*Article* **An Improved POD Model for Fast Semi-Quantitative Analysis of Carbendazim in Fruit by Surface Enhanced Raman Spectroscopy**

**Qiaoling Yang 1,2 , Hong Lin 3, Jinge Ma 4, Niannian Chen 4, Chaomin Zhao 4, Dehua Guo 4, Bing Niu 2, Zhihui Zhao 5, Xiaojun Deng 4,6,\* and Qin Chen 2,\***

**Abstract:** The current detection method of carbendazim suffers from the disadvantages of complicated preprocessing and long cycle time. In order to solve the problem of rapid quantitative screening of finite contaminants, this article proposed a qualitative method based on characteristic peaks and a semi-quantitative method based on threshold to detect carbendazim in apple, and finally the method is evaluated by a validation system based on binary output. The results showed that the detection limit for carbendazim was 0.5 mg/kg, and the detection probability was 100% when the concentration was no less than 1 mg/kg. The semi-quantitative analysis method had a false positive rate of 0% and 5% at 0.5 mg/kg and 2.5 mg/kg, respectively. The results of method evaluation showed that when the added concentration was greater than 2.5 mg/kg, the qualitative detection method was consistent with the reference method. When the concentration was no less than 5 mg/kg, the semi-quantitative method is consistent between different labs. The semi-quantitative method proposed in this study can achieve the screening of finite contaminants in blind samples and simplify the test validation process through the detection probability model, which can meet the needs of rapid on-site detection and has a good application prospect.

**Keywords:** surface-enhanced Raman spectroscopy; carbendazim; probability of detection model; semi-quantitative analysis; rapid detection method evaluation

#### **1. Introduction**

Carbendazim (CBZ) is a broad-spectrum fungicide that is effective against diseases caused by fungi (e.g., Demodex, Polychaeta) in various crops. However, its residues are toxic to mammals and can cause liver disease and chromosomal aberrations [1,2]. Prashantkumar et al. found that exposure to CBZ in male goats caused testicular damage and impaired liver, kidney and blood function [3]. Meanwhile, the study found that CBZ can change the antioxidant defense system [4]. Hence, in the field of food safety, CBZ is restricted in different countries and foods. China stipulates that the maximum residue limit (MRL; The maximum legal allowable residue concentration of pesticides in an agricultural product, food and feed.) of CBZ in apple is <5 mg/kg [5], and the EU stipulates that the

**Citation:** Yang, Q.; Lin, H.; Ma, J.; Chen, N.; Zhao, C.; Guo, D.; Niu, B.; Zhao, Z.; Deng, X.; Chen, Q. An Improved POD Model for Fast Semi-Quantitative Analysis of Carbendazim in Fruit by Surface Enhanced Raman Spectroscopy. *Molecules* **2022**, *27*, 4230. https:// doi.org/10.3390/molecules27134230

Academic Editor: Daniel Cozzolino

Received: 8 June 2022 Accepted: 28 June 2022 Published: 30 June 2022

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**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/).

MRL is <2 mg/kg [6]. The existing detection methods of CBZ include high performance liquid chromatography (HPLC) [7], liquid chromatography-tandem mass spectrometry (LC-MS/MS) [8], electrochemistry [9], immunosensor [10] and so on. Lee et al. used QuEChERS and LC-MS/MS to simultaneously detect the concentrations of thiophanatemethyl and CBZ in pears. The results showed that the detection limit of CBZ was as low as 0.0012 mg/kg [11]. Liu et al. applied HPLC with fluorescence detection to determinate CBZ and thiabendazole in apple juice. The limit of detection was 0.8 μg /kg for CBZ [12]. Although these detection techniques are highly sensitive and reliable, their preprocessing is complex, time-consuming and costly. Furthermore, these detection techniques cannot perform rapid detection for a large number of samples. Nowadays, rapid detection techniques including near-infrared spectroscopy, Raman spectroscopy, hyperspectral, etc. have been widely applied in the field of food quality and safety monitoring. Among them, Surfaceenhanced Raman spectroscopy (SERS) [13,14] developed rapidly due to its outstanding sensitivity, specificity, real-time response and "fingerprint" identification characteristics, and is widely performed in food safety assessment [15], especially the qualitative analysis of finite contaminants food [16,17], including antibiotics [18], metal ions [19], microorganisms [20], pesticides [21], etc. For example, Chen et al. detected CBZ in oolong tea by SERS, and used the partial least squares method to quantitatively analyze the content of CBZ. The results showed a good linear relationship between the spiked and predicted carbendazim in methanol–water solution (R = 0.972; slope = 0.975; RMSEP = 0.819 mg/L) [22].

Quantitative analysis of limited finite contaminants has been widely applied in the field of food finite contaminants analysis [23]. The quantitative analysis of Raman spectroscopy is based on a linear proportional relationship between the Raman characteristic peak intensity and the concentration of the analyte. Although SERS technology has high sensitivity and advantages in the field of trace detection, the Raman peak intensity detected is affected by some factors such as the randomness of surface-enhanced particle aggregation, matrix, sample transparency and so on [24]. Hence, Raman peak intensities collected by SERS cannot form a clear linear proportional relationship with the concentration. To resolve this problem, existing standard curve-based quantification methods need to be improved according to the requirements of rapid testing. Recent studies suggested that the Raman peak intensity detected by SERS conforms to a Gaussian distribution [25], which inspired us to use Raman characteristic peak intensity of CBZ for semi-quantitative modeling to achieve rapid screening of limited finite contaminants, thereby avoiding complex quantitative analysis of SERS and improving detection efficiency.

As one of the rapid detection methods for food, SERS technology requires implemental technical specifications to validate its applicability in the screening of food finite contaminants. Both qualitative and semi-quantitative SERS assays are binary outputs, which can be evaluated by a validation system based on binary outputs. At present, there are many methods that can effectively evaluate the accuracy of binary results, such as Wilrich based on ISO 5725 [26], Fleiss's Kappa statistics [27], probability of detection (POD) model [28], Wieringen [29] and so on. POD is a model that reflects the change of detection probability with concentration [30] which can characterize metrics such as sensitivity, specificity, false positive and false negative rates. The POD model can plot qualitative data as a centralized function, and coordinate with the statistical parameters of quantitative method validation simultaneously, providing a unified statistical method for all method validation, solving the statistical problem of the unpaired test part. The evaluation process is concise, efficient, easy to understand, and implemented by non-statisticians, which has been successfully applied to the validation of chemical and microbiological methods [31,32] and to assess the reliability of non-destructive testing [33]. The evaluation method of SERS based on POD model not only satisfied the requirements of regulations, but also can make up for the poor quantitative ability of SERS and the lack of applicable evaluation methods.

Here, the intensity of Raman signature peak at the MRL of CBZ is used as the threshold, and the result is a quickly and effective method of determining whether the added concentration exceeds the MRL. Based on POD model, a standard SERS assay validation

system was established to promote the commercial application of SERS in the field of rapid food detection.

#### **2. Results**

#### *2.1. Establishment of a Rapid Detection Method for CBZ in Apple*

#### 2.1.1. Selection of Raman Characteristic Peaks

The SERS results of CBZ standard, spiked sample (5 mg/kg) and blank sample are shown in Figure 1. Six Raman shift peaks (630 cm−1, 728 cm−1, 1000 cm−1, 1218 cm−1, 1260 cm−1 and 1315 cm−1) were observed for CBZ standard and spiked sample compared to the blank sample. The six Raman shift peaks can be used as the qualitative characteristic peaks of CBZ in apple due to the clear peak shapes and distinct intensities. According to the SERS spectrum of CBZ and referring to the relevant literature [34–36]., the assignment of Raman shift and Vibrational Description were calculated and shown in Table 1.

**Figure 1.** Raman spectra of different samples.

#### 2.1.2. Establishment of POD Model

Based on the Raman characteristic peaks of CBZ, the detection of samples with different spiked concentrations was counted (Table 2). The results showed that the higher the added concentration, the greater the POD of CBZ in the spiked samples. When the added concentration was no less than 0.5 mg/kg, the POD range was (0.975, 1), which meets the requirement of the POD (>0.95). Hence, the LOD of this method was determined to be 0.5 mg/kg.

**Table 2.** The POD of CBZ in apple at different concentrations.

Note: x is the number of positive samples with positive results detected; N is the total number of samples; POD is the probability of detection; LCL is the lower limit of the 95% confidence interval; UCL is the upper limit of the 95% confidence interval.

Based on the Table 2, a POD model was established with the additive concentration of CBZ in apple as the x-axis and the POD under different additive concentrations as the y-axis (Figure 2). According to 'Technology specification for the evaluation of food rapid detection products' (DB36/T 1334-2020) [37], when the sensitivity of the rapid detection method is greater than 95%, the additive concentration is LOD. When the added concentration is 0 mg/kg, this corresponds to a false positive rate for POD, where the sum of specificity and false positive rate is 100%. When added at concentration other than 0 mg/kg, sensitivity and POD were equal and the sum of the false negative rate and sensitivity is 100%.

**Figure 2.** The POD model diagram of SERS detection method for CBZ in apple.

Figure 2 shows that when the sample does not contain CBZ, the POD is 0, indicating that the false positive rate of this method is 0. Since the sum of the false positive rate and specificity is 100%, so the specificity is 100%. When the additive concentration is not more than 0.05 mg/kg, the detection probability is 0, the sensitivity of the method at this concentration is 0, and the false negative rate is 100%. When the additive CBZ concentration is in the range of 0.05 mg/kg–1 mg/kg, the POD is different under different additive concentrations, the sensitivity and false negative rate are different, of which 0.5 mg/kg (sensitivity > 95%) is the LOD. When the added concentration is greater than 1 mg/kg, the POD is 100%. Based on the POD model plots, the specificity, sensitivity, false positive and false negative rates of SERS detection of CBZ in apples at different spiked concentrations can be visually analyzed.

At different additive concentrations, the qualitative POD of SERS method and HPLC [38] (GB/T 23380-2009) is shown in Table 3. It can be seen from Table 3 that when the concentration of CBZ is less than 0.01 mg/kg, the POD of the two methods is 0, and the dPOD is 0. The POD of the SERS method was lower than that of the HPLC method when the additive concentration was in the range of (0.01,2.5) mg/kg, and the POD of the HPLC was 100%. When the additive concentration was not lower than 2.5 mg/kg, the POD of both the SERS method and the reference method was 100%, and the dPOD is 0. This suggests that the SERS qualitative assay method has good reproducibility and is the same as the reference method.

**Table 3.** Evaluation of the consistency between the Raman qualitative method and the reference method for CBZ in apple.

Note: x is the number of positive samples with positive results detected; N is the total number of samples; POD is the probability of detection; LCL is the lower limit of the 95% confidence interval; UCL is the upper limit of the 95% confidence interval.

The POD curve and dPOD curve of Raman qualitative method and reference method were obtained (Figure 3) according to the consistency evaluation table of Raman qualitative method and reference method for CBZ in apple (Table 3). Based on the analysis of the POD (Figure 2), the false positive rate of SERS and HPLC in Figure 3A is 0, and the specificity is 100%. When the additive concentration was between 0.01 mg/kg and 2.5 mg/kg, the false negative rate of SERS was higher than that of HPLC. When the additive concentration was 5.0 mg/kg, the POD of SERS and HPLC are both 100%, the sensitivities are both 100% (Figure 3A). It can be seen from Figure 3B that when the added concentration is no less than 2.5 mg/kg, the dPOD of the two detection methods is 0, indicating that the SERS qualitative detection method and the reference method have the same detection results. This suggests that the SERS qualitative assay can meet the needs of limited CBZ detection at the MRL level of CBZ (5 mg/kg), while improving detection efficiency.

**Figure 3.** The detection situation of CBZ in apple by Raman qualitative method and reference method. Note: (**A**,**B**) are the POD curve and the dPOD curve of the Raman qualitative detection method and the reference method, respectively.

(2) Consistency evaluation of qualitative method among different labs

Table 4 shows the qualitative POD of SERS detection method between two different labs with different additive concentrations. The environments of lab I and lab II are different, and the same Raman instrument was always used during the experiment and the acquisition parameters were the same. The results showed that the POD of the Raman qualitative method in the two labs was 0 when the additive concentration was not more than 0.05 mg/kg. When the added concentration was in the range of 0.05 mg/kg to 2.5 mg/kg, the POD between the two labs was different, and the POD of lab II was higher than that of lab I. When the additive concentration was no less than 2.5 mg/kg, the POD between the two labs was 100%, and the dPOD was 0, which shows that the Raman qualitative method has the same detection results among different labs and has good repeatability.

**Table 4.** The POD of CBZ in apple by Raman qualitative method among different labs.

Note: x is the number of positive samples with positive results detected; N is the total number of samples; POD is the probability of detection; LCL is the lower limit of the 95% confidence interval; UCL is the upper limit of the 95% confidence interval.

The POD curve and the dPOD curve between different labs were obtained based on the POD of the qualitative methods between the different laboratories (Figure 4). Compared with the analysis results in Figure 3A, it can be seen from Figure 4A that the false positive rate of the method between different labs is 0, and the specificity is 100%. When the concentration is from 0.05 mg/kg to 2.5 mg/kg, the method had higher false negatives in lab I and higher sensitivity in lab II. When the concentration is no less than 2.5 mg/kg, the dPOD is 0 (Figure 4B), indicates that the detection results of this method between different labs are consistent. In other words, at the MRL (5.0 mg/kg) of CBZ, the Raman qualitative detection method can meet the requirements of limited detection and the results are not affected by the environment.

**Figure 4.** The detection situation of CBZ in apple by Raman qualitative method in different labs. Note: (**A**,**B**) are the POD curve and the dPOD curve of the Raman qualitative detection method between different labs, respectively.

#### *2.2. Establishment of a Semi-Quantitative Analysis Method for Carbendazim in Apple*

The SERS rapid detection method allows accurate and qualitative analysis based on the characteristic peaks of CBZ. However, as CBZ is a finite contaminant in food, screening for finite contaminant concentrations is necessary for practical applications. In order to further judge whether the concentration of SERS qualitative detection reaches the MRL of CBZ, a semi-quantitative analysis method of CBZ based on Raman intensity threshold was developed. The method can determine whether the concentration of CBZ in the sample exceeds the MRL according to the characteristic peak intensity, which can avoid complex quantitative analysis and meet the needs of actual finite contaminant detection.

#### 2.2.1. Establishment and Screening of Semi-Quantitative Models

The intensity distribution of the characteristic peaks of CBZ was obtained at the MRL level (5 mg/kg) based on the Raman spectral information of the semi-quantitative model training set (Figure 5). The actual intensity distribution was fitted with the theoretical Gaussian distribution, and the theoretical threshold for semi-quantitative analysis was obtained. The results showed that the actual distribution curve of Raman intensity at 630 cm−1 fit well with the theoretical Gaussian curve, and the fitting degree of the remaining five characteristic peaks was poor. When the CI was higher than 95%, the semi-quantitative model had a Raman intensity threshold of 1.4 × 104 at 630 cm−1, indicating that the concentration of CBZ in apple was no less than 5 mg/kg when the intensity of characteristic peak at 630 cm−1 of CBZ in apple was greater than 1.4 × 104.

**Figure 5.** Semi-quantitative model of CBZ in apple at the MRL (5 mg/kg). Note: (**A**–**F**) represent the semi-quantitative models established by different Raman shifts, respectively. (**A**) 630 cm−1; (**B**) 728 cm−1; (**C**) 1000 cm−1; (**D**) 1218 cm−1; (**E**) 1260 cm−1; (**F**) 1315 cm−1. SD: Standard deviation.

The concentration discrimination results of the three test sets of the semi-quantitative model are shown in Figure 6 based on these semi-quantitative models of CBZ in apple at the MRL (5 mg/kg). As shown in Figure 6, the discriminant results of the semi-quantitative models established by different characteristic peaks are different. The semi-quantitative model established at 630 cm−1 can well distinguish 0.5 mg/kg, 2.5 mg/kg and 5 mg/kg, and the concentration distribution results discriminated by the model are consistent with the reality. The test sets of the semi-quantitative models established at the remaining characteristic peaks have different degrees of overlap, and the results of model discrimination do not match the actual ones.

**Figure 6.** Validation of the semi-quantitative model on the MRL (5 mg/kg) of CBZ in apple. (**A**–**F**) represent the validation of semi-quantitative models established by different Raman shifts, respectively. (**A**) 630 cm−1; (**B**) 728 cm−1; (**C**) 1000 cm−1; (**D**) 1218 cm−1; (**E**) 1260 cm−1; (**F**) 1315 cm−1. SD: Standard deviation.

According to the Raman intensity thresholds of different semi-quantitative models (Figure 5) and the validation results of different semi-quantitative models (Figure 6), the POD that the concentration of CBZ in the three test sets exceeds the MRL and the scores of different semi-quantitative models are shown in Table 5. According to the calculation Formula (1) of the score, POD3 and score raised along with decrease of POD1 and POD2. Therefore, the higher the score of the model, the lower the false positive rate of the model, the higher the sensitivity, the better the model. The semi-quantitative model scores at 630 cm−1 and 1315 cm−1 are 97 and 100, respectively. However, the semi-quantitative model at 1315 cm−1 cannot distinguish the spiked samples at 0.5 mg/kg and 2.5 mg/kg (Figure 6). The semi-quantitative model at 630 cm−1 was well fitted (Figure 5) and the concentration distribution is consistent with reality, so the semi-quantitative model at 630 cm−1 is chosen as the optimal model for the semi-quantitative analysis of CBZ in apple. The model has a false positive rate of 0 at 0.5 mg/kg, a false positive rate of 5% at 2.5 mg/kg, and a POD of 100% at 5 mg/kg.

**Table 5.** Scores for semi-quantitative models.

Note: x is the number of positive samples with positive results detected; POD is the probability of detection.

#### 2.2.2. Consistency Evaluation of Semi-Quantitative Methods among Different Labs

Table 6 shows the detection probability (whether the concentration of CBZ exceeds the MRL) of the semi-quantitative method in different labs under different additive concentrations. As shown in Table 6, when the added concentration is less than 5 mg/kg, the POD of the semi-quantitative method is different among different labs, and the POD of lab II is higher than that of lab I. When the additive concentration is 5 mg/kg, the POD of the semi-quantitative methods among different labs is 100%, and the dPOD was 0. Although the semi-quantitative model has higher false positives in lab II, it can accurately determine whether the concentration of CBZ exceeds the MRL between the two labs, and the repeatability is good.

**Table 6.** Detection situation between different labs by semi-quantitative method for CBZ in apple.

Note: x is the number of positive samples with positive results detected; N is the total number of samples; POD is the probability of detection; LCL is the lower limit of the 95% confidence interval; UCL is the upper limit of the 95% confidence interval.

According to the POD of the semi-quantitative method between labs, the POD curve and the dPOD curve between different labs were obtained (Figure 7). It can be seen from Figure 7A that when the added concentration is less than 5 mg/kg, the POD of the semiquantitative method in different labs is different which suggested that the sensitivity of lab II is higher than that of lab I if sensitivity is different. The POD of both labs is 100% at 5 mg/kg. As shown in Figure 7B, when the concentration of CBZ in the sample is no less than 5 mg/kg, dPOD = 0, which indicates that the semi-quantitative method has the same detection situation among different labs. Therefore, the semi-quantitative method can effectively distinguish whether the additive concentration of the blind sample exceeds the MRL according to the intensity of the characteristic peak at 630 cm−1, and the results are not affected by the environment.

**Figure 7.** The detection situation of CBZ in apple by semi-quantitative method in different labs. Note: (**A**,**B**) are the POD curve and dPOD curve of semi-quantitative detection method between different labs, respectively.

#### **3. Discussion**

CBZ is widely used in agriculture and is a common pesticide residue that threatens human and animal health. At present, some classical detection methods such as HPLC [7] can provide accurate qualitative and quantitative analysis of CBZ, but these traditional methods all involve complex pretreatment process, long detection cycles and complicated instrumentation. In order to meet the needs of rapid screening of finite contaminants in practical applications, it is necessary to develop a rapid detection method with a short detection cycle and simple operation.

SERS has been widely performed in the qualitative, quantitative and semi-quantitative analysis of food finite contaminants due to its specificity, sensitivity, non-destructive sample, and no interference from aqueous solutions [39]. The basis of SERS qualitative analysis is based on the Raman characteristic peaks of the target, selecting the characteristic shifts of the spiked sample and the target but not in the blank matrix as the characteristic peak of the target. In this study, the peak intensities at 630 cm−1, 728 cm−1, 1000 cm−1, 1218 cm−1, 1260 cm−1 and 1315 cm−1 in the CBZ standard and spiked samples were obvious. Therefore, these six Raman shifts were selected as the Raman characteristic peaks of CBZ in apple. The characteristic peaks of CBZ obtained here are the same as those of existing research [34]. For example, the peak at 630 cm−1 is related to the C–C–C in-plane bending and the peak at 728 cm−1 is attributed to the out-of-plane bending of the C-H bond in the benzene ring. The LOD of CBZ was 0.5 mg/kg, which was lower than the MRL of CBZ in apple (5 mg/kg). At the same time, it is four times lower than the LOD (2 mg/kg) of previous research methods [40].

Quantitative analysis of SERS is of great significance in the detection of finite contaminants in food. However, the Raman intensity stability of SERS is easily affected by enhanced matrix activity and environment, which makes quantitative analysis difficult. To overcome these issues, existing researches mainly focused on developing curing techniques to improve the stability and reproducibility of reinforced matrices. For example, Sivashanmugan et al. [41]. developed novel Au nanodot arrays on graphene substrates for highly active enhanced Raman scattering. By using Rhodamine 6G (R6G) as a molecular probe, the LOD was as low as 10−12 M and the Raman enhancement factor was as high as 108. Wu et al. developed a simple and effective SERS tape based on biconical gold nanoparticles (BP-AuNPs) for monitoring methyl parathion residues on the surfaces of vegetables and fruits [21]. In real world applications, the screening of finite contaminants in food mainly depends on the MRL. If the added concentration of the sample is higher than the MRL, it is judged as a non-conforming product, otherwise it is judged as a qualified product. In order to fulfill the detection requirements of finite contaminants, we developed a threshold-based semi-quantitative analysis method for finite contaminants, which reduces the difficulty and cost of developing new materials and improves the detection efficiency. The model

established at 630 cm−1 was selected as the basis for semi-quantitative analysis of CBZ in apple after screening and verification. When the Raman intensity at 630 cm−1 was greater than 1.4 × 104, the concentration of CBZ in the sample was higher than the MRL (5 mg/kg). When the additive concentration was 5 mg/kg, the POD of this semi-quantitative method was 100%. The semi-quantitative method developed in this study only requires modelling based on a large number of samples from MRL, and the semi-quantitative results are determined by the Raman characteristic peak intensity. Compared to existing CBZ semi-quantitative analysis methods of CBZ, such as PLS-DA [42], this semi-quantitative does not require complex classification models and classification parameters such as the variable importance of variables in projection fraction, so it is simpler and more tractable. In order to ensure the SERS rapid detection method and semi-quantitative analysis method satisfy the evaluation standards of rapid detection methods (released by the State Food and Drug Administration in 2017). POD curve and dPOD curve were constructed for different methods or different labs within a certain concentration range, and the consistency of the methods was determined based on whether the POD is the same. The results showed that at the MRL level of CBZ (5 mg/kg), the SERS qualitative detection method was consistent among different labs, and the results were the same as the reference method. Compared with 'Technology specification for the evaluation of food rapid detection products' [37] , this evaluation method can show the change of sensitivity with concentration, and the LOD is well defined. It can compare the consistency of each concentration interval within the detection concentration range and can be applied to the consistency analysis between methods, environments, and instruments. The obtained POD curve can display the results visually, which is more statistically significant.

The semi-quantitative analysis method based on SERS technology can perform rapid primary screening of samples according to the characteristic peak intensity of CBZ at 630 cm−1 in samples, which can improve the detection efficiency, and can be extended to other finite contaminants such as melamine in liquid milk and other pesticide residues in food. However, the training set of the semi-quantitative model requires a large amount of sample size (the number of samples >50), which leads to consume most of the time and energy before modeling. How to use less time to obtain the more sample information and reduce the preparation time is the bottleneck and future development direction of this research. Raman hyperspectral imaging technology is an advanced non-destructive testing technology that combines conventional imaging and spectroscopy to collect Raman spectral information of each pixel in space, so as to conduct qualitative, quantitative and localized analysis of samples [43]. Compared with SERS, the advantage of Raman hyperspectral imaging technology is that it can continuously collect a large number of spectral information through an automated sample platform, so as to obtain more sample information in less time. For example, Yang et al. applied Raman hyperspectral imaging technology to continuously collect spectral information of 100 pixels in 10 min [44]. However, it would have taken at least 50 min to collect the spectral information with SERS. Therefore, the semi-quantitative method developed in this study can be combined with the Raman hyperspectral imaging to shorten the sample information acquisition time and further improve the efficiency of sample screening.

#### **4. Materials and Methods**

#### *4.1. Samples, Reagents and Instruments*

Sample: Apple (commercially available); Reagents: Carbendazim solid standard, ethanol (AR), dichloromethane (AR), NaCl solution (1 mol/L), nano-gold solution (Shanghai Oceanhood opto-electronics tech Co., Ltd., Shanghai, China).

Instruments: Portable Raman spectrometer (SEED 3000, Shanghai Oceanhood optoelectronics tech Co., Ltd., China), precision electronic balance (Sartorius, Germany), vortex mixer (VORTEX-GENIE2, Scientific Industries, New York, NY, USA), Eppendorf 5810R centrifuge (20050647GZ, Eppendorf, Germany).

#### *4.2. Methods*

#### 4.2.1. Sample Preparation

Apple pulp was put into a 50 mL tube, and it was broken up with a homogenizer. 2 g of pulp was weighed and added with 20 μL of standard solutions of CBZ with different concentrations, so that the concentrations of CBZ in samples were 5 mg/kg, 2.5 mg/kg, 1 mg/kg, 0.5 mg/kg, 0.1 mg/kg, 0.05 mg/kg. The samples with 3 mL of ethanol (5%, *v*/*v*) were vortexed for 3 min and centrifuged at 4000 rpm for 2 min. 2 mL of sample supernatant was mixed with 2 mL of dichloromethane in a 5.0 mL tube. After layering, 1 mL of liquid in the lower layer was taken into a gas-phase injection vial, dried with a nitrogen blower, and reconstituted with 500 μL of ethanol (20%, *v*/*v*). The reconstituted liquid was vortexed for 1 min and used for SERS detection.

#### 4.2.2. SERS Detection

50 μL reconstituted solution with 200 μL of nano-gold solution and 50 μL of NaCl solution (1 mol/L) were added to the detection vial. The samples were quickly mixed with a pipette and then tested on the SEED 3000. To avoid the aggregation of gold nanoparticles, the SERS detection was completed within 1 min. The excitation wavelength of the Raman spectrometer was 785 nm, the wavelength range of data acquisition was 200–4000 cm−1, the integration time was 1 s, and the laser power was 200 mw. Spectral data were collected by Uspecral-PRO software. (Shanghai Oceanhood opto-electronics tech Co., Ltd., Shanghai, China).

#### 4.2.3. Data Processing

(1) Screening of Raman characteristic peaks of CBZ in apple

Based on the positions of the Raman characteristic peak of CBZ standard, the Raman shift peaks (the number varies from 2 to 6) specific to the blank samples were selected as the characteristic peaks of CBZ, and a library of Raman characteristic peak was established. After obtaining the Raman spectral of the sample, the search for the first order derivative peak and matching of the characteristic peak were performed. When the Raman characteristic peaks of the sample had all the characteristic peaks of CBZ, it was determined that the sample contains CBZ. The tolerance range of Raman shift is 3–10 cm−1 can be considered as the same characteristic peak.

(2) Establishment and screening of semi-quantitative models

The semi-quantitative model of the target at a specific concentration (such as MRL, etc.) is used to determine whether the concentration of target in the sample exceeds a specific concentration. To establish a semi-quantitative model at a specific concentration, it is first necessary to determine the applicable concentration of the Raman detection method and to confirm that the discriminant concentration is within the detection range of the current method. In practice, it is necessary to obtain Raman spectral data of spiked samples at specific concentrations to establish semi-quantitative models at different characteristic peaks. Then the optimal semi-quantitative model was determined according to the fitting of Gaussian distribution and the discrimination of different additive concentrations. These steps for establishing a semi-quantitative model at a specific concentration are as follows:

1. The establishment of semi-quantitative models. Raman spectral data of spiked samples (number of samples > 50) at specific concentrations were collected as a training set for a semi-quantitative model. A histogram of the Raman intensity of each Raman characteristic peak at a specific additive concentration was obtained and the distribution of the intensity was viewed. If the intensity of the characteristic peak does not obey the Gaussian distribution, Raman intensity of the characteristic peak is not only affected by the random error of detection, so it is not suitable for the semi-quantitative model and should be eliminated. For the characteristic peaks whose Raman intensity follows a Gaussian distribution, calculate the intensity mean and standard deviation, determine the confidence level, and draw the confidence interval (CI). The Raman

intensity corresponding to the lower limit of the confidence interval (CI > 95%) is used as the semi-quantitative threshold.

2. Screening of semi-quantitative models. Raman data of spiked samples (number of samples > 20) at low concentration, half of specific concentration and specific concentration were collected as the test set for the semi-quantitative models of different characteristic peaks. The threshold value of the semi-quantitative model was used to determine whether the concentration in the sample exceeds a specific concentration, the POD was calculated, and the semi-quantitative model score under different characteristic peaks was computed. The higher the score of the model, the more accurate the semi-quantitative model will be. The model with the highest score and the Raman intensity conforming to the Gaussian distribution was selected as the optimal semi-quantitative model.

The formula for calculating the score is as follows: S is the score of the semi-quantitative model, POD1 is the probability of detection at low concentration, POD2 is the probability of detection at half of specific concentration, POD3 is the probability of detection at specific concentration.

$$\mathbf{S} = \mathbf{2}^{(\text{POD}^3 - \text{POD}^2 - \text{POD}^1)} \times \mathbf{50} \tag{1}$$

The evaluation of Raman rapid detection method based on POD model specifically includes three steps: establishment of POD model, determination of blind sample results and calculation of evaluation result.

1. Establishment of POD model. According to the qualitative results of samples at blank, low concentration, half of a specific concentration, a specific concentration, etc. (the number of samples > 20), the number of test samples, positive samples and negative samples at each concentration were counted. The POD was calculated, and POD curve of the POD changing with the added concentration was drawn.

2. Determination of blind sample results [45]. According to the qualitative discrimination results of spiked blind samples (the number of samples >20) in different labs and reference methods, the number of test samples, positive samples and negative samples under each concentration was counted. At the same time, the number of samples exceeding a specific concentration is calculated from the semi-quantitative identification of the low concentration, half of the specific concentration and the spiked sample at the specific concentration.

3. Calculation of evaluation result. The POD of qualitative and semi-quantitative methods was drawn, and it was established for the method evaluation system of Raman detection. Sensitivity, specificity, and LOD were evaluated by the POD model, and the consistency with the reference method and the inter-laboratory consistency were evaluated by the difference of POD (dPOD).

The POD and its confidence interval are calculated as follows: x is the number of positive samples with positive results detected, N is the total number of samples, POD is the probability of detection, LCL is the lower limit of the 95% confidence interval, and UCL is the upper limit of the 95% confidence interval:

(1) when x = 0,

$$\text{POD} = 0,$$

$$\text{LCL} = 0,$$

$$\text{UCL} = 3.8415/(\text{N} + 3.8415) \tag{2}$$

$$(2) \quad \text{When } \mathbf{x} = \mathbf{N}\_{\prime}$$

$$\text{POD} = 1;$$

$$\text{LCL} = \text{N} / (\text{N} + 3.8415),$$

$$\text{UCL} = 1;$$

(3) When 0 < x < N,

$$\text{POD} = \text{x} / \text{N} \tag{4}$$

$$\text{LCL} = \frac{\text{x} + 1.9207 - 1.9600\sqrt{\text{x} - \frac{\text{x}^2}{\text{N}} + 0.9604}}{\text{N} + 3.8415} \tag{5}$$

$$\text{UCL} = \frac{\text{x} + 1.9207 + 1.9600\sqrt{\text{x} - \frac{\text{x}^2}{\text{N}} + 0.9604}}{\text{N} + 3.8415} \tag{6}$$

The dPOD and its confidence interval are calculated as follows: dPOD is the difference of POD, I is the method to be evaluated/laboratory, II is the reference method/laboratory:

$$\text{dPOD} = \text{POD}\_{I} - \text{POD}\_{II\prime} \tag{7}$$

$$\text{LCL} = \text{dPDD} - \sqrt{\left(\text{POD}\_I - \text{LCL}\_I\right)^2 + \left(\text{POD}\_{II} - \text{UCL}\_{II}\right)^2} \tag{8}$$

$$\text{UCL} = \text{dPOD} + \sqrt{\left(\text{POD}\_{I} - \text{UCL}\_{I}\right)^{2} + \left(\text{POD}\_{II} - \text{LCL}\_{II}\right)^{2}};\tag{9}$$

#### **5. Conclusions**

This study carried out qualitative and semi-quantitative analysis of CBZ in apple and the method was evaluated by a binary output-based validation system. The LOD of the qualitative method was 0.5 mg/kg. When the additive concentration was 5 mg/kg, the POD of the semi-quantitative method was 100%. When the added concentration was greater than 5 mg/kg, the SERS method and the reference method are consistent and the detection results are not affected by the lab. The threshold-based semi-quantitative method proposed can quickly determine whether the finite contaminants in blind samples exceed the MRL. Meanwhile, the evaluation method based on binary output provided a reference for the evaluation system of Raman spectroscopy rapid detection technology, which is of great significance for food rapid detection technology and has some significance in other rapid detection fields.

#### **6. Patents**

The work reported in this manuscript resulted in a patent, which has been granted under the patent number ZL201911005222.1

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

**Funding:** This research study was supported by the National Key Research and Development Plan Project, grant number 2018YFC1603503; Shanghai Agricultural Research Project, grant number<PHONE_NUMBER>0; Science and Technology Joint Project of the Yangtze River Delta, grant number<PHONE_NUMBER>0; and Shanghai Technical Standard Project, grant number 18DZ2201200.

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The dataset used and/or analyzed during the current study are available from the corresponding author on reasonable request.

**Acknowledgments:** The authors wish to thank anonymous reviewers for their constructive comments on the presentation of this article.

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

**Sample Availability:** Samples of the compounds are not available from the authors.

#### **References**

## *Review* **A Review of The Application of Spectroscopy to Flavonoids from Medicine and Food Homology Materials**

**Lin Zou 1 , Huijun Li 1, Xuejie Ding 1, Zifan Liu 1, Dongqiong He 2, Jamal A. H. Kowah 2, Lisheng Wang 2,\*, Mingqing Yuan 1,\* and Xu Liu 1,\***

**Abstract:** Medicinal and food homology materials are a group of drugs in herbal medicine that have nutritional value and can be used as functional food, with great potential for development and application. Flavonoids are one of the major groups of components in pharmaceutical and food materials that have been found to possess a variety of biological activities and pharmacological effects. More and more analytical techniques are being used in the study of flavonoid components of medicinal and food homology materials. Compared to traditional analytical methods, spectroscopic analysis has the advantages of being rapid, economical and free of chemical waste. It is therefore widely used for the identification and analysis of herbal components. This paper reviews the application of spectroscopic techniques in the study of flavonoid components in medicinal and food homology materials, including structure determination, content determination, quality identification, interaction studies, and the corresponding chemometrics. This review may provide some reference and assistance for future studies on the flavonoid composition of other medicinal and food homology materials.

**Keywords:** flavonoid; medicine and food homology; spectroscopy

#### **1. Introduction**

In recent years, as people have become more conscious of food safety and health, more and more people are looking at foods with health benefits and therapeutic effects. In Chinese traditional medicine concepts, this is referred to as "medicine and food come from the same source" or "medicinal and food homology". In the earliest records of the Yellow Emperor's Internal Classic, food can be used as a complementary function to medicine to treat disease. In the Tang dynasty, the "Qianjin recipe", and then in the Yuan dynasty, the "principles of correct diet"(Yin Shan Zheng Yao), the efficacy and contraindications of food were documented, and the theoretical system of the medicinal and food homology was gradually improved [1]. In November 2021, China's National Health and Wellness Commission issued the "Regulations on the Management of the Catalogue of Substances that are Traditionally Both Food and Chinese Medicinal Herbs", listing 110 medicinal and food homology materials [2].

These herbs, which have been popular in China for millennia, possess high medical value. Unfortunately, however, their clinical development remains highly limited. An important influencing factor is that the composition of herbal medicines is often not a single but a complex mixture, thus making quality control of herbal medicines more difficult. Therefore, qualitative identification and quantitative analysis of herbs is essential to ensure quality standards and safe efficacy. For medicinal and food homology materials, it is even more important to carry out quality assessments to ensure safety due to the particular characteristics of the materials that differ from those of ordinary food products. The Pharmacopoeia of the People's Republic of China and the Law of the People's Republic of

**Citation:** Zou, L.; Li, H.; Ding, X.; Liu, Z.; He, D.; Kowah, J.A.H.; Wang, L.; Yuan, M.; Liu, X. A Review of The Application of Spectroscopy to Flavonoids from Medicine and Food Homology Materials. *Molecules* **2022**, *27*, 7766. https://doi.org/10.3390/ molecules27227766

Academic Editors: Weiying Lu and Yanping Chen

Received: 31 August 2022 Accepted: 27 October 2022 Published: 11 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/).

China on the Administration of Medicinal Products have strict regulations on medicinal and food homology materials.

Secondary metabolites in medicinal plants include flavonoids, polysaccharides, terpenoids, quinones, steroids and others, of which flavonoids are one of the most important active ingredients [3]. Flavonoids are a large class of polyphenols, which are widely present in various plants in free form or as glycosides. The basic skeletal structure of flavonoids consists of two benzene rings (A and B) which are linked by an oxygen-containing heterocyclic pyran ring (C) [4]. Flavonoids can be divided into different six major groups depending on the unsaturation of the linking chain, the connection between the rings and the chemical structure, including flavanones, isoflavonoids, flavanols, flavonols, flavones and anthocyanidins [5] (Figure 1).

**Figure 1.** Classification of flavonoids.

Flavonoids derived from food and herbs have been found to contain a variety of pharmacological activities, such as antioxidant [6], anti-inflammatory [4], anti-cancer [5,7], modulation of intestinal immune function [8] and cardiovascular protection [9] (Figure 2).

The flavonoids extracted from various herbal medicines are numerous and have complex and diverse structures. Therefore, a range of analytical technical tools and methods, such as high-performance liquid chromatography (HPLC), gas chromatography (GC), mass spectrometry (MS) and spectroscopy, are used to determine the basic structure and characterization of flavonoid components. Traditional analytical techniques, such as HPLC [10], GC [11], liquid chromatography-mass spectrometry (LC-MS) [12,13], gas chromatographymass spectrometry (GC-MS) [14], have been used to determine the concentration as well as identify the structure of the substance. However, these methods are expensive, timeconsuming, require complex operation skills and consume large amounts of solvents and reagents. In addition, these methods usually measure a small number of samples to represent a larger sample, so it is not suitable for large-scale measurement applications. At present, various spectroscopic techniques are frequently used for the detection and iden-

tification of biological molecules, such as UV, NIR, fluorescence spectroscopy, terahertz time-domain spectroscopy, etc.

**Figure 2.** Pharmacological activities of flavonoids.

Compared to these techniques, spectroscopic analysis has the advantage of being relatively simple, inexpensive and does not require extended preparation methods of samples or other chemicals [15]. On the other hand, because each compound has its own specific characteristic spectral information, it is easy to differentiate classes of functional groups, the structure of bonds within compounds and their conformation by corresponding spectral profiles, which has led to the widespread use of spectroscopic analysis for the development and study of flavonoid components [16].

This paper provides a review of spectroscopic techniques used in recent years in the quality control and development applications of flavonoid in medicinal and food homology materials and details the advantages and research progress of each technique. These findings provide an important scientific basis for future research and development of flavonoid components in medicinal and food homology materials.

#### **2. Qualitative and Quantitative Analysis**

The analytical identification of active compounds extracted from herbal medicines also depends on various analytical techniques. The detection of flavonoids in herbal medicines has received increasing attention. Many studies related to the qualitative and quantitative analysis of flavonoid components in medicinal and food homologous materials have been published. Compared with the traditional time-consuming, labor-intensive and expensive chromatography methods, the spectroscopic method is simple, fast and environmentally friendly [17]. Spectral analysis is based on their characteristic spectra and is used to study the structure of compounds or to determine their chemical composition [18]. Various organic compounds with different structures have their own unique characteristic spectra. Table 1 has summarised the spectroscopic techniques used for quantitative and qualitative analysis of medicinal and food homology materials.

(PCA); Support vector machine (SVM), Partial

least-squares

 regression (PLSR); Artificial neural network (ANN); Multiple linear regression (MLR);

Sampling error profile analysis (SEPA).

#### *2.1. Nuclear Magnetic Resonance*

Nuclear magnetic resonance (NMR) spectroscopy can detect and quantify molecular interactions to accurately characterize the structure of flavonoid molecules in complex samples, although it has disadvantages such as high cost and may not be suitable for all applications [41]. Duan et al. isolated and purified 21 compounds from the Platycladi Cacumen extract and identified the chemical structures of several flavonoids by NMR [19]. Shang et al. studied the chemical composition and biological activities in four different parts of the roots, stems, leaves and seeds of *Glycyrrhiza uralensis* [20]. Three flavonoid Cglycosides were identified by NMR spectroscopic analysis. Bao et al. isolated 14 isoflavone components from the root of *Astragalus membranaceus*, the planar structures of which were determined by detailed analysis of 1D and 2D NMR data [21]. Xie et al. first analyzed four major flavonoids in *Capsella bursa-pastoris* (L.) by UPLC, 1H-NMR and 13C-NMR spectroscopic techniques to investigate the effect of *Capsella bursa-pastoris* (L.) on the clinical treatment of cataracts [22]. Wang et al. isolated and identified four new compounds and eight known flavonoids from the leaves of *Glycyrrhiza uralansis* and determined their structures by high-resolution electrospray mass spectrometry (HR-ESI-MS) and UV spectrum and 1H-nuclear magnetic resonance (NMR) and 13C-NMR spectroscopy [23]. Yi et al. determined the 1H and 13C NMR chemical shifts and NMR shielding parameters of daidzein and puerarin, the major flavonoid active ingredients in Radix puerariae, by NMR analysis [24]. Xu et al. reported 109 chemical constituents, including 21 flavonoids, 6 flavones and 35 isoflavonoids, from the mature fruit of *Psoralea corylifolia* L. The structures of the compounds were elucidated by 1H NMR, 13C NMR, 2D NMR and Rh2(OCOCF3)4 and Mo2(OAc)4-induced circular dichroism spectroscopic methods [25].

#### *2.2. Terahertz Time-Domain Spectroscopy*

Terahertz (THz) spectroscopy is an emerging and powerful investigation technology that contains abundant information about the physics, chemistry and structure of materials. In contrast to conventional far-infrared spectroscopy, terahertz spectroscopy exploits a part of the electromagnetic spectrum that lies between the microwave and infrared regions. In the terahertz band, biological molecules conduct complex molecular vibrations such as rotations, low frequency bond vibrations, hydrogen bonds and van der Waals forces. Based on terahertz characteristic spectra, biomolecules can be identified effectively, especially for those with similar chemical structures. In recent decades, terahertz spectroscopy has been widely used in the fields of physics, chemistry, materials science and biomedicine on account of its rapid, safe and non-destructive advantages [42,43]. However, terahertz spectroscopy also has limitations in terms of limited penetration, scattering effects and limited sensitivity [44]. Furthermore, the cost of terahertz spectroscopy is still high compared to other applications and the current terahertz database on natural active compounds is incomplete and needs further improvement and development.

Yin et al. used terahertz time-domain spectroscopy (THz-TDS) for the qualitative identification and quantitative analysis of ten common flavonoids, including baicalein, baicalin, apigenin, quercetin, naringenin, hesperetin, daidzein, genistein, puerarin, and gastrodin [26]. These flavonoids with similar molecular structures had significantly different characteristic absorption peaks in the terahertz band and were distinguished by the terahertz absorption spectrum. Yan et al. identified three flavonols with similar structures, including myricetin, quercetin, and kaempferol, and determined their concentrations by terahertz spectroscopy [27].

#### *2.3. Fluorescence Spectroscopy*

Fluorescence spectroscopy is now increasingly used for quality control and monitoring analysis of food and pharmaceuticals as a sensitive, simple and fast detection technique [45]. Flavonoid components will produce fluorescence when exposed to constant excitation light due to their native fluorescence properties. Shan et al. used a fluorescence spectrophotometer to obtain synchronous fluorescence spectra of tea infusion to quan-

tify the content of flavonoids in green tea. The excitation/emission spectra of flavonols were in the range of 365–390 nm/450–470 nm and those of flavanols were in the range of 480–500 nm/510–520 nm [46]. In recent years, as nanotechnology becomes more sophisticated, various new nano-fluorescent sensor-based fluorescence spectroscopy has found more applications in quality assessment [47]. Lan et al. used a nanomaterial-based fluorescent sensor combined with spectral splicing to successfully evaluate the quality of *Citri Reticulatae Pericarpium* and identify their storage year [28]. Specifically, nanogold particles and cadmium telluride quantum dots were chosen as nanosensors and mixed with aqueous extracts of *Citri Reticulatae Pericarpium* to collect fluorescence quenching spectra. Then, the self-fluorescence and fluorescence quenching spectra of the same sample were combined to integrate the spectra of different fluorescence sensing systems at the same coordinate axes to obtain spliced spectra. This new strategy achieved accurate recognition of different *Citri Reticulatae Pericarpium* samples by identifying the interaction between the nanoparticles and the fluorescent components in the *Citri Reticulatae Pericarpium* sample.

#### *2.4. UV Spectrophotometry*

Ultraviolet-visible (UV-Vis) spectrophotometry is used to qualitatively and quantitatively analyze compounds by utilizing the absorption spectra of compounds that will absorb energy in the UV or visible region and undergo an electron energy leap. In addition to simple operation and fast analysis speed, UV-Vis has the advantage of greater sensitivity and selectivity [48].

In a study to determine whether *Pueraria lobata* could protect human umbilical vein endothelial cells (HUVECs) from apoptosis, Gao et al. used UV spectrophotometry and HPLC to determine the content of isoflavones in the ethanolic extracts of *Pueraria lobata*. Puerarin, daidzin and daidzein were found to be the major isoflavonoid components of *Pueraria lobata*, accounting for 84.94% of the extract [29]. Wang et al. isolated four known isoflavone analogs, a new isoflavone and a new flavone hydrate from *Pueraria lobata* (Willd.) ohwi, and their structures were characterized by IR, UV, HR-ESI-MS, 1D and 2D NMR spectroscopic methods [34]. Xie et al. measured the total flavonoids content of *Capsella bursa-pastoris* (L.) extract to 65.18±2.16% based on UV-VIS spectrophotometry [22]. Witkowska-Banaszczak et al. used NMR, UV spectroscopy and electrospray ionization tandem mass spectrometry (ESI-MS/MS) for the structural identification of 10 flavonoids in the extracts from the flowers of *Trollius europaeus* [31]. El Shoubaky et al. isolated a flavonoid from the acetone extract of marine red alga *Acanthophora spicifera* and identify the structure of the flavone compound by infrared, mass and UV spectroscopy. This flavonoid was confirmed to be apigenin and showed promising analgesic, anti-inflammatory and antiproliferative activities [32]. Luteolin, a flavonoid widely occurring in natural plants, has a variety of activities including anti-inflammatory, cardioprotective and can interact with certain metals and biomolecules. Jomova et al. used UV-vis spectroscopy to characterize the interaction between Cu(II) and luteolin in their study of the effect of luteolin on DNA damage in the copper-catalyzed Fenton reaction [33].

#### *2.5. Near Infrared Spectroscopy*

Near-infrared (NIR) spectroscopy is a simple, fast, accurate and non-destructive technique that has been used in a variety of fields for process analysis and quality control in recent years [49–51]. Compared to the mid-infrared (MIR) range, the shorter NIR wavelengths increase the depth of penetration. NIR spectroscopy covers the wavelength range from 800 to 2500 nm and mainly records the spectral bands corresponding to the molecular vibrations of hydrogen bonds (e.g., C-H, N-H, O-H) to obtain the characteristic information of the hydrogen-containing groups in compounds [52]. Compared to IR spectroscopy, which requires the sample powder and KBr powder to be mixed and ground and pressed into tablets first, IR spectroscopy requires no sample preparation and will not produce any waste products [17]. However, NIR spectra usually rely on reference methods and need to be combined with chemometrics to build models [53].

Arslan et al. performed quantitative analyses of flavonoid components in black wolfberry by Fourier-transform near-infrared (NIR) spectroscopy combined with chemometric algorithms [34]. Wang et al. used near-infrared (NIR) spectroscopy to quantitatively monitor the content of flavonoid active ingredients in the water-ethanol extraction process of *Pueraria lobata* [35]. Betances-Salcedo et al. analysed the total contents of flavones and flavonols, flavanones and dihydroflavonols by using the methods of NIR methodology in 99 samples of propolis from Spain and Chile [36].

In general, herbal medicines contain a large number of active ingredients and other components that make spectroscopic measurements very complex, and it is difficult to achieve accurate measurements from NIR spectroscopy results alone. Therefore, multiple techniques are often used to obtain more complete and comprehensive information. UV-Vis detects only information about specific groups in the molecule, so UV-Vis cannot obtain information about all active compounds, but it is possible to get rid of the interference of solutions during spectroscopic analysis. In contrast, NIR is greatly affected by the solution, but more signals of the compound can be obtained. Combining NIR and UV-Vis, therefore, gives more comprehensive and effective information in a dual spectrum. Nie et al. developed a rapid method for the determination of flavonoid compounds (chrysin and galangin) in poplar propolis by means of visible and near-infrared spectroscopy (Vis-NIR) and a total of 114 propolis samples from China were analysed [37].

The effective extraction of Chinese medicinal ingredients is the primary premise behind identifying the structure and studying the biological activity, which directly determines the quality of the preparations in production and the actual clinical efficacy. Therefore, a reasonable extraction time is crucial, too long or too short an extraction time will affect the extraction yield. Therefore, a simple, rapid and efficient means of monitoring and analysis is required in the extraction process of herbal medicines.

In this context, Xu et al. developed a dual-spectrum portable spectrometer technology based on near-infrared (NIR) and ultraviolet-visible (UV-Vis) spectroscopy. With the spectral acquisition analysis, a quantitative analysis model of total flavonoids in *Pueraria lobata* was developed to enable online monitoring of the extraction process of flavonoid active compounds from *Pueraria lobata* [38]. In particular, they have chosen to use cylindrical cuvettes instead of traditional quartz cuvettes or fibre optic probes when measuring spectroscopy, making it easier to clean the solid residues produced during the extraction process, while reducing the cost of detection and facilitating its widespread application. The results showed that the dual-spectrum online monitoring system is simple to operate, has fast sampling speed, low cost and provides more comprehensive information than individual NIR or UV–Vis spectrum. It is a promising tool for the quantitative analysis of some traditional Chinese medicines with complex compositions. It should be noted, however, that if the compound component to be measured does not absorb in the UV–Vis region, then the dual-spectrum system will not be able to identify it.

#### *2.6. Hyperspectral Imaging*

Hyperspectral imaging (HSI) combines imaging and spectroscopic techniques so that spectral and spatial information about the sample can be obtained simultaneously. Compared to conventional analytical methods such as liquid chromatography, hyperspectral imaging is faster and more non-destructive and has been used to detect various compositions of samples, such as food [54], agriculture [55] and herbal medicine [56,57].

Near-infrared (NIR) spectroscopy generally collects spectral information from small sampling points and is unable to obtain the spectra of the entire sampled region; the final spectra of the sample are represented by the average spectra measured from multiple small sampling points. In hyperspectral imaging, a 3D data cube (x × y × λ) is created by combining two spatial (x; y) and one wavelength (λ) dimension, where images are collected as a function of wavelength [57–59]. Hyperspectral imaging has the advantage of acquiring spectral information from the entire sampling area within the hyperspectral images. As a result, hyperspectral imaging enables more representative spectral information to be

obtained than near-infrared spectra acquired from a single point. In addition, based on the character that each pixel within the hyperspectral images has a spectrum, a distribution map can be formed to explore the composition distribution differences within and among samples [60–62].

Zhang et al. determined the total flavonoid content of dried black goji berries by near-infrared hyperspectral imaging (NIR-HSI) [39]. In the work of He et al. near-infrared hyperspectral imaging was used to determine the total polysaccharide and total flavonoid content of *Chrysanthemum morifolium* [40]. Compared to reference methods using UV–Vis spectroscopy for total polysaccharides and total flavonoids, hyperspectral imaging was more environmentally friendly, and more efficient in handling large numbers of samples and online predictions could be made for different *Chrysanthemum morifolium*.

#### **3. Identification of Quality**

Generally, the quality of herbs can be strictly influenced by the culture conditions, growth year, and geographic origin, thus resulting in significant differences in their medicinal quality and clinical efficacy [63]. Differences in culture conditions, such as light, temperature, and air humidity, can affect the number of secondary metabolites accumulated in herb plants, while the content of these active ingredients increases with the number of growth years. The composition and content of active compounds in the same herb may vary depending on the geographical origins [64]. Hence, the cultivation methods, growth years and geographical origin of herbs have attracted more and more attention from consumers. It is necessary to establish a reliable and effective analytical method for identification.

#### *3.1. Cultivation Methods and Growth Years*

Hai et al. collected a total of 320 samples of *Dendrobium huoshanense* (DHS), which were mainly divided into greenhouse cultivation and wild-like cultivation, each cultivation containing four growth years [65]. It is difficult to identify and distinguish between samples of *Dendrobium huoshanense* from different cultivation methods and growth years due to the high similarity of these samples and the limited information obtained from a single spectrum. Therefore, Hai et al. synthesized metallized tetraphenylporphyrin (ZnTPP) to obtain nano-effect near and mid-infrared spectroscopy by axial coordination, hydrogen bonds or electrostatic interactions with flavonoids, amplifying the differences in spectral signals of *Dendrobium huoshanense* samples. The nano-effect near and mid-infrared spectral data were then fused to obtain nano-effect feature fusion spectra, which successfully identified samples of *Dendrobium huoshanense* (DHS) with different growth years and cultivation methods, achieving 100% accuracy. Tetraphenyl zinc porphyrin (ZnTPP) is a metalloporphyrin formed by the introduction of Zn2+ into the cavity in the porphyrin ring. ZnTPP achieved complementary properties of both substances, with highly large π-conjugated properties and excellent optical properties [66,67]. The results showed that the addition of ZnTPP increased the peak difference, and the overlap of the nano-effect mid-infrared spectra was reduced compared to the original spectra. For the nano-effect NIR spectra, the peaks were more dispersed overall and the difference in spectral properties increased. It indicated that the nano-effect of ZnTPP amplifies the variability of the near and mid-infrared spectra.

#### *3.2. Geographic Origin*

NIR spectroscopy, with the advantages of simple, rapid, high efficiency and no sample preparation, has been a powerful analytical tool in use for identifying and analysing the geographical origin of food and herbal medicines [68]. Chen et al. collected a total of 250 raw material samples of notoginseng from four main geographical origins (Yunnan, Xizang, Guangxi and Guizhou provinces of China). The geographical origin of notoginseng was successfully identified by near-infrared spectroscopy [69].

However, for those herbs with complex and similar compositions, their NIR spectral peaks often overlap severely and some significant differences in the spectral peak cannot be obtained, and the classification accuracy is low. Therefore, the original NIR spectra are difficult to be used directly for the quality evaluation of herbal medicines.

At present, a large number of sensors based on nanomaterials are widely used to monitor food safety and authenticity due to their low cost, high sensitivity, and convenience [70–72]. As a very popular nanomaterial, carbon dots(CDs) have many merits, such as remarkable optical properties, excellent biocompatibility, good stability, and environmental friendliness, and they have been widely used in chemical sensors. Porphyrins possess a rigid macrocyclic structure, a large π-conjugation system, and various functional groups and substituents, enabling them to recognize multifarious molecules.

In order to solve this dilemma, Long et al. completed the identification of the geographical origin of lily using carbon dot-tetramethoxyporphyrin nanocomposite(CDs-TMPP) based nano-effect near-infrared spectroscopy sensor [73]. Long et al. first collected a total of 120 lily samples from 12 different geographic origins in China and then collected nano-effect NIR spectra in the presence of CDs-TMPP. The results show that the nano-effect spectroscopy sensor method had better classification performance compared with the original NIR spectra, with 100% accuracy in identifying the geographical origin of the lily samples. The active components in the lily interact with CDs-TMPP through hydrogen bonding, electrostatic interaction, and steric hindrance, enhancing the differences in near-infrared spectroscopy peaks of lily samples from different geographical sources. In previous studies, Lv et al. developed a near-infrared spectroscopic sensor combined with porphyrin to realize the identification of dendrobes from 12 different geographical sources [74]. Compared with traditional NIR spectra, the NIR spectra with TMPP can obtain more characteristic information, which greatly improves the accuracy of model identification. After adding TMPP, the accuracy rate of NIR spectra reached 100%. The possible mechanism was that the π-π conjugated system and the methoxy groups of TMPP interact with the chemical components of dendrobes, which increase the specificity of NIR spectra.

#### **4. Interaction Studies**

Spectroscopy techniques can be used not only for quantitative and qualitative analysis, but also in combination with bioactivity analysis to study the interaction of flavonoids bound to biological macromolecules, including lipid membranes, human serum albumin, and hyaluronidase.

#### *4.1. Interaction of Flavonoids with Membrane Lipids*

The amphiphilic character of flavonoids allows them to intercalate into or bind with lipid bilayers. Many of the biological effects of flavonoids have been assumed to result from interactions with the membranes [75–77]. In previous studies, IR spectroscopy has been used extensively to elucidate the interactions between flavonoids and membrane lipids, but the conclusions drawn from IR spectroscopy about the way flavonoids are incorporated onto or into lipid bilayers are rather vague and are generally not far reaching [78,79]. The main reason lies in the fact that the actual state in which the flavonoid molecules are bound to lipids in a buffer solution is not known.

When there are interactions between flavonols and their surroundings, changes in molecular structure may occur, resulting in changes in the spectrum. Such structural changes such as structural rearrangements, conformational changes and deprotonation may give rise to hydroxyl and carbonyl vibrations. The vibrations of anyone hydroxyl group do not occur in isolation but are mixed with ring vibrations and other neighboring hydroxyl groups to form very complex vibrational patterns. The structural changes caused by the interactions do not remain in a narrow part of the spectrum, but actually affect the whole spectrum. This leads to very different spectra of structurally similar compound molecules. Therefore, it can hardly be satisfactorily to discuss the spectra of flavonoids in terms of characteristic vibrations of either the hydroxyl or carbonyl group. Baranovi´c

et al. performed the infrared spectroscopic analysis of seven flavones (flavone, 3-and 5-hydroxyflavone, chrysin, apigenin, fisetin and luteolin) and five flavonols (galangin, kaempferol, quercetin, morin and myricetin) and reported that hydroxyl and carbonyl vibrations in the interaction of these flavonoids with membrane lipids [80]. By monitoring spectral changes brought about by the interaction of flavonoids with membrane lipids it revealed structural details of how the flavonoids incorporate onto or into the lipid bilayer.

#### *4.2. Interaction of Flavonoids with HSA*

Human serum albumin (HSA), the most abundant protein in plasma, has the capability of binding reversibly to a large variety of drugs via its binding sites. After binding to human serum albumin, the drug compound is transported to various locations in the body for release. Therefore, the strength of the drug compound's ability to bind with human serum albumin determines the therapeutic effect [81]. A large part of the general population is exposed to some flavonoids in their daily diet. Consequently, the risk of interaction between the binding levels of these flavonoids and human serum albumin is much higher. This interaction is likely to alter the binding of another drug to human serum albumin, thus affecting the pharmacological effect of another drug.

Quercetin (QUE) is one of the most abundant flavonoids in the human diet and is also the main active ingredient in many herbal medicines, with a variety of pharmacological effects on the human organism [82]. Diosmin (DIO), a flavonoid commonly found in citrus fruits, is the active component of many drugs, especially ones used in the treatment of various blood vessel disorders [83]. Catechins (CAT) are also common plant-derived flavonoids with anti-inflammatory and antioxidant potential and have a wide range of applications in the pharmaceutical and food industries. Tigecycline (TGC), is an antibiotic drug commonly used in clinical practice. Sovrli´c et al. investigated the effect of flavonoids (catechins, quercetin and diosmin) on the binding of antibiotics (tigecycline) to HSA using multiple spectroscopic measurements, as well as their effects on the structure of the active site and the nature of interactions [84]. The formation of triple complexes of HSA–TGC– FLAVs with high binding affinity was demonstrated by UV–Vis absorption spectroscopy and fluorescence analysis. The conformational changes of HSA were analyzed by simultaneous fluorescence spectroscopy, Fourier transform infrared spectroscopy and circular dichroism, and it was found that the triple complex of HSA–TGC–FLAVs did not affect the microenvironment around the tryptophan (Trp) and tyrosine (Tyr) residues of HSA.

#### 4.2.1. UV–Vis Absorption Spectra

UV–Vis absorption spectroscopy, a convenient, rapid and effective technology, is commonly used to study protein-drug interactions and complex formation [85]. Redshift and blue-shift changes observed in UV–Vis spectra can explain the interaction mode between drugs and proteins. UV–Vis spectral results showed that the absorption intensity of HSA- flavonoids (QUE, CAT and DIO) decreased as the concentration of TGC increased, demonstrating the formation of triple protein–drug–drug complexes between HSA, TGC and flavonoids (QUE, CAT and DIO) [84].

#### 4.2.2. Fluorescence Quenching Measurements

The presence of Trp and Tyr amino acids in the HSA structure provides HSA with fluorescent properties. This fluorescence property is sensitive to the microenvironment of the HSA molecule and when the local environment of the HSA molecule changes, such as protein denaturation or biomolecular binding, its fluorescence is quenched [86,87]. In previous studies, Matei et al. found that the fluorescence of HSA was quenched in a concentration-dependent manner upon interaction with kaempferol by fluorescence measurements [88]. Kaempferol, a natural flavonoid compound with a wide range of biological activities, is widely found in plants and foods. Therefore, the change in the fluorescence intensity of HSA can determine whether small molecules are bound to it, affecting the environment around the Trp and Tyr amino acid residues. The results showed

the fluorescence quenching of HSA-flavonoids (QUE, CAT and DIO) in the presence of varying concentrations of TGC, indicating the formation of the HSA-TGC-flavonoids (QUE, CAT and DIO) complex [84].

#### 4.2.3. Synchronous Fluorescence Spectra

The synchronous fluorescence method allows information on the molecular environment in the vicinity of the Trp and Tyr fluorophores of HSA to be obtained and to determine whether polarity change around the microenvironment has occurred by measuring the position of its maximum emission wavelength. Synchronous fluorescence of HSA can provide characteristic information around tyrosine (Tyr) and tryptophan (Trp) residues when the scanning wavelength intervals (Δλ) are fixed at 15 nm and 60 nm, respectively [89]. The red or blue shift in the maximum fluorescence emission (wavelength) of HSA indicates enhanced hydrophilicity or hydrophobicity of the microenvironment around Tyr or Trp residues, respectively [84]. In the findings of Matei et al., no significant shifts in the position of the maximum emission wavelength were registered, but the fluorescence burst of Trp was stronger than that of Tyr, suggesting that the binding site of kaempferol to HSA is nearer to the Trp residue [88]. The results of the synchronous fluorescence spectra of the ternary HSA–TGC–flavonoids (QUE, CAT and DIO) systems showed no significant changes in the maximum emission wavelength of Tyr and Trp residues, indicating that the interaction of HSA-TGC with flavonoids does not affect the conformation of the micro-region of Tyr and Trp [84].

#### 4.2.4. Circular Dichroism Measurements

It is known that protein–ligand interactions can alter the secondary structure, resulting in changes in the protein conformation, which are reflected by the circular dichroism (CD) spectrum. The circular dichroism spectrum of HSA observed two negative minima at 208 and 222 nm, and represents α- helix structure transition of π → π\* and n → π\*. In the findings of Matei et al., the percentage of α-helices decreased progressively upon binding of kaempferol to HSA, compared to free HSA molecules, indicating a degree of protein folding [88]. Sovrli´c et al. found that the CD spectra for the ternary HSA-TGC-flavonoids (QUE, CAT and DIO) systems showed no significant change in signal and shape from the original spectra, indicating that the binding of HSA–TGC–flavonoids had a negligible effect on the secondary structure of the protein [84].

#### 4.2.5. Fourier Transform Infrared Spectroscopy (FT-IR)

The investigation of the secondary structure of HSA was performed using the FT-IR spectroscopic technique [88]. The protein amide I band at 1650–1654 cm−1 and amide II bands at 1548–1560 cm−1 are attributed to the secondary structure of all proteins. FT-IR spectra of HSA showed no significant difference between the ternary HSA-TGC-FLAV system and free HSA, indicating no conformational change in the HSA protein [84].

#### *4.3. Interaction of Flavonoids with Hyaluronidase*

It has been reported that there were several enzymes known to be involved in promoting inflammatory pathways, of which hyaluronidase (HAase) is one of the most important enzymes, cleaving hyaluronic acid in the extracellular matrix and improving the permeability of cell membranes and blood vessels [90]. During the development of inflammation, the level of HAase in the body increases dramatically. It has been found that some flavonoids exhibit strong anti-inflammatory effects while being able to inhibit the activation of HAase [91].

#### 4.3.1. Fluorescence Spectra

In the study by Zeng et al., the interaction between eight flavonoids(apigenin, luteolin, keampferol, quercetin, morin, naringenin, daidzein, genistein) and HAase was investigated by fluorescence spectroscopic and molecular modeling methods [90]. The results showed

that the eight flavonoids formed flavonoid–HAase complexes mainly by binding to HAase interactions through electrostatic forces, hydrophobic interactions and hydrogen bonding. According to synchronous and three-dimensional fluorescence spectra, the presence of flavonoids significantly altered the microenvironment and conformation of HAase, leading to reduced enzyme activity.

Li et al. studied the binding of three flavonoids(baicalin, liquiritin and isoliquiritigenin), extracted from *Scutellaria baicalensis Georgi* and *Glycyrrhiza uralensis* to hyaluronidase by steady state fluorescence, time-resolved fluorescence and circular dichroism (CD) spectroscopy [92]. The results of the fluorescence spectra showed that when baicalin and liquiritin were bound to HAase, the strongest fluorescence emission peak of HAase was red-shifted and fluorescence quenching was stronger. When isoliquiritigenin interacted with HAase, it acted as quencher to decrease the fluorescence intensity of HAase with no significant change in the position of the emission peak. The fluorescence quenching mechanism of HAase by the three flavonoids is a static quenching procedure.

#### 4.3.2. Synchronous Fluorescence Spectra

Synchronous fluorescence spectra present information about the molecular microenvironment in the vicinity of the fluorophore by measuring the emission wavelength shift. The wavelength interval (Δλ) between the excitation wavelength and the emission wavelength is fixed individually at 15 and 60 nm, which gives the characteristic information of tyrosine (Tyr) or tryptophan (Trp), respectively.

Synchronous fluorescence spectra showed that with the addition of baicalin, the maximum emission wavelengths of Trp- and Tyr- are both observed to have a redshift, indicating that the polarity around Trp-and Tyr-residues increases and the hydrophobicity decreases. For the liquiritin–HAase system, the maximum emission wavelength of Trpresidues has an obvious red shift, but that of Tyr- has no obvious change. The results suggested that the interaction of liquiritin with HAase increases the polarity and decreases the hydrophobicity around Trp-residue, but has no effect on the microenvironment around Tyr-residue. For the isoliquiritigenin–HAase system, the synchronous fluorescence peaks of Trp- and Tyr- do not change significantly, indicating that the microenvironment of Trp-and Tyr-is not disturbed by isoliquiritigenin [92].

#### 4.3.3. Circular Dichroism

Circular dichroism (CD) is usually executed to investigate the secondary structural changes of protein because of its accuracy and sensitivity [93]. The results of CD spectroscopy showed that the binding of flavonoids(baicalin, liquiritin and isoliquiritigenin) with HAase leaded to changes in the secondary structure of HAase with an increase in the α-helix content in HAase.

#### **5. Chemometrics**

Due to the complex composition of herbal raw materials, the obtained spectra are difficult to identify, have severe overlaps of spectrum bands and contain a lot of useless information, which brings a great challenge for the identification of spectral information. Therefore, it is necessary to analyse the spectral information of the samples by chemometric methods in order to obtain valid spectral information, improve the selectivity of fluorescence spectroscopy and achieve rapid multi-component analysis. Table 1 has summarised the chemometric methods used for the spectroscopic analysis of medicinal and food homology materials.

#### *5.1. PLS-DA Analysis*

Partial least squares discriminant analysis (PLS-DA) is a very common classification method that is used in various fields of analysis [94–96]. PLS-DA is based on the classical PLS regression algorithm, which combines PLS regression with discriminant analysis to look for latent variables (LV) with a maximum covariance with the dependent Y variable. The number of LVs is usually determined based on the optimal correct classification rate of the cross-validation procedure [97]. The dependent variable Y is a dummy matrix composed of binary values, with the one-hot encoding used to represent the class belonging of the samples. In dummy matrix Y, a value of 1 means that the sample belongs to a specific class and 0 means the opposite. However, the estimated response values of PLS regression models are usually not exactly equal to 0 or 1. When the estimated response value for a sample is closer to 1, the sample is considered to belong to the corresponding class, and the opposite is true when it is closer to 0.

The model based on the traditional raw fusion spectrum is poor, resulting in low accuracy in the training and prediction sets. It means that traditional spectra struggle to identify DHS from different cultivation years. To enable feature fusion spectra of DHS samples of different growth years to be identified by classification, PLS-DA was used to build a PLS-DA model based on nano-effects feature fusion spectra. Of these, 70% of the DHS samples were classified as the training set and 30% were classified as the prediction set, with the training and prediction sets proceeding by random classification. The results showed that the PLS-DA model based on nano-effects feature fusion spectroscopy can achieve accurate discrimination of DHS samples of different growth years with 100% accuracy [65].

Long et al. used partial least squares discriminant analysis (PLS-DA) to identify the geographical origin of the lily based on the collected nano-effect NIR spectroscopy, achieving 100% classification accuracy [73]. Lan et al. collected nanomaterial-based fluorescence splicing spectra of *Citri Reticulatae Pericarpium* samples and achieved 100% sample species identification and 98.04% storage year identification by partial least squares discriminant analysis [28]. In order to classify dendrobe of different geographical origins, Lv et al. used partial least squares to process the NIR spectral data of dendrobe samples and achieved 100% accuracy in identifying the origin of the samples [74]. Arslan et al. used partial least squares(PLS) to establish the quantitative analysis models of the total flavonoid content and other components in black goji berries based on near-infrared fourier transform spectroscopy [34]. Yin et al. developed a quantitative prediction terahertz time-domain spectroscopy model of the ternary flavonoid mixtures (genistein, naringenin, daidzein) by means of partial least squares regression, achieving simultaneous prediction of the concentrations of these three analytes [26]. Wang et al. used partial least squares regression (PLSR) to develop a calibration model for near-infrared spectroscopy that could be used to rapidly monitor the concentration of isoflavone compounds during *Pueraria lobata* extraction [35]. Betances-Salcedo et al. used a modified partial least squares (MPLS) regression method to develop a data calibration model to evaluate the NIR spectral data of 99 propolis samples, which well quantified the composition of flavonoids and flavonols, flavanones and dihydroflavonols in propolis [36]. The MPLS model calculates and standardized the NIR residuals for each factor and wavelength and is typically more stable and accurate than the standard PLS algorithm.

#### *5.2. OPLS-DA Analysis*

Orthogonal partial least squares discriminant analysis (OPLS-DA) is a very effective supervised analysis method that is commonly used to deal with classification and discrimination problems [98]. The orthogonal partial least squares discriminant analysis (OPLS-DA) adds a positive exchange algorithm to the partial least squares discriminant analysis (PLS-DA), which can filter out signals that are not relevant in the model's classification matrix [99]. Therefore, the OPLS-DA model is able to maximize the classification differences between groups and can better identify differences in chemical composition between groups compared to the PLS-DA model. The classification indicators of the OPLS-DA model include accumulated explanatory power parameters (R2X, R2Y) and predictive ability parameters (Q2) [100]. Among them, R2X and R2Y respectively represent the percentage of X and Y matrix information that the OPLS-DA classification model can

explain, and Q2 is calculated through cross-validation to evaluate the predictive ability of the OPLS-DA model.

The closer these indicators are to 1, the better the OPLS-DA model fits the data and predictive power. When these indicators are greater than 0.5 the model is considered to have good results. In the results of Hai et al., an OPLS-DA model for nano-effects feature fusion spectroscopy was developed with model parameters including R2X, R2Y and Q2 are both greater than 0.9, indicating that the model has good goodness-of-fit and prediction ability to successfully distinguish between different cultivation mode of DHS samples [65].

#### *5.3. VIP Value Analysis*

The variable importance for the projection (VIP) value describes the importance of each variable in the models, and variables with a VIP score above 1 will be considered to contribute significantly to the PLS-DA model, thereby classifying and screening important spectral information [101]. Screening valid information through VIP can improve the model classification accuracy for subsequent chemometric analysis.

In order to improve the accuracy of identifying the cultivation methods and growth years of *Dendrobium huoshanense* (DHS), Hai et al. processed the spectral data by chemometrics after obtaining nano-effect near-infrared spectroscopy and nano-effect mid-infrared spectroscopy of 320 DHS samples [65]. After screening by variable importance for the projection (VIP greater than 1), multivariate data extraction and integration are performed to obtain the feature vectors of the fusion spectra. The feature vectors were then combined with partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) to identify DHS in different growth years and cultivation methods.

#### *5.4. Data Fusion Strategy*

A data fusion strategy is the integration of data from all sources to provide complementary data on the overall chemical signature [102]. It can be divided into three levels: low, medium and high. Compared with single data analysis, the data fusion strategy provides more efficient and accurate chemometric characterization, which is advantageous for tracing geographical origin [103] and quality identification [104]. Low-level data fusion is the concatenation of signals from different analysis instruments to form a new matrix where the rows represent the number of samples to be analysed and the columns represent the signal variables. Medium-level data fusion extracts the desired features from the signals of different analysis instruments and then concatenated these features into a new matrix for multivariate classification analysis. High-level data fusion calculates the classification results for each dataset separately and then finishes the combined analysis by assigning ratios based on discriminant accuracy.

The original data of the DHS samples were screened with VIP to retain nearly half of the important variables [65]. The extracted feature vectors were then subjected to data fusion to obtain the NIR-MIR fusion spectra and nano-effect NIR-MIR fusion spectra, which contain more comprehensive and valid information compared to the original spectra.

#### *5.5. Sampling Error Profile Analysis (SEPA) Method*

The sampling error profile analysis (SEPA) method is based on the Monte Carlo sampling(MCS) strategy and error profile analysis and can be used in outlier detection, CV, pretreatment method and wavelength selection, and model evaluation [105]. Multiple sub-models and their resulting sub-errors were obtained from the MCS. Error distribution analysis was performed on these sub-errors, from which the median, variance and bias of the errors were estimated. In spectral analysis, the choice of the number of latent variables (LV) and the evaluation of the model are essential for the optimization of the spectral analysis model. Cross validation (CV) is a commonly used method for selecting the number of LVs and can be used for model optimization. In both model optimization and evaluation, error analysis of statistical models is required. The median is more robust and can evaluate

the model more accurately instead of the mean. The variance and bias of the model determine the predictive power of the model. As the complexity of the model increases, the bias of the model becomes smaller and the variance higher, but the generalization ability decreases and the prediction error becomes more significant, resulting in over-fitting of the data. Therefore, an optimal model should have a good complexity and a small prediction error, which means that the model is accurate and robust. Meanwhile, after the data have been fitted, the parameters selected in the model need to be tested for adequacy. The SEPA method can make the model more predictive and stable.

Xu et al. established a SEPA-PLS model based on the near-infrared (NIR) and ultraviolet–visible (UV–Vis) dual spectra of *Pueraria lobata* and achieved online quantitative monitoring of total flavonoids during the extraction of *Pueraria lobata* with good accuracy and precision [38]. SEPA-PLS models were developed using individual nearinfrared (NIR) and ultraviolet-visible (UV–Vis) spectra from the extraction process of *Pueraria lobata*. Possible outliers were screened by error analysis. Finally, NIR and UV–Vis spectral data were fused to construct the dual-spectrum model.

#### *5.6. Principal Component Analysis*

Principal Component Analysis (PCA) is a classical multivariate statistical and data processing method that is widely used in a variety of analytical fields [106,107]. PCA can be used to extract feature variables, reduce the dimensionality and removes overlapping information from the data set without reducing the variance, and highlights the characteristics of the data through principal components. PCA is an orthogonal transformation method that changes the original correlated variables to uncorrelated components, which are named principal components (PCs), namely, a linear combination of the original variables. PCs are arranged in descending order of variance. When the cumulative variance contribution rate is more than 85%, these PCs can be considered to be able to replace the original data set [108].

Yin et al. collected terahertz time-domain spectroscopy (THz-TDS) of 10 common flavonoids, including baicalein, baicalin, apigenin, quercetin, naringenin, hesperetin, daidzein, genistein, puerarin, and gastrodin. Then the THz-TDS of these flavonoids were qualitatively identified and quantitatively analysed by chemometric methods, including principal component analysis (PCA), support vector machine (SVM), partial least squares regression (PLSR) and artificial neural networks (ANN) [26]. They performed a PCA method of the THz-TDS data of all samples and extracted the top five principal components representing the important information of the original data based on the three-dimensional score graph.

#### *5.7. Support Vector Machine*

Support vector machine (SVM) is a promising classification and regression technique for solving linear and non-linear multivariate calibration problems with excellent generalization capabilities [109]. Compared with other statistical methods, SVM does not require a large number of training samples for modeling [108]. Generally, PCA is combined with SVM techniques in order to improve the prediction accuracy of classification models.

Yin et al. used the THz-TDS spectral feature variables extracted by principal component analysis (PCA) as input variables of SVM to classify and identify 10 flavonoids. The model had 100% classification accuracy compared to the original spectral results [26]. Yan et al. performed quantitative analyses of three structurally similar flavonols by terahertz spectroscopy combined with partial least squares regression (PLSR) and least squares support vector machine (LS-SVM) [31]. The LS-SVM model demonstrated better results compared to the PLSR model for myricetin, quercetin, and kaempferol, respectively. He et al. used partial least squares (PLS) and least squares support vector machine (LS-SVM) to build prediction models and combined with near-infrared hyperspectral imaging to determine the total polysaccharides and total flavonoids content in *Chrysanthemum morifolium*, and obtained good prediction results [40]. Zhang et al. performed feature extraction by

principal component analysis (PCA), developed partial least squares (PLS) and least squares support vector machine (LS-SVM) models, and used near-infrared hyperspectral imaging (NIR-HSI) techniques to determine the total flavonoid content in black goji berries [39].

#### *5.8. Artificial Neural Network*

Artificial neural network (ANN) is a commonly used non-linear econometric method, often used to solve machine learning problems such as regression and classification [110]. ANN is based on the operating principles of biological nerve cells and uses mathematical expressions to simulate the signal transmission between neurons, thus constructing interconnected hierarchical artificial neural networks. In spectral analysis, spectral data are introduced into the ANN model as an input layer and the output layer is the predicted result. The performance of the model is evaluated by the coefficient of determination and root mean square in the calibration (RMSEC) and the prediction set (RMSEP) [111]. Yin et al. used ANN regression models combined with THz-TDS for the quantitative detection of ternary mixtures of flavonoids (genistein, naringenin, daidzein) and showed good predictions [26]. Nie et al. used visible and near-infrared spectroscopy (Vis–NIR) combined with several chemometric models, including partial least squares (PLS), artificial neural networks (ANN), multiple linear regression(MLR) and least square-support vector machine (LS-SVM), to perform a rapid determination of the content of chrysin and galangin in poplar propolis. Among them, the ANN model achieved the best results [37].

#### *5.9. Soft Independent Method of Class Analogy*

The soft independent method of class analogy (SIMCA) is a classical class modeling technique that incorporates principal component analysis (PCA) to reduce the dimensions of the spectral data and provides a high dimensional variations classification [112]. The SIMCA model contains a collection of mutually independent PCA datasets. The training set is modeled by PCA and new samples can be fitted to the model and classified according to their similarity or dissimilarity to the training set. Chen et al. used partial least squares discriminant analysis (PLSDA) and soft independent modeling of class analogy (SIMCA) to construct the discriminant models that combined NIR spectral data to classify 250 notoginseng samples from different geographical origins, and the models achieved 100% sensitivity and 100% specificity on both the training and test sets [69].

#### **6. Conclusions**

The development and application of natural products have always been an area of significant interest to researchers. The abundant pharmacological activities of natural flavonoids demonstrate their adequate potential for future development and application in therapeutic drugs, functional foods and cosmetic additives. Phytochemical and pharmacological studies of medicinal and food homology materials are becoming increasingly attractive because of their combination of medicinal and food health effects. Nevertheless, to date, there are few reviews on the application of spectroscopic analysis of medicine and food homology flavonoids. This paper reviewed common spectroscopic methods applied to the analytical study of flavonoid components in medicinal and food homology materials, including qualitative and quantitative analysis of the compound structure and content, identification of herbal cultivation methods, growth years and geographical origin, interaction studies when combined with biomolecules, and chemometric methods used in combination with spectroscopic techniques.

Nuclear magnetic resonance (NMR) spectroscopy is a common and effective method for accurately identifying the chemical structure of multi-component complex samples. However, NMR has limited applicability in other analytical applications and is expensive. Terahertz spectroscopy is a very powerful technique that has emerged in recent years and has the advantages of being fast, safe and non-destructive, enabling the effective identification of structurally similar biomolecules. However, terahertz spectroscopy also has limitations in terms of limited penetration, scattering effects and limited sensitivity, and

is very costly. Fluorescence spectroscopy is a sensitive, simple and rapid detection technique that is commonly used for quality control of food products and monitoring and analysis of the environment. In recent years, as nanotechnology becomes more sophisticated, various new nano-fluorescent sensor-based fluorescence spectroscopy has found more applications in quality assessment. Near-infrared (NIR) spectroscopy is a simple, fast, accurate, nondestructive and will not produce waste products technique that has been used in a variety of fields for process analysis and quality control in recent years. However, NIR spectra usually rely on reference methods and need to be combined with chemometrics to build models. In addition to simple operation and fast analysis speed, UV–Vis has the advantage of greater sensitivity and selectivity. UV–Vis detects only information about specific groups in the molecule, so UV–Vis cannot obtain information about all active compounds, but it is possible to get rid of the interference of solutions during spectroscopic analysis. In contrast, NIR is greatly affected by the solution, but more signals of the compound can be obtained. Combining NIR and UV–Vis, therefore, gives more comprehensive and effective information in a dual spectrum. Hyperspectral imaging (HSI) combines imaging and spectroscopic techniques so that spectral and spatial information about the sample can be obtained simultaneously. In addition, hyperspectral imaging enables more representative spectral information to be obtained than near-infrared spectra acquired from a single point. Compared to conventional analytical methods such as liquid chromatography, hyperspectral imaging is faster and more non-destructive.

A growing number of flavonoid components of medicinal and food homology materials have been isolated, identified and studied, based on the development of various techniques of spectroscopic analysis and other analytical methods. Nevertheless, continuous efforts are still needed to develop more analytical techniques with development potential to ensure quality control of medicinal and food homology materials and further clarify the potential molecular mechanisms. This will provide favorable conditions for better product development and market application of medicinal and food homology materials. This review may contribute to a rapid understanding of the application of spectroscopic techniques to flavonoid components in medicinal and food homology materials and provides an important reference for the research and development of flavonoids in medicinal and food homology materials.

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

**Funding:** This work was financially supported by Guangxi Innovation-Driven Development Special Fund Project (Funding Number: GUANGXI AA18242040, Funder: Guangxi Science and Technology Department), Guangxi Natural Science Foundation (Funding Number: 2020GXNSFAA297178, Funder: Guangxi Science and Technology Department) and National Natural Science Foundation of China (Funding Number: 22078073, Funder: National Natural Science Foundation of China). This work was also supported by Fund of High-level talents of Youjiang Medical College for Nationalities (Funding Number: YY2021SK02, Funder: Youjiang Medical College) and Fund of Science and Technology Planning Project of Nanning Science and Technology Bureau (Funding Number: 2020023, Funder: Nanning Science and Technology Bureau).

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** The authors gratefully thank the financial support provided by Guangxi Innovation-Driven Development Special Fund Project, Guangxi Natural Science Foundation, National Natural Science Foundation of China, Fund of High-level talents of Youjiang Medical College and Fund of Science and Technology Planning Project of Nanning Science and Technology Bureau.

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

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