Analysis of Differences in Volatile Components of Five Lauraceae Plants From Different Genera Based on HS-SPME-GC-MS

Abstract

Lauraceae plants are diverse in species and rich in volatile components, which possess functions such as insect repellency, antioxidant activity, and antibacterial properties. However, currently, the methods for analyzing the volatile components of Lauraceae plants are relatively single. The essential oils are mainly extracted by steam distillation, but the pretreatment is relatively complex and cumbersome. Therefore, it is essential to find a simple and cost-effective method. By comparing different extraction methods, HS-SPME-GC-MS was selected as the optimal extraction condition. Regarding Head-space Solid-Phase Microextraction and Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS), single-factor condition optimization and response surface analysis were carried out for different fiber coatings, equilibrium time, extraction temperature, and extraction time. Eventually, 75-μm CAR/PDMS fiber head was chosen, with an equilibrium time of 15 min, and extraction was conducted at 70°C for 57 min as the optimal HS-SPME extraction conditions. Furthermore, a differential analysis of the volatile components of five Lauraceae plants from different genera was performed, and differential metabolites were screened out respectively. This can effectively separate Cinnamomum and Litsea from the other three genera, providing certain assistance for the chemotaxonomy of the volatile components of Lauraceae plants and the subsequent development of medicinal plant resources.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

1. Introduction

The Lauraceae family refers to a group of plants within the class Dicotyledoneae and the subclass Magnoliidae [1]. Most of them are trees or shrubs, and a large number of species within this family possess oil-containing cells in their fruits and leaves, from which a substantial quantity of volatile components can be extracted. These components mainly consist of linalool, citral, camphor, cinnamaldehyde, eucalyptol, and so on. These volatile substances exhibit functions like repelling insects and mosquitoes [2], antioxidation and anti-inflammation [3, 4], as well as antibacterial and antiviral effects [5–7]. Lauraceae plants can be categorized into around 45 genera, with a distribution across tropical and subtropical regions. They show remarkable diversity in China. In the recently published “Inventory of Higher Plants in China”, a total of 513 species belonging to 25 genera, including subspecies groups, have been recorded. Lauraceae plants are abundant in various chemical components, including aliphatic and aromatic aldehydes, ketones, alkenes, monoterpenes, and sesquiterpenes, all of which possess certain medicinal value [8]. Lauraceae plants are rich in aliphatic and aromatic aldehydes, ketones, alkenes, monoterpenes, and sesquiterpenes chemical components, which have certain medicinal value [9]. At present, cinnamon is the Lauraceae plant with the most thoroughly studied medicinal properties. Research has revealed that cinnamon branches are rich in compounds such as cinnamic acid amides and cinnamaldehyde [10], which can be used to treat microbial infections [11]. Moreover, cinnamon essential oil has a significant antibacterial effect [12], and has been used in the development and utilization of antibacterial composite films, significantly inhibiting the growth of Bacillus cereus, Staphylococcus aureus, Escherichia coli, and Salmonella typhimurium [13]. In addition, cinnamon extracts can inhibit the proliferation of tumor cells by upregulating pro-apoptotic molecules, and research has proven that it has strong anticancer ability [14]. The volatile oil of Lindera aggregata has been found to alleviate cardiomyopathy caused by diabetes in mice by inhibiting the MAPK/ATF6 pathway [15]. The sesquiterpenoids of Litsea cubeba have a certain inhibitory effect on cancer cells [16], and Laurus nobilis also has certain effects in helping sleep and protecting the liver [17]. Evidently, this group of plants holds significant medicinal value.

China abounds in species resources of Lauraceae plants. Nevertheless, currently, our comprehension of Lauraceae plants is predominantly confined to the research on the chemical components and medicinal properties of certain medicinal plants. There is a dearth of systematic research on the chemical components of Lauraceae plants in general, and numerous nonmedicinal plants await development and utilization. Thus, it is imperative to detect the volatile components of nonmedicinal Lauraceae plants. Our anticipation is to discover species that can substitute for medicinal plants during the processes of component identification and chemotaxonomy. At present, the principal research methods still center around the GC-MS identification of volatile oils, including techniques like the steam distillation method and the Soxhlet extraction method [18]. Although these methods boast high accuracy, are widely acknowledged by the majority of researchers, and are of vital importance in content determination, their intricate sample-processing procedures are time-consuming and labor-intensive. This poses a rather significant challenge for the qualitative research on the chemical components of a large quantity of Lauraceae plants. Consequently, it is extremely essential to seek a relatively simple and convenient detection method.

Odor detection methods such as Head-space Solid-Phase Microextraction and Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS), Automatic Thermal Desorption-Gas Chromatography-Mass Spectrometry (ATD-GC-MS), and Electronic nose were initially widely applied in the research of the environmental science field [19], Due to their high sensitivity in detecting volatile components, they have also been applied in the food and pharmaceutical fields in recent years. For example, HS-SPME-GC-MS has been used in the monitoring of food production and processing, such as flaxseed baking [20] and the dynamic change of green tea aroma [21]. It has also been involved in the pharmaceutical field, such as the detection of tangerine peel aging [22] and the metabolomics research of Houttuynia cordata [23]. This demonstrates that HS-SPME-GC-MS has certain research potential in the development of medicinal plants mainly composed of volatile components and is suitable for the analysis of a large number of chemical components. In the realm of volatile component differential analysis, there exists a broad spectrum of opportunities for further exploration. For example, by examining the variations in terpenoid constituents between green and red Sichuan peppercorns and employing chemometric techniques, one can effectively differentiate among various cultivars and geographical origins of these spices [24]. Moreover, conducting comparative analyses of chemical constituents from distinct plant parts—such as the leaves, stems, and flowers of Pogostemon cablin—has revealed notable differentiation efficacy through differential analysis and metabolic pathway investigations [25]. These analytical approaches are equally applicable to HS-SPME-GC-MS methodologies, thereby furnishing novel research avenues for the taxonomic classification of Lauraceae species. Therefore, in this research project, different extraction methods of volatile components will be screened. Based on the detection abundance and the number of compounds by GC-MS, the practicality of HS-SPME-GC-MS in the qualitative analysis of volatile components of Lauraceae plants will be verified, and the conditions of SPME will be optimized. The aim is to screen out the most suitable HS-SPME-GC-MS extraction conditions for most Lauraceae plants, which can be used for the qualitative detection of volatile components of Lauraceae plants, laying a prerequisite for the subsequent chemotaxonomy of the collected Lauraceae plants and the development of medicinal plant resources.

2. Materials and Methods

2.1. Materials and Reagents

The cassia twig used in the experiment is packaged decoction pieces from a pharmacy, sourced from Rong County, Guangxi Zhuang Autonomous Region, and complies with the implementation standards of Part 1 of the Chinese Pharmacopoeia (2020 Edition). The extraction solvents used are HPLC-grade n-hexane (imported from Germany, SIMARK) and HPLC-grade acetone. Anhydrous sodium sulfate is provided by Tianjin Zhiyuan Chemical Co., Ltd. The instruments used in the experiment are as follows: 7890A/5975C Gas Chromatograph-Mass Spectrometer (GC-MS), produced by Agilent Technologies; BSA224S Electronic Analytical Balance, provided by Sartorius Scientific Instruments (Beijing) Co., Ltd; DC-200 High-speed Multifunctional Crusher, manufactured by Zhejiang Wuyi Dingcang Daily-use Metal Factory; KMD Regulable Temperature Electrothermal Mantle, produced by Shandong Juancheng Hualu Electrothermal Instrument Co., Ltd; KQ-500DE Ultrasonic Cleaner, manufactured by Kunshan Ultrasonic Instrument Co., Ltd; C-MAG HS4 Magnetic Stirrer, from IKA Group, Germany; and 57318-type 50/30-μm DVB/CAR/PDMS, 65-μm PDMS/DVB, 75-μm CAR/PDMS, and 100-μm PDMS SPME fiber heads, provided by Supelco, USA.

2.2. Comparison of Different Extraction Methods

2.2.1. Ultrasound-Assisted Extraction

Accurately weigh 0.5 g of the prepared powdered herbal sample (sieved through a 60-mesh screen) and transfer it into a 10-mL centrifuge tube. Add 5 mL of two extraction solutions (n-hexane and acetone), mix thoroughly, and allow it to soak for 6 h. Perform ultrasonic extraction for 50 min with the aid of a water bath, followed by centrifugation at 10,000 r/min for 5 min. Collect the supernatant and set it aside for further use.

2.2.2. The Volatile Oil Extraction Method

The extraction was performed according to Method A in the “Determination of Volatile Oils” outlined in the Chinese Pharmacopoeia (2020 edition, General Rule 2204, Part IV). Accurately weigh 10 g of the powdered sample and soak it in distilled water at a ratio of 10:1 (distilled water to sample) for 1 h. The volatile oils were extracted using a volatile oil extraction apparatus, with distillation carried out for 6 h until no further increase in oil volume was observed in the extractor. The oil was extracted three times with petroleum ether. The combined petroleum ether extracts were dehydrated using anhydrous Na₂SO₄, followed by filtration. The filtrate was subjected to solvent recovery using a rotary evaporator, yielding a yellow volatile oil with a characteristic aroma. The oil was diluted with n-hexane and set aside for further use.

2.2.3. HS-SPME Extraction

Accurately weigh 0.5 g of the prepared powdered herbal sample and transfer it into a 15-mL extraction vial, and then seal the vial. Place the extraction vial onto the SPME apparatus. After aging the SPME fiber for 5 min, insert the fiber through the vial cap into the headspace above the sample, positioning the fiber approximately 1.0 cm above the surface of the sample. Set the stirring speed to 150 r/min and allow the system to equilibrate for 15 min. The extraction was conducted for 40 min at 60°C. After sampling, insert the SPME fiber into the GC-MS injection port, desorb the analytes at 250°Cfor 5 min, and proceed with GC-MS analysis.

2.3. GC-MS Conditions

2.3.1. Gas Chromatography Method

The sample analysis was performed utilizing a 7890A-5975C GC-MS system from Agilent Technologies, USA. The chromatographic column employed was a TR-5MS (60 m × 0.25 mm, 0.25 μm). High-purity helium (purity ≥ 99.999%) served as the carrier gas. The temperature program was established as follows: the initial temperature was set at 50°C, followed by an increase at a rate of 10°C/min to 160°C, where it was maintained for 10 min; subsequently, the temperature was raised at a rate of 1°C/min to 161°C and held for 5 min, and finally, the temperature was ramped up at 20°C/min to 260°C and held for 2 min, resulting in a total run time of 2 min. With the exception of HS-SPME injections, n-hexane was utilized as the solvent for all other samples, with a solvent delay of 5 min.

2.3.2. Mass Spectrometry Parameters

The mass spectrometer model was 5975C Network MSD (Agilent Technologies). The following parameters were set for the MS: ionization source: electron impact (EI) with an energy of 70 eV; MS interface temperature: 250°C; ion source temperature: 230°C; quadrupole temperature: 150°C; acquisition mode: full scan; and mass scan range: m/z 40–650.

2.4. Screening of the Different Extracted Fibers

Volatile organic compounds from Cinnamomum cassia were extracted using four types of SPME fibers: 50/30 μm DVB/CAR/PDMS, 65 μm PDMS/DVB, 75 μm CAR/PDMS, and 100 μm PDMS. A 0.5-g sample of Cinnamomum cassia was weighed and placed in a 15-mL extraction vial, with the temperature of the magnetic stirrer set to 80°C and the stirring speed set to 150 rpm. The SPME fibers were exposed to the headspace of the vial for 50 min to adsorb the volatile organic compounds, which were then analyzed by SPME. The fibers were subsequently desorbed at 250°C for 5 min, followed by GC-MS separation and identification.

2.5. Single-Factor Condition Optimization

2.5.1. Extraction Temperature

Extraction was carried out at temperatures of 20°C, 40°C, 60°C, 80°C, and 100°C, while the remaining conditions were kept consistent with the method outlined in Section 2.4.

2.5.2. Extraction Time

The extraction process was conducted over durations of 20, 30, 40, 50, and 60 min, with all other parameters held constant in accordance with the methodology outlined in Section 2.4.

2.5.3. Equilibrium Time

The extraction was performed with equilibrium times set to 10, 15, 20, 25, and 30 min, while the other conditions remained consistent with the method described in Section 2.4.

2.6. Response Surface Methodology Experiment

Based on the results obtained from the single-factor experiments, the extraction temperature, extraction time, and equilibrium time were selected as the influencing factors according to the principles of Box–Behnken design in central composite experiments. The factors and their levels are shown in Table 1.

Table 1

The factors and levels of response surface test.

Level	Factor
	A: Extraction temperature (°C)	B: Extraction time (min)	C: Equilibrium time (min)
−1	50	30	10
0	60	40	15
1	70	50	20

2.7. Analysis and Identification of VOCs

The NIST 11 library in the GC-MS software was used to automatically search and analyze the mass spectral data of the components. All results were searched, and the data were checked and supplemented by referencing relevant standard spectra. The composition of each volatile compound was determined qualitatively, and the relative percentage content of each component in the total volatile substances was calculated using the total ion chromatogram (TIC) peak area normalization method.

2.8. Differential Analysis of Lauraceae Plants in Five Different Genera

According to the above optimization method, the representative plants of five different genera were analyzed, and the differences of metabolites were analyzed by stoichiometry, and the different metabolites were classified and analyzed.

3. Results and Discussion

3.1. Comparison of Different Extraction Conditions

Figure 1(a) shows a comparison of the TIC diagrams obtained under four different extraction methods. The differences in the TICs derived from the four extraction methods are clearly discernible. For the samples extracted via ultrasonic-assisted extraction with n-hexane and acetone, only a small number of compounds were detected, with 18 and 21 compounds detected respectively, as presented in Table 2. In contrast, when SPME is employed, most of the TIC peaks are well separated in the signal. By setting a matching degree of ≥ 80, potentially existing volatile compounds were screened, and a total of 59 compounds were identified. Figure 1(c) depicts the compound component histogram under different extraction methods. Except for aliphatic compounds, the number of compounds measured by the SPME method is higher than that measured by other methods. Moreover, in the research on the medicinal components of Lauraceae plants, terpenoids and aldehyde compounds play more significant roles, and the SPME method can acquire more compound information with a smaller sample quantity. Overall, it is more advisable to select SPME as the optimal extraction method.

[figure(s) omitted; refer to PDF]

Table 2

The number and total peak area of resolved components under different extraction conditions.

Extraction method	Number of resolved components	Total peak area
n-Hexane	18	1.97 × 108
Acetone	21	5.84 × 108
HS-SPME	59	1.14 × 1010
Volatile oil	42	1.36 × 1010

3.2. Screening of Different Fiber

The TIC profiles obtained from different fiber coatings are quite similar in terms of peak appearance. As shown in Figure 1(b), the 75-μm CAR/PDMS fiber was able to adsorb a greater number of volatile compounds. Table 3 lists the number of compounds and total peak area of separated components obtained with different extraction fibers. Moreover, by comparing the number of compounds captured and the total peak area across the four fiber coatings, the 75-μm CAR/PDMS fiber detected 62 compounds, with a total peak area significantly higher than those of the other three fibers. Therefore, the 75-μm CAR/PDMS fiber was selected as the SPME fiber for sample extraction.

Table 3

The number and total peak area of resolved components under different extraction fibers.

Extraction fiber	Number of resolved components	Total peak area
50/30-μm DVB/CAR/PDMS	54	8.67 × 109
65-μm PDMS/DVB	55	7.45 × 109
100-μm PDMS	54	5.69 × 109
75-μm CAR/PDMS	62	1.04 × 1010

3.3. Optimization of the Single-Factor Test Conditions

Before the analysis, the adsorption conditions of SPME were also optimized. Single-factor experiments were carried out on three parameters: extraction time, extraction temperature, and equilibrium time.

3.3.1. Selection of Different Extraction Temperatures

The extraction temperature has a significant impact on the extraction process. As shown in Figure 2(a), the total peak area of the compounds increases with the rise in extraction temperature. It is worth noting that although higher temperatures result in a larger total peak area, a decrease in the number of detectable compounds occurs after the temperature reaches 80°C. To further validate this observation, we conducted experiments at 120°C, detecting only 52 compounds. Moreover, temperatures exceeding 80°C generate significant amounts of water vapor during sample preparation, hindering the experimental process. Therefore, we conclude that 80°C is the likely optimal temperature condition.

[figure(s) omitted; refer to PDF]

3.3.2. Selection of Different Extraction Times

Figure 2(b) shows the number of compounds and total peak area under different extraction times. The optimal extraction effect was observed at 50 min. A comparison of the number of detected compounds and total peak area shows that 58 compounds were detected at 50 min, with a total peak area of 1.13 × 10¹⁰, both representing the highest values among the five conditions tested. Therefore, 50 min was selected as the optimal extraction time.

3.3.3. Selection of Different Equilibration Times

As shown in Figure 2(c), the optimal equilibration time was reached at 15 min. After 15 min, both the number of compounds and the total peak area gradually declined, likely due to prolonged heat exposure leading to compound degradation. Therefore, 10 min was selected as the optimal equilibration time.

3.4. Optimization of the Response Surface Test

A response surface experiment was designed using Design Expert 8.0.6, with a total of 17 experimental points, including 12 factorial experiments and 5 center points. The results are shown in Table 4.

Table 4

Response surface test design and results.

Std	Run	A (°C)	B (min)	C (min)	Number of resolved components
1	17	−1 (60)	−1 (40)	0 (15)	57
2	14	1 (100)	−1	0	56
3	8	−1	1 (60)	0	61
4	16	1	1	0	57
5	2	−1	0 (50)	−1 (10)	58
6	10	1	0	−1	55
7	4	−1	0	1	60
8	5	1	0	1	56
9	12	0 (80)	−1	−1	56
10	6	0	1	−1	58
11	3	0	−1	1 (20)	57
12	9	0	1	1	60
13	11	0	0	0	64
14	13	0	0	0	62
15	7	0	0	0	63
16	1	0	0	0	62
17	15	0	0	0	63

A multiple regression analysis was conducted using Design Expert 8.0.6 on the experimental data, yielding a quadratic polynomial regression model for the volatile components of Cinnamomi ramulus as a function of extraction temperature (A), extraction time (B), and equilibration time (C). The resulting regression equation is Y = −80.2 + 1.26A + 2.625B + 3.43C − 0.00375AB − 0.0025AC + 0.005BC − 0.006937$A^2$ − 0.02275$B^2$ − 0.111$C^2$. An analysis of variance (ANOVA) was performed on the regression model and its contributing factors, with the results summarized in Table 5.

Table 5

Variance analysis of regression equation of response surface test.

Source	Sum of squares	df	Mean square	F-value	$p$ value	Significance
Model	61.55	9	14.94	31.69	< 0.0001	∗∗
A	4.91	1	18	38.18	0.0005	∗∗
B	1.92	1	12.5	26.52	0.0013	∗∗
C	1.47	1	4.5	9.55	0.0176	∗
AB	0.038	1	2.25	4.77	0.0652
AC	1.17	1	0.25	0.5303	0.4901
BC	0.0006	1	0.25	0.5303	0.4901
$A^2$	24.97	1	32.42	68.78	< 0.0001	∗∗
$B^2$	5.54	1	21.79	46.23	0.0003	∗∗
$C^2$	16.59	1	32.42	68.78	< 0.0001	∗∗
Residual	2.73	7	0.4714
Lack of fit	0.9056	3	0.1667	0.2381	0.866	Not significant
Pure error	1.82	4	0.7
Cor total	64.27	16
$R^2$ = 0.9760 $R_{\text{Adj}}^2$ = 0.9452 $R_{\text{Pre}}^2$ = 0.9102  C.V. = 1.16%

^∗A significant difference ($p<0.05$).

^∗∗An extremely significant difference ($p<0.01$).

As shown in Table 5, the $p$ value of the model is less than 0.0001, indicating that the regression model is highly significant ($p<0.01$). Meanwhile, the lack of fit term has a $p$ value of 0.6137, which is not significant ($p>0.05$), confirming the good fit of the regression equation. The model’s coefficients of determination $R^2$ = 0.9760 and $R_{\text{Ad}}^2$ = 0.9452 demonstrate a high degree of fit between the regression equation and the actual data. Therefore, this model can be used to analyze and predict the extraction of volatile components from Lamiaceae plants using the HS-SPME-GC-MS method. Additionally, the linear terms A and B, along with the quadratic terms $A^2$, $B^2$, $C^2$ have highly significant effects on the extraction efficiency of volatile substances ($p<0.01$), whereas term C shows a significant effect ($p<0.05$). The ranking of the three factors in terms of their influence on the total peak area of volatile components is: extraction temperature (A) > extraction time (B) > equilibration time (C). The response surface results for the pairwise interactions between these factors are shown in Figures 2(d), 2(e), 2(f).

The prediction of the regression model gave the best condition for HS-SPME-GC-MS analysis of 0.5 g of samples equilibrated at 70.17°C for 14.7 min and extracted for 57.41 min.

3.5. Modeling Verification

As can be seen from Figure 3(a), this optimized condition is applicable to most Lauraceae plants. Judging from the representative plant samples of five genera currently tested, the peak shape is good and the distribution is uniform. As shown in Figures 3(b), 3(c), they are the qualitative result verification and repeatability verification of some standards, respectively. The qualitative results are accurate and the repeatability is good. Since both temperature and time need to be rounded, for the convenience of the experiment, the response surface test model was modified and verified according to the actual conditions. The extraction temperature was selected as 70°C, and after 15 min of equilibration, extraction was carried out for 57 min. Under these conditions, a total of 62 volatile substances could be detected finally, indicating that this mathematical model has good predictability and accuracy. Finally, this condition was selected as the optimal HS-SPME extraction condition.

[figure(s) omitted; refer to PDF]

3.6. Differential Analysis of Volatile Components in Samples From Five Different Genera

The plant samples of five genera in the Lauraceae family are mainly composed of sesquiterpenoids and monoterpenoids. The peak areas of the samples from the five genera (with three replicates for each sample, and the average value was used for input) were selected for chemometric analysis. The results are shown in Figure 4(a). From the PCA, it can be found that due to the large differences in compounds, the samples of the Litsea genus and the Cinnamomum genus are evenly distributed on the positive and negative semi-axes, while the Phoebe genus, Machilus genus, and Lindera genus are clustered together, indicating that these three genera have a relatively high similarity compared with the Litsea genus and the Cinnamomum genus. The PLS-DA results in Figure 4(b) are also quite similar. Therefore, these three genera were grouped into one category, named “other category”, and PLS-DA analysis was carried out on the newly generated three groups of samples, as shown in Figure 4(c). The three groups can be well distinguished. And as shown in Figure 4(d), the values of $R^2$, $Q^2$, and the accuracy demonstrate that the model is in good condition and the results are stable and reliable. Next, chemical classification will be carried out pairwise among the three groups. The following are the specific analysis results.

[figure(s) omitted; refer to PDF]

3.6.1. Analysis of Differences Between Litsea Genus and Cinnamomum Genus

Further comparative analysis was performed on the volatile components of the branch parts of 33 species of the Litsea genus and 16 species of the Cinnamomum genus, and the results are shown in Figure 5. The PLS-DA analysis diagram in Figure 5(a) clearly shows that there are significant differences between the two genera. This indicates that in the supervised two-dimensional analysis, there is still a large difference between the Litsea genus and the Cinnamomum genus, which is consistent with the previous PCA results, and according to the cross-validation results shown in Figure 5(b), no overfitting phenomenon was observed. The relatively concentrated aggregation of Litsea genus samples proves that there is little difference in the composition of the Litsea genus itself. Although the Cinnamomum genus can be distinguished from the Litsea genus, it can be found to be more dispersed in the vertical direction, so there are relatively large differences within the Cinnamomum genus.

[figure(s) omitted; refer to PDF]

According to the PLS-DA, there are 70 compounds with VIP > 1. Figure 5(c) shows the top 15 compounds with VIP scores, which are candidate compounds for distinguishing between the Litsea genus and the Cinnamomum genus. The top 15 compounds were selected as the finally screened differential compounds, and the specific information is shown in Table 6.

Table 6

15 Differential metabolites in the genera Litsea and Cinnamomum.

No.	Chemical compound	Formula	Classification	VIP value
1	(+)-2-Bornanone	C10H16O	Oxygenated monoterpenoids	2.861
2	D-Carvone	C10H16O	Oxygenated monoterpenoids	2.7131
3	Eucalyptol	C10H18O	Oxygenated monoterpenoids	2.5178
4	p-Cymene	C10H14	Aromatics	2.4202
5	Benzyl benzoate	C14H12O2	Aromatics	2.3318
6	(Z)-para-2-Menthen-1-ol	C10H18O	Oxygenated monoterpenoids	2.2769
7	Safrole	C10H10O2	Aromatics	2.1535
8	4-Isopropylbenzaldehyde	C10H12O	Aromatics	2.1494
9	Benzylsalicylate	C14H12O3	Aromatics	2.1391
10	D-Limonene	C10H16	Monoterpenoids	2.0988
11	Hexanoic acid	C6H12O2	Aliphatics	2.0011
12	Geraniol	C10H18O	Oxygenated monoterpenoids	1.9629
13	Ethylpalmitate	C15H30O2	Aliphatics	1.9625
14	Guaiol	C15H26O	Oxygenated sesquiterpenoids	1.9595
15	β-Terpineol	C10H18O	Oxygenated monoterpenoids	1.8817

The sample distribution of 15 differential metabolites was analyzed, and the results are shown in the violin diagram in Figure 5(d). Hexene, D-limonene, geraniol, and carvone are the volatile components unique to the Litsea genus compared with the Cinnamomum genus. This is consistent with the odor characteristics of the Litsea genus and the Cinnamomum genus. For example, the common Chinese herbal medicine L. cubeba of the Litsea genus has a strong lemon fragrance. Different from the lemon flavor of the Litsea genus, the Cinnamomum genus has a strong camphor odor. Compounds such as camphor, eucalyptol, p-cymene, benzyl benzoate, (Z)-p-2-menth-1-ol, safrole, cuminaldehyde, benzyl salicylate, ethyl palmitate, guaiol, and β-terpineol are the compounds that distinguish the Cinnamomum genus from other genera.

3.6.2. Analysis of Differences Between the Litsea Genus and Other Genera

Further analysis was conducted on 33 species of the Litsea genus and 63 species of other genera, and the results are shown in Figure 6(a). The PLS-DA analysis diagram in the above figure clearly shows that there are significant differences between the two genera. Moreover, Figure 6(b) demonstrates favorable cross-validation results.

[figure(s) omitted; refer to PDF]

According to the PLS-DA analysis in Figure 6(c), a total of 75 compounds had VIP > 1. The top 15 compounds with the highest VIP scores were selected as differential metabolites to distinguish between the two sample groups, with detailed information provided in Table 7.

Table 7

15 differential metabolites between the genus Litsea and other genera.

No.	Chemical compound	Formula	Classification	VIP value
1	Nerolidol	C15H26O	Oxygenated sesquiterpenoids	3.3973
2	Carveol acetate	C12H18O2	Oxygenated monoterpenoids	3.2243
3	Styrene	C8H8	Aromatics	3.1373
4	Heptanal	C7H14O	Aliphatics	3.062
5	Isoledene	C15H24	Sesquiterpenoids	2.3654
6	(E)-β-Farnesene	C15H24	Sesquiterpenoids	2.2928
7	p-Cymene	C10H14	Aromatics	2.275
8	D-Carvone	C10H16O	Oxygenated monoterpenoids	2.0832
9	Verbenone	C10H14O	Oxygenated monoterpenoids	2.0482
10	(R)-(+)-Citronellol	C10H18O	Oxygenated monoterpenoids	2.0336
11	β-Cubebene	C15H24	Sesquiterpenoids	2.0119
12	Geraniol	C10H18O	Oxygenated monoterpenoids	1.9406
13	(R)-α-Campholenaldehyde	C10H16O	Oxygenated monoterpenoids	1.9319
14	α-Bisabolol	C15H24	Oxygenated sesquiterpenoids	1.8935
15	(+)-Calarene	C15H24	Sesquiterpenoids	1.8882

As shown in Figure 6(d), it is the violin plot of the Litsea genus and other genera. Among them, nerolidol is widely distributed in the Litsea genus and can be used to distinguish the two types of samples. Nerolidol, carvyl acetate, carvone, verbenone, and α-bisabolol can be used to distinguish the other three genera. Isoledene, p-cymene, β-cubebene, campholenaldehyde, and calamenene are commonly found in the Phoebe genus, Machilus genus, and Lindera genus and can be distinguished from the Litsea genus.

3.6.3. Chemical Difference Analysis Between the Cinnamomum Genus and Other Genera

Differential compounds are screened based on the VIP values and $p$ values obtained from the PLS-DA model. According to the criteria of VIP > 1 and $p<0.05$, 15 main differential compounds are finally obtained, most of which are aliphatic compounds, mainly including n-hexane and n-undecanal. The specific information of the compounds is shown in Table 8.

Table 8

15 Differential metabolites between the genus Cinnamomum and other genera.

No.	Chemical compound	Formula	Classification	VIP value
1	(+)-2-Bornanone	C10H16O	Oxygenated monoterpenoids	3.0211
2	Benzyl benzoate	C14H12O2	Aromatics	2.9954
3	Styrene	C8H8	Aromatics	2.9737
4	Safrole	C10H10O2	Aromatics	2.7502
5	(Z)-para-2-Menthen-1-ol	C10H18O	Oxygenated monoterpenoids	2.5621
6	Benzyl salicylate	C14H12O3	Aromatics	2.5455
7	(Z)-Citral	C10H16O	Oxygenated monoterpenoids	2.5255
8	Eucalyptol	C10H18O	Oxygenated monoterpenoids	2.4909
9	(E)-Citral	C10H16O	Oxygenated monoterpenoids	2.4802
10	Undecane	C11H24	Aliphatics	2.3714
11	Heptanal	C7H14O	Aliphatics	2.2188
12	Isoledene	C15H24	Sesquiterpenoids	2.1818
13	α-Terpineol	C10H18O	Oxygenated monoterpenoids	2.1744
14	Hexanal	C6H12O	Aliphatics	2.0435
15	Methyleugenol	C11H14O2	Aromatics	2.0374

As shown in Figure 7, it is the comparison among the Cinnamomum genus and the Lindera genus, Machilus genus, and Phoebe genus. It can be seen that the difference between the Cinnamomum genus and the other genera in the figure is obvious. Most of the differential metabolites of the other genera are mainly aliphatic compounds, indicating that the other three genera have a better enrichment of aliphatic compounds compared with the Cinnamomum genus, while the Cinnamomum genus is rich in more oxygen-containing monoterpenoid compounds. However, since there are certain differences among the three genera themselves, a rather polarized difference is shown in the violin plot. If one wants to explore the differences among these three genera, further analysis is required.

[figure(s) omitted; refer to PDF]

3.6.4. Analysis of Differences Among Other Genera

As shown in Figure 8(a), a supervised analysis was performed on the three genera, and it was found that the Machilus genus and the Phoebe genus are closer to each other, while the Lindera genus was distinguished. As shown in Figures 8(b), 8(c), according to the criteria of VIP > 1 and $p<0.05$, 80 main differential compounds were finally obtained, and the top 15 compounds were selected by VIP score ranking, mainly including oxygen-containing monoterpenoids and sesquiterpenoids. The specific compound information is shown in Table 9. According to the violin plot to explore the distribution of differential components among the three genera, it can be found that there is a large gap between the Lindera genus and the Phoebe genus and the Machilus genus, especially geranyl acetate, geraniol, β-elemene, β-selinene, and linalyl geranyl acetate. It indicates that the Lindera genus contains more oxygen-containing monoterpenoid compounds compared with the Phoebe genus and the Machilus genus. Myrtanal, calamenene, guaiol, isocaryophyllene, and 2-isopropyl-5-methylanisole have a relatively high content distribution in the Phoebe genus and the Machilus genus. Calamenene and guaiol are also the differential metabolites between them and the Cinnamomum genus and the Litsea genus, indicating that such compounds may serve as the differential metabolites that distinguish the Phoebe genus and the Machilus genus from other genera.

[figure(s) omitted; refer to PDF]

Table 9

15 differential metabolites among the remaining three genera (Phoebe, Machilus, and Lindera).

No.	Chemical compound	Formula	Classification	VIP value
1	Geranyl acetate	C12H20O2	Oxygenated monoterpenoids	3.8288
2	Geraniol	C10H18O	Oxygenated monoterpenoids	2.8112
3	β-Selinene	C15H24	Sesquiterpenoids	2.7091
4	Ethyl palmitate	C15H30O2	Aliphatics	2.5423
5	α-Selinene	C15H24	Sesquiterpenoids	2.112
6	Myrtenal	C10H14O	Oxygenated monoterpenoids	2.0991
7	Undecane	C11H24	Aliphatics	2.0164
8	(+)-Calarene	C15H24	Sesquiterpenoids	2.0065
9	Guaiazulene	C15H18	Aromatics	2.0048
10	Isocaryophyllene	C15H24	Sesquiterpenoids	1.956
11	2-Isopropyl-5-methylanisole	C11H16O	Aromatics	1.8641
12	Eicosane	C20H42	Aliphatics	1.8594
13	β-Myrcene	C10H16	Monoterpenoids	1.7722
14	cis-Linaloloxide	C10H18O2	Oxygenated monoterpenoids	1.7528
15	Linalool	C10H18O	Oxygenated monoterpenoids	1.7187

3.6.5. Comparison of Differential Metabolites

The differential metabolites of each group were aggregated, and the landmark differential metabolites among each genus were searched through Figure 9, Venn diagrams. Detailed compounds can be found in Table 10. There are a total of eight common compounds, including carvone and ethyl palmitate. There are 19 unique compounds in the Litsea genus, such as D-limonene, geraniol, and nerolidol. While the Cinnamomum genus has 27 landmark differential metabolites, such as camphor, eucalyptol, and safrole, there are 10 differential metabolites in other genera, such as styrene, n-heptanal, and isoledene. It can be seen that there are huge differences in volatile components among the genera of Lauraceae plants. Compared to trait-based differential analysis [26], this method provides an in-depth analysis of five genera within the Lauraceae family from a chemical composition perspective. Currently known detection methods for the classification of Lauraceae plants often rely on UV and nuclear magnetic resonance (NMR) techniques [27]. In comparison, the HS-SPME-GC-MS detection method offers greater convenience and cost-effectiveness. This method enables the rapid detection of compound compositions in various samples and utilizes differential compounds to effectively distinguish among different genera. Furthermore, the unique chemical composition profiles can provide a significant material basis for the further development and utilization of plants within each genus.

[figure(s) omitted; refer to PDF]

Table 10

The specific and common metabolite compounds of different genus detected by HS-SPME-GC-MS in Lauraceae.

No.	Litsea(16)	Cinnamomum(29)	Others(10)	Common (11)
1	p-Cymene	Bornanone	Styrene	D-Carvone
2	Geraniol	Eucalyptol	Heptanal	D-Limonene
3	Nerolidol	Benzyl benzoate	Isoledene	Ethyl palmitate
4	α-Calacorene	(Z)-para-2-Menthen-1-ol	α-Himachalene	Guaiol
5	Carveol acetate	Safrole	α-Muurolene	α-Cubebene
6	Spathulenol	4-Isopropylbenzaldehyde	Linalool	Alloocimene
7	Humulene epoxide II	Benzyl salicylate	Carveol	Calarene
8	β-Elemene	Hexanoic acid	Octanal	Undecane
9	γ-Muurolene	β-Terpineol	β-Patchoulene	α-Terpineol
10	Hexadecane	Benzaldehyde	2-Carene	(Z)-Citral
11	Campholenaldehyde	Methyleugenol		(E)-Citral
12	Muurola-3,5-diene	Undecanoicacid
13	α-Copaene	Hexanal
14	α-Cedrene	α-Thujene
15	Myrtenal	α-Cadinol
16	Geranyl acetate	Tetradecanal
17		Dodecene
18		(+)-Camphene
19		para-Menthatriene
20		Nerol
21		Eugenol
22		1,5,5-Trimethyl-6-methylene-cyclohexene
23		4-Carene
24		1-Tetradecanol
25		β-Neoclovene
26		Myrcenol
27		(E)-para-2-Menthen-1-ol
28		β-Ionone
29		Selina-3,7(11)-diene

4. Conclusion

This study is based on the established HS-SPME, combined with gas chromatography-mass spectrometry. Eventually, 75-μm CAR/PDMS was determined as the optimal adsorption fiber coating. Taking 0.500 g of the sample, after equilibration for 15 min, extraction at 70°C for 57 min can yield better results. Representative plant samples of five genera were verified and subjected to chemometric analysis, and differential metabolites that can be used to distinguish each genus were found, proving that the genera of Lauraceae can be distinguished by compounds and confirming the feasibility of chemical classification. This optimized content provides a relatively practical experimental condition for the analysis of volatile components of Lauraceae plants and also provides certain help for further chemical classification of volatile components of Lauraceae plants and the development of nonmedicinal plants.

Author Contributions

Zhengjiu Wang was responsible for conceptualization and methodology. Le Li and Lu Yang reviewed and edited the manuscript and supervised the study. The following is the specific situation:

Conceptualization: Zhengjiu Wang, Lu Yang.

Data curation: Zhengjiu Wang, Hao Liu.

Formal analysis: Zhengjiu Wang, Anping Liu.

Funding acquisition: Lu Yang, Le Li.

Investigation: Zhengjiu Wang, Shaoli Fan, Bahetiyar Keremu.

Methodology: Peng Liu, Jiuyang Zhao.

Project administration: Lu Yang, Le Li.

Resources: Jinhui Wang.

Supervision: Lu Yang, Le Li.

Visualization: Zhengjiu Wang.

Writing–original draft: Zhengjiu Wang.

Writing–review and editing: Lu Yang, Le Li.

Funding

This research was funded by Innovation of Mulberry Germplasm and Breeding of Excellent New Varieties (2023A02008-2), which belongs to the Xinjiang Academy of Forestry Sciences, and this research topic was provided by the Xinjiang Academy of Forestry Sciences and also supported by Guiding Science and Technology Program of the Xinjiang Production and Construction Corps (2022ZD050), and Shihezi University (ZZZC202089A). The Xinjiang Academy of Forestry Sciences provided partial support for this research.

Acknowledgments

This research was funded by the Xinjiang major project “Innovation of Mulberry Germplasm and Breeding of Excellent New Varieties” (grant number 2023A02008-2), the Guiding Science and Technology Program of the Xinjiang Production and Construction Corps (grant number 2022ZD050), and the university-level research project of Shihezi University (grant number ZZZC202089A). The Xinjiang Academy of Forestry Sciences provided partial support for this research. We gratefully acknowledge Dr. Le Li, Dr. Na Zhao, and Dr. Lu Yang for their valuable suggestions on this research.

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