|Year : 2022 | Volume
| Issue : 79 | Page : 713-728
A study on the quality evaluation of agarwood of Aquilaria sinensis and Aquilaria malaccensis induced by different inducers based on gray correlation degree and TOPSIS
Zhiling Zhuang1, Shenghong Wu2, Shimin Deng1, Pengjian Zhu1, Xin Zhou1, Xiaoying Chen1, Weimin Zhang3, Xiaoxia Gao4
1 School of Pharmacy, Guangdong Pharmaceutical University, Guangzhou, China
2 School of Pharmacy, Guangdong Pharmaceutical University, Guangzhou; Research Institution of Biology, Zhuhai United Laboratories Co., Ltd., Zhuhai, China
3 State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, Guangdong Open Laboratory of Applied Microbiology, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, China
4 School of Pharmacy, Guangdong Pharmaceutical University; State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, Guangdong Open Laboratory of Applied Microbiology, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou, China
|Date of Submission||23-Jul-2021|
|Date of Decision||18-Nov-2021|
|Date of Acceptance||29-Apr-2022|
|Date of Web Publication||19-Sep-2022|
State Key Laboratory of Applied Microbiology Southern China, Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, Guangdong Open Laboratory of Applied Microbiology, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou - 510070
School of Pharmacy, Guangdong Pharmaceutical University, Guangzhou - 510006
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Background: Agarwood is a resinous heartwood produced in certain Aquilaria species that is often used as a spice and Chinese medicine materials. Objectives: This study aimed to comprehensively evaluate the agarwood quality of different species under the same inducer as well as different inducers of the same species. Materials and Methods: The GC-MS data of agarwood were retrieved by AMDIS to obtain 47 secondary metabolites. Combined with multivariate statistical analysis, 12 secondary metabolites were identified as potentially representing the differences between A. malaccensis and A. sinensis. The gray correlation degree and TOPSIS method were used to comprehensively grade 18 characters, including the AEC, the content of agarotetrol, the apparent abundance of GC-MS fingerprint, and the 15 secondary metabolites representing the 58 batches of samples. The OD values representing the overall desirability of ri (gray correlation degree) and ci (TOPSIS) were ranked, with a higher ranking reflecting, better agarwood quality. Results: The ranking results demonstrated that the agarwood samples of the top 33 in OD value were all induced by FAM71, whereas the agarwood samples of the top 23 were all from A. malaccensis. The agarwood of A. sinensis induced by FA had the lowest OD value. Conclusion: The study demonstrated that the quality of agarwood from A. malaccensis was better than that of A. sinensis using the same inducer of FAM71. In the same species of A. sinensis, the quality of agarwood produced by FAM71 was better than that induced by formic acid alone or NA8 alone. This study provided a theoretical basis for the selection of high-quality agarwood inducer and tree species, as well as a reference basis for the efficient production of agarwood in the actual production process.
Keywords: Agarwood, agarwood inducer, Aquilaria malaccensis, Aquilaria sinensis, quality evaluation
|How to cite this article:|
Zhuang Z, Wu S, Deng S, Zhu P, Zhou X, Chen X, Zhang W, Gao X. A study on the quality evaluation of agarwood of Aquilaria sinensis and Aquilaria malaccensis induced by different inducers based on gray correlation degree and TOPSIS. Phcog Mag 2022;18:713-28
|How to cite this URL:|
Zhuang Z, Wu S, Deng S, Zhu P, Zhou X, Chen X, Zhang W, Gao X. A study on the quality evaluation of agarwood of Aquilaria sinensis and Aquilaria malaccensis induced by different inducers based on gray correlation degree and TOPSIS. Phcog Mag [serial online] 2022 [cited 2022 Dec 8];18:713-28. Available from: http://www.phcog.com/text.asp?2022/18/79/713/356392
- The agarwood qualities of different species under the same inducer as well as different inducers of the same species were be evaluated in this study. The results showed that A. malaccensis had better quality than A. sinensis and the quality of agarwood induced by FAM71 was better than that of NA8 or FA. The quality of agarwood could be quickly and comprehensively evaluated by gray correlation degree and TOPSIS analysis.
Abbreviations used: GC-MS: gas chromatography mass spectrometry; AMDIS: automatic mass spectral deconvolution and identification system; TOPSIS: technique for order preference by similarity to an ideal solution; AEC: the ethanol-soluble extraction content; OD: overall desirability; FAM71: formic acid combined with Hypocrea jecorina M71; FA: formic acid; NA8: Nigrospora oryzae A8; RIs: retention indices; comp.: compound; PCA: principal components analysis; OPLS-DA: orthogonal partial least-squares discrimination analysis; VIP: variable important plot; TCM: traditional Chinese medicine; PECs: 2-(2-phenylethyl) chromones; THPECs: tetrahydro-2-(2-phenylethyl) chromones; EPECs: epoxy-(2-phenylethyl) chromones; DEPECs: diepoxy-(2-phenylethyl) chromones.
| Introduction|| |
Agarwood, also known as Gaharu in Southeast Asia and Chen Xiang in China, is the dark fragrant resinous heartwood secreted by Aquilaria spp. Trees. The chemical composition of agarwood includes sesquiterpenes, chromones, flavonoids, benzophenones, diterpenoids, triterpenoids, and lignans., Agarwood has been renowned for its aroma since ancient times, mainly due to its sesquiterpenoids. In addition, agarwood has been widely used as an ingredient in traditional herbal medicines for its sedative, carminative, digestive aid, gastropathy, and pain reliever.,, In recent years, growing number of studies have shown that the crude extracts or isolates compounds of agarwood have antiasthma, antioxidant, antimicrobial, antidiabetic, and antiatherosclerosis properties. Furthermore, the agarwood essential oil possesses antiinflammatory and antioxidant properties, as well as having certain effects on the central nervous system.,,, The technology of artificial agarwood induction is mainly inspired by the natural agarwood formation process. Compared to natural agarwood, artificial agarwood has a shorter resin production cycle, relatively stable quality, and high yield. In recent years, greater attention has been paid to sustainable planting and management of agarwood to solve the shortage of agarwood in the market, with China and Southeast Asian countries increasing the yield of agarwood through artificial induction.,
Nowadays, inducers are increasingly being used to produce agarwood in Aquilaria trees, which mainly include fungal inoculum and chemical formulations. The effective biological agents that can induce agarwood formation in healthy Aquilaria trees are mainly the pure-culture strains of fungi isolated from natural agarwood. Meanwhile, the fungal inoculum is generally considered both safe and eco-friendly. Liu et al. analyzed agarwood and fungi from five different parts of the same Aquilaria tree, identifying many terpenoids, such as guaiol, agaruspirol, and α-eudesmol using GC-MS. Correlation analysis of the detected compounds with different types of fungi on these five sites indicated that the compounds in agarwood were related to the types of fungi, such as (+)-valencene, which was found to be significantly related to the fungal genus Thaxteriella. The chemical inducers generally include phytohormones, salts, minerals, and biological-derived substances., A satisfactory yield and good quality can be obtained by applying appropriate inducers with special devices. The chemical composition of agarwood is related to the tree species, inducer, and induction duration. Chen et al. investigated the relationship between the expression of chalcone synthase genes and dynamic changes in chromone content in agarwood induced by formic acid stimulation combined with Fusarium sp. A2 inoculation. Chromones were not detected until 2 months later, and their content increased with time, peaking at 12 months, which was consistent with the relative gene expression level of CHS1 also peaking at 12 months. Sun et al. used GC-MS to analyze and identify 232 compounds in agarwood samples from eight different regions across four countries. The sample classification was proven to be regional when combined with factor analysis. Wang et al. used GC-MS to examine the chemical constituents of volatile components and ethanol extracts from different organs of A. sinensis and agarwood grown in different regions. Sesquiterpenoids, an aromatic species, were discovered as the active ingredients in agarwood from different habitats.
Initially, grading of agarwood quality was mainly based on the characters of color, resin proportion, submerged water or not, as well as smell and shape, all of which were highly subjective. At present, the grade and quality evaluation of agarwood are mainly determined by investigating the content of ethanol-soluble extraction and the color reaction. The Standard Nasional Indonesia of Gaharu (SNI 7631:2011) has five grades based on color, weight, and smell: Double Super, Super A, Super B, Super tanggung (under water), and Super tanggung A (up water). Siti Nazirah Ismail et al. used 1H NMR to classify the agarwood from A. malaccensis, reporting that agarwood samples with high contents of kusunol, jinkohol. and 10-Epi-γ-eudesmol could be reclassified as the “High-Grade” Group, while the “intermediate grade” group was dominated by fatty acids and vanillic acid. The “low-grade” group had higher contents of aquilarone derivatives and phenylethyl chromones.
In previous studies, the majority of agarwood collected for the analysis of agarwood did not indicate the composition of the inducer or the induction mode. The uncertainty of the inducer and induction approaches of artificial agarwood have a distinct impact on the study of the chemical composition of agarwood. As a result, there may be some variation in species identification. In this study, the same inducer (FAM71) was injected into two different tree species (A. malaccensis and A. sinensis) using be pinhole-infusion technique. In addition, A. sinensis trees were stimulated to produce resin by the different inducers (FA, NA8, and FAM71). In order to identify the differential secondary metabolites, agarwood trichloromethane extracts were analyzed using the GC-MS and multivariate statistical analysis method. The gray correlation degree method and TOPSIS were used to synthetically evaluate the quality of agarwood produced by different Aquilaria species and inducers. The aim was to provide a basis for the promotion and application of superior quality tree species and efficient agarwood inducers.
| Materials and Methods|| |
Agarwood materials and reagents
Fifty-eight artificial agarwood samples corresponding to two species were selected and analyzed from a farm in Xinyi, China, as well as two states in Malaysia, Penang, and Kedah. The FAM71 method was used to induce the formation of resinous in 5-year-old matured trees, including the two tree species of A. malaccensis and A. sinensis, and 50 batches of agarwood samples that were finally collected. In addition, eight batches of agarwood samples from A. sinensis were induced by FA or NA8. H. jecorina M71 and N. oryzae A8 were selected to inoculate the A. sinensis trees, while the fungal strains were isolated from A. sinensis (Xinyi, China) that was provided by Prof. Zhang (Institute of Microbiology, Guangdong Academy of Sciences) and preserved at the Guangdong Provincial Key Laboratory of Microbiol Culture Collection and Application. The trees were approximately 3–4 m high, more than 10 cm in diameter, and 50–70 cm apart from each other. A drill was used to make a hole that was 0.5 cm in diameter and 4–5 cm deep in the trunks of trees at a height of 1 m. The induced liquid was injected slowly into the xylem of the tree to stimulate resinous secretion. After several months of induction, the trees were harvested and the dark brown resins of artificial agarwood were collected [Figure 1]. The samples were identified as A. malaccensis and A. sinensis by Prof. Yan (College of Traditional Chinese Medicine, Guangdong Pharmaceutical University, China). The detailed information of the sample is shown in [Table 1]. Ethanol and trichloromethane (purity >99.0%) were purchased from Guangzhou Chemical Reagent Factory (China). The agarotetrol standard (>98.6% purity) was purchased from the National Institutes for Food and Drug Control, China. The alkane standards (C10–C31) were purchased from AccuStandard Inc. (USA).
|Table 1: The detailed information of agarwood samples included in this study and the relevant test results|
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All samples were cut into small pieces and ground into powder using a grinder and then filtered using 50-mesh sieves. The powder samples of agarwood (0.5 g) were extracted with trichloromethane (10 mL, 24 h) at room temperature. The solvent was evaporated in a water bath (65°C) to obtain viscous semisolid masses, which were then dissolved in 2 mL of trichloromethane and stored in a dark, air-tight sealed vial at 4°C.
The AEC (%), color reaction, and the content of agarotetrol (%) were tested in accordance with the provisions of The Pharmacopoeia of People's Republic of China (2020) [Table 1].
Apparatus and chromatographic conditions
The GC-MS analysis was performed using a GCMS 7890A-5957C (Agilent Technologies) equipped with a capillary fused silica column HP-5MS (30 m × 0.25 mm I.D. × 0.25 μm film thickness, Agilent Technologies). The oven temperature program was initiated at 90°C, held for 4 min before rising at 2.5°C/min to 130°C, maintained for 20 min before rising at 0.5°C/min to 180°C. Following that, the temperature was maintained for 5 min before rising at 2.0°C/min to 200°C. Finally, the temperature increased at 1.0°C/min to 230°C and was maintained for 120 min.
The other operating conditions included the carrier gas, He (99.999%), at a flow rate of 1 mL/min and an injector temperature of 260°C. A solvent delay of 5 min was used, and a 1 μL sample was injected. The samples were processed using the electron ionization (EI) mode (70 eV). The m/z values were recorded in the 50–500 amu range.
The apparent abundance of GC-MS fingerprint (R)
The ratio of sum peak area in the 130–305 min range of individual agarwood to that in the range of 0–130 min was determined as the apparent abundance of GC-MS fingerprints.
Identification of secondary metabolites
The trichloromethane extract of agarwood was analyzed according to the GC-MS conditions, and the GC-MS data were imported into AMDIS software. The components eluting within the total ion chromatogram were extracted using AMDIS, the matrix interference was resolved, and the overlapping components were removed. The mass spectral fragmentation patterns were compared to those stored in the NIST Mass Spectral Library (NIST14), which was built up using pure substances and mass spectra from literature. The RIs of GC on HP-5MS columns were compared to the RI(s) of pure substances in the library. In order to obtain the linear RI values of the volatile compounds, a series of n-alkanes (C10–C31) were run in similar conditions. The chromatographic peaks were confirmed and the chemical components in the chloroform extract of agarwood were identified. A GC-MS and library search could be used to identify the volatile components, while the chromone could be inferred from its fragments and references. The relative percentage content of each component was calculated using the area normalization method. Finally, the retrieval results were summarized and integrated to produce the secondary metabolites identification table.
The relative percentage contents of the 47 retrieved compounds were analyzed by PCA and OPLS-DA using SIMCA-P+ 14 software (Umetrics, Sweden). PCA generated a scores plot that provides a visual determination of similarity among the secondary metabolite profiles. When a new secondary metabolite exhibited unexpected characteristics that differed significantly from the major good secondary metabolite, it was excluded from the model and diagnosed as something different. Therefore, the PCA score plot could distinguish between different categories of samples. After PCA analysis, a more sophisticated OPLS-DA model (the systematic variation of X is divided into two parts: one is linearly related to Y and the other is orthogonal to Y) with specific discriminant information between different groups was obtained. The substitution test was used to verify whether the OPLS-DA model was overfitting, then the V-plot and S-plot were used to analyze significant differences between the agarwood samples from A. malaccensis and A. sinensis.
Gray relational degree and TOPSIS analysis
The SPSSAU V20.0 online analysis software (https://spssau.com/) was used to analyze TOPSIS and gray correlation degree. The AEC, the content of agarotetrol, R, the relative peak area of 6 groups of common components, and 12 groups of different components were included in the analysis. However, because three compounds were both common components and differential secondary metabolites, so only 18 characters were included in the analysis. Due to the lack of data on the AEC and the content of agarotetrol in 7 out of 58 batches of samples, only 51 batches of samples were available.
The TOPSIS method needed to ensure that the evaluation characters showed all in a positive trend (the greater the value, the better). As agarwood samples with R values of less than or equal to 1 were mostly natural agarwood, the lower the value of R is, the better the quality of agarwood. Therefore, the R fell under the category of low-priority targets. In this study, the counting backward technique was used to transform low-priority targets into high-priority targets. The formula was used to carry on normalization processing of the data, to obtain the normalization matrix value. In formula 1, “n” was the number of indices, “Xij” represented the value of the ith sample on the jth character and “aij” represented the normalized value of the ith sample on the jth evaluation character. The matrix was then imported into the SPSSAU software for TOPSIS analysis [Table 2]. The positive and negative ideal solution distances, D+ and D-, determined, followed by the relative proximity ci, which represented the degree to which the evaluation object is close to the optimal scheme.
However, according to TOPSIS, ci could only reflect the internal relative closeness of each evaluation object, and it was necessary to analyze the gray correlation degree. The gray correlation degree was mainly through selecting the best quality of each character from 51 batches of agarwood to serve as the ideal sample. Based on the ideal sample position, the correlation coefficient between each character of the sample and the ideal sample was calculated. The data were analyzed using the SPSSAU software to obtain ri values. The geometric mean of the ci and ri values was calculated to obtain the OD using formula 2. In formula 2, the “X” represented the character included in the investigation and “k” represented the number of characters.
| Results|| |
Analysis of secondary metabolites
The GC-MS original data were converted into corresponding format files and imported into the AMDIS software to retrieve each peak. The components that eluted in the total ion chromatogram were extracted in AMDIS. The secondary metabolites were identified by comparing their mass spectra and retention index with those of commercial standards. Finally, the results were sorted into a total secondary metabolite retrieval table (see supplementary materials for details). A total of 47 secondary metabolites were retrieved, including 17 sesquiterpenes, 24 2-(2-phenylethyl) chromone compounds, 4 aromatic acids, and 2 fatty acid compounds. There were 6 common peaks in 58 batches of samples [Table 3], with 12 common peaks detected in 26 batches of A. malaccensis and 6 common peaks detected in 32 batches of A. sinensis. The similarity evaluation system for the chromatographic fingerprint of TCM (2004 A) was used to overlap the GC-MS chromatogram [Figure 2]a and [Figure 2]b.
|Figure 2: Overlapped GC-MS chromato gram for 26 batches of agarwood in A. malaccensis (A1-A26) (a), 32 batches of agarwood in A. sinensis (B1–D5) (b), Box plot of 58 (c), and the Percentiles (d) of the relative content of four different types of compounds in 58 samples|
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|Table 3: Identification of common components of fingerprints from 58 batches of agarwood samples|
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Under the same FAM71 inducer, the total contents of aromatic compounds and sesquiterpenes in A. malaccensis were higher than in A. sinensis, but the total content of 2-(2-phenylethyl) chromone compounds were lower than in A. sinensis [Figure 2]c and [Figure 2]d. The relative content of sesquiterpenoids in A. malaccensis was 53% higher than in A. sinensis. The top seven relative contents of sesquiterpenoids were all A. malaccensis, with sample A3 ranking highest and having the lowest R-value (R = 0.75). The total relative content of 2-(2-phenylethyl) chromone compounds in A. sinensis was 10% compared to A. malaccensis, and the R-value of the B24 sample of A. sinensis was the highest (R = 25.07).
In the same species of A. sinensis, the total relative content of sesquiterpenoids in agarwood induced by FA was the highest, followed by the NA8 and the lowest was FAM71 [Figure 2]c and [Figure 2]d. The number of sesquiterpenoids in A. sinensis induced by FA was 37% higher than in agarwood induced by NA8 and was three times that of agarwood induced by FAM71. The total content of chromone compounds in A. sinensis induced by FAM71 was 35% higher than in NA8 induced agarwood and 20% higher than in FA induced agarwood.
Multivariate statistical analysis of differential secondary metabolites
The PCA score plot reveals several trends [Figure 3]a. According to the X-axis, A. malaccensis and A. sinensis can be separated even with the same FAM71 inducement method. There was a tendency to separate the samples of NA8 inoculation (C samples), FA stimulation (D samples), and FAM71 (B samples). In addition, the 26 batches of A. malaccensis agarwood samples were also found to be divided into two groups by the Y-axis. In addition to A20 (resin formation time of 9 months) and A26 (resin formation time is unknown), the resin formation time of A. malaccensis in the negative half of the Y-axis was more than or equal to 11 months. In the positive half of the Y-axis, the resin formation time of A. malaccensis was less than 11 months.
|Figure 3: PCA score plots of 47 secondary metabolites (a), OPLS-DA results (b), Permutation test (c), Variable Important Plot (VIP > 1.00) (d), S-plot from OPLS-DA (e), OPLS-DA scores plots (f), and Biplot (g) of 15 secondary metabolites representing 58 batches of samples|
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The OPLS-DA model was established to further understand the difference in agarwood between A. malaccensis and A. sinensis [Figure 3]b. The 58 agarwood samples were divided into two groups based on A. malaccensis and A. sinensis, with these two groups clustering well and separating significantly. There were three mode parameters for the OPLS-DA model: R2X (cum) and R2Y (cum) represented the explanatory ability of the OPLS-DA model's principal component to variables in the direction of X-axis or Y-axis, respectively, while Q2 (cum) represented the predictive ability of the OPLS-DA model for grouping. The closer the three values were to one, the stronger the explanatory and prediction of the model. In this model, two principal significant components described 38.4% of the variation in X (R2X = 0.384), 92.2% of the variation in Y (R2Y = 0.922), and predicted 64.7% (Q2(cum) = 0.647) according to cross-validation. Therefore, this revealed that the grouping model was capable of interpretation and prediction, and the clustering results were reliable. The permutation test was used for internal validation of the model to prevent overfitting of the model [Figure 3]c. Generally, the Q2 and R2 values of the model must to be validated. When the intercept of the result of the permutation test on the y axis did not exceed 0.05 (Q2 < 0.05), the model could be considered as not overfitted. After 800 permutation tests, the intercept values of R2 and Q2 were 0.614 and -1.13, respectively, all the R2 and Q2 values on the left were lower than the rightmost value, and the intercept of the regression curve of Q2 was less than 0.00. These indicated that the established OPLS-DA model established was not overfitted and had good predictive ability.
The variable importance in the projection method was used to determine the significant differential secondary metabolites that were differentially produced between the agarwood from A. malaccensis and A. sinensis. A VIP was used to select the significant secondary metabolites that were differentially produced between A. malaccensis and A. sinensis [Figure 3]d. A VIP value greater than 1.00 was used as the screening index and 13 differential components were obtained. Moreover, in order to observe the contribution rate of variables in the model to grouping, an S-plot of the relative peak area of all secondary metabolites was generated using the OPLS-DA model [Figure 3]e. The ordinate P (corr) in the S-plot represented the correlation coefficient of each component. The further the component was from the origin, the greater its contribution to grouping. Among them, compounds 27 [P (corr) = -0.705], 6 [P (corr) = -0.701], 30 [P (corr) = -0.424], 43 [P (corr) = 0.542], and 38 [P (corr) = 0.590] contributed to more than 58 batches of samples. Combined with VIP and S-plot, 12 secondary metabolites representing the differences between the samples of A. malaccensis and A. sinensis were screened.
However, 3 of the 12 secondary metabolites screened [Table 4] were also presented in the six common components, resulting in 15 secondary metabolites that represented 58 batches of samples and were then verified using OPLS-DA again [Figure 3]f. The parameters of the OPLS-DA model established in this study were R2X (cum) = 0.653, R2Y (cum) = 0.833, and Q2(cum) = 0.708, indicating that the grouping model had strong interpretation and prediction ability, and the clustering results were reliable. Following that, the Biplot [Figure 3]g was generated using the OPLS-DA model and the 58 sample batches were grouped into two groups based on different tree species. The distribution positions of different components in the Biplot revealed their corresponding contribution rates to the grouping of the two groups of samples. Compounds 6, 11, 12, 16, 20, 27, 28, 30, 31, 33, and 34 were grouped with samples from A. malaccensis, which contributed the most to the grouping. In addition, compounds 23, 38, 43, and 47 were clustered together with A. sinensis and contributed the most to the grouping.
|Table 4: Identification of differential secondary metabolites from 58 batches of agarwood samples|
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Analysis of differential secondary metabolites
In order to prove the differences between A. malaccensis and A. sinensis, as well as to compare the secondary metabolites in agarwood induced by FAM71, NA8, and FA, 12 differential secondary metabolites were divided into two parts: nonchromone compounds and the 2-(2-phenylethyl) chromone [Figure 4].
The relative peak area of sesquiterpenes in A. malaccensis was higher than A. sinensis using the same FAM71 inducer [Figure 4]a. The contents of α-cedrene epoxide and agarospirol in A. malaccensis were eight times that of A. sinensis, whereas the contents of 10-Epi-γ-eudesmol and (-)-aristolene in A. malaccensis were two and four times those of in A. sinensis, respectively. Compared to the three different inducement methods in A. sinensis, the relative peak areas of agarospirol and (-)-aristolene were the highest in A. sinensis induced by FA, which was twice that of NA8 inoculation, and three times that of FAM71. The relative peak areas of α-Cedrene epoxide and 10-Epi-γ-Eudesmol in agarwood induced by NA8 and FAM71 were 10 and 40 times than the FA method, respectively.
|Figure 4: Box-plot of four nonchromone compounds in different components (a). Box-plot of eight chromone compounds in different components (b)|
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There were 8 chromone compounds among the 12 different components, with 6-methoxy-2-(2-phenylethyl) chromone (comp. 30), 7-hydroxy-2-(2-phenylethyl) chromone (comp. 33), and 6-methoxy-2-[2-(3-methoxyphenyl) ethyl] chromone (comp. 38) accounting for a large proportion in all samples [Figure 4]b. Using the same FAM71 inducer, the relative contents of compound 30 and 33 in A. malaccensis were 41% and 26% higher than those in A. sinensis, respectively. However, the relative content of compound 38 in A. malaccensis was 49% lower than in A. sinensis. The relative percentages of the first five differential secondary metabolites were all higher than those of A. sinensis, while the last three differential secondary metabolites were lower. A comparison of different inducers in A. sinensis revealed that the relative percentage of compound 30 in the FAM71-induced agarwood was three times that of the agarwood induced by NA8 and 1.6 times that of the FA-induced agarwood. The relative percentages of compounds 33 and 38 in the FA-induced agarwood were 29% higher than those in the NA8-induced agarwood and similar to those in the FAM71-induced agarwood.
Analysis of TOPSIS and gray correlation degree
According to the TOPSIS analysis, the top five samples with the highest ci values were all FAM71-induced agarwood samples, with the first four samples all from A. malaccensis [Table 5]. The ci values of the three batches of samples induced by NA8 ranked third, sixth, and seventh from the bottom. The FA-induced agarwood sample D5 ranked last out of the 51 batches of samples. The larger the ci value was, the closer it was to the ideal sample. Under the same inducer FAM71 conditions, the agarwood samples from A. malaccensis were closer to the ideal sample than A. sinensis. For A. sinensis, the agarwood induced by FAM71 was closer to the ideal samples than those induced by NA8 or FA.
|Table 5: TOPSIS evaluated the calculation results (ci), correlation coefficient results (ri), OD results, and the ranking of the respective result values|
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The correlation coefficient reflects the degree of coincidence between each character and the ideal value. The average of each character is taken as the correlation degree (ri). The larger the correlation coefficient, the closer it is to the ideal value. In the gray correlation degree ranking, all 26 batches of samples induced by FAM71 from A. malaccensis ranked in the top 26, with A10 ranking first. All 17 batches of A. sinensis induced by FAM71 ranked between 27 and 43. The remaining samples were all in the bottom eight, including three batches of NA8-induced samples and five batches of FA-induced samples with D3 ranking last. Using the same FAM71 inducer, the close degree of each character to the optimal character of A. malaccensis was higher than that A. sinensis, indicating that A. malaccensis had better quality than A. sinensis. For A. sinensis, agarwood induced by FAM71 had better quality than that induced by NA8 or FA.
As the values of ri and ci differed in order, the OD value was used to represent the overall desirability of ri and ci. The top 33 agarwood samples based on OD value were all induced by FAM71, with the top 23 agarwood samples all from A. malaccensis, whereas the FA-induced sample D5 had the lowest OD value. Overall, A. malaccensis had better quality than A. sinensis and the quality of agarwood induced by FAM71 was better than that of NA8 or FA.
| Discussion|| |
At present, various inducers are used in the production of agarwood. It is known that Trichoderma is an effective fungus that can produce agarwood. In addition, when compared to chemical method or fungi inoculation method alone, the quality of agarwood obtained by the combination of chemical reagent stimulation combined with fungal inoculation was closer to that of natural agarwood. Therefore, the FAM71 inducer was used to induce the agarwood in this study. The same compounds, such as baimuxinal and 2-(2-phenylethyl) chromone, were detected in different tree species.
Previous studies on agarwood identification mainly focused on the accumulation of certain sesquiterpenes and PECs in high-quality agarwood. Sesquiterpenes, including benzylacetone, agarospirol, hinesol, (-)-aristolene, guaiol, baimuxinal, and others, cannot be detected in healthy Aquilaria trees, but only in the early stage of cell death in callus., Sesquiterpenes are one of the main pharmacological active components of agarwood, with central inhibitory, sedative, hypnotic, and stomach-strengthening effects.,, There were four sesquiterpenes among the 12 different components, with Agarospirol being the main component of high-grade agarwood that is internationally recognized. Agarospirol and hinesol are mutually opposite isomers that have antigastric ulcer properties and can improve cerebral blood circulation. In addition, some studies have shown that the higher the content of 10-Epi-γ-eudesmol, the higher the quality grade of agarwood. The special aroma of agarwood mainly depends on sesquiterpenoids, but PECs with high boiling points also enhance the stability of the aroma. Furthermore, PECs degrade into aromatic compounds when agarwood is heated; therefore, chromone compounds contribute significantly to the aroma and its duration when heated. The chromone compounds in agarwood are grouped into four types according to their backbone structures: THPECs, EPECs, DEPECs, and PECs. The GC-MS used in this study was only capable of detecting PECs. A comparison by Yang et al. on the quality of agarwood produced from China and Southeast Asian countries revealed that the AEC and total chromone in A. sinensis were both higher than the five agarwood production areas in Southeast Asian countries, including A. crassna and A. khasiana. Yan et al. analyzed agarwood obtained through four different induction methods and discovered that the content of sesquiterpene was the highest in the agarwood obtained by wounding using an axe. Furthermore, the relative content of PECs in the agarwood obtained by both the fungus induction and chemical methods exceeded 60%. At present, the “Qi Nan” agarwood is considered to be high-quality natural agarwood in the agarwood industry, with the sum of the relative contents of 2-(2-phenylethyl) chromone and 2-[2-(4-methoxybenzyl) ethyl] chromone of 51.57–84.71%. In this study, the relative contents of PECs in agarwood induced by FAM71 were all greater than 60% (60.6% for A. malaccensis and 66.3% for A. sinensis). The relative contents of PECs in agarwood induced by NA8 and FA were 49.2 and 55.3%, respectively, which were lower than those obtained using the comprehensive method of FAM71.
Ethanol can extract a large number of chromone and sesquiterpene compounds from agarwood. According to the local standard of Hainan Province of China, the ethanol-soluble extraction content (T, referred to as AEC in this paper) and the total content of 2-(2-phenylethyl) chromone and 2-[2-(4-methoxy) phenylethyl] chromone of agarwood can be divided into five grades (Super grade: T ≥30.0%, total chromone content ≥1.0%. First grade: T ≥30.0%, 1% > total chromone content >0.1% or 30.0 >T ≥20.0%, total chromone content ≥1.0%. Second grade: 30.0% > T ≥20.0%, total chromone content <0.1%. Third grade: 20.0% >T ≥10.0%. Fourth grade: 10.0% >T ≥4.0%). Based on the Group Standard of Zhongshan City (China), agarwood was divided into three grades by AEC (Super grade: T >20.0%, First Grade: 20.0% >T ≥15.0%, Qualified: 15% >T ≥10.0%). In the previous work of this group, 98 batches of agarwood were analyzed by GC-MS. According to the AEC, agarwood samples were divided into three grades (First grade: T≥30.0%, Second grade: 30.0% > T ≥20.0%, Third grade: 20.0% > T≥10.0%). In this study, there were significant variations in the AEC of all samples, ranging from 5.0% to 36.1%. Comprehensive analyses of the various species, different inducement methods, and the AEC were performed [Figure 5]a. Using the same FAM71 inducer, the AEC of A. malaccensis was higher than A. sinensis, and there was a significant correlation between the AEC and species (P = 0.007 (P < 0.01)). A comparison of different inducers revealed that the AEC of A. sinensis was the highest when induced by FA, followed by FAM71, and finally by NA8. However, there was no significant difference in AEC between the three different induction methods.
|Figure 5: The AEC of all samples (a). The correlation curve between the resin formation time and AEC of the agarwood of FAM71 (b). Box plot of the content of agarotetrol and AEC in A. malaccensis and A. sinensis (**P < 0.01) (c)|
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The AEC did not increase even with a longer resin production time. Therefore, analyses of the resin formation time and AEC were carried out, as well as fitting of the relevant curves. The data were presented as an average ± SEM [Figure 5]b. The results showed that the longer the resin formation time, the higher the AEC 11 months ago. At 12 months, the AEC was significantly lower than at 11 months, with AEC declining after the 11th month. The average value of AEC was at its lowest in the 24th month. There may also have been a reduction during the resin production process; a longer resin formation time did not necessarily mean, a higher AEC value. Therefore, the 11th month is recommended as the best time to collect based on the analysis of dynamic changes in AEC and resin formation time.
In the correlation data analysis, there was a significant correlation between agarotetrol and AEC (P = 0.000 (P < 0.01)). Agarotetrol is one of the THPECs commonly found in agarwood. However, because PECs in chromone compounds can only be detected using GC-MS, agarotetrol was determined using the HPLC method. Using the same FAM71 inducer, the agarwood samples were compared according to A. malaccensis (A1–A26) and A. sinensis (B1–B24), respectively. It was discovered that agarotetrol significantly correlated with AEC (P < 0.01) [Figure 5]c. However, C1–C3 and D1–D5 had no correlation with AEC, which could be due to the limited number of samples. In addition, it was possible that there was no correlation between agarotetrol and AEC under FA or fungal-induced conditions. Therefore, further investigations are still required.
| Conclusion|| |
In this paper, the gray correlation degree and TOPSIS analysis were used to comprehensively analyze the 18 characters, including AEC, the content of agarotetrol, R, and 15 secondary metabolites representing 58 batches of samples. It was demonstrated that the quality of agarwood from A. malaccensis induced by FAM71 was better than that of A. sinensis. Furthermore, the quality of agarwood induced by FAM71 was also better than that induced by FA or NA8 alone. These findings provided a theoretical basis for the selection of high-quality agarwood inducer and tree species, as well as a reference basis for efficient production of agarwood in the actual production process.
Thanks are due to Caiyun Hu and Jinghua Hu for assistance with the experiments and to Hua Li for valuable discussion.
Xiaoxia Gao and Weimin Zhang conceived and designed the experiments; Weimin Zhang contributed reagents and materials; Shimin Deng, Pengjian Zhu, Xin Zhou, and Xiaoying Chen performed the experiments; Zhiling Zhuang and Shenghong Wu analyzed the data; and Zhiling Zhuang wrote the paper.
Financial support and sponsorship
This work was supported by the Guangdong Province Basic and Applied Basic Research Fundation 2022A1515011268, the Special Project of International Science and Technology Cooperation Guidance of Guangdong Academy of Sciences (Grant No. 2019GDASYL-0503002), the Open Fund Project of the State Key Laboratory of Applied Microbiology Southern China (Grant No. SKLAM002-2018), the National Basic Research Program of China (Grant No. 973 Program), and the National Natural Science Foundation of China (Grant Nos. 81102418, 31100496).
Conflicts of interest
There are no conflicts of interest.
| References|| |
Tan CS, Isa NM, Ismail I, Zainal Z. Agarwood induction: Current developments and future perspectives. Front Plant Sci 2019;10:122.
Kristanti AN, Tanjung M, Aminah NS. Review: Secondary metabolites of Aquilaria
, a Thymelaeaceae genus. Mini Rev Org Chem 2018;15:36-55.
Li W, Chen HQ, Wang H, Mei WL, Dai HF. Natural products in agarwood and Aquilaria
plants: Chemistry, biological activities and biosynthesis. Nat Prod Rep 2021;38:528-65.
Naziz PS, Das R, Sen S. The scent of stress: Evidence from the unique fragrance of agarwood. Front Plant Sci 2019;10:840.
Hashim YZ, Kerr PG, Abbas P, Mohd Salleh H. Aquilaria
spp. (agarwood) as source of health beneficial compounds: A review of traditional use, phytochemistry and pharmacology. J Ethnopharmacol. 2016;189:331-60.
Xie B, Luo HT, Huang XL, Huang F, Zhang QY, Wu XF, et al
. Pharmacokinetic studies of six major 2-(2-phenylethyl) chromones in rat plasma using ultra high performance liquid chromatography with tandem mass spectrometry after oral administration of agarwood ethanol extract. J Sep Sci 2021;44:2418-26.
Liu YY, Chen DL, Yu ZX, Wang CH, Feng J, Meng Y, et al
. New 2-(2-phenylethyl) chromone derivatives from agarwood and their inhibitory effects on tumor cells. Nat Prod Res 2020;34:1721-7.
Lei ZD, Zhang S, Liu DL, Gao XX, Zhao YM, Cui Y. Evaluation of three different artificial agarwood-inducing methods from Aquilaria sinensis
using antimicrobial activity. Pak J Pharm Sci 2019;32:905-10.
Wang S, Wang CH, Peng DQ, Liu XM, Wu CM, Guo P, et al
. Agarwood essential oil displays sedative-hypnotic effects through the GABAergic system. Molecules 2017;22:2190.
Wang S, Wang CH, Yu ZX, Wu CM, Peng DQ, Liu XM, et al
. Agarwood essential oil ameliorates restrain stress-induced anxiety and depression by inhibiting HPA axis hyperactivity. Int J Mol Sci 2018;19:3468.
Wang S, Yu ZX, Wang CH, Wu CM, Guo P, Wei JH. Chemical constituents and pharmacological activity of agarwood and Aquilaria
plants. Molecules 2018;23:342.
Hishamuddin MS, Lee SY, Ng WL, Ramlee SI, Lamasudin DU, Mohamed R. Comparison of eight complete chloroplast genomes of the endangered Aquilaria
tree species (Thymelaeaceae) and their phylogenetic relationships. [sci rep: 13034]. Sci Rep 2020;10:13034.
Gao XX, Xie MR, Liu SF, Guo XL, Chen XY, Zhong ZJ, et al
. Chromatographic fingerprint analysis of metabolites in natural and artificial agarwood using gas chromatography-mass spectrometry combined with chemometric methods. J Chromatogr B Analyt Technol Biomed Life Sci 2014;967:264-73.
Lin CP, Hsiao YT, Hsiao YA, Chou SJ, Chen AP, Kuo CI, et al
. The complete chloroplast genome of agarwood producing species, Aquilaria sinensis
(Lour.) Gilg: A species on IUCN red list. Mitochondrial DNA B Resour 2019;4:2992-3.
Liu J, Zhang X, Yang J, Zhou JH, Yuan Y, Jiang C, et al
. Agarwood wound locations provide insight into the association between fungal diversity and volatile compounds in Aquilaria sinensis
. R Soc Open Sci 2019;6:190211.
Faizal A, Esyanti RR, Adn'ain N, Rahmani S, Azar AWP, Iriawati, et al
. Methyl jasmonate and crude extracts of Fusarium solani
elicit agarwood compounds in shoot culture of Aquilaria malaccensis
Lamk. Heliyon 2021;7:e06725.
Chen XY, Sui C, Liu YY, Yang Y, Liu PW, Zhang Z, et al
. Agarwood formation induced by fermentation liquid of Lasiodiplodia theobromae
, the dominating fungus in wounded wood of Aquilaria sinensis
. Curr Microbiol 2017;74:460-8.
Chen XD, Zhu XL, Feng MR, Zhong ZJ, Zhou X, Chen XY, et al
. Relationship between expression of chalcone synthase genes and chromones in artificial agarwood induced by formic acid stimulation combined with Fusarium
sp. A2 inoculation. Molecules 2017;22:686.
Sun YA, Zhang HM, Li ZX, Yu WH, Zhao ZJ, Wang K, et al
. Determination and comparison of agarwood from different origins by comprehensive two-dimensional gas chromatography-quadrupole time-of-flight mass spectrometry. J Sep Sci 2020;43:1284-96.
Wang MR, Li W, Luo S, Zhao X, Ma CH, Liu SX. GC-MS study of the chemical components of different Aquilaria sinensis
(Lour.) Gilgorgans and agarwood from different Asian countries. Molecules 2018;23:2168.
Badan Standardisasi Nasional. Gaharu Jakarta Badan Standardisasi New As. 2011;7631.
Ismail SN, Maulidiani M, Akhtar MT, Abas F, Ismail IS, Khatib A, et al.
Discriminative analysis of different grades of gaharu (Aquilaria malaccensis
Lamk.) via 1
H-NMR-based metabolomics using PLS-DA and random forests classification models. Molecules 2017;22:1612.
Wang L, Zhang WM, Gao XX, Wu GQ, Li HH, Chen Q. An artificial induced method of Aquilaria sinensis
. Chinese Patent 102302041A; Jul 1 2011.
National Pharmacopoeia Committee. Pharmacopoeia of the People's Republic of China (I). Beijing, China: Chinese Medical Science and Technology Press; 2015. p. 192.
Zhou X, Chen LY, Chen XD, Zhong ZJ, Li HH, Zhang WM, et al
. Analysis and evaluation of chemically induced artificial agarwood. Zhongguo Zhong Yao Za Zhi 2017;42:3159-66.
Fan YF, Dong L, Zhou X, Chen XY, Zhong ZJ, Li HH, et al
. Quality evaluation of artificial agarwood induced by formic acid stimulation combined with different fungal inoculation. Chin J Pharm Anal 2016;36:1544-54.
Dai HF. Research Progress of Agarwood. Beijing, China: Science Press. ISBN: 978-7-03-053024-0; 2017. p. 80-110.
Chhipa H, Kaushik N. Fungal and bacterial diversity isolated from Aquilaria malaccensis
tree and soil, induces agarospirol formation within 3 months after artificial infection. Front Microbiol 2017;8:1286.
Yan TT, Yang S, Chen Y, Wang Q, Li GY. Chemical profiles of cultivated agarwood induced by different techniques. Molecules 2019;24:1990.
Yang JS, Chen YW. Studies on the constituents of Aquilaria sinensis
(Lour.) Gilg. I. Isolation and structure elucidation of two new sesquiterpenes, baimuxinic acid and baimuxinal. Acta pharmacol Sin 1983;18:191-8.
Wang CH, Wang S, Peng DQ, Yu ZX, Guo P, Wei JH. Agarwood extract mitigates intestinal injury in fluorouracil-induced mice. Biol Pharm Bull 2019;42:1112-9.
Guo R, Zhao YF, Li J, Gu YF, Huo HX, Li SS, et al
. GYF-21, an epoxide 2-(2-phenethyl)-chromone derivative, suppresses innate and adaptive immunity via inhibiting STAT1/3 and NF-κB signaling pathways. Front Pharmacol 2017;8:281.
Wu YX, Lu WW, Geng YC, Yu CH, Sun HJ, Kim YJ, et al
. Antioxidant, antimicrobial and anti-inflammatory activities of essential oil derived from the wild rhizome of Atractylodes macrocephala
. Chem Biodivers 2020;17:e2000268.
Okugawa H, Ueda R, Matsumoto K, Kawanishi K, Kato A. Effect of jinkoh-eremol and agarospirol from agarwood on the central nervous system in mice. Planta Med 1996;62:2-6.
Yang Y, Feng J, Zhu JX, Liu YY, Chen DL, Wei JH. Quality of agarwood produced by whole tree agarwood-inducing technique (agar-wit) from China and Southeast Asian countries. Chin Pharm J 2019;54:1988-94.
Wang YG, Wang J, Yang JL, Cai CH, Gai CJ, Mei WL, et al
. The GC-MS analysis of the chemical composition of agarwood from Aquilaria sinensis
'Reke2'. Chin J Trop Agric 2020;40:79-89.
Hainan provincial quality and technical supervision bureau, China. Quality Grade of Agarwood. 2017. DB46/T 422-2017.
Agarwood Association of Zhongshan, Identification and Quality Grade of Agarwood. 2018. T/ZSCX 001-2018.
Huang YY, Wu SH, Yang SH, Chen XY, Zhou X, Zhang WM, et al
. Study on classification of commercial specifications of Aquilaria sinensis
. J Chin Med Mater 2020;43:546-52.
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5]