensure the results had good reproducibility. The parameters and levels were as follows: 78.5 ± 0.5% of phase B content in mobile phase at 0 min (*X*1), 8.5 ± 0.5 min of the closing time of the first gradient (*X*2), 73.5 ± 0.5% of phase B content in mobile phase at the beginning of the second gradient (*X*3), 30.5 ± 0.5 min of the closing time of the second gradient (*X*4), 30.0 ± 1.0 ◦C of column temperature (*X*5), 0.60 ± 0.01 mL/min of flow rate (*X*6). The experimental design is shown in Table 4.

**Table 4.** Experimental conditions and results of Plackett–Burman designed experiment.

#### *3.4. LC-Q-TOF-Ms Analysis*

A LC-Q-TOF-MS (AB Sciex Triple TOF 5600+, AB Sciex, Framingham, MA, USA) was used to analyze the sugar components in Xiaochaihu capsules. The optimal condition within MODR was selected as the analysis condition. The specific analysis condition is described in Section 3.2. The mass spectrometry conditions are as follows. The electron spray ionization was chosen as ion source. The acquisition mode was set at negative ion. The scan mode was MS. The scan range was set at m/z 100–1500. The drying gas temperature was set at 320 ◦C. The drying gas flow rate was set at 8 L/min. The nebulizer pressure was set at 35 psi. The sheath gas temperature was set at 350 ◦C. The sheath gas flow rate was set at 11 L/min. The capillary voltage was set at 3500 V. The nozzle voltage was set at 1000 V. The crushing voltage was set at 175 V. The cone hole voltage was set at 65 V. The octapole radio frequency voltage peak value was set at 750 V.

#### *3.5. Method Validation*

Validation of the fingerprinting method was carried out in terms of precision, repeatability, and stability. The validation of the content determination method was carried out in terms of linear examination, precision, repeatability, stability, and recovery. Detailed experimental methods are shown in the supplementary material.

#### **4. Results**

#### *4.1. Identification of CMAs*

The peak number was chosen as a CMA to fully reflect the chemical composition of samples (*Y*1). The percentage of common peak (*Y*2) was chosen to ensure the representativeness of the fingerprint. The retention time of the last peak (*Y*3) was chosen to optimize the separation time.

The experimental results of DSD are shown in Table 3. The peak number ranged from nine to 17. The percentage of common peak ranged from 85.26 to 93.88%. The retention time of the last peak ranged from 21.884 to 28.681 min. The value of CMAs varied considerably with different method parameters. Thus, method parameters need to be further optimized.

#### *4.2. Influence of Method Parameters*

The quantitative mathematical models between each CMA and method parameters were established according to Equation (1). The regression coefficients and analyses of variance (ANOVA) of the models are shown in Table 5. The coefficients of determination (R2) of the three models were 0.9322, 0.9925, and 0.9999, respectively. The adjusted coefficients of determination (R2adj) were 0.8712, 0.9795, and 0.9998, respectively. These indicated that the models were all well-fitted and could explain most of the variance.

Contour plots of each response variable can be obtained from the established mathematical models. Some of the contour plots are shown in Figure 2. The *p* value of each parameter was less than 0.1. *X*1, *X*2, *X*5, and *X*<sup>6</sup> all showed significant effects on the peak number. *X*1–*X*<sup>6</sup> all showed significant effects on the percentage of common peaks. *X*1, *X*2, *X*3, *X*4, and *X*<sup>6</sup> all showed significant effects on the retention time of the last peak.

**Figure 2.** Contour plots of each response variable. In order to better show the relationship between the parameters and the response values, the other parameters are fixed (**a**) Peak number. Closing time of the first gradient was 10 min; closing time of the second gradient was 30 min; column temperature was 28 ◦C; (**b**) The percentage of common peak. Closing time of the first gradient was 10 min; column temperature was 28 ◦C; (**c**) Retention time of the last peak. Phase B content in mobile phase at 0 min was 80%; phase B content in mobile phase at the beginning of the second gradient was 73%; closing time of the second gradient was 30 min; column temperature was 28 ◦C.

**Table 5.** Regression coefficients and ANOVA for each model.

#### *4.3. MODR and Validation*

A larger peak number indicates more information in the fingerprint. Thus, the lower limit was set at 14. The percentage of the common peak should be larger. Therefore, the lower limit was set at 0.87. The upper limit of the retention time of the last peak was set at 27 min in order to shorten the analysis time.

To be able to better demonstrate the MODR, three of the parameters were fixed. The calculated MODR is shown in Figure 3.

**Figure 3.** The MODR calculated using the experimental error simulation method. (**a**) Closing time of the second gradient was 28 min; column temperature was 30 ◦C; rate of flow was 0.6 mL/min; (**b**) Phase B content in mobile phase at 0 min was 81%; phase B content in mobile phase at the beginning of the second gradient was 74%; rate of flow was 0.6 mL/min; (**c**) Phase B content in mobile phase at 0 min was 81%; phase B content in mobile phase at the beginning of the second gradient was 75%; rate of flow was 0.6 mL/min; (**d**) Phase B content in mobile phase at 0 min was 81%; column temperature was 28 ◦C; rate of flow was 0.6 mL/min.

Within the MODR, three optimal combinations of method parameters were selected for validation experiments. The conditions and results of the validation method are shown in Table 6. Among them, the column temperature was set at 30 ◦C and the flow rate was set at 0.60 mL/min. The measured values were closer to the predicted values. Most of the indicators met the range requirements of the MODR, indicating that the established MODR is reliable.

**Table 6.** Validation of experimental conditions and results.

#### *4.4. Plackett–Burman Designed Experiment Result*

The results of the Plackett–Burman designed experiment are shown in Table 4. Most groups of experiments showed that the peak number was greater than or equal to 12, the percentage of the common peak was greater than 89%, and the retention time of the last peak was less than 27 min. The CMAs obtained can still meet the analytical requirements when the analytical parameters are varied within MODR. In other words, the established analytical method has good robustness.

#### *4.5. LC-Q-TOF-MS Analysis*

The total ion chromatogram obtained using LC-Q-TOF-MS is shown in Figure S2. Based on the accurate relative molecular masses and chemical reference substances, seven compounds were inferred. Their numbers and inferred results are shown in Table S2. Peaks 2–9 were inferred to be ribitol, fructose, glucose, sucrose, maltose, raffinose, and stachyose, respectively.

#### *4.6. Method Validation*

#### 4.6.1. Fingerprint Method Validation

Different batches of Xiaochaihu capsules numbered S1–S10 were studied. Seven common peaks were identified under the conditions. The peak of fructose (No. 3) was designated as the reference peak for its relatively large peak area and good separation from neighboring peaks. The results were expressed as the relative standard deviation (RSD) of the relative retention time and relative peak areas of each common peak with respect to the reference peaks. As shown in Tables S4 and S5, in the precision, repeatability, and stability tests of injection, the RSD values of relative retention time and relative retention peak area of each peak were less than 4%. The results met the requirements of chromatography fingerprinting, indicating that the test solution was stable within 24 h.

#### 4.6.2. Application of Fingerprinting

Ten batches of Xiaochaihu capsules were prepared into sample solution according to Section 3.1. They were analyzed under the conditions of Section 3.2 to establish fingerprints. As shown in Figure 4, the original data of ten batches (S1–S10) of Xiaochaihu capsules were imported into the similarity evaluation system software (v2012.130723, Chinese Pharmacopoeia Commission, Beijing, China). The reference fingerprint was generated using the average method. The similarity results between the reference fingerprint and the sample fingerprints are shown in Table S3. The values of similarity were all above 0.90. They indicated that the sugar components of Xiaochaihu capsules from each batch had good-quality consistency.

**Figure 4.** Reference fingerprint of Xiaochaihu capsule. Peaks 2, 3 and 5–9 were inferred to be ribitol, fructose, glucose, sucrose, maltose, raffinose, and stachyose, respectively.

4.6.3. Content Determination Method Validation

According to the retention times of the chemical reference substances and mass spectrometry, peaks 2, 3, 5, 6, 7, 8, and 9 were identified as ribitol, fructose, glucose, sucrose, maltose, raffinose, and stachyose, respectively. According to the principle of establishing fingerprint, fructose and sucrose are the main components of four components in Xiaochaihu capsules. Their detection can reflect the chemical composition of the Xiaochaihu capsule. This follows the principle of systematicity. Ribitol is a characteristic component of *Bupleurum* root. The detection of ribitol reflects the medicinal material of *Bupleurum* root. It is in line with the principle of characterization. Stachyose has the effect of regulating intestinal flora. This can be used to explain the effect of Xiaochaihu capsules in the treatment of loss of appetite, irritability, vomiting, etc. The detection of the effective ingredient is also in line with the principle of systematicity. In summary, ribitol, fructose, sucrose and stachyose were selected as the components for content determination.

The regression equations, linear range, limit of detection (LOD), and limit of quantitation (LOQ) of content determination components are shown in Table S6. The linear fit results were all greater than 0.999. The results of the injection precision experiments are shown in Tables S7 and S8. The results of the reproducibility experiments are shown in Table S9. The results of the solution stability experiments are shown in Table S10. The RSD values of precision, reproducibility, and stability were less than 3%. The results were in accordance with the requirements of the Chinese Pharmacopoeia. The results of the recovery experiments are shown in Table 7. The average recovery value of each component met the requirements, and the RSD values were less than 4%. These proved that the optimized method is accurate and reliable and can be used for the determination of sugar components in Xiaochaihu capsules.

The control strategy of the analysis method can be realized in the following two ways. First, the system suitability needs to be paid attention to before tests, including system precision, signal–noise ratio, tailing factor, and other parameters. Second, the parallel sample and reference substances can be used to observe whether the retention time is offset. When the chromatographic analyzer works abnormally, it is important to act in time.

**Table 7.** Results of recovery experiments.

4.6.4. Applications of Content Determination

Twelve batches of Xiaochaihu capsules were prepared into sample solution according to Section 3.1. They were analyzed under the conditions of Section 3.2. The content determination results of the quantitative component are shown in Table 8. In each batch of Xiaochaihu capsules, the content of ribitol ranged from 0.8985 to 2.281%, fructose ranged from 1.815 to 9.018%, and sucrose ranged from 2.054 to 5.320%. The content of stachyose was lower than 2.430%. Among them, the contents of fructose and sucrose were higher.

**Table 8.** Quantitative component content determination results for 12 batches of Xiaochaihu capsules.

\* 0 means the result is below LOQ.

#### **5. Conclusions**

In this study, a HPLC-CAD analytical method for quantitative fingerprinting of the sugar components of Xiaochaihu capsules was established based on AQbD. First, the peak number, the percentage of common peak, and the retention time of the last peak were chosen as CMAs. According to the results of definitive screening designed experiments, the critical parameters affecting the peak number were phase B content in mobile phase at 0 min, closing time of the first gradient, column temperature, and flow rate. The critical parameters affecting the percentage of common peak were phase B content in mobile phase at 0 min, closing time of the first gradient, phase B content in mobile phase at the beginning of the second gradient, closing time of the second gradient, column temperature, and flow rate. The critical parameters affecting the retention time of the last peak were phase B content in mobile phase at 0 min, closing time of the first gradient, phase B content in mobile phase at the beginning of the second gradient, closing time of the second gradient, and flow rate. Then, quantitative mathematical models between each CMA and each method parameter were established using multiple regression analysis. The R2 of all three models exceeded 0.93, which could explain most of the variation. The MODR was calculated using the experimental error simulation method. Three experimental conditions within it were

selected and successfully validated, indicating that the established MODR was reliable. Considering various factors, ribitol, fructose, sucrose, and stachyose were identified as the content determination components. The HPLC conditions for quantitative fingerprint analysis were as follows: The mobile phase consisted of solvent A (water) and solvent B (acetonitrile). The gradient elution program was as follows: 0–10 min, 78–74% B; 10–28 min, 74–50% B; 28–33 min, 50% B. The flow rate was set at 0.6 mL/min. The injection volume was set at 10 μL. The column temperature was set at 30 ◦C. The evaporation temperature was set at 35 ◦C. The peak of fructose was chosen as a reference peak to establish the fingerprint with seven common peaks. The method validation results showed that the performance of the fingerprint and content determination methods were good. Twelve batches of Xiaochaihu capsule samples were determined using the developed analysis method. The results showed that the content of fructose and sucrose were higher. In the established analytical method, the influence of the analytical parameter variation on the method's performance has been investigated. Most groups of experiments showed that the CMAs obtained can still meet the analytical requirements when the analytical parameters are varied within MODR. The proposed method is expected to be robust in the quality control of Xiaochaihu capsules.

**Supplementary Materials:** The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/separations10010013/s1, Table S1: Source merchant and lot number information of Xiaochaihu capsules; Table S2: LC-Q-TOF-MS analysis of some sugar components of Xiaochaihu capsules; Table S3: Fingerprint similarity evaluation results of 10 batches of Xiaochaihu capsule sample solution; Table S4: The relative retention time of injection precision, method repeatability, and sample stability; Table S5: The relative peak areas of each common peak of injection precision, method repeatability, and sample stability; Table S6: The linear equation, coefficient of determination, and analytical range of each component; Table S7: Injection precision of the peak area; Table S8: Injection precision of retention time; Table S9: Method repeatability of content determination; Table S10: Sample stability of content determination; Figure S1: Fishbone diagram of potential critical method parameters; Figure S2 The total ion chromatogram of LC-Q-TOF-MS.

**Author Contributions:** Conceptualization, X.G. and G.W.; methodology, J.L.; software, J.L.; validation, J.L. and L.W.; formal analysis, J.L.; investigation, P.G., P.Z. and Y.X.; resources, Y.X., P.G. and P.Z.; data curation, J.L.; writing—original draft preparation, J.L.; writing—review and editing, J.L., L.W., X.G. and H.Q.; visualization, J.L. and P.Z.; supervision, X.G. and H.Q.; project administration, G.W. and X.G.; funding acquisition, H.Q. and X.G. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Innovation Team and Talent Cultivation Program of the National Administration of Traditional Chinese Medicine (ZYYCXTD-D-202002) and the Fundamental Research Funds for the Central Universities (226-2022-00226). The APC was funded by Xingchu, Gong.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** The authors are grateful for the support of Yanni Tai and Feng Ding.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

#### **References**

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## *Article* **In-Line Vis-NIR Spectral Analysis for the Column Chromatographic Processes of the** *Ginkgo biloba* **L. Leaves. Part II: Batch-to-Batch Consistency Evaluation of the Elution Process**

**Wenlong Li 1,2,3, Xi Wang 2,3, Houliu Chen 1, Xu Yan <sup>1</sup> and Haibin Qu 1,3,\***

**Abstract:** An in-line monitoring method for the elution process of *Ginkgo biloba* L. leaves using visible and near-infrared spectroscopy in conjunction with multivariate statistical process control (MSPC) was established. Experiments, including normal operating batches and abnormal ones, were designed and carried out. The MSPC model for the elution process was developed and validated. The abnormalities were detected successfully by the control charts of principal component scores, Hotelling T2, or DModX (distance to the model). The results suggested that the established method can be used for the in-line monitoring and batch-to-batch consistency evaluation of the elution process.

**Keywords:** Vis-NIR spectroscopy; *Ginkgo biloba* L. leaves; in-line analysis; column chromatographic elution; consistency evaluation; multivariate statistical process control (MSPC)

#### **1. Introduction**

Botanical drugs are widely used around the world. Different from chemical drugs, the raw materials of botanical drugs are from natural products with complicated components. Therefore, taking only one or a few indicators as the quality indices is not suitable during process monitoring for botanical drug production, and more comprehensive information of the in-process materials should be taken into consideration [1–3]. Visible-near infrared (Vis-NIR) spectroscopy is increasingly widely used in the food industry, chemical engineering, and process analysis of the pharmaceutical process [4–6]. Traditionally, the application of the spectral analysis technique includes the establishment, optimization, validation, and maintenance of the calibration models [7,8], which are complex, time-consuming, and require professionals to operate. To overcome these disadvantages, there is an urgent need to develop new process analytical technology for the pharmaceutical process.

Multivariate statistical process control (MSPC) is a process control technology tool for monitoring the process performance using multidimensional data collected from the processes. The data can be process parameters, such as pH, temperature, pressure, etc., and can also be spectra of the materials in process. In a previous study [9–11], we applied the technique to the manufacturing process of traditional Chinese medicine, which uses herbal medicines as raw materials. Several NIR spectroscopy-based process trajectories have been developed for monitoring pharmaceutical processes [12]. They can correctly identify the normal operation condition (NOC) batches and abnormal operation condition (AOC) batches and can also be used for the consistency evaluation of different manufacturing processes [13–16].

*Ginkgo biloba* L. preparation is widely used for its potential effects on memory and cognition, and in the treatment of many cardiovascular diseases [17–19]. In the production

**Citation:** Li, W.; Wang, X.; Chen, H.; Yan, X.; Qu, H. In-Line Vis-NIR Spectral Analysis for the Column Chromatographic Processes of the *Ginkgo biloba* L. Leaves. Part II: Batch-to-Batch Consistency Evaluation of the Elution Process. *Separations* **2022**, *9*, 378. https://doi.org/10.3390/ separations9110378

Academic Editors: Victoria Samanidou and Aleksandra Mišan

Received: 15 August 2022 Accepted: 9 November 2022 Published: 18 November 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

process of *Ginkgo* preparation, column chromatography is a widely used technique for its separation and purification. However, due to the lack of on-line monitoring means, column chromatography techniques are always operated based on operator skills, and it is difficult to reduce the batch-to-batch variability, which ultimately affects the consistency of the product quality. This is a technical problem to be solved urgently in the column chromatography technique of *Ginkgo biloba* L. extract [20].

In the present study, Vis-NIR spectroscopy-based process trajectories were developed for the monitoring and control of the column chromatographic processes of *Ginkgo biloba* L. leaves extract. The in-line spectra of liquid materials were collected in the production process and, combined with the MSPC method, key information that can reflect the status of the production process was extracted. Principal component (PC) scores and Hotelling T2 and DModX (distance to the model) control charts were quickly and effectively built and used for the real-time monitoring of the production process. The presented method provides a promising tool for the recognition of abnormal batches and the consistency evaluation of different batches for the column chromatographic processes of *Ginkgo biloba* L. leaves extract, and it can also be consulted for solving similar problems.

#### **2. Materials and Methods**

#### *2.1. Experimental Design of NOC and AOC Batches*

Experimental apparatus and experimental procedures were described in part I [21], and detailed operating conditions are listed in Table 1. Eleven batches of column chromatography processes of *Ginkgo biloba* L. leaves extracts were designed and executed, which included six NOC batches (batch 1–6) and five AOC batches (batch 7–11). Five NOC batches were used to develop the MSPC models and the AOC batches were used to investigate the prediction performance of the models. The five AOC batches covered common problems that often occurred in practical production. Batch 7 gives a low loading sample, whereas batch 8 gives a high one. Batches 9 and 10 were designed to simulate the case of abnormal elution solvent. In the elution solvent of batch 9, the concentration of ethanol is low, whereas it is high in batch 10. In batch 11, the elution flow rate is low, which is used to simulate the blocking of the chromatography column.

**Table 1.** Experimental design of the column chromatographic processes of *Ginkgo biloba*.

#### *2.2. In-Line Spectra Acquisition*

The in-line Vis-NIR spectra were collected as described in part I with a Sartorius X-One Vis-NIR spectrometer [22]. For every batch, a matrix composed of spectral data at different time-points was obtained. The original spectrogram of eluent of batch 1 is shown in Figure 1.

#### *2.3. Data Processing*

Five NOC batches were selected as calibration set randomly in order to establish the MSPC models. The remaining one batch of normal operation (batch 4) and other five AOC batches were used as a validation set for model evaluation.

**Figure 1.** In-line original spectrogram of eluent of batch 1.

#### *2.4. Unfolding of the Batch Data*

Principal component analysis (PCA), partial least squares (PLS), and other multivariate projection algorithms were applied for the two-dimensional data analysis. However, the data of batch production process are composed of 3 dimensions: batch No., time, and process variables. Single-batch data are composed of a dimension of sampling points and a dimension of process variables, which are ordered by time to form an independent two-dimensional data block, whereas multi-batch process data make up a 3D data matrix X (I(nt), where I is the experimental batch, J represents the process time, and K represents the number of process variables (spectral variables) [23,24], as shown in Figure 2.

**Figure 2.** Two different ways to unfold 3-D spectral data.

The 3D data matrix should unfold in two ways, named "variable-wise" or "batchwise", before the multiway principal component analysis (MPCA) or multiway partial least squares (MPLS) analysis. In the batchwise method, the data of the time and process variable dimensions are combined as one, and the 3D data matrix is decomposed into 2D matrix XB (I(e), in which, every row includes all data of a batch. However, this method cannot monitor the technical process at each time point, and can only be used to analyze the whole batch, which is the limitation of the batchwise method [25]. On the other hand, in the variable-wise method, the data of time and batch dimensions are combined, and two-dimensional matrices XA (K(d) are constituted, in which, each time point corresponds

to a Y value, respectively. In this way, the matrix can be used to monitor the whole process of a batch at each time-point.

#### *2.5. Multivariate Statistical Process Control Charts*

The control charts, including PC scores, Hotelling T2, DModX, or SPE, are important tools for the MSPC model. The PC scores trajectory reflects the change trend of the main components with time. If the used PCs can explain the information of the process variables enough, the change trend of the process can be visualized with the PC scores trajectory [26]. However, if too many principal components are taken into account, the monitoring will be more complex. Therefore, in the actual application, only the first principal component is often used. In the MPLS and MPCA method, the control limits of PC scores are different from those of PCA and PLS, which are determined by the average value and standard deviation of the modeling data. In the MPLS and MPCA methods, the control limits of PC scores were determined according to the average values and standard deviations at the same time point of different batches [27].

Hotelling T2 statistic is always used to determine how similar new observations are to the historical data collected under normal conditions. The control limit of Hotelling T2 statistic can be determined using the F distribution [28]. Hotelling T2 can be described as

$$\left|T\_K\right|^2 = T\_K \lambda\_A^{-1} T\_K \,^T \, \text{s} \tag{1}$$

where *A* represents the number of the selected PCs; *λA*−<sup>1</sup> and *TK* represent the diagonal matrix composed of the eigenvalues corresponding to the first *A* PCs and the score vector of the Kth PC, respectively [29].

SPE statistic, also known as Q statistic, is the sum of squares of model residuals, representing changes in sampling points that are not explained by the model. There are N sampling points, *Xn* (1 × K), and the calculation formula of SPE statistics is

$$\text{SPE}n = \boldsymbol{e}\_{n}\boldsymbol{e}\_{n}^{T} = \sum\_{k=1}^{K} \left(\boldsymbol{x}\_{nk} - \overset{\wedge}{\boldsymbol{x}}\_{nk}\right) \tag{2}$$

where *en* is the residual term of sampling point *xn*, *xnk* is the observed value of the *k*th variable of sampling point *xn*, and <sup>∧</sup> *xnk* is the model predictive value of the *k*th variable of sampling point *xn*.

In SIMCA software (Stockholm, Sweden), DModX statistics replace SPE statistics for analysis. DModX is the distance of a given observation to the model plane. DModX statistic and its control limit can be determined according to the formula found in reference [16]. DModX can be described as

$$\text{DModX}\_{\text{n}} = \frac{\varepsilon\_{n}\varepsilon\_{\text{n}}^{T}}{K - A} = \sum\_{K=1}^{K} \frac{\left(\chi\_{nK} - X\_{nK}\right)^{2}}{K - A} \tag{3}$$

where *xn*, *en*, and *XnK* represent the process variable, residual vector of *xn*, and estimate of *xn*. The control limit of DModX statistic is calculated as

$$\text{DMod}\mathbb{X}\_{\text{lim}} = D\_{\text{cal}} \pm \text{\mathcal{3}SD}\_{\text{\textdegree}} \tag{4}$$

In Equation (4), *Dcal* and *SD* represent the mean value and the standard deviation of the standardized DModX values, respectively [29].

All of the computations were performed using SIMCA-P (V12.0.1, Umetrics, Umea, Sweden) software package.

#### **3. Results and Discussion**

#### *3.1. Spectral Data Pretreatment*

The PC score was performed on the original spectra of batch 2, and Figure 3A shows the trajectories of the first two PC scores, in which, there are obvious spectral fluctuations at approximately 180 min, which may be caused by a baseline drift. In comparison with the first derivative, Savitzky–Golay (SG) smoothing, SNV, MSC, and other pretreatment methods, the 2nd derivative can eliminate the baseline drift effectively, which can be seen from the PC score diagram after pretreatment (Figure 3B).

**Figure 3.** The first 2 PC scores variation trend during the chromatographic process of batch 2 raw spectral data; ((**A**)—Track map of the first 2 PC scores; (**B**)—Second derivative spectral data).

#### *3.2. PCA of the NOC Batches*

Seen from the in-line spectra of the elution process in part I, the whole band spectra have obvious fluctuations in the elution process. To monitor the whole elution process more comprehensively, whole band spectra were selected for subsequent modeling. To understand the change in trend of the elution process and analyze the batch process consistency more clearly, 3D data of the five NOC batches were unfolded with the variablewise method according to the description in the "*Unfolding of the batch data*" section, and the data were pretreated with the second derivative before the MPLS analysis.

The PCA results of the NOC batches are shown in Figure 4, where the blue points represent the starting points of every batch, the green points are the endpoints, and the color changes according to the time sequence of every batch. As seen from Figure 4, along with the process of elution, the PC scores of the in-line spectra showed an obvious trend, and different batches had similar trajectories. The elution process can be divided into four stages. At the beginning of the elution, the elution solvent (70% ethanol) needed a certain time from entering into the column to flow out of the column, so the spectra were in a relatively stable stage. After 60 min, the solvent began to gradually change from water to 70% ethanol, and a large amount of active components were eluted out simultaneously. The spectra in this stage changed greatly. After the elution solvent converted into 70% ethanol completely, the main changes in the spectra were caused by changes in the chemical substance contents in the elution liquid, which were relatively small; and when the elution process reached the end-point, the score was concentrated in a small space, and the spectra changed slightly.

**Figure 4.** The spectral principal component score trajectories of the 5 NOC batches.

#### *3.3. MPCA of the NOC Batches*

To investigate the overall fluctuation in the batches, the process spectra cubes were unfolded in a batchwise way according to the description in the "*Unfolding of the batch data*" section, and the PCA model was established using the NOC batches. The cross-validation results indicated that the best PC number is 2, with which, the cumulative contribution rate of variance reached 60.5%. The PC score, Hotelling T2, and DModX control charts are shown in Figure 5, from which, it can be concluded that all three statistics of the NOC batches were within the control limits. For the AOC batches, the DModX values were all beyond the control limit, whereas the PC scores and Hotelling T2 statistics of batches 7, 8, and 11 were normal, which may be due to the model variance accumulation rate not being high enough to detect the abnormal sensitively. The results indicate that the DModX control chart can monitor the abnormal part, which was not explained by the model and is complementary to the PC scores and Hotelling T2 control charts; therefore, a comprehensive analysis of the three control charts is needed for a new batch.

**Figure 5.** Three PCA model statistics of the 11 batches of column chromatographic processes ((**A**)—PC1; (**B**)—Hotelling T2; (**C**)—DModX).

Although the PCA model can identify the AOC batches with abnormal initial materials or technical parameters, it can only be used to analyze the whole batch after it finishes and cannot be used to monitor the process synchronously [15]. For this reason, a more convenient and effective method should be developed.

#### *3.4. Establishment of the MSPC Model*

The modeling waveband (400–1800 nm) includes 141 wavelength points; therefore, the 3D matrix of the process spectral data (5 × 420 × 141) can be unfolded into a 2D matrix with 5 × 420 rows and 141 columns in the variable-wise way. The validation batches were also unfolded in the same way.

After unfolding the 3D matrix, the MPLS model of the five NOC batches, which take the spectral data matrix as X and the time vector as Y, was established. According to the cross-validation results, three latent variables were used in the model, which explains the 74.4% variance. The obtained PC1, Hotelling T2, and DModX control charts (see Figure 6) can be used for the real-time monitoring of new batches.

**Figure 6.** Three control charts of MSPC models for the column chromatographic processes of *Ginkgo biloba* ((**A**)—PC1; (**B**)—Hotelling T2; (**C**)—DModX).

#### *3.5. Validation of the Established MSPC Model* 3.5.1. NOC Batch

To examine whether the process monitoring model will give a false alarm, Batch 4 (an NOC batch) was used for verification. The process control charts are shown in Figure 7, from which, it can be concluded that, for a randomly selected normal validation batch, in the three control charts, the trajectories are all within the control ranges, which indicates that the overall operation of the batch is in normal conditions, and the quality of intermediates can be considered in the acceptable range.

**Figure 7.** Three control charts of the batch 4 column chromatographic processes of *Ginkgo biloba* ((**a**)—PC1; (**b**)—Hotelling T2; (**c**)—DModX).

#### 3.5.2. AOC Batches with Abnormal Starting Materials

Batches 7 and 8 were used to simulate the AOC batches by changing the volume of the starting materials. As seen from the PC1 score trajectory (Figure 8), the two batches deviated from the normal range at the beginning stage of the elution. However, after 60 min, the elution solvent gradually changed from water to an ethanol solution, and the spectra fluctuated greatly, so the abnormality of the starting material was masked. Two hours later, the whole solvent system was replaced by 70% ethanol, and the anomalies in the elution were mainly caused by the different contents of the chemical substances. After 200 min, the abnormality could still be observed in the first PC score and DModX control charts. The Hotelling T2 trajectories lie within the 95% confidence limits during the entire process. However, compared with the NOC batch, the values are significantly larger.

**Figure 8.** The control charts of batch 7 and batch 8 with abnormal initial materials ((**A**,**D**)—PC1; (**B**,**E**)—Hotelling T2; (**C**,**F**)—DModX).

#### 3.5.3. AOC Batches with Abnormal Ethanol Concentrations

Figure 9 shows the process control charts of two AOC batches with abnormal ethanol concentrations. From PC1 and Hotelling T2 control charts, no obvious anomaly can be found. In the first 60 min, as the eluent liquid is the solvent of the starting materials, both trajectories laid in the normal range. However, the solvent changed after 60 min, which resulted in the process trajectories changing; this phenomenon is fully reflected in both PC1 and Hotelling T2 control charts.

#### 3.5.4. AOC Batches with Abnormal Flow Rates

The chromatography column will be blocked if the raw solution is insufficiently flitted, the concentration is too high, or the column is polluted by the irreversible adsorption of lipids, polysaccharides, and other impurities after repeated use. The blockage is a gradual process, in which, the elution rate will slow down. During this process, if the abnormal flow rate can be effectively monitored, complete blockage can be avoided through the refreshing of the resin, which will also increase the service life of the resin.

**Figure 9.** The control charts of batch 7 and batch 8 with abnormal ethanol concentrations ((**A**)—PC1; (**B**)—Hotelling T2).

Batch 11 was used to simulate the blockage fault of column by reducing the flow rate of the elution solvent; the process monitoring charts of the batch are shown in Figure 10. As seen from the DModX chart, the effect of the flow rate on the process gradually increased with time. The anomaly was not obvious at the beginning of the elution, but it was quite obvious after 2–3 h. However, after 6 h, the elution process approached the end-point, and the chemical contents and the spectra were no longer changed; therefore, the DModX returned to a normal level. The Hotelling T2 values are larger than average during the entire process, which indicates that the process is in an abnormal state.

**Figure 10.** The control charts of batch 11 with abnormal flow rate of elution solvent ((**A**)—Hotelling T2; (**B**)—DModX).

#### **4. Discussion**

The ingredients contained in plant medicines are complex. The target compound needs to be separated and purified by chemical means [30,31], and column chromatography technology is a traditional and very effective method for separating natural products [32]. However, because the slogan of green environmental protection goes hand in hand with the policy of eliminating outdated technologies, column chromatography has had to face a series of challenges in recent years, and the process of column chromatography needs to be monitored and controlled in order to avoid consuming too much time and solvent [33]. As one of the important means of process analysis technology (PAT), NIRs are often used to monitor the key quality and performance characteristics of raw materials, intermediates, and processes in real time [34].

#### **5. Conclusions**

In this study, a Vis-NIR spectroscopy-based MSPC method was developed for the in-line monitoring and control of the column chromatographic processes of *Ginkgo biloba*. The established model can provide effective supervision for the process and reflect the

running state of the process accurately. The PC score, Hotelling T2, and DModX trajectories control charts were mutually supplemented. A combined use of the three statistics will help to obtain more accurate results. It is undeniable that there were still problems that can be improved in this study: the number of batches used for calibration and verification was small, and the more data used to establish the calibration set, the higher the accuracy. This is where we need to improve.

**Author Contributions:** Conceptualization, W.L., X.W., X.Y. and H.Q.; methodology, X.Y. and X.W.; software, H.C., X.W. and X.Y.; validation, X.Y. and H.Q.; formal analysis, W.L. and H.C.; investigation, W.L. and H.Q; resources, W.L., X.Y. and H.Q.; data curation, W.L. and X.W.; writing—original draft preparation, W.L., H.C. and H.Q.; writing—review and editing, W.L., H.C. and X.W.; visualization, W.L. and H.Q.; supervision, W.L. and H.Q; project administration, W.L., H.C., X.W., X.Y. and H.Q.; funding acquisition, W.L. and H.Q. All authors have read and agreed to the published version of the manuscript.

**Funding:** This study was supported by the Key R & D Project of the Zhejiang Science and Technology Program (2018C03075), Hebei Industrial Innovation and Entrepreneurship Team (No. 215A2501D), Wuhan Science and Technology Project (No.<PHONE_NUMBER>012116), the Key Project from National Project for Standardization of Chinese Materia Medica (ZYBZH-C-JIN-43) and the National S&T Major Project of China (No. 2018ZX09201011).

**Data Availability Statement:** Data are contained within the article.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

## *Article* **Development and Validation of a Near-Infrared Spectroscopy Method for Multicomponent Quantification during the Second Alcohol Precipitation Process of** *Astragali radix*

**Wenlong Li 1,2,3, Yu Luo 1, Xi Wang 2, Xingchu Gong 1, Wenhua Huang 4, Guoxiang Wang <sup>4</sup> and Haibin Qu 1,\***

**Abstract:** The objective of this study was to develop and validate a near-infrared (NIR) spectroscopy based method for in-line quantification during the second alcohol precipitation process of *Astragali radix*. In total, 22 calibration experiments were carefully arranged using a Box–Behnken design. Variations in the raw materials, critical process parameters, and environmental temperature were all included in the experimental design. Two independent validation sets were built for method evaluation. Validation set 1 was used for optimization. Different spectral pretreatments were compared using a "trial-and-error" approach. To reduce the calculation times, the full-factorial design was applied to determine the potential optimal combinations. Then, the best parameters for the pretreatment algorithms were compared and selected. Partial least squares (PLS) regression models were obtained with low complexity and good predictive performance. Validation set 2 was used for a thorough validation of the NIR spectroscopy method. Based on the same validation set, traditional chemometric validation and validation using accuracy profiles were conducted and compared. Conventional chemometric parameters were used to obtain the overall predictive capability of the established models; however, these parameters were insufficient for pharmaceutical regulatory requirements. Then, the method was fully validated according to the ICH Q2(R1) guideline and using the accuracy profile approach, which enabled visual and reliable representation of the future performances of the analytical method. The developed method was able to determine content ranges of 8.44–39.8% at 0.541–2.26 mg/mL, 0.118–0.502 mg/mL, 0.220–0.940 mg/mL, 0.106–0.167 mg/mL, 0.484–0.879 mg/mL, and 0.137–0.320 mg/mL for total solid, calycosin glucoside, formononetin glucoside, 9, 10 dimethoxypterocarpan glucopyranoside, 2 -dihydroxy -3 , 4 -dimethoxyisoflavan glucopyranoside, astragloside II, and astragloside IV, respectively. These ranges were specific to the early and middle stages of the second alcohol precipitation process. The method was confirmed to be capable of achieving an in-line prediction with a very acceptable accuracy. The present study demonstrates that accuracy profiles offer a potential approach for the standardization of NIR spectroscopy method validation for traditional Chinese medicines (TCMs).

**Keywords:** near-infrared spectroscopy; *Astragali radix*; alcohol precipitation; validation; accuracy profile

#### **1. Introduction**

*Astragali radix* is one of the most extensively used Chinese herbal medicines because of its effect of increasing the overall vitality of the system, and it has been prescribed for general debility and chronic illnesses for centuries [1]. In recent years, it has been used clinically for spinal cord injury [2], tissue fibrosis, and other diseases [3]. Alcohol precipitation is a vital separation unit that is widely used in the manufacture of botanical medicines to

**Citation:** Li, W.; Luo, Y.; Wang, X.; Gong, X.; Huang, W.; Wang, G.; Qu, H. Development and Validation of a Near-Infrared Spectroscopy Method for Multicomponent Quantification during the Second Alcohol Precipitation Process of *Astragali radix*. *Separations* **2022**, *9*, 310. https://doi.org/10.3390/ separations9100310

Academic Editor: Nguyen Van Quan

Received: 15 August 2022 Accepted: 7 October 2022 Published: 14 October 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

purify the water extracts of medicinal plants [4]. In the manufacturing of Chinese patented drugs derived from *Astragali radix*, a second ethanol precipitation is performed after the first ethanol precipitation to remove more impurities, such as saccharides or proteins. The quality of the intermediates in the following processes and the finished products is thought to be affected by the second ethanol precipitation. To ensure a precise and reproducible alcohol precipitation process, the composition of the alcohol precipitation liquid should be closely supervised during the process.

Due to the complicated ingredients in *Astragali radix*, it is insufficient to realize quality control using a single indicator. The total solid (TS) content represents the total soluble solids (mostly saccharides) that are partially removed during the precipitation process. Flavonoids and saponins are bioactive components that are responsible for pharmacological activities and therapeutic efficacy [5,6]. In this study, the contents of TS, four flavonoid compounds, and two saponin compounds were taken as critical quality attributes (CQAs) of the alcohol precipitation liquids. However, these seven quality indices are often determined by the time-consuming, loss-on-drying method or by high-performance liquid chromatography (HPLC), which fail to satisfy the need for real-time monitoring.

With its advantages of nondestructive and high-speed acquisition, near-infrared (NIR) spectroscopy is a good process analytical technology (PAT) tool that has long been used in the pharmaceutical industry. To develop a sound NIR spectroscopy method, representative samples should be carefully selected, which need to be robust with the expected variation [7]. In the modeling, spectral pretreatments should also be properly selected. Among the different types of selection approaches, the "trial-and-error" approach [8] is a fit-for-use oriented approach, which applies all the possible pretreatments to the data set and selects the optimal one according to the goal of the analysis. However, this approach may be computationally intensive.

Prior to routine analysis, the established NIR spectroscopy method should be validated to demonstrate that it is suitable for its intended purpose [9]. However, validation of the chemometric method is not straightforward compared with the validation of conventional analytical techniques, such as chromatography or titrimetry. In pharmaceutical applications, validation based on traditional chemometric parameters is widely used to assess the performance of the developed NIR spectroscopy method [10]. Such parameters include the correlation coefficient (R), root mean square error of calibration (RMSEC), root mean square error of cross-validation (RMSECV), and root mean square error of prediction (RMSEP). However, the model performance evaluation is an area that has not been fully explored. Some studies have demonstrated that all these parameters provide insufficient information to guarantee the suitability of the method for the intended purpose [11–13]. For example, R is an index affected by unwanted factors, such as data distribution. The more centralized the data distribution is, the more difficult it is to obtain a higher R, while the more dispersed the data distribution is, the more likely it is to obtain a higher R.

The validation strategy of an accuracy profile, which was introduced by the commission of the Société Française des Sciences et Techniques Pharmaceutiques (SFSTP) [14–16], involves acquiring the content ranges over which future measurements will be sufficiently accurate. The accuracy profile is based on *β*-expectation tolerance intervals, which reflect the total measurement error.

A method is considered to be valid when the *β*-expectation tolerance intervals are fully included within the predefined acceptance limits. As described by De Bleye et al., accuracy profiles have been used for many NIR spectroscopic methods in pharmaceutical applications [10].

In the manufacturing of traditional Chinese medicine (TCM) preparations, NIR spectroscopy has been extensively applied [17]. However, most NIR spectroscopy methods have been considered valid when satisfactory traditional chemometric parameters were obtained, and few of the methods were further validated according to the ICH Q2(R1) guideline. In reported studies, there have been some cases of using the accuracy profile approach during the validation stage, such as methods for the determination of baicalin

in Yinhuang oral solution [18], chlorogenic acid in the ethanol precipitation solution of *Lonicera japonica* [19], and licorice acid in a blending process [20]. However, to our knowledge, there have been few studies that have focused on a thorough validation of an in-line NIR spectroscopy method for multicomponent quantification.

The objective of this study was first to develop an NIR spectroscopy method for multicomponent quantification during the second alcohol precipitation process of *Astragali radix*, meanwhile during which a selection method based on the design of experiments (DOE) was used to reduce the calculation times for the selection of pretreatments. The second aim was to fully validate the in-line method for the seven analytes and to compare the traditional chemometric validation with the accurate profile approach.

#### **2. Materials and Methods**

#### *2.1. Materials*

The concentrated supernatants of the first ethanol precipitation of *Astragali radix* were supplied by Livzon (Group), Limin Pharmaceutical Factory (Shaoguan, China). Anhydrous alcohol was purchased from Changqin Chemical Co., Ltd. (Hangzhou, China). Standard substances of calycosin-7-O-β-D-glucoside (CG), formononetin–7–O–β–D-glucoside (FG), 9,10-dimethoxypterocarpan–3–O-β-D-glucopyranoside (DPGP), 2 -dihydroxy-3 ,4 dimethoxyisoflavan-7-O-β-D- glucopyranoside (DDIFGP), astragloside II (AG II), and astragloside IV (AG IV) were purchased from Shanghai Winherb Medical Technology Co., Ltd. (Shanghai, China).

#### *2.2. Alcohol Precipitation Process and Experimental Setup*

Typical operating conditions for the second alcohol precipitation process were as follows. First, 300 g of concentrated supernatant of the first ethanol precipitation of *Astragali radix* (TS content was 40%) was placed intoa2L jacketed glass container. The solution was maintained at a constant temperature of 25 ◦C using a circulation bath. A 95% (*v*/*v*) alcohol solution that was 900 g was added into the glass container at a constant speed using a peristaltic pump, and the mixed solution was stirred using a mechanical stirrer at a speed of approximately 350 rpm. The alcohol adding time was 20 min, and the mixed solution was allowed to stand for 10 min without stirring. The supernatant was obtained as the second alcohol precipitation liquid.

The experimental setup is shown schematically in Figure 1. An NIR immersion transflectance probe with a 2 mm optical path length (Hellma, Müllheim, Germany) was directly inserted into the glass container and connected to the spectrometer by optic fibers. During the precipitation process, a sample of approximately 5 mL was collected near the probe every 5 to 10 min for reference assays.

**Figure 1.** Schematic of the experimental setup for the second alcohol precipitation process.

#### *2.3. NIR Spectral Acquisition*

NIR spectra were collected in transflectance mode using an Antaris FT-NIR spectrophotometer (Thermo Nicolet Corporation, Waltham, MA, USA) fitted with an InGaAs detector and a 2 mm optical path length immersion probe. The instrument resolution was specified at 8 cm−1. Each spectrum was acquired by averaging 32 scans over the wavenumber range of 4500–10,000 cm<sup>−</sup>1, and background spectra were obtained in air.

#### *2.4. Reference Assays*

The seven quality indicators were measured by corresponding standard assays. A gravimetric-based loss-on-drying method was run to determine the contents of TS via hot air dying at 105 ◦C for 3 h. Quantitative analyses of CG, FG, DPGP, DDIFGP, AG II, and AG IV were performed using the HPLC-UV-ELSD method [19]. The flavonoids of CG, FG, DPGP, and DDIFGP were detected using UV. The saponins of AG II and AG IV were detected using ELSD.

#### *2.5. Calibration Protocol*

Calibration models should be developed using carefully selected and representative samples, and method development procedures need to be robust with respect to the expected variability of the products to be analyzed and the manufacturing processes used to prepare them [7]. Variations of raw materials, process parameters, and environmental temperature were introduced into the calibration sample set using a Box–Behnken design. As shown in Table 1, experiments of 29 runs (the central operating conditions were repeated 5 times) were performed to cover the different sources of variability. The concentrated supernatants of the first ethanol precipitation with TS contents of 45%, 40%, and 35% were prepared to obtain different raw materials for the second precipitation process. As illustrated in our previous study [20,21], the concentration of ethanol and the mass ratio of ethanol to concentrated raw materials were considered to be the two critical process parameters for the alcohol precipitation process. Additionally, the spectral acquisition position of each experiment was randomly set to include the different positions of the samples in the container. In the meantime of spectral acquisition, samples were collected for reference assays. Seven samples were collected for each experiment for batches 1 to 22, as shown in Table 1, and in total, 154 samples were included in the calibration set.

**Table 1.** Conditions for the alcohol precipitation experiments according to the Box–Behnken design.

**Table 1.** *Cont.*

#### *2.6. Validation Protocol*

Validation set 1 was constructed to optimize the calibration model. It encompassed samples collected from batches 23 to 29. Independent raw material with TS contents of 40% were used in the experiments, and 3 to 7 samples were randomly collected for each batch. Finally, 38 samples were included in external validation set 1.

Validation set 2 was constructed for a thorough validation of the in-line NIR method. Three batches with normal operating conditions for the second alcohol precipitation process were repeated and performed over three days. The position of the optical probe was fixed. One spectrum was collected every 1 min to provide real-time spectral information. Samples for reference assays were collected at 6 time points (0, 4, 8, 13, 18, and 30 min) near the probe in each validation batch to obtain 6 different content levels. Samples collected at the same time point during the 9 repeated batches were considered to have the same content levels. Finally, 54 samples (9 batches × 6 content levels) were included in validation set 2.

#### *2.7. Multivariate Data Treatment*

Partial least square regression (PLSR) was used to build the prediction models based on the calibration set. Appropriate pretreatments of the spectra were selected via a "trialand-error" approach. To reduce the computational complexity, an experimental design was first set up to determine the optimal directions. The parameters for the improved algorithms were adjusted to select the best pretreatments.

Model validation consisted of traditional chemometric validation and validation using accuracy profiles. Conventional chemometric parameters were first used to obtain the global predictive capability of the established models based on validation sets 1 and 2. Then, the accuracy profiles computed based on validation set 2 were used for model assessment and thorough validation. Conventional statistical parameters, such as R, RMSEC, RMSECV, RMSEP, and the relative standard error of prediction (RSEP), were calculated to evaluate the model performance.

In validation set 2, for each content level, the average content values of the 9 samples were used as the true reference values. Due to process variation, it was not possible to obtain exactly the same contents when we repeated the 9 validation batches. Therefore, all prediction values were normalized in Equation (1) when calculating the accuracy profile for each content level [13].

$$y\_{ij\text{ norm}} = \frac{\overline{y}\_{ij}}{y\_{ij}} \cdot \overline{y}\_{j} \tag{1}$$

In the *i*th validation batch, for a sample of the *j*th content level, *yij norm* is the normalized NIR predicted value, *yij* is the reference value, *<sup>y</sup>ij* is the NIR predicted value, and *yj* is the true reference value of the *j*th content level.

The TQ analyst software package (Thermo Fisher scientific, Madison, WI, USA) and ChemDataSolution chemometrics software (Dalian ChemDataSolution Information Technology Co. Ltd., Dalian, China) were used for spectral data treatments. The accuracy profiles were computed with e.noval V3.0b demo (Arlenda, Liège, Belgium).

#### **3. Results and Discussion**

#### *3.1. The Second Alcohol Precipitation Process and NIR Spectra*

Due to the addition of ethanol, the system changed as alcohol-insoluble impurities emerged. The NIR spectra obtained under the main operating conditions are shown in Figure 2. The peak at 6900 cm−<sup>1</sup> is attributed to water and ethanol. The peaks at 5200–5500 cm−<sup>1</sup> and 8500 cm−<sup>1</sup> appeared after ethanol addition, which correspond to the 1st and 2nd overtones of C-H stretching in ethanol. [22] The system was clear before the critical point when the alcohol-insoluble impurities emerged. The whole system became turbid, and the spectra shifted after the critical point (at approximately 12 min). During the standing period (20–30 min) after the completion of ethanol addition, the impurities gathered and began to precipitate. The upper part of the system was clear again, and the precipitate was in the bottom part. The corresponding NIR spectra for this period became smooth, and the spectral shifts decreased. Therefore, the NIR spectra reflected the changing process state. The regions of 5000–5095 cm−<sup>1</sup> and 5300–10,000 cm−<sup>1</sup> were selected for model construction after removing the saturated absorption regions.

**Figure 2.** The NIR spectra during the process under normal operating conditions.

#### *3.2. Spectral Pretreatments*

Different spectral pretreatments were employed to remove unwanted artifacts caused by small particles and other interference factors during the process. The typical methods used to preprocess the raw spectral data include: baseline correction, scatter correction, smoothing, and scaling [23,24]. Table 2 lists some pretreatment algorithms that are often used. Appropriate combinations of different pretreatments were selected via a "trial-anderror" approach; however, they may be computationally intensive. To reduce the number of calculations, DOE was used to determine the optimization directions. The four pretreatment steps in Table 2 were used as factors, and the full factorial design was applied to obtain 48 combinations of different pretreatments. The Norris–Williams derivation was only used to process the first or second derivation spectra. The combinations were used to process the spectra, and 48 PLSR models were obtained. The models with better performance were selected compared to the model constructed from the raw data without any pretreatments. Using the TS contents as an example, the potential optimal combinations are listed in Table 3.

Next, predictive performance and model complexity were used as evaluation indices to further optimize the parameters of the pretreatment algorithms shown in Table 3. As a result, there were a total of 80 models constructed, as included in Figure 3. Take the red triangle as reference, which represents the model built from raw data. The performances of the models in the lower left part were improved by the pretreatments. Finally, the best pretreatments that yielded a simple model with the best predictive performance were chosen. For the seven analytes, the best pretreatment combinations are listed in Table 4.

**Table 2.** Overview of some commonly used algorithms for each pretreatment method.

<sup>a</sup> First derivation (1st D). <sup>b</sup> Second derivation (2nd D). <sup>c</sup> Multiplicative scatter correction (MSC). <sup>d</sup> Standard normal variate (SNV). <sup>e</sup> Savitzky–Golay polynomial derivative filter (SG) using a 9-point window and a third-order polynomial as the initial default parameters. <sup>f</sup> Norris–Williams derivation (NW) using 5-point smoothing and a gap size of 5 as the initial default parameters.

**Table 3.** Potential optimal pretreatment combinations for the TS content models.

<sup>a</sup> The number of latent variables (LVs) reveals the complexity of the models.

**Table 4.** Performance parameters of the seven calibration models.

<sup>a</sup> NW using a 7-point smoothing and a gap size of 7. <sup>b</sup> SG using an 11-point window and a second-order polynomial. <sup>c</sup> NW using a 3-point smoothing and a gap size of 5. <sup>d</sup> NW using a 5-point smoothing and a gap size of 3. <sup>e</sup> SG using a 9-point window and a third-order polynomial.

**Figure 3.** Overview of the TS content models for all pretreatment combinations. (The red triangle represents the model built from raw data, and the blue dot represents the final model built from the best pretreatments).

#### *3.3. Development of Calibration Models*

A four-fold cross-validation was used to choose the number of LVs for each PLSR model. The quantitative models were constructed for NIR spectra and the seven analytes. The conventional statistical parameters, such as R of calibration (RC), R of cross-validation (RCV), R of prediction (RP), RMSEC, RMSECV, and RMSEP, were calculated to evaluate the model performance. The detailed performance parameters of the seven models are summarized in Table 4. The obtained models have a small number of LVs, which limit the risk of overfitting. Promising results in terms of high correlation coefficients and low prediction errors were obtained. Figure 4 shows the correlation plots of reference values versus the NIR predictions for the seven analytes. The plots present the fitting and predictive ability of the seven models for the entire content range. The models demonstrate good global predictive performance for the seven analytes of the target process samples in the external validation set 1.

**Figure 4.** Correlation plots for reference values versus NIR predictions for the PLSR models of TS (**a**), CG (**b**), FG (**c**), DPGP (**d**), DDIFGP (**e**), AG II (**f**), and AG IV (**g**).

#### *3.4. In-Line Monitoring of the Second Alcohol Precipitation Process and Chemometric Validation*

The established calibration models were used to predict the contents of the seven analytes during the second alcohol precipitation process under normal operating conditions. Figure 5 shows the in-line monitoring results of nine alcohol precipitation batches. To further evaluate the model performance, 54 samples in validation set 2 were collected for reference assays from the nine batches. The newly added parameter of RSEP together with RMSEP and R were used to evaluate the different aspects of the model quality. Table 5 shows the chemometric validation parameters of the quantitative models for the seven analytes. The results demonstrate that the models still present a good overall predictive performance for the samples in the independent new batches. Compared with the parameters in Table 4, the difference in RMSEP values is low. In addition, the first four models for TS, CG, FG, and DPGP perform better than the models for DDIFGP, AG II, and AG IV regarding the RSEP values.

**Figure 5.** In-line monitoring results of 9 validation batches of the second alcohol precipitation.

**Table 5.** Chemometric validation parameters for the 7 quantitative models.

#### *3.5. Validation Based on Accuracy Profiles*

The accuracy profile approach using the concept of total error (bias and standard deviation) is fully compliant with the ICH Q2(R1) requirements in which different validation characteristics should be considered. The following sections discuss in detail the validation criteria for the established models.

#### 3.5.1. Trueness

Trueness represents the systematic error in the measurements. It refers to the closeness in agreement between the average of the measured results and the accepted reference value [25,26]. Trueness is generally expressed in terms of relative bias and recovery.

The calculated results of the six different content levels for each analyte are listed in Tables 6 and 7. All seven models exhibited a higher relative bias as a function of increasing content. The relative bias became much higher for the lower content range of 0.246–0.386 mg/mL for the AG II model, and for the other six models shown in Table 8, all values were within 12.5%. Most values fell within 5%.

**Table 6.** Validation criteria of trueness, precision, and accuracy of the models for the 7 analytes.

**Table 7.** Validation criteria of trueness, precision, and accuracy of the models for the 7 analytes.

**Table 8.** The valid range for each analyte and its proportion over the studied content range.

#### 3.5.2. Precision

Precision represents the random error in the measurements. It refers to the closeness in agreement between a series of measurements of the same homogeneous sample obtained under various conditions [25]. Precision is evaluated at two levels: repeatability and intermediate precision, and the results are listed in Tables 6 and 7. The relative standard deviation (RSD%) shows good precision at high content levels for the seven analytes, whereas at low content levels, some random errors were observed. Of the seven models, the AG II model was the most precise model.

#### 3.5.3. Accuracy

Accuracy expresses the closeness in agreement between a single measured result and the accepted reference value [25], and it represents the total measurement error, which is the sum of the trueness and precision. The accuracy at different content levels for the seven analytes calculated at the relative 95% *β*-expectation tolerance limits are shown in Tables 6 and 7. The obtained intervals produced error ranges that suggested that the future NIR predicted results will fall within a 95% probability. The accuracy profile was built by integrating the total error and the calculated tolerance limits at each content level in one plot, as shown in Figure 6. These profiles constitute a visual decision tool when compared with the predefined acceptance limits. The acceptance limits for the in-line determination of the seven analytes were fixed at 15%, and the NIR quantitative method was considered to be valid when the relative errors of the predicted values were within 15% of the studied content range. As shown in Figure 6, for the first four analytes, the relative *β*-expectation tolerance limits for most content levels were included within the acceptance limit of ±15%. For the last three analytes, the accuracy did not fulfill the acceptance limits for some content levels, especially for lower levels.

**Figure 6.** Accuracy profiles for the 7 PLSR models. (The plain red line represents the relative bias, the dashed blue lines represent the β-expectations tolerance limits (β = 95%), and the dotted black lines represent the acceptance limits (±15%)).

#### 3.5.4. Linearity

The linearity of an analytical procedure is its ability within a definite range to obtain results that are directly proportional to the amount of the analyte in the sample [24]. Figure 7 presents the linear profiles of the seven models with R<sup>2</sup> values and the linear equations. The R2 values are larger than 0.95, which indicate the overall high linearity of the models. The intercepts in the equations are close to 0, confirming the absence of a constant systematic error, and all slopes are close to 1 in the equations except the slope for DDIFGP, which indicates a certain proportional systematic error in that quantification model. However, the method can be considered linear within the content range where the *β*-expectation tolerance limits are within the absolute acceptance limits.

**Figure 7.** Linear profiles for the 7 PLSR models. (The dashed blue lines on this graph correspond to the accuracy profiles, i.e., the β-expectations tolerance limits expressed in absolute values. The dotted black lines represent the acceptance limits at ±15% expressed in concentration units. The solid line is the identity line for y = x).

#### 3.5.5. Range

The range of an analytical procedure is the interval between the upper and lower amounts of analyte in a sample for which it has been demonstrated that the analytical procedure has a suitable level of precision, accuracy, and linearity [26]. According to Figure 6, the range between the lower and upper limits of quantification can be obtained where the relative *β*-expectation tolerance limits are included within the acceptance limits. The range for each quantification model is listed in Table 8. For the first four analytes, the established NIR models were able to predict accurate results over more than 90% of the content range for the second alcohol precipitation process. However, for the last three models, accurate results could be guaranteed within the higher content ranges for the process. Of the seven analytes, the content of DDIFGP was the lowest in the samples, which may be close to the sensitivity limit of the NIR spectrometer, which may be prone to large prediction errors. For AG II and AG IV, as the HPLC-ELSD method was applied as the reference quantitative method, which is less accurate than the HPLC-UV method, the prediction models yielded narrower valid ranges than the three models for CG, FG, and DPGP. Moreover, the complexity of the changing system during the in-line analysis and the existence of the alcohol-insoluble impurities made it more difficult to obtain more accurate models.

#### 3.5.6. Robustness

The robustness of an analytical procedure is a measure of its capacity to remain unaffected by small but deliberate variations in the method parameters during normal usage [26]. At the stage of method development, the expected variability was built into the calibration set by DOE, and the obtained models had a small number of LVs, which characterized their robustness. At the stage of validation, stable RMSEP values calculated from two independent validation sets were obtained for the seven models, which indicated the robustness. To further evaluate method robustness, critical process parameters, and temperature were deliberately altered. As listed in Table 1, batch 23 and 24 were selected, and seven samples were randomly collected from each batch. The content results of the seven analytes in the samples were compared and are shown in Figure 8, which were obtained via the off-line reference methods and in-line NIR analysis. The established NIR spectroscopy method retained good in-process performance even with deliberate process variations.

#### 3.5.7. Specificity

The specificity refers to the ability to unequivocally assess an analyte in the presence of components that are expected to be present [26]. The NIR spectrum is characterized by wide and overlapping absorption bands, and it is quite difficult to assign a value to a specific chemical component because of the complexity of TCMs. The specificity of the models was demonstrated by the variance in the reference data that was covered by the LVs [27]. Three LVs were used for quantitative models of TS, CG, FG, DPGP, DDIFGP, and AG IV, explaining 94.3%, 92.9%, 94.3%, 93.9%, 92.6%, and 88.7% of the total variance in the data, respectively. Six LVs were used for quantitative models of AG II, explaining 93.6% of the total variance in the data. This result indicated that each model contained enough content information of the target analyte and demonstrated the specificity [28].

**Figure 8.** Robustness of the in-line NIR spectroscopy method. (In-line monitoring results are shown as the lines, where the results of the randomly collected 7 samples were obtained via off-line reference methods).

#### *3.6. Method Uncertainty Assessment*

The uncertainty characterizes the dispersion of the values that can reasonably be attributed to the measurement [29]. The uncertainties in the bias of the NIR spectroscopy method at each content level for the seven analytes are displayed in Tables 9 and 10. The expanded uncertainty refers to an interval around the results where an unknown true value can be observed with a confidence level of 95% [30], and the relative expanded uncertainties obtained by dividing the expanded uncertainties with the corresponding true reference content values were not higher than 11.5% over the entire TC validated range, which means that at a confidence level of 95%, the unknown true value is located at a maximum of ±

11.5% around the NIR result. For the other six analytes, the relative expanded uncertainties did not exceed 8.5%, 8.6%,8.6%, 13.0%, 11.0%, or 7.6% over the respective valid ranges between the lower and upper limits of quantification.

**Table 9.** NIR method uncertainties for the 7 analytes.

**Table 10.** NIR method uncertainties for the 7 analytes.

#### **4. Conclusions**

This study explored an in-line NIR spectroscopy method for multicomponent quantification during the second alcohol precipitation process of *Astragali radix*.

At the stage of method development, a calibration set that encompassed enough variation was built, and models were optimized using the DOEs, reducing the calculation times via a trial-and error approach. Finally, the established PLS models had a small number of LVs, and promising results in terms of high correlation coefficients and low prediction errors were obtained.

At the validation stage, traditional chemometric validation and the accurate profile approach were compared. The general good predictive capability of the seven models was demonstrated using the conventional statistical parameters, and the models were further validated using accuracy profiles. According to the predefined acceptance limits, the accuracy profiles produced a reliable representation of the future performances of the NIR spectroscopy method. A visual decision tool to select valid content ranges showed the following results: 8.44–39.8%, 0.541–2.26 mg/mL, 0.118–0.502 mg/mL, 0.220–0.940 mg/mL, 0.106–0.167 mg/mL, 0.484–0.879 mg/mL, and 0.137–0.320 mg/mL for TS, CG, FG, DPGP, DDIFGP, AG II, and AG IV, respectively. Generally, the developed NIR spectroscopy method can be applied for in-line prediction of the early and middle stage of the second alcohol precipitation process. Additionally, the validation results demonstrated acceptable trueness, precision, accuracy, linearity, specificity, and robustness over the ranges, which were in compliance with the ICH Q2(R1) guideline.

**Author Contributions:** Conceptualization, W.L., Y.L. and X.W.; methodology, Y.L. and X.W.; software, X.W. and Y.L.; validation, Y.L., X.W. and X.G.; formal analysis, W.L., X.G. and H.Q; investigation, W.L. and H.Q.; resources, W.H. and G.W.; data curation, W.L., Y.L. and X.W.; writing—original draft preparation, W.L., Y.L. and H.Q.; writing—review and editing, W.L., Y.L., W.H., G.W. and H.Q.; visualization, W.L., X.G. and W.H.; supervision, W.L. and H.Q.; project administration, W.H., G.W. and H.Q.; funding acquisition, W.H. and G.W. All authors have read and agreed to the published version of the manuscript.