*Article* **Physical and Chemical Properties of** *Acacia mangium* **Lignin Isolated from Pulp Mill Byproduct for Potential Application in Wood Composites**

**Nissa Nurfajrin Solihat 1,\* , Eko Budi Santoso 2 , Azizatul Karimah 1,2, Elvara Windra Madyaratri 2 , Fahriya Puspita Sari 1 , Faizatul Falah 1 , Apri Heri Iswanto 3,4 , Maya Ismayati 1 , Muhammad Adly Rahandi Lubis 1 , Widya Fatriasari 1,\* , Petar Antov 5 , Viktor Savov 5 , Milada Gajtanska 6,\* and Wasrin Syafii 2**

**Abstract:** The efficient isolation process and understanding of lignin properties are essential to determine key features and insights for more effective lignin valorization as a renewable feedstock for the production of bio-based chemicals including wood adhesives. This study successfully used dilute acid precipitation to recover lignin from black liquor (BL) through a single-step and ethanolfractionated-step, with a lignin recovery of ~35% and ~16%, respectively. The physical characteristics of lignin, i.e., its morphological structure, were evaluated by scanning electron microscopy (SEM). The chemical properties of the isolated lignin were characterized using comprehensive analytical techniques such as chemical composition, solubility test, morphological structure, Fourier-transform infrared spectroscopy (FTIR), 1H and 13C Nuclear Magnetic Resonance (NMR), elucidation structure by pyrolysis-gas chromatography-mass spectroscopy (Py-GCMS), and gel permeation chromatography (GPC). The fingerprint analysis by FTIR detected the unique peaks corresponding to lignin, such as C=C and C-O in aromatic rings, but no significant differences in the fingerprint result between both lignin. The 1H and 13C NMR showed unique signals related to functional groups in lignin molecules such as methoxy, aromatic protons, aldehyde, and carboxylic acid. The lower insoluble acid content of lignin derived from fractionated-step (69.94%) than single-step (77.45%) correlated to lignin yield, total phenolic content, solubility, thermal stability, and molecular distribution. It contradicted the syringyl/guaiacyl (S/G) units' ratio where ethanol fractionation slightly increased syringyl unit content, increasing the S/G ratio. Hence, the fractionation step affected more rupture and pores on the lignin morphological surface than the ethanol-fractionated step. The interrelationships between these chemical and physicochemical as well as different isolation methods were investigated. The results obtained could enhance the wider industrial application of lignin in manufacturing wood-based composites with improved properties and lower environmental impact.

**Keywords:** acid precipitation; single and fractionation step; kraft lignin; physical and chemical properties; *A. mangium* black liquor

**Citation:** Solihat, N.N.; Santoso, E.B.; Karimah, A.; Madyaratri, E.W.; Sari, F.P.; Falah, F.; Iswanto, A.H.; Ismayati, M.; Lubis, M.A.R.; Fatriasari, W.; et al. Physical and Chemical Properties of *Acacia mangium* Lignin Isolated from Pulp Mill Byproduct for Potential Application in Wood Composites. *Polymers* **2022**, *14*, 491. https:// doi.org/10.3390/polym14030491

Academic Editor: Nathanael Guigo

Received: 22 December 2021 Accepted: 24 January 2022 Published: 26 January 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/).

#### **1. Introduction**

In 2019, Indonesia was ranked among the top 10 countries concerning pulp and paper production. In 2018, Indonesia produced 16 million tons of paper and 11 million tons of pulp [1]. For every 1 ton of pulp produced, about 7 tons of black liquor (BL) were generated as a residue at 15% solids by weight, with two-thirds of the solids consisting of organic chemicals, and the remains were inorganic chemicals [2]. In recent decades, lignin derived from BL has been considered a natural biopolymer, a viable alternative to the fossil-based chemicals due to its abundance in BL, reaching 45% dry weight [3].

Most BL is incinerated for boiler heating sources and energy, and only 5% of the BL is used for value-added applications [4]. The economical consideration of lignin isolation from BL includes recovery yield, purification, non-uniform structure, and unique reactivity [5]. The three main phenolic hydroxyl precursors in lignin are coniferyl alcohol (G), p-coumaryl alcohol (H), and sinapyl alcohol (S), which are linked to each other mostly by aryl ether linkage (β-O-40 ) [6,7]. The actual properties of lignin, such as thermal stability, reactivity, molecular distribution, and solubility, depend on the ratio of these aromatic units. It varies depending on the technique of extraction and the plant source. For instance, softwoods contain mostly G units; hardwoods include both S and G, while non-wood plants have all three units [8].

Precipitation by dilute acid such as sulphuric acid is a common and feasible technique to isolate lignin from BL [9]. However, using sulphuric acid can increase lignin's ash and sulfur content. Therefore, lignin for sulfur-sensitive utilizations should be restricted [5]. Haz et al. evaluated the effect of four different dilute acids (chloric, sulphuric, acetic, and nitric) on lignin properties. Lignin precipitated by nitric and chloric acid obtained high phenolic hydroxyl both in non-conjugated and conjugated forms (>2 mmol/g), suitable for phenolic polycondensates production and rubber stabilizer [10]. Handika et al. [11] reported that high free-phenolic hydroxyl in lignin increased its reactivity to produce the high-thermal stability of polyurethane resin for textile application. As a polyphenol molecule, lignin contains high free-phenolic hydroxyl groups that are favorable for modifications, such as phenolation and methylolation [12], tailored for increasing its chemical reactivity to formaldehyde in formaldehyde-based resins used in the production of wood composites such as particleboards [13], oriented strand boards [14], and flame-retardant composites [15]. Besides, lignin modification either with poly(butylene succinate) or polypropylene biocomposites increased the thermal stability of kenaf core fiber [16,17]. According to Tejado et al., kraft lignin is suitable for phenol-formaldehyde resin due to its higher amount of free phenolic content, molecular weight, and thermal properties [18]. Therefore, it is necessary to understand the specific chemical structure of lignin to achieve optimal utilization. However, the lack of understanding of the lignin structure-property– application relationship (SPARs) is a major roadblock to further development of lignin [19]. Of these, one characterization technique is insufficient to produce coherent data to identify the feature of lignin because of its complex structure and variation. Therefore, a comprehensive analytical technique is pivotal to understanding the properties of isolated lignin, allowing its large-scale utilization.

This study emphasizes the efficient isolation method of lignin from industrial residues of the pulp and paper industry. Our fundamental comprehensive analytical technique provides knowledge for industries to produce superior lignin-based, value-added products, especially for wood composites. Lignin was isolated by two different methods of dilute chloric acid precipitation. The physical characteristics of lignin, such as morphological structure, were conducted by scanning electron microscopy (SEM). Chemical features, including its total hydroxyl phenolic content and solubility in the organic and base solvent, were determined by ultraviolet-visible spectrophotometer (UV-Vis). The functional group was identified by attenuated total reflection Fourier-transform infrared (ATR-FTIR). 1H and 13C nuclear magnetic resonance (NMR) were employed to predict structural properties of lignin corresponding to its fingerprint signal. The elucidation aromatic precursors unit in lignin structure was analyzed by pyrolysis-gas chromatography-mass spectrometry

(PyGC/MS). Meanwhile, the thermal features of lignin were characterized by thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). Gel permeation chromatography (GPC) was used to measure the molecular distribution of lignin.

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

#### *2.1. Material*

A derived BL from *Acacia mangium* was collected from a pulp and paper mill factory in Sumatra, Indonesia. Hydrochloric acid (HCl), dioxane, sodium hydroxide (NaOH), acetic acid anhydride, dimethyl sulfoxide (DMSO), and tetrahydrofuran (THF) were purchased from Merck (Darmstadt, Germany), while pyridine was obtained from Wako Pure Chemical Industries (Osaka, Japan). Lignin alkali (kraft) from Sigma-Aldrich (Saint Louis, MO, USA) was used for the lignin standard. All chemical materials used in this study were analytical grade without any purification.

#### *2.2. Lignin Isolation*

Dilute acid precipitation was used to isolate lignin from BL through a single-step and fractionated-step based on Hermiati et al. [20] with one major modification. For the single step, HCl 1 M was poured into BL until pH 2. The solution was kept overnight and the residue was separated by decantation. The deionized (DI) water was added to the residue with similar acid volume, and the decantation process was conducted again after 24 h. This process was repeated six times. The residue was kept in the refrigerator overnight and separated by vacuum filtration. Wet lignin on the filter paper was washed with DI water and dried in an oven at 45 ◦C for 24 h. The lignin yield percentage was measured by dividing the dry weight of lignin (g) by the dry weight of BL. Dried lignin was kept in sealed plastic for further analysis.

For fractionated-step, HCl 1 M was added into BL until pH 7. Ethanol, as much as four times the volume of the acid, was added to the solution. Non-lignin components such as sugar and carbohydrates were filtrated as a residue. The filtrate was evaporated until the ethanol completely dried up. The acidification was continued by adding acid until pH 2. Lignin precipitate was separated without decantation six times by water, unlike the single-step. The following step is similar to the procedure from the single-step method.

#### *2.3. Chemical Features Measurement: Chemical Component, Total Phenolic Hydroxyl, and Solubility*

The water content of lignin was determined according to TAPPI T 264 cm-97 [21], and ash content was calculated following the TAPPI T211 om-02 method [22]. Contents of acid-insoluble lignin (AIL) and acid-soluble lignin (ASL) were analyzed based on the method of Sluiter et al. (NREL/TP-510-42618) [23]. Triplicate analysis was performed for all chemical features measurements. Lignin alkali (kraft) from Sigma-Aldrich (370958) (Saint Louis, MO, USA) was used as a reference.

Total phenolic hydroxyl (phOH) was determined by the UV-Vis method [24]. A total of 1 mg/mL of lignin was diluted in dioxane: 0.2 M NaOH (1:1), and the mixture was filtered by microfiltration (0.45 µm). The filtrate was diluted in 0.2 M NaOH until the 0.08 mg/mL concentration was reached. The UV spectrum was recorded in a 200–600 nm range by Shimadzu UV vis-1800 spectrophotometer, where lignin in pH6 was used as reference. The absorbance of maximum spectra at 300 and 350 nm was used to calculate total phOH by the following equation:

$$\text{Total phOH (mmol/g)} = (0.425 \times A\_{300 \text{ nm}}) + (0.812 \times A\_{350 \text{ nm}}) \times \frac{1}{c \times a} \times \frac{10}{17}$$

where *A* is absorbance, *c* stands for lignin concentration, and *a* is path length (1 cm) [24].

The solubility test of lignin in the base and the organic solvent was conducted according to the method by Hermiati et al. Lignin 7 mg/5 mL was dissolved in NaOH pH 12 as the alkali solvent and mixture of dioxane water (9:1). Each solution was diluted 50 times by DI water. The UV spectrum was measured in the range of wavelength 200–400 nm [20].

## *2.4. Morphological Assessment by SEM*

A scanning electron microscope (JSM-IT200, JEOL, Tokyo, Japan) was used to observe morphological surfaces and particle size of the reference lignin and the isolated lignins. Lignin samples were placed on the carbon tube, and the surface was coated with gold using Ion Coater iB2. The micrograph of the sample was recorded at 200 and 5000 magnifications under a high vacuum and working distance of 11 mm with 5.0 kV accelerating voltage.

#### *2.5. Functional Group Analysis by UATR-FTIR*

Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopic equipped with UATR unit cell from PerkinElmer (spectrum two) (PerkinElmer Corporation, Waltham, MA, USA) was employed to investigate the functional group of lignin. The sample was placed on the diamond crystal, and the spectrum at a wavelength of 400–500 cm−1 was taken by pressing the torque knob with the same pressure. An average of 32 scans with 4 cm−1 resolution were used to acquire the spectrum. The same average scanning was carried out for background correction and scanning before analysis.

#### *2.6. Fingerprint Observation by 1H and 1C NMR*

Lignin samples were acetylated before undergoing nuclear magnetic resonance (NMR) and molecular weight distribution test based on Wen et al. [25] method with a minor adjustment. A total of 200 mg lignin was dissolved in an 8 mL mixture of acetic acid anhydride: pyridine (1:1) for 72 h in the dark bottle. Ethanol was added until the mixture was concentrated. Acetylated lignin was obtained by slowly dropping the mixture into ice acid (pH 2) and separated through centrifugation. The wet acetylated lignin was washed with 50 mL DI water three times and freeze-dried until dry acetylated-lignin (AL) was obtained.

Each 20 mg AL sample was diluted in 0.7 mL DMSO. The solution was transferred to a 3 mm tube. 1H NMR (JEOL JNM-ECZR 500, Tokyo, Japan) data points were acquired with an acquisition time of 1.75 s, a relaxation time of 5.0 s, and 24 scans. For typical 13C NMR, 20,480 spectra scanning were averaged to increase the signal-to-noise ratio with 2.0 s delayed relaxation and 0.9 s acquisition time.

### *2.7. Thermal Investigation by TGA and DSC*

The thermal investigation of the isolated lignin was conducted using a thermogravimetric analyzer (TGA 4000, PerkinElmer, Waltham, MA, USA) and differential scanning calorimetry (DSC) (DSC 4000, PerkinElmer, Waltham, MA, USA). For TGA analysis, about 4 mg lignin sample was placed on the crucible ceramics sample holder, and the measurement was conducted under argon atmosphere with the flow of 20 mL/min. The sample was heated from 25 ◦C to 750 ◦C at a 10 ◦C/min rate. The automatic curve of weight loss versus temperature was generated from the instrument.

DSC analysis was carried out with ~4 mg lignin samples on a standard aluminium pan to determine the glass transition temperature (Tg) and curing properties of lignin. Each sample was heated until 300 ◦C with a 10 ◦C/min heating rate under a nitrogen atmosphere (flowrate = 20 mL/min). Tg value was automatically calculated by DSC 4000 pyris 1 PerkinElmer software (Pyris 11 software Version <IP_ADDRESS>2, PerkinElmer, Shelton, CT, USA).

#### *2.8. Chemical Elucidation by Py-GCMS*

Chemical elucidation analysis of lignin was studied by pyrolysis-gas chromatographymass spectrometry (PyGC/MS) (Shimadzu GC/MS system QP-2020 NX, Shimadzu, Kyoto, Japan) equipped with multi-shot pyrolyzer EGA/PY-3030D. Between 500–600 µg of lignin was placed in eco-cup SF PY1-EC50F, and the cup was sealed by glass wool. The ecocup was pyrolyzed at 500 ◦C for 0.1 min using helium as carrier gas and SH-Rxi-5Sil MS column (30 m × 0.25 mm i.d. film thickness. 0.25 µm). The PyGC/MS temperature was programmed as follows: 50 ◦C for 1 min, 5 ◦C/min to 280 ◦C, and 13 min at 280 ◦C. The

mass spectrum was taken at 70 eV with a pressure of 20.0 kPa (15.9 mL/min, column flow 0.61 mL/min). The obtained pyrolysis product was identified by approaching mass spectra and retention times using the data library in NIST LIBRARY 2017.

#### *2.9. Molecular Weight by GPC*

Gel permeation chromatography (GPC) is a rapid and versatile tool to provide information on the molecular weight of lignin. Lignin was dissolved in the THF, and Shimadzu LC-20 (Shimadzu, Kyoto, Japan) equipped with a UV-RID detector was used to quantify the molecular weight distribution acetylated lignin. The analysis was employed using the LF-800 column with an injection volume of 20 uL. Polystyrene standard was used to create a calibration curve and GPC system calibration.

#### **3. Result and Discussion**

#### *3.1. Chemical Composition and Lignin Solubility*

Lignin recovery was one critical factor for selecting the lignin isolation method that is economically feasible. Lignin yield recovery from BL by single and fractionated-step (oven dry based) was 35.39% and 16.34%. Both isolation methods reported lignin recovery yield at the expected range of 20–40% [5]. Ethanol fractionation resulted in lignin depolymerization in the liquid solution, decreasing the solid lignin residue by acid. Lignin yield recovery is related to the larger size of the fractionated-step lignin based on the SEM micrograph. Large lignin particle size results in a smaller reaction surface area for precipitation, reducing the amount of lignin recovered after acid precipitation. This suggestion was in correlation with the ASL content. This finding agreed with Hamzah et al. (2020), where lignin recovery from *Miscanthus x giganteus* decreased from 75% to 25% with the increased ethanol concentration from 0% to 50% [26].

The chemical composition of lignin Is presented in Table 1, where the data is the average value from a triplicate experiment with a deviation standard less than ±5%. Ethanol was added in fractionated-step to precipitate non-lignin components such as sugar and carbohydrates, theoretically increasing lignin purity. However, the total lignin from single-step lignin (~99%) was slightly higher than from the fractionated step. Besides, the impurities component in the single-step lignin, represented as ash content, was lower than the fractionated step. It suggests that the single-step isolation method effectively isolates lignin with high purity.

**Table 1.** Chemical composition of lignin.

Interestingly, lignin isolation from BL by dilute hydrochloric acid obtained high ASL content while reference lignin had low ASL content. Different isolation methods likely obtained the different proportions of AIL and ASL content. Similar results were reported by Sameni et al. [8] where isolation lignin from BL by using dilute sulfuric acid resulted in low ASL (<4%) and high AIL ~91. Sulfuric acid is popular acid to isolate lignin and obtain a high concentration of AIL. Consequently, it will increase sulfuric and ash content [5].

In this study, the highest free phOH content was obtained from the isolated lignin, where the lowest was from the reference lignin. This finding agreed with the previous report where Kraft pulping process and precipitation lignin by HCl enhanced condensed structure and the phenolic hydroxyl group [10,27]. Unfortunately, we could not find the source and isolation process of reference lignin from Sigma-Aldrich. The total phOH content is correlated to the Tg value because a higher condensed structure in polymer

created a high char amount in high temperature. Eventually, the combustion rate can be reduced by the presence of char [11,28,29]. UV spectroscopy is used to monitor the lignin purity and molecular distribution. A similar pattern of UV-Vis spectra from both commercial and isolated lignin is observed in

**ASL (%)**

**Total phOH (mmol/g)**

UV spectroscopy is used to monitor the lignin purity and molecular distribution. A similar pattern of UV-Vis spectra from both commercial and isolated lignin is observed in Figure 1a. The distinct absorption at 215–222 nm corresponded to non-conjugated phenolic groups (excitation of π-π\*) that appear due to shifting band is an effect of hypsochromic NaOH. The spectrum of single-step lignin is slightly higher than others which may correlate to the total phOH (Table 1). Another intensive peak was observed in the range of 296–303 nm, originating from the conjugated phenolic group due to n-π\* excitation [30]. Figure 1a. The distinct absorption at 215–222 nm corresponded to non-conjugated phenolic groups (excitation of π-π\*) that appear due to shifting band is an effect of hypsochromic NaOH. The spectrum of single-step lignin is slightly higher than others which may correlate to the total phOH (Table 1). Another intensive peak was observed in the range of 296–303 nm, originating from the conjugated phenolic group due to n-π\* excita-

*Polymers* **2022**, *14*, x FOR PEER REVIEW 6 of 20

by the presence of char [11,28,29].

**Water Content**

tion [30].

purity.

*3.2. SEM Micrograph of Lignin*

**Table 1.** Chemical composition of lignin.

**(%) Ash Content (%) AIL**

Lignin reference 2.60 ± 0.27 2.44 ± 0.00 96.02 ± 0.50 1.54 ± 0.06 6.00 ± 0.50

Lignin fraction method 15.79 ± 0.74 1.94 ± 0.08 69.94 ± 5.55 28.12 ± 0.94 7.31 ± 0.78

In this study, the highest free phOH content was obtained from the isolated lignin, where the lowest was from the reference lignin. This finding agreed with the previous report where Kraft pulping process and precipitation lignin by HCl enhanced condensed structure and the phenolic hydroxyl group [10,27]. Unfortunately, we could not find the source and isolation process of reference lignin from Sigma-Aldrich. The total phOH content is correlated to the Tg value because a higher condensed structure in polymer created a high char amount in high temperature. Eventually, the combustion rate can be reduced

**(%)**

(**b**)

**Figure 1.** Lignin solubility in base (**a**) and organic solvent () determined by UV-Vis.

A glance at Figure 1b reveals an identical UV-Vis spectrum among three lignin samples when lignin is diluted in dioxane/water. Unlike the lignin in the base solution, solubilization lignin in dioxane/water is limited to wavelengths above 250 nm seen in the spectra Figure 1b. This finding is similar to lignin Alfa grass kraft from industrial waste [31] and Kraft-anthraquinone (AQ) lignin [32]. According to Ammar et al.'s report, the large absorbance of lignin in dioxane/water at 280 nm corresponded to non-conjugated phenolic hydroxyl groups. In comparison, the presence of both ferulic acids and p-coumaric acids could be attributed to the presence of the second type region of lignin absorption at about 300 nm [31]. Lignin reference has slightly higher absorbance than isolated lignin regarding and pores on the lignin surface were observed in more concentrated ethanol [26]. **Figure 1.** Lignin solubility in base (**a**) and organic solvent (**b**) determined by UV-Vis. A glance at Figure 1b reveals an identical UV-Vis spectrum among three lignin samples when lignin is diluted in dioxane/water. Unlike the lignin in the base solution, solubilization lignin in dioxane/water is limited to wavelengths above 250 nm seen in the spectra Figure 1b. This finding is similar to lignin Alfa grass kraft from industrial waste [31] and Kraftanthraquinone (AQ) lignin [32]. According to Ammar et al.'s report, the large absorbance of lignin in dioxane/water at 280 nm corresponded to non-conjugated phenolic hydroxyl groups. In comparison, the presence of both ferulic acids and p-coumaric acids could be attributed to the presence of the second type region of lignin absorption at about 300 nm [31]. Lignin reference has slightly higher absorbance than isolated lignin regarding purity.

had a smaller particle of 59 µm than lignin from fractionated-step isolation (~72 µm). Interestingly, the single-step lignin had the lowest particle size, ~51 µm, indicating that ethanol impacted the behavior of lignin aggregates. At 5000-times magnification (Figure 2d– f), the morphological image of reference lignin (Figure 2d) and lignin from a single-step (Figure 2e) showed more rupture and pores on the lignin surface. Lignin from fractionated-step (Figure 2f) depicted smooth and rigid surfaces. This finding was following lignin isolation from *Miscanthus x giganteus* where the particle size of lignin increased with higher ethanol concentration, from 306 to 2050 nm. Besides, more crystalline structures

#### *3.2. SEM Micrograph of Lignin*

SEM micrograph in Figure 2a–c, shown in 200-times magnification, described irregular and not uniform particles in terms of size from lignin samples. The reference lignin had a smaller particle of 59 µm than lignin from fractionated-step isolation (~72 µm). Interestingly, the single-step lignin had the lowest particle size, ~51 µm, indicating that ethanol impacted the behavior of lignin aggregates. At 5000-times magnification (Figure 2d–f), the morphological image of reference lignin (Figure 2d) and lignin from a single-step (Figure 2e) showed more rupture and pores on the lignin surface. Lignin from fractionatedstep (Figure 2f) depicted smooth and rigid surfaces. This finding was following lignin isolation from *Miscanthus x giganteus* where the particle size of lignin increased with higher ethanol concentration, from 306 to 2050 nm. Besides, more crystalline structures and pores on the lignin surface were observed in more concentrated ethanol [26]. *Polymers* **2022**, *14*, x FOR PEER REVIEW 8 of 20

**Figure 2.** SEM micrographs show the morphological surface of reference lignin (**a**), single-step (**b**), fractionated-step (**c**) at 200× magnification and reference lignin (**d**), single-step (**e**), and fractionatedstep (**f**), at 5000× magnification. **Figure 2.** SEM micrographs show the morphological surface of reference lignin (**a**), single-step (**b**), fractionated-step (**c**) at 200× magnification and reference lignin (**d**), single-step (**e**), and fractionatedstep (**f**), at 5000× magnification.

#### *3.3. Functional Group of Lignin 3.3. Functional Group of Lignin*

H asymmetric in a methyl group [4].

described in the Py-GCMS section.

FTIR is a versatile analytical tool to investigate functional groups and the general structure in lignin. The functional group of lignin may vary depending on the source of lignin. The main functional group in lignin is hydroxyl, methoxyl, carboxylic acid, and carbonyl. Figure 3 shows the FTIR spectra of reference lignin, and two isolated lignin, while a summary of peaks interpretation is available in Table 2. As shown in Figure 3, most of the peaks were similar between three samples, such as the broadband corresponding to hydroxyl group stretching (O-H) from aliphatic and aromatic in lignin structure detected at the wavelength 3500–3400 cm−1 (a), while the sharp peak at 2918 (b) and 2854 (c) cm−1, respectively, were attributed to C-H stretching in methylene from side chain and aromatic methoxyl groups [33]. A small band assigned to carbonyl (C=O) stretching in unconjugated aldehyde and ketone in the ester group at 1716–1704 cm−1 (d) was found in the reference lignin. Still, a pronounced peak was obtained from both isolated lignin due to different lignin structures. Noticeable peaks attributed to vibration of the aromatic skeleton in all types of lignin appeared in the range of 1590–1460 cm−1 (e–g) [34]. An intense FTIR is a versatile analytical tool to investigate functional groups and the general structure in lignin. The functional group of lignin may vary depending on the source of lignin. The main functional group in lignin is hydroxyl, methoxyl, carboxylic acid, and carbonyl. Figure 3 shows the FTIR spectra of reference lignin, and two isolated lignin, while a summary of peaks interpretation is available in Table 2. As shown in Figure 3, most of the peaks were similar between three samples, such as the broadband corresponding to hydroxyl group stretching (O-H) from aliphatic and aromatic in lignin structure detected at the wavelength 3500–3400 cm−1 (a), while the sharp peak at 2918 (b) and 2854 (c) cm−1 , respectively, were attributed to C-H stretching in methylene from side chain and aromatic methoxyl groups [33]. A small band assigned to carbonyl (C=O) stretching in unconjugated aldehyde and ketone in the ester group at 1716–1704 cm−1 (d) was found in the reference lignin. Still, a pronounced peak was obtained from both isolated lignin due to different lignin structures. Noticeable peaks attributed to vibration of the aromatic skeleton in all types of lignin appeared in the range of 1590–1460 cm−1 (e–g) [34]. An intense peak at 1430–1420 cm−1 (h) referred to aromatic skeletal vibration with deformation of C-H asymmetric in a methyl group [4].

peak at 1430–1420 cm−1 (h) referred to aromatic skeletal vibration with deformation of C-

lignin was observed in adsorption at 1326 cm−1 (i) and 1111 cm−1 (l) as the breathing of C-O and deformation C-H in syringyl rings. The band was absent in the reference lignin, but it appeared in two isolated lignins. Conversely, stronger C-O stretching in the guaicyl unit at 1266 cm−1 (j) and 1213 cm−1 (k) was recorded in reference lignin but not in isolated lignin since the lignin was extracted from *Acacia mangium* (hardwood). This finding suggests that the reference lignin may be derived from softwood. This result correlates with Sameni et al. [35] finding where syringyl unit portion was absent in lignin from softwood and abundant in lignin from hardwood. Furthermore, higher peak absorption of unconjugated C-O (at 1030 cm−1 (m)) and CH out-of-plane bending (at 855 cm−1 (n)) in the guaicyl ring in reference lignin suggests a higher concentration of guiacyl in softwood than hardwood. Besides, these peaks were also slightly sharper in the lignin fractionated-step than in the single-step. Further semi-quantitative analysis of syringyl versus guaicyl percentage is

**Figure 3.** Functional group peaks of reference lignin, isolated lignin from single-step and fractionated-step by UATR‒FTIR. **Figure 3.** Functional group peaks of reference lignin, isolated lignin from single-step and fractionatedstep by UATR-FTIR.

**Table 2.** Interpretation bands of UATR-FTIR spectra. **Table 2.** Interpretation bands of UATR-FTIR spectra.

*3.4. 1H and 13C NMR*  NMR analysis is frequently used to predict lignin's structural details concerning its molecular characteristics, reactivity, and composition. The acetylation of lignin before NMR analysis aims to decrease the impurities in lignin that may interfere with the spectrum [25]. Due to the complex structure of polymer lignin, typically simple proton 1H NMR resulted in overlapping spectra which is difficult to justify the structure. Hence, 13C NMR is needed to support the hypothesis of 1H NMR. The presence of condensed and uncondensed aliphatic and aromatic carbon and aryl ethers can be detected by natural 13C isotope NMR. However, longer scanning and acquisition times are required to improve signal sensitivity due to the low abundance of carbon isotope in the lignin molecule [4]. Still, quantitative 13C-NMR can be a useful technique for lignin structural investigation, particularly in determining molecular alterations caused by different isolation procedures and biomass sources [8,25,35]. However, an obvious difference in lignin structure between reference and isolated lignin was observed in adsorption at 1326 cm−1 (i) and 1111 cm−1 (l) as the breathing of C-O and deformation C-H in syringyl rings. The band was absent in the reference lignin, but it appeared in two isolated lignins. Conversely, stronger C-O stretching in the guaicyl unit at 1266 cm−1 (j) and 1213 cm−1 (k) was recorded in reference lignin but not in isolated lignin since the lignin was extracted from *Acacia mangium* (hardwood). This finding suggests that the reference lignin may be derived from softwood. This result correlates with Sameni et al. [35] finding where syringyl unit portion was absent in lignin from softwood and abundant in lignin from hardwood. Furthermore, higher peak absorption of unconjugated C-O (at 1030 cm−1 (m)) and CH out-of-plane bending (at 855 cm−1 (n)) in the guaicyl ring in reference lignin suggests a higher concentration of guiacyl in softwood than hardwood. Besides, these peaks were also slightly sharper in the lignin fractionatedstep than in the single-step. Further semi-quantitative analysis of syringyl versus guaicyl percentage is described in the Py-GCMS section.


2.2.1. Parameter Definition

Let *qh*,*i*(*d*) be defined as a random variable which describes the water volume consumed by a single household *i* within a specific hour *h* of a specific day *d*; if *D* is the number of days with hourly registrations, the recorded sample for the hour *h* is made up of a maximum of *D* data. *M* random samples of *N* households are drawn from the database of *Nmax* households (1 ≤ *N* ≤ *Nmax*) so that each

household can belong to different samples, but every household can only be extracted once within each sample. The aggregated water demand for each day *d* at hour *h* of the random sample *m* is:

$$Q\_{h,N}^m(d) = \sum\_{i=1}^N q\_{h,i}(d) \qquad \qquad h = 1, \dots, 24 \tag{1}$$

For a group of *N* households, the hourly peak water demand *Qm p*,*N*(*d*) of the random sample *m* for each day *d* is defined as:

$$Q\_{p,N}^{\rm m}(d) = \max\_{h=1,\ldots,24} \left[ Q\_{h,N}^{\rm m}(d) \right] \tag{2}$$

where *Qm* μ,*N*(*d*) is the daily mean water demand of the random sample *m* for a group of *N* households for each day *d*, expressed as:

$$Q^{\rm m}\_{\mu,N}(d) = \frac{\sum\_{h=1}^{24} Q^{\rm m}\_{h,N}(d)}{24} \tag{3}$$

Then, for a group of *N* households, the dimensionless hourly peak water demand factor *CPm*,*N*(*d*) of the random sample *m* for each day *d* is defined as:

$$CP\_{m,N}(d) = \frac{Q^m\_{p,N}(d)}{Q^m\_{\mu,N}(d)}\tag{4}$$

By the adopted notation, *CPm*,*N*(*d*) stands for a *CP* value belonging to the *m*-th sample of size *N* and referring to day *d*. According to the purpose of the analysis, it could be either seen as part of a sub-sample of size *D* made up of all the daily observations of *CP* within one specific sample *m*, or, alternatively, it can be considered as part of a sub-sample of size *M* made up of all the observations referring to one specific day *d* across all the extracted samples. In all cases, *CPm*,*N*(*d*) is a single realization drawn from the population of the random variable *CPN* with expected value μ*N*.

#### 2.2.2. Expected Value, Variance, and Distribution of the Sample Mean

For a group of *N* households, the sample mean of the hourly peak water demand factor related to a sample *m* of size *D* is:

$$
\overline{CP}\_{m,N} = \frac{\sum\_{d=1}^{D} CP\_{m,N}(d)}{D} \tag{5}
$$

If *CPN* is an independent random variable, the mean (i.e., the expected value) of the sample means *CPm*,*N* coincides with the population mean μ*N*:

$$
\mu\_N = \frac{\sum\_{m=1}^{M} \overline{\mathbb{C}P}\_{m,N}}{M} \tag{6}
$$

Literature suggests an empirical relationship between μ*N* and the number *N* of aggregated households in the following form [7,10,11,16,28]:

$$
\mu\_N = \frac{a}{N^b} + c \tag{7}
$$

where, *a*, *b*, and *c* are the estimated regression coefficients. *c* is the horizontal asymptote of the function, representing the expected value of the sample mean of peak factor *CPN* for a very large *N*.

According to the classic sampling theory, the standard deviation of the sample mean, usually referred to as, "standard error of the sample mean" [47], *ESD*,*N*, is directly related to the population variance and to sample size *D*:

$$\text{ES}\_{D,N}^2 = Var\left\{ \overline{\text{CP}}\_{m,N} \right\} = Var\left\{ \frac{\sum\_{d=1}^D \text{CP}\_{m,N}(d)}{D} \right\} = \frac{1}{D^2} \sum\_{d=1}^D Var\left\{ \text{CP}\_{m,N}(d) \right\} = \frac{\sigma\_N^2}{D} \tag{8}$$

where σ2 *N* is the population variance of *CPN*.

If the random variable *CPN* is normally distributed, the sample mean will be normally distributed too, with *CPm*,*N* ∼ *N*(μ*N*, *ESD*,*N*) independently of sample size *D*. Otherwise, based on the central limit theorem, when the dimension of the random sample becomes sufficiently large (*D* ≥ 30), the distribution of the sample mean can be approximated by a normal distribution independently of the specific distribution of the random variable *CPN*. To verify the normality of the sample mean *CPm*,*N*, well-known statistical tests can be adopted such as the Kolmogorov-Smirnov (KS) test [48].

If the random variable *CPN* is not independent (as will be demonstrated in the present paper), Equation (6) is still valid, whereas the standard error of the sample mean can be estimated according to the following Equation [45] that explicitly accounts for the covariance matrix:

$$\begin{aligned} \text{ES}\_{D,N}^2 &= \text{Var}\left\{\frac{\sum\_{d=1}^D \text{CP}\_{mN}(d)}{D}\right\} \\ &= \sum\_{d=1}^D \frac{\text{Var}\{\text{CP}\_{mN}(d)\}}{D^2} + \sum\_{i=1}^D \sum\_{\substack{j=1 \\ j=1}^D}^D \frac{\text{Cov}\{\text{CP}\_{mN}(i), \text{CP}\_{mN}(j)\}}{D^2} \\ &= \frac{1}{D^2} \left[ \sum\_{d=1}^D \text{Var}\{\text{CP}\_{mN}(d)\} + \sum\_{i=1}^D \sum\_{\substack{j=1 \\ j=1 \\ j \neq i}}^D \text{Cov}\{\text{CP}\_{mN}(i), \text{CP}\_{mN}(j)\} \right] \\ &= \frac{1}{D^2} \left[ \sum\_{i=1}^D \sum\_{\substack{j=1 \\ j \neq i}}^D \text{Cov}\{\text{CP}\_{mN}(i), \text{CP}\_{mN}(j)\} \right] \end{aligned} (9)$$

where the first term sums up the cross-sample variance for each day *d*, and the second term sums up the cross-correlation among pairs of samples.

Equation (9) estimates the standard error of sample means. When sample data are extracted from a finite population, as in the present paper, the values of the standard error can be influenced and underestimated, because there is a high probability that the same elements are extracted from the total population. Indeed, for *N* = *Nmax* Equation (9) gives a null value for the standard error, which is a degeneration caused by the fact that the *M* samples are made up of exactly the same *CPN* values. Instead, for an infinite population, a finite, although small, value for the standard error should be expected even for very high *N* values.

In case there is no spatial correlation among water demands, the covariance term in Equation (9) is null and the variance collapses back to Equation (8), with an inverse dependence on sample size *D*. In any other case, also accounting for the finite population effect (i.e., a null asymptotical value for *ES*) the dependence of *ES* on *D* can be formulated for each *N* in the generic form:

$$ES\_D = \alpha\_1 \times D^{\beta\_1} \tag{10}$$

where the coefficients depend on the structure of the spatial correlation [30,41]. Since Equation (10) can be applied for each fixed *N*, the following general equation is proposed to consider the additional dependence of the variance on *N*:

$$ES\_{D\mathcal{N}} = \frac{\alpha\_1 \times D^{\beta\_1}}{(\alpha\_2 + N)^{\beta\_2}} \tag{11}$$

When the distribution of the sample mean *CPm*,*N* for a group of *N* households is (at least approximately) normal, the lower and upper limits *CPm*,*N p* of a confidence interval centered on the mean μ*N*, for a predefined probability *p*, are:

$$\left[\overline{\mathbb{C}P}\_{m,N}\right]\_p = \mu\_N \pm \xi\_p \times ES\_{D,N} \tag{12}$$

where ξ*p* is the normal *p*-th quantile; the standard error *ESD*,*N* can be estimated as the square root of either Equation (8) or Equation (9), or directly by its empirical approximation provided by Equation (11), based on the probability distribution of random variable *CPN*. If the sample mean *CPm*,*N* is normally distributed, substituting in Equation (12) the 2.5-th and 97.5-th normal percentile values ξ*p* = ±1.96, the 95% confidence interval is obtained.


Contents

**Preface XI**

Preface

*Ultimate Control*.

have a better quality of life.

Cervical cancer is widely known, highly prevalent, largely targeted, successfully prevented,

The sheet anchor for the success of cervical cancer prevention and control programs is "screening." However, in most of the programs the participation rate of women for screen‐ ing is low because of the following reasons. Lack of awareness in the community should be overcome by well-planned, structured, organized, and focused health education programs. "Availability" is ensured by organizing community-based screening camps. "Affordability" should be taken care of by government and non-government sources. "Accessibility" is en‐ sured by arranging camps in local health posts. "Acceptability" is made possible by health

Other issues, such as precancer treatment with doubtful efficiency, should be subjected to intensive research. Prevention becomes costly with the inclusion of *human papillomavirus*

Hence, this book: *Cervical Cancer—Screening, Treatment and Prevention—Universal Protocols for*

Authors from different countries have given their valuable contributions, explaining the sta‐

The InTech publisher has completed yet another mission along with the authors and Editor

We sincerely hope that our mission and vision are achieved and women worldwide will

**Dr. Rajamanickam Rajkumar** Professor, Community Medicine

Kancheepuram, Tamil Nadu, India

Meenakshi Medical College Hospital and Research Institute Meenakshi Academy of Higher Education and Research-MAHER

effectively screened, efficiently treated, and eligible for control.

vaccine and this should be addressed by health care planners.

tus of cervical cancer screening in limited resource settings.

in striving towards their universal vision of cervical cancer control.

care providers who are local, well-trained women.


**2.1. Spinal cord injury pathological events and timing sequence**

Spinal cord injury (SCI) is often mentioned among the first conditions for which stem cells may provide a new therapy. While recent decades have brought significant improvements in rescuing neuronal activity after SCI at preclinical phases testing several individual approaches, translation to the clinic still remains inefficiently explored. Management for SCI efficient treatment is a difficult task by the intrinsic nature of the pathological cascade of events that makes the SCI a dynamic and progressive disorder. The sentence "Time is spine" defines the crucial importance of timing to rapidly diagnose patients and implement neuroprotective interventions during the acute injury phase (≤2 h) in order to diminish the devastating effects of the secondary phase of the injury (≥2–48 h) which are known to be key determinants of the final extent of neurological deficits. The secondary injury leads to necrosis and/or apoptosis of neurons and glial cells, such as oligodendrocytes, which can lead to demyelination and the loss of neural circuits. Later, in a subacute phase (2–4 days after injury), further ischemia occurs owing to ongoing edema, vessel thrombosis, and vasospasm. Persistent inflammatory cell infiltration causes further cell death and formation of very toxic cystic microcavities over time. Astrocytes, fibroblast, and pericytes proliferate and deposit extracellular matrix molecules into the perilesional area in the already intermediate and chronic phases, few weeks after SCI, when axons continue degenerating (**Figure 1a**) [11].

Spontaneous regeneration during and after having reached the chronic stage occurs due to the neuroplasticity capacity of the central nervous system (CNS); however, very limited gain of function is obtained decreasing advancing age, attributed to both extrinsic and intrinsic factors that modulate further onset, severity, and progression of the injury [12]. The cumulative myelin-associated protein anchorage to myelin sheet debris, in and around the epicenter of the injury, has a strong inhibitory nature. Nogo-A (reticulon-4 isoform A) and myelin-associated glycoprotein (MAG), among other myelin-associated proteins, bind to NOGO receptors to activate the GTPase Rho A, which activates Rho-associated protein kinase (ROCK), a regulator of further downstream effectors, leading to apoptosis and growth-cone collapse of regenerating axons involving neurite retraction [13–17]. Additional external barriers are potently adding to the inhibition of regeneration like the hypertrophic astrocytes and the reactive chemical scar with a number of axonal growth inhibitory chondroitin sulfate proteoglycans (CSPGs) [18].

The chronic SCI repair demands an intensive effort to overcome the impediments and enhance the intrinsic axon regeneration involving an efficient anatomical reorganization [19, 20]. Fortunately, although long distances for axonal reconnection or spared degenerated tracts are normally required, involving a long-term process (a rate of 1 mm/month for axon growth is estimated), it has been shown that as little as 10% of particular tracts can subserve substantial function [19, 20]. This in fact allows hypothesizing for a real recovery, mediated by bridging and partially reconnecting the spared axons allowing subsequent plasticity. Additionally, both, humans and rats, can regain a degree of function after incomplete injury, thought to be mostly due to local structural rearrangements, such as collateral sprouting from remaining axons in the gray matter, rather than by long-distance regeneration of axons in the white matter (**Figure 1b**) [21].


*4.4. Measurement of Flower Anthocyanin*

Sample preparation and extraction methods were as follow, the freeze dried tissues were crushed using a mixer mill (MM 400, Verder Shanghai Instruments and Equipment Co., Ltd., Shanghai, China) with a zirconia bead for 1.5 min at 30 Hz. A 100 mg sample of the powder was weighed and extracted overnight at 4 ◦C with 1.0 mL 70% aqueous methanol. Following centrifugation at 10, 000× *g* for 10 min, the extracts were absorbed and filtered. Then the sample extracts were analyzed using a UPLC system, the analytical conditions were as follow, HPLC: column, Waters ACQUITY UPLC HSS T3 C18 (1.8 μm, 2.1 mm × 100 mm); solvent system, water (0.04% acetic acid): acetonitrile (0.04% acetic acid); gradient program, 95:5 *v*/*v* at 0 min, 5:95 *v*/*v* at 11.0 min, 5:95 *v*/*v* at 12.0 min, 95:5 *v*/*v* at 12.1 min, 95:5 *v*/*v* at 15.0 min; flow rate,. 0.40 mL/min; temperature, 40 ◦C; injection volume: 2 μL [32,50]. The mass spectrometry data was analyzed using the software Analyst 1.6.1 (AB Sciex Pte. Ltd., Concord, Ontario, Canada) and based on the Metware Database (MWDB, Metware Biotechnology Co., Ltd., Wuhan, China), a local self-built database, and a public metabolite information database, to identify anthocyanin compounds. The experiments were repeated three times. 1.8 μm


**9. Laboratory data**

### **9.1. Advantages of animal models**

Testing in animal models has two big advantages:

### **9.2. Showing accuracy and trending**

Bland-Altman and concordance analysis can still be used to assess accuracy and trending. However, the ability to perform multiple readings over a range of cardiac output and condi‐ tions against a gold standard method allow the test method to be fully assessed. Regression analysis and correlation now are the appropriate methods for analyzing the data. Regression plots from each animal experiment are used to show how the test method behaves over a range of cardiac output. The regression line defines the relationship between test and flow probe methods. Correlation reflects the repeatability and trending ability of the test method, rather than the agreement between methods. Either r or R2 are quoted. R2 is used when a relationship exists between the two methods. The correlation coefficient (R2 ) ranges from 0 to 1, where a value > 0.9 signifies good correlation. Ideally, if the test and reference (i.e. flow probe) methods are correctly calibrated, their data should lie along the line of identity y=x and correlation can also be performed along this line, which is known as Lin's concordance. Alternatively, the interclass correlation coefficient (ICC) is used. These methods were used in our 2005 paper to validate the supra-sternal Doppler method in anaesthetized dogs [52].


**Legume Genetics and Biology From Mendel's Pea to Legume Genomics**

Edited by

Petr Smýkal, Eric J. Bishop von Wettberg and Kevin McPhee

Printed Edition of the Special Issue Published in *International Journal of Molecular Sciences*

www.mdpi.com/journal/ijms

**Legume Genetics and Biology**


**Preface to "Antioxidant Capacity of Anthocyanins and other Vegetal Pigments: Modern Assisted Extraction Methods and Analysis"**

"Hence we must believe that all the Sciences are so inter-connected, that it is much easier to study the all together than to isolate one from the others."Rene Descartes (1596–1650), in Rules for ´ the Direction of the Mind (1701).

The investigation of phenolic compounds from the chemical, biochemical and biological points of view during the last few decades has focused intensely on the study of anthocyanins. Interest in understanding the medicinal, therapeutic, and nutritional values of these naturally present phytochemical compounds has increased notably, with the number of papers published in this regard experiencing a significant increase. It is important to highlight the interdisciplinary nature of the research that is currently being carried out in this prolific area of work.

The highly bioactive anthocyanin compounds present in all plant tissues including leaves, stems, flowers, roots and fruits, e.g., berries, impart the colors orange, red, purple, and blue to many fruits, vegetables, grains, flowers, and plants. Anthocyanins are part of a group of secondary metabolites known as flavonoids, a subclass of the polyphenol family, being common components of the human diet. The identification and quantification of anthocyanins in natural products, as well as the elucidation of their effects in vivo and in vitro, requires the development of analytical techniques, and constitutes a challenge.

Anthocyanins also show an antioxidant activity largely dependent on their chemical structure. Numerous epidemiological studies have shown the beneficial effects of a diet rich in fruits and vegetables on human health, and in the prevention of various disorders related to oxidative stress, such as cancers and cardiovascular diseases. On the other hand, extracts rich in anthocyanins are highly valued in the food industry, given their coloring properties, as an alternative to the use of dyes and synthetic lakes. This is because various adverse effects on human health are attributed to synthetic dyes.

Anthocyanins are found in solution as a mixture of different secondary structures: a flavylium ion, a quinoid base, a carbinol base, and a chalcone pseudobase. Various stabilization mechanisms occur through self-association, and intermolecular and intramolecular copigmentation of anthocyanins, leading to the formation of tertiary structures. Anthocyanins show enormous potential for the food, pharmaceutical and cosmetic industries, although their use is limited by their relative instability (the Achilles heel) and low extraction percentages. Much progress has been made in the pretreatment of samples, having also developed some novel extraction techniques, as well as in stabilization techniques.

Thus, this reprint shows studies (Urbonaviciene et al.; Carmona et al.; Fanyuk et al. and Urbstaite et al.) applied to exotic or wild fruits, in which the aim is to elucidate how different external factors, such as climatic and/or geographical factors, affect the production, antioxidant capacity and physicochemical properties of anthocyanins and polyphenols. Moreover, other studies (Gonzalez-de-Peredo et al.; Salazar-Bermeo et al.; Carrera et al. and Ortiz et al.) show the feasibility of ´ applying certain extraction methods to obtain anthocyanins from different wild fruits or by-products. Finally, other authors (Tena et al.; Enaru et al.; Morata et al. and Roca et al.) review the state of the art of extraction methods for these precious compounds, not only polyphenols and anthocyanins, but also carotenes and chlorophyll pigments. These studies provide, among other things, interesting information on the experimental parameters to be applied for each type of extraction, including material preparation, extraction procedures, different recommendations and information on recovery rates. A critical discussion on the effects of biotic and abiotic stressors on living organisms, in which chlorophylls and carotenoids are involved, was also included in the last revision of this reprint.

In short, the possibilities of these vegetable chameleons, as Mikhail Tsvet (father of chromatography) baptized them, are enormous, and some interesting applications can be seen in this reprint published today by *MDPI*, which covers a fairly wide space, honoring the interdisciplinary nature of this polyhedric subject.

Last but not least, we would like to thank the authors for their brilliant contributions, which have made this reprint possible. We would also like to thank *Antioxidants* for trusting us as editors with the publication of this reprint. In addition, we cannot fail to mention the reviewers for the important work they have done, thus ensuring that the results shown in this reprint have sufficient rigor and scientific quality, and for which we also show our gratitude.

> **Agust´ın G. Asuero and Noelia Tena** *Editors*

## *Editorial* **Antioxidant Capacity of Anthocyanins and Other Vegetal Pigments: Modern Assisted Extraction Methods and Analysis**

**Noelia Tena and Agustin G. Asuero \***

Departamento de Química Analítica, Facultad de Farmacia, Universidad de Sevilla, Prof. García González 2, 41012 Sevilla, Spain<EMAIL_ADDRESS>
**\*** Correspondence<EMAIL_ADDRESS>
Anthocyanins [1,2], chlorophylls, and carotenoids [3] are pigments responsible for the colour of many fruits, flowers, and plant tissues. In particular, anthocyanins show potential utility as natural dyes and for having a beneficial impact on health (as demonstrated by many epidemiological studies), as in addition to being safe and innocuous molecules, they have antioxidant properties and exert an effect on the gut microbiome. This special issue of Antioxidants contains twelve contributions—eight research articles and four reviews—including recent advances in the field. The interdisciplinary nature of the subject and the breadth of the content presented by the authors make this special issue very interesting and comprehensive. Modern methods of analysis of anthocyanins, their geographical variability, the improvement of their antioxidant properties, the valorisation of by-products, stability studies, and the metabolomics of chlorophylls and carotenoids are the subject of research or review. "Today, science has few boundaries and collaboration is the name of the game" [4].

Urbonaviciene et al. [5] carried out an evaluation of the antioxidant capacity and physicochemical properties of biologically active compounds (total polyphenol and total anthocyanin content) of wild blueberries by developing chemometric tools that make it possible to relate authenticity and quality control to geographical origin. This is the first study of its kind carried out on wild blueberries from several northern European countries.

The production of anthocyanins from blood oranges requires low temperature conditions, thus being a useful postharvest strategy to be applied in hot climates. Peña et al. [6] found a different response in the case of pear and Moorish oranges at the biochemical and molecular level, the changes being more prominent in the latter case. Blood orange has found use in traditional Asian medicine due to the vital bioactivity of the polyphenols it contains.

The red skin colour of some fruits, such as mango and apple, is vital for marketing and consumer acceptance. Alkan et al. [7] sought to determine whether external pre-harvest treatment of apples and mangoes with phenylalanine can promote red skin colouring of the fruit, and experimentally proved the hypothesis to be true, especially when combined with exposure to sunlight. The level of anthocyanin content of the treated peel of Cripps pink or May Kent apples, as determined by HPLC, increased in both cases.

Janulis et al. [8] performed a qualitative and quantitative analysis of anthocyanin and anthocyanidin composition in a variety of cultivars and genetic clones of American blueberries. The novelty is the growth under Lithuanian climatic conditions. Chemometric tools, such as hierarchical cluster analysis and principal component analysis, indicate that the Woodman cultivar is different from other cranberry cultivars, as its samples contain twice the average total amounts of anthocyanins. A correlation was observed between the total anthocyanin content and the anti-radical and reducing activity of the in vitro extracts.

*Allium cepa* L. has a wide abundance worldwide, its versatility being a feature in culinary uses with the bulbs also showing many interesting medicinal uses, due to their high content of bioactive compounds. Barbero et al. [9] developed assisted extraction methods for the phenolic and anthocyanin compounds present, making use of a Box–Behnken design

**Citation:** Tena, N.; Asuero, A.G. Antioxidant Capacity of Anthocyanins and Other Vegetal Pigments: Modern Assisted Extraction Methods and Analysis. *Antioxidants* **2022**, *11*, 1256. https://doi.org/10.3390/ antiox11071256

Received: 22 June 2022 Accepted: 23 June 2022 Published: 26 June 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/).

for their optimisation. Both methods show high repeatability and intermediate precision, short extraction times, and good recoveries.

Food by-products with high content of dietary fibre and free and bound bioactive compounds are usually discarded. Persimmon by-products are an interesting source of fibre and bioactive compounds, as demonstrated by Valero et al. [10]. The effects of solvent extraction of persimmon dietary fibre by-products after in vitro gastrointestinal digestion and probiotic bacterial fermentation on techno- and physico-functional properties were evaluated.

The purple potato variety is not well known although it is as rich in nutrients, amino acids, and starches as other potato varieties. In addition, it has a high anthocyanin content and its consumption is attractive in relation to human health. Barbero et al. [11] developed a methodology based on ultrasound-assisted extraction to achieve a higher anthocyanin yield. The method has been applied to successfully extract and quantify anthocyanins found in Vitelotte, Double Fun, Highland, and Violet Queen potatoes.

Native Chilean berries (rich in total polyphenols and anthocyanins) were studied by Alcudia et al. [12], and a large-scale extract of maqui berries was tested on intestinal epithelial and immune cells and shown to have potential as a nutraceutical agent with health benefits for the treatment of inflammatory bowel disease (IBD). Total polyphenol content (Folin–Cioculteau) and antioxidant capacity (DPPH, FRAP, and ORAC) were estimated, and the anthocyanin profile was assessed by ultra-high-performance liquid chromatography (UHPL-MS/MS).

Non-conventional extraction techniques meet the requirements of the food industry in terms of legal aspects, waste policy, safety, and environmental protection. However, the selection of a particular process is not an easy task and multiple factors are involved in planning the choice of the most suitable one. Tena et al. [13] provided an overview of recent applications in the field of anthocyanins extracted from different natural matrices, both by conventional and non-conventional techniques. Aspects such as the principles of the techniques involved, optimisation, technical progress, and industrial applications were considered and some useful recommendations were made.

The Achilles heel of anthocyanins is their lack of stability, which is affected by a number of factors such as pH, light, co-pigmentation, sulphites, ascorbic acid, oxygen, and enzymes. Diaconeasa et al. [14] reviewed all these factors affecting anthocyanin stability and degradation, assessing the impact of each parameter in order to minimise negative behaviour and consequently enhance the beneficial health effects.

The unstable nature of anthocyanins, which are affected as we have seen by changes in pH, oxidation, or high temperatures, requires the application of gentle non-thermal technologies for their extraction. Morata et al. [15] reviewed the characteristics, advantages, and disadvantages in the extraction of anthocyanins from grapes by applying non-thermal technologies such as Hugh hydrostatic pressure (HHP), ultra-high pressure homogenisation (UHPH), pulsed electric fields (PEF), ultrasound (US), irradiation, and pulsed light (PL). These techniques significantly increase extraction capacity while reducing extraction times and maintaining antioxidant capacity.

Chlorophylls and carotenoids are two families of antioxidants that include a large and complex number of compounds, present in daily food intake, with added value ingredients and functional properties. Their extraction and analysis require more powerful, precise, and accurate methods at hand, as well as a better understanding of the technical and biological context. Roca and Pérez-Gálvez [16] reviewed recent advances in the metabolomics of chlorophylls and carotenoids (pigmentomics), including material preparation and extraction procedures, and the use of instrumental techniques, e.g., spectroscopic and spectrometric (mass spectrometry to pigment metabolomics). The review also covered a critical account of studies showing the effects of biotic and abiotic stressors on living organisms, in which chlorophylls and carotenoids are involved.

Many thanks to the authors for their brilliant contributions, which have made this special issue possible. Many thanks also to Antioxidants for having us as guest editors for this special issue. Finally, we cannot leave out the reviewers for the important work they have done, which is worthy of mention and praise, and for which we also show our gratitude.

**Funding:** This research received no external funding.

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

## **References**


*4.1. Mental Health in Ukrainian University Students*

This study aimed to examine PA's relationship with the mental health of university students during the COVID-19 pandemic. Results suggest that 37% of the study sample suffered from moderate to severe anxiety and/or depression. Symptoms of depression were significantly more frequently found than symptoms of anxiety. In addition, more people had dual clinically significant mental disorders (anxiety and depression simultaneously) than those who met the singular criteria of the PHQ-9 and GAD-7. Approximately one-quarter of students may suffer from general anxiety disorders, while nearly one-third may be affected by depression. Furthermore, almost one-fifth of university students manifested symptoms of anxiety and depression concurrently. This means that the vast majority of individuals with anxiety symptoms experienced the comorbidity of depression.

The hypothesis that approximately one-third of the university student population suffered from various forms of depression and anxiety (from moderate to severe symptoms) was confirmed in this research regarding depression, but anxiety levels were lower than expected. The present result is consistent with the study of Feng et al. [25] but differs from certain previous studies performed in the student population during the COVID-19 pandemic [18,20,21,28]. The prevalence of depression in the present study sample was 32%. In comparison, between 23% [18] and 32% [25] of depression was reported among college and university students in China. General anxiety disorder was found in approximately 24% of Ukrainian university students, as compared to 4% [24], 28% [25] and 45% [18] among Chinese college students, 35% of Polish university students [20], and 56% in nursing students from Israel [28]. The mean anxiety score was 6.43 in the present sample of Ukrainian university students, while Jordanian students reported a mean anxiety score of 8.4 [21]. Anxiety in the present study was lower than in most previous studies reported for both students and general populations.

A review indicated that the prevalence of anxiety and depression ranged between 16–28% of the Chinese population during the COVID-19 pandemic at the time of publishing [13]. However, differences between particular studies can be seen in mental health symptoms during the COVID-19 pandemic. More specifically, moderate to severe anxiety was noted in 29%, whereas moderate

to severe depression was reported in 16% of the population living in China during the COVID-19 pandemic outbreak [16,17]. Among Chinese people younger than 35, 38% reported general anxiety disorder (GAD), and 22% reported depression symptoms [9,10]. Ahmed et al. [6] found 19% of people suffered from anxiety and 27% suffered from symptoms of depression ranging from moderate to severe among 1074 Chinese people living in Hubei province. The prevalence of depression, anxiety, and comorbidity of depression and anxiety was 48.3%, 22.6%, and 19.4%, respectively, among people from Wuhan province in China [52]. Approximately 23% of individuals from Cyprus reported moderate to severe anxiety symptoms, and 9% reported moderate to severe depression symptoms [22].

The differences between particular studies may be determined, besides cross-cultural differences, by the various methods used to measure anxiety and depression, the distinct quality of the study, and the various cut-off criteria for diagnosing anxiety and depression. A recent meta-analysis indicated that the prevalence of depression assessed using the PHQ-9 scores ≥10 are often overestimated when compared to a diagnosis of major depression on the base of Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders (SCID) [63]. On the other hand, the PHQ is a self-reported screening questionnaire used to detect a risk of depression by an average non-clinician person, so overestimating seems to be a desired feature in this case. Another systematic review and meta-analysis [64] showed that PHQ-9 has acceptable diagnostic properties for major depressive disorders (with cut-off points between 8–11), and diagnostic accuracy was reasonably consistent despite the clinical heterogeneity of the included studies. Most likely, certain anxiety and depression measures present better sensitivity, specificity, and reliability than others. A previous meta-analysis showed a wide range of differences in the global prevalence of anxiety among medical students, which may also be related to cross-cultural differences [65]. Moreover, research was conducted at different stages of the COVID-19 outbreak. Many countries in the world introduced several coronavirus-related restrictions in traveling; shopping; access to education, medical and social services; outdoor physical activity; gatherings; and meetings with friends and family. However, these restrictions were changed systematically within each country. As a consequence, a reliable comparison of mental health during the COVID-19 pandemic may be very difficult to achieve.

Consistent with most of the previous research, the analysis of variance showed that female Ukrainian university students score higher than men in anxiety and depression [16,18–23]. However, it is essential to note that these differences are rather weak, since the effect size was very small (η*p* 2 = 0.03), and sex was not found to be a predictor for both anxiety and depression. Previous research indicated that the fear of the COVID-19 disease was positively related to a younger age, being female, and those more likely to keep smoking and drinking alcohol among medical students in Vietnam [26]. Among Jordanian university students, higher anxiety was reported in females, individuals with the lowest monthly income, those with less coronavirus knowledge, and those who believed the disease was part of a conspiracy [21].

The present results do not differ substantially from previous research conducted before the coronavirus pandemic outbreak [30–33]. It is confirmed that university and college students experience consistently higher rates of mental health disorders than other populations, which may be slightly elevated by the COVID-19 pandemic and lockdown restrictions. The findings from the regression analysis suggest that exposure to COVID-19 and individual differences in the perception of the coronavirus impact (PCI) showed significant but weak associations with anxiety and depression, so other factors may be more important in explaining mental health among Ukrainian university students.

However, the COVID-19 pandemic has given rise to many new concerns among the academic population. Besides the usual restrictions, university students have to contend with virtual learning during the COVID-19 pandemic. Online learning was not available to many students due to the lack of computers or internet access. During the pandemic, neither the students nor the teachers had been trained in the use of large-scale web-based teaching platforms and technology. Teachers were not familiar with the new online learning methodology. They had to change learning plans to adapt to the unique situation rapidly. Thus, the virtual classes were stressful for both teachers and students [18,66–69].

Furthermore, most of the students lost their part-time jobs which supported them while studying and offered them financial independence [70]. Adults who lived in dormitories had to return to their family homes and be dependent on their parents. During the first months of the outbreak of the pandemic, a prevalence of uncertainty regarding the near future, examinations, completing classes and finishing studies, the financial situation, and housing and social situation was evident [69]. Cao et al. [24] showed that economic factors affected daily life during the COVID-19 pandemic and that delays in academic activities were positively related to anxiety symptoms among Chinese college students. Furthermore, Qiu et al. [19] suggested that young adults tend to obtain a large amount of information from social media, which may elevate stress. Overall, the SARS-CoV-2 was perceived as a moderately dangerous disease by most students [21].


**Author details**

Karim Keshavjee1 \*, Dustin Johnston-Jewell2 , Brian Lee2 and Robert Kyba3

1 University of Toronto, InfoClin Inc., Toronto, ON, Canada

2 Normative Inc., Toronto, ON, Canada

3 Strategic Global Counsel Inc., Toronto, ON, Canada

\*Address all correspondence to<EMAIL_ADDRESS>
© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

*Designing Disease-Specific mHealth Apps for Clinical Value DOI: http://dx.doi.org/10.5772/intechopen.99945*


2.2.1. Preparation of Co2+ and Ni2+ Solutions

The sulfate solutions containing 1 mM metal ions were prepared by dissolving CoSO4·7H2O (99.0%, Wako Pure Chemical Industries, Ltd., Osaka, Japan) or NiSO4·6H2O (99.0%, Wako Pure Chemical Industries, Ltd., Osaka, Japan) in deionized (DI) water (18 MΩ·cm, Mill-Q® Integral Water Purification System, Merck Millipore, Burlington, Vermont, USA). For the preparation of 1 mM metal chloride solutions, CoCl2·6H2O (99.0%, Kishida Chemical Co., Ltd., Osaka, Japan), or NiCl2·6H2O (98.0%, Kishida Chemical Co., Ltd., Osaka, Japan) was dissolved in DI water. The initial pH of sulfate and chloride solutions was adjusted to 4.0 using 1 M H2SO4 and HCl (Wako Pure Chemical Industries, Ltd., Osaka, Japan), respectively. The total concentration of SO4 2− and Cl− were fixed to 0.1 M using Na2SO4 (99.0%, Wako Pure Chemical Industries, Ltd., Osaka, Japan) and NaCl (99.5%, Wako Pure Chemical Industries, Ltd., Osaka, Japan) to normalize their effects on experiments.

#### 2.2.2. Cementation Tests

The cementation tests were carried out in a 50 mL Erlenmeyer flask using a thermostat water bath shaker (Cool bath shaker, ML-10F, Taitec Corporation, Saitama, Japan) with 40 mm of shaking amplitude and 120 min−1 of shaking frequency at 25 ◦C for 24 h. (Note that these parameters were selected based on our preliminary experiments). Ten milliliters of the prepared solution were added to the flask, then ultrapure nitrogen gas (99.9%) was introduced for 15 min before experiments to maintain an oxygen-free environment. One-tenth gram of Al powder and/or a predetermined amount (0.01, 0.05, 0.1, 0.2, 0.4 g)

of additive (i.e., AC, TiO2, and SiO2) were added to the solution. Ultrapure nitrogen gas (99.9%) was further introduced to the flask for 5 min, then the flask was tightly capped with a rubber cap and sealed with parafilm, and an experiment was conducted. After 24 h, the suspension was filtered using a syringe-driven membrane filter (pore size: 0.2 μm, LMS Co., Ltd., Tokyo, Japan); final pH of the filtrate was measured. The filtrate was diluted with 0.1 M HNO3, and the concentrations of metal ions were analyzed by inductively coupled plasma atomic emission spectroscopy (ICP-AES, ICPE-9820, Shimadzu Corporation, Kyoto, Japan). The recovery efficiency of Co2+ and Ni2+ was calculated based on Equation (4):

$$\text{Recovery efficiency}, \ R = \frac{\mathcal{C}\_i - \mathcal{C}\_f}{\mathcal{C}\_i} \tag{4}$$

where *Ci* and *Cf* are the initial and final concentrations of metal ions, respectively.

#### 2.2.3. Surface Analysis

The solid products obtained by filtration were washed 5 times with DI water, dried in a vacuum oven at 40 ◦C for 24 h, and then analyzed by Auger electron spectroscopy (AES) using JAMP-9500F (JEOL Co., Ltd., Tokyo, Japan). The dried residue was mounted on an AES holder using conductive carbon tape. The analysis was conducted under the following conditions: ultrahigh vacuum condition, ~1 × 10−7 Pa; probe energy, 10 kV; and probe current, 19.7 nA. The spectra were analyzed by using Spectra Investigator AES software.

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