PEER-REVIEW ARTICLE PEER-REVIEWED ARTICLE bioresources.com Pan et al. (2023). “Extraction of agarwood,” BioResources 18(4), 6741-6760. 6741 Supercritical Extraction Technique of Agarwood Essential Oil Induced by Plant Hormones Zhihong Pan,a Xian Wang,b Qingde Li,c,* Jichang Li,d and Haikun Wang e Samples of agilawood (agarwood), which were studied in this work, were produced in Zhongshan City, Guangdong Province, China. To enhance incense production, a specific concentration of plant hormone is employed for induction. The extraction technology of agilawood essential oil was explored using supercritical carbon dioxide fluid, which exhibited a more pronounced induction effect. The pressure, temperature, and flow rate, respectively, were 8, 16, and 24 MPa; 35, 45, and 55 °C; and 20, 30, and 40 L/h. A Box-Behnken analysis was adopted for experimental data, which involved 33 experiments. The data were fitted with the equation Y = 2.18 + 0.1312X1 - 0.025X2 + 0.1236X3 - 0.0025X1X2 - 0.0125X2X3 + 0.0175X12 + 0.035X22 - 0.1275X32. Hence, the optimal process parameters in the supercritical extraction of agarwood essential oil were as follows: the pressure, temperature, and flow rate of 24 MPa, 35 °C, and 33 L/h, respectively. An analysis was conducted with the statistical analysis software Design-Expert 11, which indicated that the extraction yield of agarwood essential oil by supercritical carbon dioxide was mainly affected by the pressure and flow rate. The yield was proportional to the pressure and flow, and inversely proportional to the temperature. DOI: 10.15376/biores.18.4.6741-6760 Keywords: Agarwood essential oil; Supercritical extraction; Box-Behnken regression analysis; Design- Expert 11 Contact information: a: School of Art and Design, Zhongshan Polytechnic, Zhongshan, Guangdong, China; b: School of Material Engineering, Southwest Forestry University, Kunming, Yunnan, China; c: School of Industrial Design and Ceramic Art, Foshan University, Foshan, Guangdong, China; d: School of Information Engineering, Zhongshan Polytechnic, Zhongshan, Guangdong, China; e: Faculty of Science, University of New South Wales, Sydney, New South Wales, Australia; Indah; * Corresponding author: liqingde@fosu.edu.cn INTRODUCTION The Aquilaria sinensis tree holds medicinal significance and is officially recognized in China's Pharmacopoeia as the agilawood tree, which is also known as agarwood. However, due to the declining availability of wild agilawood resources and the increasing market demand, there has been rapid progress in the development of artificial cultivation technology for agilawood and artificial incense production. Nevertheless, the industry still faces challenges related to low incense production and yield. In the field of wood discipline, a novel incense production method using plant hormone induction has been introduced, surpassing the traditional non-mechanical and microorganism induction methods. This cutting-edge technique has garnered significant scientific attention. Specifically, the Aquilaria sinensis tree is induced to produce agilawood through the application of plant hormones. Through observation, the cumulative formation process of the main compounds involved in incense production from the PEER-REVIEWED ARTICLE bioresources.com Pan et al. (2023). “Extraction of agarwood,” BioResources 18(4), 6741-6760. 6742 Aquilaria sinensis tree has been studied, and the chemical composition of agilawood produced via plant hormone induction has been analyzed. Additionally, extensive research has been conducted on the systematic extraction of supercritical agilawood essential oil. With the increasingly serious environmental issues, green industry is gradually becoming the focus for development of industrial processes, so non-polluting solvents that are harmless to humans have replaced previous organic solvents (Chen et al. 2022). At present, new alternative solvents are usually adopted in the supercritical fluid technology, such as water and carbon oxide. Supercritical carbon dioxide has been found useful for the extraction of natural products, and its solubility is related to its density, pressure, and temperature. Supercritical fluid technology could reduce damage to the environment, as it is non- toxic and harmless. It could reduce the waste discharge to a certain extent. It is often used for the isolation and purification of materials, preparation of micro and nano particles, preparation of special functional products, wafer surface cleaning, replacing the previous organic solvent, food processing, extraction, and other technical fields (Dahham et al. 2015; Xiao et al. 2021). Hence, supercritical fluid technology was selected for preparing agarwood essential oil. The extraction process is carried out under high pressure, so it has higher requirements of pressure resistance for the whole tubing system of the device. The microcomputer automatic monitoring is being used in the production process, which could greatly improve the safety and reliability of the system and decrease the operation costs. Plant essential oil is extensively applied in different fields, and its application is preferred due to the advantages of safety and other factors (Hashim et al. 2014; Hidayat et al. 2021; Wigati et al. 2022). Essential oil could be divided into the categories of single essential oil and compound essential oil, and is mainly composed of terpenes, aldehydes, esters, alcohols, and other chemical components. The refining and preparation of essential oil mainly refers to the extraction and production of a part of the plant (such as a seed, petal, leaf, bark, etc.). Different essential oils have different effects due to different chemical components (Hashim et al. 2021; Kao et al. 2021; Zanan et al. 2022). The common preparation methods of plant essential oil include water evaporation, pressing, freezing compression, immersion, solvent extraction, and supercritical fluid extraction (Yoswathana 2013; Dahham et al. 2015; Arumugham et al. 2021). There are no relevant reports on essential oil extraction with supercritical carbon dioxide. In this study, the authors used supercritical carbon dioxide to extract agarwood essential oil. EXPERIMENTAL Materials Aquilaria sinensis is a member of the Aquilaria genus within the Thymelaeaceae family, and it is an exclusive tree used for incense production in China. It is primarily found in regions such as Hainan, Guangdong, Guangxi, Fujian, Taiwan, and Yunnan. The experimental materials for this study were derived from the Aquilaria sinensis trees at the Innovation Training Base of Postgraduates located in Guangdong Zhongshan Southwest Forestry University. These selected trees are situated within the geographical coordinates of northern latitude 22°11’ to 22°47’ and eastern longitude 113°09’ to 113° 46’. The trees grow at an altitude of 11 meters and have a tree age of 7 years. The diameter at breast height for these trees ranges from 7.6 to 10.8 centimeters. PEER-REVIEWED ARTICLE bioresources.com Pan et al. (2023). “Extraction of agarwood,” BioResources 18(4), 6741-6760. 6743 Experimental Methods Hormone induction reagent processing For this experiment, 18 Aquilaria sinensis trees were carefully selected as the test subjects and divided into three distinct processing groups, namely Group A, Group B, and Group C. The hormone induction reagent used for each group varied as follows: Group A: The trees in this group were treated individually with methyl jasmonate. Group B: The trees in this group underwent a treatment where methyl jasmonate and ethephon were mixed in a volume ratio of 1:1. Group C: The trees in this group were treated individually with ethephon. In all three groups, different concentrations of the reagent were used, specifically 0% (control group), 1%, 2%, and 5%. Each group consisted of 6 trees, and the specific processing method applied to each tree is illustrated in Fig. 1. To determine the dosage of the reagent for coating, the size required for the application was standardized to 4 cm in length, 4 cm in width, and 3 mm in thickness. A slid-walk microtome was used to slice the sample, and then the slices were observed with Nikon 80i biological microscope. The slices were photographed with Nikon 80i built-in software to measure the area of dark secondary metabolites. Table 1 outlines the corresponding reagent preparation based on the dosage calculations, and a thickness gauge, conforming to the specified size, was employed for uniform coating during the experiment. Fig. 1. Sketch map of chemical treatment (a) and picture of treatment in the scene (b) (c) (1) On May 1, 2020, the experiment commenced by selecting a relatively flat area on the surface of each tree trunk to apply the corresponding reagent using a thickness gauge. The reagent was applied at intervals of more than 20 cm between two adjacent treatments, and each application was staggered transversely by approximately 90°. The starting treatment position was approximately 30 cm above the ground. The 18 tree trunks (No. 1- 18) were subjected to different concentrations of reagents, including 0% (control group), 1%, 2%, and 5%, arranged from the lowest to the highest concentration. Specifically, tree trunks No. 1-6 were treated with 0%, 1%, 2%, and 5% Me-JA (methyl jasmonate) from lowest to highest concentration, while tree trunks No. 7-12 received the mixed reagent (1:1 volume ratio of Me-JA and Et (ethephon)) from lowest to highest concentration. Tree trunks No. 13-18 were treated with 0%, 1%, 2%, and 5% Et reagent (ethephon) from lowest to highest concentration. The lower part of each tree trunk was used as the control group and treated only with lanolin. The processing parts were not subjected to repeated irritation during the experiment. PEER-REVIEWED ARTICLE bioresources.com Pan et al. (2023). “Extraction of agarwood,” BioResources 18(4), 6741-6760. 6744 Sample tree No. Treatment reagent Concentration MeJA (g) 1.3 times MeJA(g) *4 ET (g) 1.3 times ET(g) *4 Lanolin (g) 1.3 times Lanolin (g) *4 Gross weight (g) 1.2 1.3 times total weight (g) 1-2 Me-JA 1% 0.048 0.0624 0.2496 0 0 0 4.752 6.1776 24.7104 4.8 5.76 6.24 3- 4 Me-JA 2% 0.096 0.1248 0.4992 0 0 0 4.704 6.1152 24.4608 4.8 5.76 6.24 5- 6 Me-JA 5% 0.24 0.312 1.248 0 0 0 4.56 5.928 23.712 4.8 5.76 6.24 7- 8 Me-JA&ET 1% 0.024 0.0312 0.024 0.0312 0.1248 4.752 6.1776 24.7104 4.8 5.76 6.24 9- 10 Me-JA&ET 2% 0.048 0.0624 0.2496 0.048 0.0624 0.2496 4.704 6.1152 24.4608 4.8 5.76 6.24 11- 12 Me-JA&ET 5% 0.12 0.156 0.624 0.12 0.156 0.624 4.56 5.928 23.712 4.8 5.76 6.24 13- 14 ET 1% 0 0 0 0.048 0.0624 0.2496 4.752 6.1776 24.7104 4.8 5.76 6.24 15- 16 ET 2% 0 0 0 0.096 0.1248 0.4992 4.704 6.1152 24.4608 4.8 5.76 6.24 17- 18 ET 5% 0 0 0 0.24 0.312 1.248 4.56 5.928 23.712 4.8 5.76 6.24 1- 18 Lanolin (control) 0% 0 0 0 0 0 0 4.8 6.24 24.96 4.8 5.76 6.24 Table 1. Calculation of Chemical Quality in Every Group PEER-REVIEWED ARTICLE bioresources.com Pan et al. (2023). “Extraction of agarwood,” BioResources 18(4), 6741-6760. 6745 The higher part of each tree trunk, treated with 1%, 2%, and 5% reagent, constituted the experimental group. For tree trunks with even numbers (No. 2, 4, 6, 8, 10, 12, 14, 16, and 18), the reagent was replaced, and the original reagent was replaced with the same concentration about 60 days and 120 days after the initial processing. (2) On June 25, 2020, the reagent on the tree trunks in the experimental group with even numbers was gently scraped off to prevent damaging the surface of the Aquilaria sinensis. Subsequently, the same concentration, composition, and size of reagent was reapplied to the same parts. Specifically, for tree trunks No. 2, 4, and 6, the 1%, 2%, and 5% Me-JA reagent at higher positions was replaced. For tree trunks No. 8, 10, and 12, the 1%, 2%, and 5% Me-JA and Et reagent at higher positions was replaced. Finally, for tree trunks No. 14, 16, and 18, the 1%, 2%, and 5% Et reagent at higher positions was replaced. (3) On August 20, 2020, the same reagent replacement steps were repeated as done previously in the experiment. Supercritical extraction method The supercritical extraction equipment (SC5+1, Deyang Sichuang Technology Co., Ltd., Sichuan, China) was selected for extraction of agarwood essential oil under the supercritical carbon dioxide (Ma et al. 2021). The device mainly is composed by a CO2 cylinder, a refrigeration device, a temperature control system, a safety protection device, a carrier tank, a storage tank, a purifier, a mixer, a heat exchanger, a plunger pump, an extraction cylinder, a separator, and a mass flowmeter. The experimental procedure consisted of three parts: 1. Crushing the raw material of agilawood wood; 2. Supercritical fluid extraction; 3. Calculating the essential oil yield. Fig. 2. The experimental procedure for extraction of agarwood essential oil The experimental procedure is shown in Fig. 2. The ultrasonic vibrator (JP-070S, Kunshan Yinghua Electronic Equipment Co., Ltd., Jiangsu, China) was selected for impurity separation and extraction of the agilawood sample (Yuanyi Agilawoods Co., Ltd., Zhongshan, China). After preparing 60-mesh agilawood powder, it was placed in the dry extraction device for supercritical fluid extraction. Then, the pressure reducing valve of carbon dioxide cylinder was turned on (Xinwang Gas Co., Ltd., Wenzhou, China), with the purity > 99.5%. Thereafter, the centrifugal air compressor (CHORUS90, Denair Energy-saving Technology Co., Ltd., Shanghai, China) was used to compress carbon dioxide and push it forward with agilawood (Waluyo et al. 2021; Wigati et al. 2022). Finally, the system was heated by the temperature controller vacuum oven (HJ-ZK-2, Huajie Oven Manufacturing Co., Ltd., Jiangsu, China), to increase the temperature of carbon oxide. When the critical pressure was reached, the heating was continued while maintaining the pressure, to reach the critical temperature and state. During this process, the UV-Visible detector (The agilawood molecule was scanned and analyzed using a Cary50, Spectrophotometer/G27-200035, Agilent Technologies Australia Pty Ltd., Mulgrave, Australia) to obtain an accurate critical value, while maintaining the time and flow rate to obtain the extracted agarwood essential oil. Finally, the extracted agarwood Crush agarwood slice Supercritical fluid extraction Calculate the essential oil yield PEER-REVIEWED ARTICLE bioresources.com Pan et al. (2023). “Extraction of agarwood,” BioResources 18(4), 6741-6760. 6746 essential oil was weighed with the electronic balance (AND/HR-200, Mettler-Toledo (Switzerland) GmbH, Greifensee, Switzerland) to determine the essential oil yield. The Box-Behnken method in the Design-Expert 11 (Stat-Ease Inc., Minneapolis, MN, USA) software was selected for experimental design and data processing. An orthogonal experimental design method was used to study the multiple factors and levels. Based on the orthogonality, some representative points were selected from the comprehensive test to perform the test in Design-Expert 11, and these points are featured as the “uniform and distributed, neat and comparable properties”. The agarwood essential oil extraction protocol was designed with Design-Expert 11 for the orthogonal test, and the test was controlled by the single factor to further process data. The experimental time was 2 h for supercritical fluid extraction and the sample of agilawood was in powder form, passing through 60-mesh sieve. In this study, 60 g sieved powder was weighed, the pressure, temperature and flow rate were set to 8, 16, and 24 MPa; 35, 45, and 55 °C; and 20, 30, and 40 L/h, respectively, for the numerical cross-over experiment. Then, the amount and purity of agarwood essential oil were compared to obtain the optimal extraction value of agarwood essential oil. Table 2. All-factor Level Coding and Level Pressure (kPa) Temperature (°C) Flow Rate (L/h) -1 8 35 20 0 16 45 30 1 24 55 40 RESULTS AND DISCUSSION Analysis of Anatomic Construction The anatomic construction of Aquilaria sinensis, in the absence of incense production (lanolin processing sample), was examined. The cell structure of Aquilaria sinensis includes the presence of phloem, and the parenchymatous tissue contains a significant amount of starch, which serves as a distinguishing feature. The healthy wood of Aquilaria sinensis, associated with incense production, exhibits slight apparent growth profiling and displays a slightly yellowish-white color, serving as macro features. When observed under a magnifier, the fresh wood has a subtle glossiness, emits a mild fragrance, and becomes sweet upon drying. Additionally, it features slightly smaller tube holes. In the radial section of the wood, ray stripes are evident, and the texture appears straight, representing the microscopic feature of the healthy wood of Aquilaria sinensis without incense production, as depicted in Fig. 3. The cross-section of the guide tube may take the form of circular, oval, or elliptical shapes. As for the diffuse-porous woods, solitary pores and short-diameter multiple pores (2~4), and pore clusters are occasionally observed, with simple perforation in circular and oval shapes. The pit-type column lies between the pipes, and there is an absence of tylose and vegetable glue in the guide pipe. Axial parenchyma is limited, and oil cells are not present. The wood fiber wall is thin and contains numerous bordered pits. The wood ray does not show a storied arrangement and primarily focuses on the linear ray, ranging in height from 1 to 21 cells or more. Occasionally, multi-column rays with a width of 2 cells may also be observed. The ray tissue exhibits the special-shaped III-type and few heterotype III. https://link.zhihu.com/?target=https://baike.baidu.com/item/%25E6%25AD%25A3%25E4%25BA%25A4%25E6%2580%25A7/3588415 https://link.zhihu.com/?target=https://baike.baidu.com/item/%25E6%25AD%25A3%25E4%25BA%25A4%25E6%2580%25A7/3588415 PEER-REVIEWED ARTICLE bioresources.com Pan et al. (2023). “Extraction of agarwood,” BioResources 18(4), 6741-6760. 6747 The presence of gums in the ray cells is limited, and there are no crystals, oil cells, or mucous cells seen. The wood includes a significant amount of phloem, which is visible to the naked eye, appearing in an island-like pattern and being evenly distributed in the basic tissues. The tree’s skin covers the secondary phloem, cortex, and periderm, with the secondary included phloem encompassing the bast fiber and phloem parenchyma cells. Fig. 3. The anatomy pictures of healthy A. sinensis Note: Cross section on the left, radial section in the middle, tangential section on the right. The anatomical structure analysis of hormone-treated and lanolin-controlled processing samples indicates that no apparent dark secondary metabolites were formed in the hormone control processing samples. However, Aquilaria sinensis is capable of forming dark secondary metabolites through two types of hormone treatments. The type and structure of cells did not exhibit significant changes, as shown in Fig. 3. After applying methyl jasmonate on the surface of the Aquilaria sinensis tree, the following cells generated dark secondary metabolites after 180 days: phellem layer cells, phloem ray cells, bast thin-walled cells, and certain cambium cells. When ethephon was applied on the surface of the Aquilaria sinensis tree, the phellem layer cells and phloem ray cells generated dark secondary metabolites after 180 days. When a mixed reagent of methyl jasmonate and ethephon was applied on the surface of the Aquilaria sinensis tree, the following cells generated dark secondary metabolites after 180 days: phellem layer cells, phloem ray cells, bast thin-walled cells, cambial cells, ray cells, included phloem cells, and guide pipes. These results indicate that the mixture of reagents induced Aquilaria sinensis to generate a wider range of cells with dark secondary metabolites. The position of the dark secondary metabolites in the cells was characterized using Photoshop and Image-Pro Plus, and the area of dark secondary metabolites was measured in the 1 mm chordwise width. The different concentrations of reagent processing include non-replacement of the reagent (no repetition) and replacement of the reagent (repeated). The area of dark secondary metabolites generated in the 1% methyl jasmonate and 1% ethephon induction experimental group showed few sporadic occurrences. When 2% methyl jasmonate and 2% ethephon were used for induction, the area of dark secondary metabolites apparently increased compared to the induction with 1% methyl jasmonate and 1% ethephon. However, when using 5% methyl jasmonate and 5% ethephon for induction, the area of dark secondary metabolites formed apparently decreased compared to the induction with 2% methyl jasmonate and 2% ethephon. It is important to note that no apparent dark secondary metabolites were generated in the samples of the control group. PEER-REVIEWED ARTICLE bioresources.com Pan et al. (2023). “Extraction of agarwood,” BioResources 18(4), 6741-6760. 6748 The area of dark secondary metabolites in the Aquilaria sinensis tree, induced by the reagent mixture of 1%, 2%, and 5%, was evidently larger than that formed through the separate induction with the same concentration of hormones. This demonstrates that the reagent mixture was more effective in inducing the generation of dark secondary metabolites. Throughout all concentrations and reagent treatment conditions, it was found that replacing the induction reagent every 60 days during the induction period significantly increased the area of dark secondary metabolites in the Aquilaria sinensis tree compared to using the same concentration of reagent without replacement. This improvement in the substance generation area of dark secondary metabolites enhanced the overall induction effect. Fig. 4. The position of cells that undergoing generation of resin (a) The surface of A. sinensis coated with methyl jasmonate; (b) the surface of A. sinensis coated with the mixed agent including methyl jasmonate and ethephon; (c) the surface of A. sinensis coated with ethephon The combination of 1% methyl jasmonate and ethephon in the reagent mixture led to the production of numerous dark secondary metabolites. In contrast, separate induction using 1% methyl jasmonate and 1% ethephon did not generate any dark secondary metabolites individually. This indicates that methyl jasmonate and ethephon play an evident cooperative role in inducing the production of dark secondary metabolites. When the concentration of the reagent mixture was increased to 2%, or the reagent was replaced every 60 days, the area of dark secondary metabolites substantially increased. Consequently, among all the processing schemes, the reagent mixture of methyl jasmonate and ethephon, with a 2% concentration and replacement every 60 days during the induction period, was considered to be the superior induction scheme for incense production in Aquilaria sinensis. Experimental Scheme of Supercritical Fluid Extraction In the total-factor experiment, the supercritical extraction time was 2 h, and the sample of agilawood powder was passed through a 60-mesh sieve. Briefly, 60 g sieved powder was weighed, and the pressure (X1), temperature (X2), and flow rate (X3) were selected. The detailed experimental plan and factor levels are shown in Tables 3 and 2, respectively. (a) (b) (c) PEER-REVIEWED ARTICLE bioresources.com Pan et al. (2023). “Extraction of agarwood,” BioResources 18(4), 6741-6760. 6749 Table 3. Total Factor Experimental Data 33 Test Serial Number Pressure (kPa) Temperature (°C) Flow Rate ( L/h) Yield (W%， g/g) 1 8 35 20 1.32 2 8 35 30 1.98 3 8 35 40 1.89 4 8 45 20 0.9 5 8 45 30 1.71 6 8 45 40 1.71 7 8 55 20 0.96 8 8 55 30 1.65 9 8 55 40 1.68 10 16 35 20 1.35 11 16 35 30 2.1 12 16 35 40 2.19 13 16 45 20 1.5 14 16 45 30 2.04 15 16 45 40 2.1 16 16 55 20 1.41 17 16 55 30 2.13 18 16 55 40 2.1 19 24 35 20 1.68 20 24 35 30 2.7 21 24 35 40 2.91 22 24 45 20 1.77 23 24 45 30 2.22 24 24 45 40 2.52 25 24 55 20 1.56 26 24 55 30 2.4 27 24 55 40 2.61 Experimental Data Processing Significance analysis The significant test indicates that a hypothesis is established for the parameters of population (random variable) or population distribution form. Then, the sample information is adopted to judge whether the hypothesis (alternative hypothesis) is reasonable, namely, whether there is significant difference between the population truth and the original hypothesis. The significant test is adopted to judge whether there is a difference between the experimental processing group and the control group, or two processing effects, and whether the difference is significant (Xu et al. 2017; Gwee et al. 2020; Sivaramakrishnan and Incharoensakdi 2020; Hashim et al. 2021). Experimental data in Table 4 was analyzed with the statistical analysis software Design-Expert 11. Taking the yield of agilawood as the variable, and the temperature, pressure and flow rate as the fixed factors, the P-value was evaluated. The F-value is the significant test of regression equation, and the P-value is the significant value. When 0.01 < P < 0.05, it indicates that the factor is significant. P < 0.01 indicates that the factor is extremely significant. The significance test was carried out for regression equation fitting model of yield, and the results are shown in Table 4. As shown in the table, the regression model corresponding to yield regression equation (P < 0.0001) was extremely significant. The determination coefficient of Box-Behnken model was R2 = 0.99840, the prediction determination coefficient was Rpre 2 = 0.7444, and the adjusted determination coefficient PEER-REVIEWED ARTICLE bioresources.com Pan et al. (2023). “Extraction of agarwood,” BioResources 18(4), 6741-6760. 6750 was Radj 2 = 0.9635, which is close to 1, which indicated that the model fitting is relatively accurate. The regression equation coefficient significance test results showed that the primary item X1 (P < 0.0001) in the yield regression equation was extremely significant, X2 (P < 0.05) was significant, X3 (P < 0.0001) was extremely significant, the interaction items X1 X2, X1 X3, and X2 X3 were not significant, the quadratic term X1 2 and X2 2 were not significant, and X3 2 was extremely significant. According to the P-value, it could be concluded that the order of influence of softening parameters on bending properties was: pressure (X1) > flow rate (X3) > temperature (X2). The yield was directly proportional to the pressure and flow and was inversely proportional to the temperature. The analysis results are shown in Table 4. Table 4. Significance Analysis Table Variance Sources Quadratic Sum Degree of Freedom Average Variance F-Value P-Value Significance Level Regression model 0.3434 9 0.0382 47.92 < 0.0001 Extremely significant X1 0.1378 1 0.1378 173.04 < 0.0001 Extremely significant X2 0.0050 1 0.0050 6.28 0.0407 Significant X3 0.1275 1 0.1275 160.11 < 0.0001 Extremely significant X2 X3 0.0000 1 0.0000 0.0314 0.8644 X1 X2 0.0000 1 0.0000 0.0000 1.0000 X1 X3 0.0006 1 0.0006 0.7848 0.4051 X1² 0.0013 1 0.0013 1.62 0.2439 X2² 0.0052 1 0.0052 6.48 0.0384 X3² 0.0684 1 0.0684 85.94 < 0.0001 Residual 0.0056 7 0.0008 Lack-of-fit value 0.0056 3 0.0019 Pure error 0.0000 4 0.0000 Total 0.3490 16 R2 = 0.99840 Radj 2 = 0.9635 Rpre2 = 0.7444 * R2 is the square of related coefficient. R is directly the correlation coefficient of the dependent variable and independent variable in the linear equation of one variable, while the multivariate is the complex correlation coefficient. According to the variance analysis of Design-Expert 11, the fitted equation of the yield Y is given by formula 1. Y = 2.18 + 0.1312X1 - 0.025X2 + 0.1236X3 - 0.0025X1X2 - 0.0125X2X3 + 0.0175X12 + 0.035X22 - 0.1275X32 (Formula 1) Design-Expert 11 was selected to substitute the formula 1 into the plot of reaction surface and contour, as shown in Figs. 5 and 13. The X1, X2, and X3 refer to the pressure (unit: MPa), temperature (unit °C), and flow rate (unit L/h). The factor level is shown in Table 2, where -1, 0, and 1 are adopted as the codes of the low, middle, and high levels. The maximum yield corresponding to each response surface is shown in Table 5. PEER-REVIEWED ARTICLE bioresources.com Pan et al. (2023). “Extraction of agarwood,” BioResources 18(4), 6741-6760. 6751 Fig. 5. X3 (flow rate) level-1, pressure, and temperature corresponding to the yield curve diagram and contour map Fig. 6. X3 (flow rate) level 0, pressure, and temperature corresponding to yield curve diagram and contour map Fig. 7. X3 (flow rate) level 1, pressure, and temperature corresponding to yield curve diagram and contour map PEER-REVIEWED ARTICLE bioresources.com Pan et al. (2023). “Extraction of agarwood,” BioResources 18(4), 6741-6760. 6752 Fig. 8. X2 (flow rate) level -1, pressure, and temperature corresponding to yield curve diagram and contour map Fig. 9. X2 (flow rate) level 0, pressure, and temperature corresponding to yield curve diagram and contour map Fig. 10. X2 (flow rate) level 1, pressure, and temperature corresponding to yield curve diagram and contour map PEER-REVIEWED ARTICLE bioresources.com Pan et al. (2023). “Extraction of agarwood,” BioResources 18(4), 6741-6760. 6753 Fig. 11. X1 (flow rate) level -1, pressure, and temperature corresponding to yield curve diagram and contour map Fig. 12. X1 (flow rate) level 0, pressure, and temperature corresponding to yield curve diagram and contour map Fig. 13. X1 (flow rate) level 1, pressure, and temperature corresponding to yield curve diagram and contour map PEER-REVIEWED ARTICLE bioresources.com Pan et al. (2023). “Extraction of agarwood,” BioResources 18(4), 6741-6760. 6754 Comprehensive parameter optimization and verification The yield of the agarwood essential oil is the ultimate goal of the extraction of agilawood. Taking the pressure (X1), temperature (X2), and flow rate (X3) as the design factors, and the highest yield as the optimization objective according to the results, the optimized process parameters could be obtained through the Design Expert 11 software: pressure of 23.7701 MPa, temperature of 35.4653 °C, flow rate of 33.3034 L/h, expected yield of 2.41369%. Modified optimal process parameters: the pressure, temperature, and flow rate were 24 MPa, 35 °C, and 33 L/h, respectively. Table 5. The Maximum Yield of the Response Surface Map No. Conditions Yield Maximum (W%，g/g) Fig. 5 X3 = -1 2.1250 Fig. 6 X3 = 0 2.3838 Fig. 7 X3 = 1 2.3975 Fig. 8 X2 = -1 2.4066 Fig. 9 X2 = 0 2.3600 Fig. 10 X2 = 1 2.3590 Fig. 11 X1 = -1 2.1699 Fig. 12 X1 = 0 2.2782 Fig. 13 X1 = 1 2.4020 * The maximum yield is calculated by the software Design-Export 11 Table 6 shows that the extraction yield of the agarwood essential oil was 2.41% according to the modified optimal softening conditions. The average deviation was -0.35%, which indicates that the predicting results of the regression model in the Box-Behnken method was close to the actual value. The predicted accuracy was high. Table 6. Response Surface Optimization Result Verification No. X1 (MPa) X2 (°C) X3 (L/h) Y (%) Deviation (%) Predicted value 24 35 33 2.414 --- Verification group 1 24 35 33 2.361 -1.87% Verification group 2 24 35 33 2.411 2.12% Verification group 3 24 35 33 2.38 -1.29% Average group 24 35 33 2.384 -0.35% Analysis of Agarwood Essential Oil Obtained by Supercritical Extraction by GC-MS GC-MS (Trace 1300 ISQ 7000) Analysis conditions of the gas chromatograph were as follows: Chromatographic column: TG-5MS column length: 30 m, inner diameter: 0.25 mm, film thickness: 0.25 m; Injection port temperature: 280 ℃; Shunting mode: Splitless; Flow rate of carrier gas: 1.2 PEER-REVIEWED ARTICLE bioresources.com Pan et al. (2023). “Extraction of agarwood,” BioResources 18(4), 6741-6760. 6755 mL/min; Carrier gas: He; Temperature programming: 40 ℃ (5min) 5 ℃/min 180 ℃ 8 ℃/min 250 ℃ (1min) 10 ℃/min 290 ℃ (10min) 10 ℃/min 320 ℃ (30min); Injection volume: 1uL. Mass spectrum condition: Temperature of ion source: 280 ℃; Ionization method: EI; Ionizing energy: 70eV. Table 7. Test Results Serial Number Peak name Retention time Chemical formula CAS Relative Peak Area 1 Ethyl Alcohol 2.87 C2H6O 64-17-5 9.06 2 Oxalic acid 2.982 C2H2O4 144-62-7 7.65 3 glycolic acid 3.154 C2H4O3 79-14-1 2.47 4 Acetic acid 3.793 C2H4O2 64-19-7 29.55 5 Methyl acetate 3.869 C3H6O2 79-20-9 1.08 6 Ethylene glycol acetate formate 6.322 C5H8O4 0.13 7 2,2-dimethoxyethanol 6.803 C4H10O3 30934-97-5 0.14 8 benzaldehyde 11.638 C7H6O1 100-52-7 0.21 9 6,7-Dimethyl-1,2,3,5,8,8a- hexahydronaphthalene 20.466 C12H18 107914-92-1 0.18 10 4-phenylbutan-2-one 20.775 C10H12O 2550-26-7 0.52 11 2,3,5-Trimethylfuran 22.468 C7H10O 10504-04-8 0.05 12 Phenol, 5-ethenyl-2-methoxy- 22.706 C9H10O2 621-58-9 0.03 13 Benzene, 1-(1,2-dimethyl-3- methylenecyclopentyl)-4-methyl-, cis- 22.792 C15H20 54808-83-2 0.05 14 Ethyl 3-phenylpropanoate 23.522 C11H14O2 2021-28-5 0.03 15 1,3-Diacetin 23.821 C7H12O5 0.22 16 3-Ethylphenol, n-propyl ether 24.698 C11H16O 0.16 17 4,6-Di-tert-butyl-2-methylphenol 26.137 C15H24O 616-55-7 0.15 18 Tricyclo[5.2.2.0(1,6)]undecan-3-ol, 2- methylene-6,8,8-trimethyl- 26.887 C15H24O 1.70 19 β-Guaiene 27.019 C15H24 88-84-6 0.03 20 Tetracyclo[6.2.1.1(3,6).0(2,7)]dodec-4- ene, 11-isopropylidene- 27.095 C15H20 0.01 21 (-)-Nootkatene 27.176 C15H22 5090-61-9 0.03 22 Valencene 27.257 C15H24 4630-07-3 0.02 23 Cycloisolongifolene, 8,9-dehydro- 27.307 C15H22 0.01 24 4-Epi-cis-Dihydroagarofuran 27.454 C15H26O 0.07 25 Anisylacetone 27.536 C11H14O2 104-20-1 0.12 26 ethyl 2,4-dihydroxy-6-methylbenzoate 27.662 C10H12O4 2524-37-0 0.00 27 α,α-Diethyl-o-methoxybenzyl alcohol 27.764 C12H18O2 53847-40-8 0.01 28 (-)-Italicene ether 27.87 C15H24O1 104188-25-2 0.07 29 Isolongifolene, 4,5,9,10-dehydro- 28.012 C15H20 156747-45-4 0.04 30 2(3H)-Benzofuranone, 3a,4,5,6- tetrahydro-3a,6,6-trimethyl- 28.159 C11H16O2 16778-26-0 0.02 31 α-Costol 28.22 C15H24O1 65018-15-7 0.04 32 1,4-Ethanoquinoxaline, 2,3-dihydro- 28.341 C10H12O2 7140-45-6 0.01 33 Tricyclo[6.3.0.0(1,5)]undec-2-en-4- one, 5,9-dimethyl- 28.529 C13H18O 0.12 34 Isolongifolene, 4,5-dehydro- 28.579 C15H24O 0.38 35 α-Costol 28.615 C15H24O 5956/12/7 0.05 36 α-Vetivone 28.939 C15H22O 15764-04-2 0.24 37 cis-Eudesm-6-en-11-ol 29.061 C15H26O 194607-96-0 0.15 38 Isolongifolene, 4,5,9,10-dehydro- 29.157 C15H20 156747-45-4 0.01 39 Benzene, 1-(1,2-dimethyl-3- methylenecyclopentyl)-4-methyl-, cis- 29.233 C15H20 54808-83-2 0.08 40 6S,10R-Dimethylbicyclo[4.4.0]decan- 1-en3-one 29.345 C12H18O 69460-62-4 0.06 https://www.chembk.com/en/chem/Methyl%20acetate https://www.chembk.com/en/chem/%CE%B2-Guaiene PEER-REVIEWED ARTICLE bioresources.com Pan et al. (2023). “Extraction of agarwood,” BioResources 18(4), 6741-6760. 6756 41 Costol 29.517 C15H24O 515-20-8 0.24 42 Cycloisolongifolene, 8,9-dehydro- 29.573 C15H22 0.18 43 Hydroxygrass extract 29.613 C15H22O 4176-16-3 0.03 44 Valerenol 29.705 C15H24O 101628-22-2 0.05 45 Arctiol 29.725 C15H26O2 36061-11-7 0.14 46 Aromadendrene oxide-(2) 29.841 C15H24O 0.07 47 trans-Valerenyl acetate 29.983 C17H26O2 101527-74-6 0.04 48 Nootkaton-11,12-epoxide 30.019 C15H22O2 0.02 49 3,5,11-Eudesmatriene 30.227 C15H22 193615-07-5 0.12 50 (+)-gamma-Eudesmol 30.485 C15H26O 117066-77-0 0.81 51 β-Guaiene 30.941 C15H24 88-84-6 0.51 52 (-)-GUAIOL 31.478 C15H26O 489-86-1 2.23 53 Diepicedrene-1-oxide 31.676 C15H24O 0.15 54 6,7-Dimethyl-1,2,3,5,8,8a- hexahydronaphthalene 31.965 C12H18 107914-92-1 0.85 55 Cycloisolongifolene, 8,9-dehydro- 32.026 C15H22 0.12 56 Acetic acid, 3-hydroxy-6-isopropenyl- 4,8a-dimethyl-1,2,3,5,6,7,8,8a- octahydronaphthalen-2-yl ester 32.081 0.02 57 Acetic acid, (1,2,3,4,5,6,7,8-octahydro- 3,8,8-trimethylnaphth-2-yl)methyl ester 32.269 C16H26O2 314773-27-8 0.41 58 1(2H)-Naphthalenone, 3,4,4a,5,6,7- hexahydro-4a,5-dimethyl-3-(1- methylethenyl)-, [3S-(3α,4aα,5α)]- 32.39 C15H22O 562-23-2 0.18 59 Tricyclo[5.2.2.0(1,6)]undecan-3-ol, 2- methylene-6,8,8-trimethyl- 32.461 C15H24O 0.07 60 (E)-2,6-Dimethoxy-4-(prop-1-en-1- yl)phenol 32.532 C11H14O3 20675-95-0 0.01 61 2aS,3aR,5aS,9bR)-2a,5a,9-Trimethyl- 2a,4,5,5a,6,7,8,9b-octahydro-2H- naphtho[1,2-b]oxireno[2,3-c]furan 32.598 C15H22O2 352457-43-3 0.04 62 6-(1-Hydroxymethylvinyl)-4,8a- dimethyl-3,5,6,7,8,8a-hexahydro-1H- naphthalen-2-one 32.791 C15H22O2 0.23 63 (R)-1,5,8-Trimethyl-6,7,8,9- tetrahydronaphtho[2,1-b]furan 33.064 C15H18O 59462-26-9 0.81 64 (E)-2-((8R,8aS)-8,8a-Dimethyl- 3,4,6,7,8,8a-hexahydronaphthalen- 2(1H)-ylidene)propanal 33.206 C15H22O 137695-18-2 0.30 65 Khusimyl methyl ether 33.384 C16H26O 300349-20-6 0.07 66 Isoaromadendrene epoxide 33.728 C15H24O 0.53 67 trans-Valerenyl acetate 33.804 C17H26O2 101527-74-6 0.14 68 Widdrenal 33.845 C15H22O 470-41-7 0.04 69 Agarotetrol 34.002 C17H18O6 69809-22-9 0.26 70 6-Isopropenyl-4,8a-dimethyl- 1,2,3,5,6,7,8,8a- octahydronaphthalene-2,3-diol 34.205 C15H24O2 1005284-62- 7 1.18 71 α-Cyperone 34.671 C15H22O 473-08-5 0.58 72 2aS,3aR,5aS,9bR)-2a,5a,9-Trimethyl- 2a,4,5,5a,6,7,8,9b-octahydro-2H- naphtho[1,2-b]oxireno[2,3-c]furan 35.021 C15H22O2 352457-43-3 0.68 73 4-(3,3-Dimethyl-but-1-ynyl)-4-hydroxy- 2,6,6-trimethylcyclohex-2-enone 35.097 C15H22O2 930090-10-1 0.08 74 Aromadendrene oxide-(2) 35.659 C15H24O 8.62 75 6-(1-Hydroxymethylvinyl)-4,8a- dimethyl-3,5,6,7,8,8a-hexahydro-1H- naphthalen-2-one 36.171 C15H22O2 0.73 76 Alloaromadendrene oxide-(2) 36.805 C15H24O 5.27 77 Acetic acid, 3-hydroxy-6-isopropenyl- 4,8a-dimethyl-1,2,3,5,6,7,8,8a- octahydronaphthalen-2-yl ester 37.377 C17H26O3 1.24 https://www.chembk.com/en/chem/Costol https://www.chembk.com/en/chem/%CE%B2-Guaiene PEER-REVIEWED ARTICLE bioresources.com Pan et al. (2023). “Extraction of agarwood,” BioResources 18(4), 6741-6760. 6757 78 5,8-Dihydroxy-4a-methyl- 4,4a,4b,5,6,7,8,8a,9,10-decahydro- 2(3H)-phenanthrenone 37.737 C17H26O3 0.40 79 Hydroxyvalerenic acid 38.087 C15H22O3 1619-16-5 3.44 80 carissone 38.289 C15H24O2 473-10-9 1.17 81 4-(2-Isopropyl-5-methylphenyl)-3- methylbutyric acid 38.842 C15H22O2 22291-58-3 2.63 82 Azatadine maleate 39.511 C20H22N2 3964-81-6 0.05 83 cis-13-Octadecenoic acid 39.866 C18H34O2 13126-39-1 0.72 84 stearic acid 40.109 C18H36O2 1957/11/4 0.83 85 Allocryptopine 40.19 C21H23NO5 485-91-6 0.27 86 2-(2-phenylethyl)chromen-4-one 42.252 C17H14O2 61828-53-3 1.56 87 Octan-2-yl palmitate 43.469 C24H48O2 55194-81-5 0.15 88 Phthalic acid, di(2-propylpentyl) ester 44.092 C24H38O4 0.42 89 6-Methoxy-2-phenethyl-4H-chromen-4- one 45.197 C18H16O3 84294-89-3 2.65 90 erythro-8-(1,2-Dibromo-2-phenylethyl)- 3,7-dihydro-1,3,7-trimethyl-1H-purine- 2,6-dione 45.567 C16H16Br2 N4O2 99765-14-7 0.21 91 Ethyl [5-hydroxy-1-(6-methoxy-4- methyl-3-quinolinyl)-3-methyl-1H- pyrazol-4-yl]acetate # 45.83 C19H21N3O 4 0.95 92 (Z)-docos-13-enamide 46.378 C22H43NO 112-84-5 0.12 93 l-Proline, n-heptafluorobutyryl-, isobutyl ester 46.484 C13H16F7N O3 0.19 94 (E,E,E,E)-Squalene 46.621 C30H50 111-02-4 0.08 95 4'-(n-Propyl)oxy-4-methoxy-2'- methylchalcone (isomer 1) 47.138 C20H22O3 0.26 96 6,7-dimethoxy-2-(2- phenylethyl)chromen-4-one 47.645 C19H18O4 84294-87-1 1.30 97 Quinazolin-4(3H)-one, 2-(4- methoxybenzylthio)-3-methyl- 47.933 C17H16N2O 2S 90852-47-4 0.12 98 24-Norursa-3,9(11),12-triene 48.582 C29H44 930591-91-6 0.15 99 6,7-dimethoxy-2-(2-(4- methoxyphenyl)ethyl)chromone 50.381 C20H20O5 117596-92-6 0.19 100 stigmasterol 51.795 C29H48O 83-48-7 0.14 The agilawood essential oil was obtained through supercritical extraction, and its GC-MS analysis was performed to authenticate 100 volatile components. The majority of these components exhibited the sesquiterpenes structure, and the relevant components are listed in Table 7. The analysis and detection results reveal that the agarotetrol content in the agilawood essential oil obtained from artificial incense production via induction of the reagent mixture hormone reached 0.26%, significantly surpassing the requirement of not being lower than 0.1%, as specified in the Chinese Pharmacopoeia (2020). This indicates that the artificial agilawood obtained through the induction of the reagent mixture hormone used in the project is a simple and efficient operation. The induction method greatly enhances the incense production velocity of Aquilaria sinensis, reducing the production cycle of agilawood, and yields a remarkable incense production effect. Moreover, it effectively addresses the imbalance between supply and demand for agilawood, leading to essential economic and social benefits. https://www.chemsrc.com/en/cas/117596-92-6_1130791.html https://www.chemsrc.com/en/cas/117596-92-6_1130791.html PEER-REVIEWED ARTICLE bioresources.com Pan et al. (2023). “Extraction of agarwood,” BioResources 18(4), 6741-6760. 6758 CONCLUSIONS 1. Among all the processing schemes, the reagent mixture of methyl jasmonate and ethephon was shown to be the most effective inductive agent. The concentration of this inductive agent was set at 2%, and during the induction period, the agent was replaced every 60 days. This particular processing scheme can be regarded as the superior induction method for incense production in Aquilaria sinensis. 2. The water-bath supercritical fluid equipment was adopted to extract the agarwood essential oil, and the supercritical extraction time was 2 h. The sample of agilawood was in powder form, passing through a 60-mesh sieve. In the experiment, 60 g sieved powder was weighed, and the pressure, temperature, and flow rate were set to 8, 16, and 24 MPa; 35, 45, and 55 ℃; and 20, 30, and 40 L/h, respectively, for a 33 total- factor experiment. The experimental data were used in the Box-Behnken analysis. The equation was obtained by fitting Y = 2.18 + 0.1312X1 - 0.025X2 + 0.1236X3 - 0.0025X1X2 - 0.0125X2X3 + 0.0175X12 + 0.035X22 - 0.1275X32. 3. With the Design Expert 11 software, the optimal process parameters were obtained in the supercritical fluid extraction of agarwood essential oil: the pressure, temperature, and flow rate were 24 MPa, 35 °C, and 33 L/h. The yield of agarwood essential oil extracted with the supercritical carbon dioxide was mainly affected by the pressure and flow rate. The yield was directly proportional to the pressure and flow, and inversely proportional to the temperature. 4. The fitting research indicated that the extraction yield was 2.41% according to the modified optimal softening conditions. The average deviation was -0.35%. It indicated that the predicting results of the regression model in the Box-Behnken method was close to the actual value. The predicted accuracy was high. 5. This study analyzed the optimal process parameters of water-bath supercritical fluid equipment in the extraction of agarwood essential oil to provide new theoretical and practical support for the extraction of agarwood essential oil. It is conducive to reduce the loss ratio of extraction and improve the utilization rate for the extraction of agarwood essential oil. 6. The gas chromatography – mass spectrometry (GC-MS) data of agilawood essential oil obtained via supercritical fluid extraction were analyzed to authenticate 100 volatile components. Most of the components had sesquiterpene structure. The analysis and detection results show that agarotetrol contents in agilawood essential oil of artificial incense production obtained via induction of reagent mixture reached 0.26％, which is far higher than the requirements of no less than 0.1％ as specified in Chinese Pharmacopoeia (2020). Funding Statement This research was supported by China National Project Fund: High-level Talents Research Project (KYG2106). PEER-REVIEWED ARTICLE bioresources.com Pan et al. (2023). “Extraction of agarwood,” BioResources 18(4), 6741-6760. 6759 Declaration of Conflicting Interests The author(s) declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Author Contributions Zhihong Pan, Xian Wang, and Qingde Li conceived and designed the experiment. Jichang Li and Haikun Wang analysed and discussed the data. Zhihong Pan and Xian Wang wrote the manuscript, Qingde Li, Jichang Li, and Haikun Wang made revisions. 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