Artificial selection and matching method for panda food source bamboo species

Abstract

The invention discloses a method for artificially selecting and matching food source bamboo species for pandas. The method comprises the following steps: feeding different bamboo samples of pandas, recording feeding behavior data of the pandas, calculating confidence intervals, and dividing the bamboo samples into a fond bamboo group, an edible bamboo group and a non-edible bamboo group according to confidence interval values; preparing bamboo samples corresponding to the fond-to-eat bamboo group and the non-fond-to-eat bamboo group into powder, adding water, heating, collecting volatile components, and detecting; preparing bamboo samples corresponding to the fond-to-eat bamboo group and the non-fond-to-eat bamboo group into powder, extracting and detecting extract components, and determining metabolites; analyzing the correlation among the volatile components, metabolites and metabolic profiles of each group, and determining markers of the bamboo fond-eating group and the bamboo non-fond-eating group; and performing marker detection on the to-be-selected bamboo species to determine the fond bamboo species. The matching method provided by the invention is high in accuracy, time-saving and labor-saving, can remarkably improve the working efficiency of raising captive pandas, and is more beneficial to carrying out the field protection work of the pandas.

Description

Technical Field

[0001] The invention belongs to the technical field of panda breeding, and particularly relates to a method for artificially selecting bamboo species as food for giant pandas. Background Art

[0002] The key to relocating giant pandas for conservation and developing captive populations is addressing their food needs. Because most captive panda facilities are located far from their habitats, the distribution and availability of edible bamboo near these facilities is relatively limited due to geographical constraints. Therefore, it is necessary to identify suitable bamboo sources in the wild near these facilities.

[0003] Field observations and research have revealed that giant pandas have a preference for bamboo. They consume different types and elevations of bamboo in different seasons, and also a variety of bamboo species within the same season. Their primary goal is to meet their nutritional needs by consuming the diverse nutrients found in a variety of bamboo species. However, the precise basis for giant pandas' bamboo selection remains unknown, making feeding them difficult in captive settings.

[0004] While some studies have proposed analyzing key components in pandas' preferred bamboo to explore the chemical mechanisms by which bamboo metabolites induce feeding preferences, such as the study "Evaluation of odorous components in bamboo that have a positive impact on giant pandas' feeding habits using PT-GC-MS combined with ROAV," the study analyzed and identified volatile compounds in five known preferred bamboo species. Using relative odor activity values, nine key volatile aroma compounds and eight major modifying volatile compounds were identified. However, further research revealed that these 17 volatile compounds are not unique to preferred bamboo, but also to non-edible and edible bamboo. Furthermore, in actual feeding, some bamboo species that do not contain these 17 volatile compounds are also preferred by pandas. Therefore, these 17 volatile compounds cannot be used as a basis for selecting preferred bamboo species for pandas.

[0005] In addition, the current analysis of the components of edible bamboo usually uses conventional analytical detection methods (such as gas chromatography, liquid chromatography, etc.). Although this method can comprehensively and accurately measure the component content of each type of bamboo, the operation is cumbersome and the samples need to be sent to a professional company for sequencing and analysis. The whole process takes 2-3 months, and the data obtained cannot directly reflect the preferences of giant pandas. Further standard comparison is required. Therefore, the existing analysis process of edible bamboo is time-consuming and costly.

[0006] In view of this, the present invention is proposed. Summary of the Invention

[0007] The purpose of the present invention is to provide a method for artificially selecting bamboo species that are food sources for giant pandas. The method can quickly, accurately and at low cost screen out bamboo species that giant pandas like to eat, solve the problem of raising captive giant pandas in northern China, and is more conducive to the relocation protection of giant pandas.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] The present invention provides a method for artificially selecting bamboo species for giant panda food, comprising the following steps:

[0010] S1. Feed giant pandas different bamboo samples, record their eating behavior data and calculate confidence intervals. Based on the confidence interval values, divide the bamboo samples into the eating bamboo group, the edible bamboo group, and the non-eating bamboo group.

[0011] S2. Separately, bamboo samples from the bamboo-loving and bamboo-non-eating groups were collected and powdered, and then heated after adding water. The volatile components were collected and tested.

[0012] S3. Separately, bamboo samples corresponding to the bamboo-loving group and the bamboo-non-eating group were collected, powdered, extracted, and the extract components were tested to determine the metabolites.

[0013] S4. Analyze the correlation between the volatile components, metabolites, and metabolic profiles of each group to determine the markers of the bamboo-loving and bamboo-averse groups;

[0014] S5. Conduct marker testing on the bamboo species to be selected to determine the preferred bamboo species.

[0015] In step S1, the types of bamboo samples include at least Bashan wood bamboo, Indocalamus truncatum, Sasa bamboo, Rongcheng bamboo, Huangcao bamboo, Huangwen bamboo, Zaoyuan bamboo, Anji golden bamboo, red bamboo, and Phyllostachys praecox;

[0016] The selection requirements of the bamboo samples are as follows: 1-2 years old, feeding stem segment length is 50±10cm;

[0017] The feeding method: random meal;

[0018] The feeding conditions are as follows: feeding once a day, each time supplying 1-3 kg / head, for a total of 4 days;

[0019] The eating behavior data include: type of bamboo eaten, eating frequency, amount of food eaten, chewing time per mouthful of food, and amount consumed per feeding.

[0020] In step S1, the confidence interval is calculated according to the following operations:

[0021] (1) Forage Ratio selection index analysis method was used to determine feeding tendency;

[0022] The expression is: i=O i / P i ;

[0023] Where:

[0024] W i : Feeding tendency on the i-th bamboo species;

[0025] O i : the proportion of the i-th type of bamboo in the giant panda's food intake;

[0026] P i : the proportion of the i-th type of bamboo in the total amount of bamboo put in;

[0027] (2) G-test was performed on the significance of the difference in bamboo species selection, with degrees of freedom df = n-1, χ 2 When the corresponding probability P≤0.05, the giant panda is selective for this group of bamboo samples;

[0028] The expression is:

[0029] Where:

[0030] χ 2 : chi-square value with (n–1) degrees of freedom;

[0031] n: number of resource types;

[0032] u i : the weight of the i-th type of bamboo used by giant pandas for food;

[0033] U=∑u i ;

[0034] m i : the weight of the i-th type of bamboo put in;

[0035] M=∑m i ;

[0036] (3) Calculate W i confidence interval of ;

[0037] The expression is: Confidence interval = Wi ± ZaSwi

[0038] Where: Sw i =[(1-O i ) / UO i +(1-P i ) / MP i ] 1 / 2 ;

[0039] Swi: standard deviation of bamboo i’s feeding utilization;

[0040] Za: quantile of the standard normal distribution. To reduce the probability of error, a Bonferroni correction is performed, and the value of the significance level α is adjusted to α / 2n, thereby obtaining the corresponding Za;

[0041] (4) According to W i The confidence interval values ​​of were used to determine the giant panda's feeding tendency on each bamboo species;

[0042] The judgment is as follows:

[0043] When W i If the lower limit of the confidence interval is >1, it is judged to be a bamboo-loving plant;

[0044] W i If the upper limit of the confidence interval is <1, it is judged that the person does not eat bamboo;

[0045] W i The confidence interval of is = 1, and it is judged to be edible bamboo.

[0046] In step S2, the mass volume ratio of the powder to water is 1 g:4 mL;

[0047] The heating conditions are: heating at 80°C water bath temperature for 30 minutes;

[0048] The volatile components are collected using an adsorbent; the adsorbent is composed of Tenax, silica gel and activated carbon in a mass ratio of 1:1:1.

[0049] In step S2, the detection is performed by purge and trap gas chromatography / mass spectrometry;

[0050] The conditions for the detection are:

[0051] Degassing: high-purity nitrogen; purge temperature: room temperature or constant temperature; purge flow rate: 40ml / min; purge time: 11min; 10 # Trap preheating temperature: 180°C; desorption temperature: 190°C; desorption time: 0.5 min; baking temperature: 220°C; baking time: 10 min; sample heating temperature: 40°C, valve temperature: 110°C, transfer line temperature: 110°C;

[0052] Gas chromatography conditions: Rtx502.2 capillary column; vaporizer temperature, 220°C; injection mode: split injection, split ratio 5:1; temperature program: initial temperature, 30°C, hold for 5 min, increase to 190°C at 10°C / min, no hold, then increase to 250°C at 20°C / min, hold for 3 min; column head pressure, 71.03 kPa;

[0053] Mass spectrometry conditions: ion source: EI source; ion source temperature: 200°C; interface temperature: 220°C, electron multiplier voltage: 1.02 kV; scanning mode: full scan or selected ion scan; scanning speed: 660 amu / s; scanning range: 45-350 amu / s; scanning time: 1-27 min.

[0054] In step S3, the extraction is performed according to the following operations: adding an extractant to the powder, grinding, ultrasonicating, centrifuging, and taking a supernatant containing the extract;

[0055] The extractant is an aqueous solution containing 0.02 mg / mL L-2-chlorophenylalanine and 75% methanol by volume;

[0056] The mass ratio of the powder to the extractant is 1 mg:8 μL;

[0057] The grinding conditions are: -10°C, 50 Hz, 6 min;

[0058] The ultrasonic conditions are: 5°C, 40KHz, 30min;

[0059] The centrifugal conditions are: 13000 g, 4° C., 15 min.

[0060] In step S3, the detection is performed by high performance liquid chromatography-mass spectrometry;

[0061] The conditions for the detection are:

[0062] Chromatographic conditions:

[0063] The chromatographic column was an ACQUITY UPLC HSS T3; mobile phase A was 95% water + 5% acetonitrile containing 0.1% formic acid; mobile phase B was 47.5% acetonitrile containing 0.1% formic acid + 47.5% isopropanol + 5% water. The injection volume was 2 μL, and the column temperature was 40°C.

[0064] Mass spectrometry conditions:

[0065] The sample was ionized by electrospray ionization, and mass spectrometry signals were collected in positive and negative ion scanning modes, respectively; scan type: 70-1050 m / z; sheath gas flow: 50 Arb; auxiliary gas flow: 13 Arb; heater temperature: 425°C; capillary temperature: 325°C; spray voltage: +3500 V; spray voltage: -3500 V; S-Lens RF level: 50; normalized collision energy eV: 20%, 40%, 60%; resolution full MS: 60000; resolution MS2: 7500.

[0066] The 21 volatile components of the bamboo group are: (1S)-1,7,7-trimethyl-bicyclohept-2-one, camphene, 1,7,7-trimethyl-tricycloheptane, sec-butyl ester-cyanate, (1R)-2,6,6-trimethylbicyclohept-2-ene, α,α-olefin, 3-methyl-2-butanol, methylsilane, 2-methyl-1-butene, N,N,N,N-tetramethyl-methanediamine, (1S) -6,6-dimethyl-2-methylene-bicycloheptane, 1-benzyl-3-amino-4-cyano-3-pyrroline, (S)-2-methyl-1-butanol, (R)-(-)-2-pentanol, 2-ethyl-furan, carbonyl sulfide, 2,2,4,4-tetramethyl-1,3-cyclobutanediol, ethanol, hexamethyldisiloxane, N-dimethylaminomethyl-tert-butylisopropylphosphine, 1,3-propylene glycol;

[0067] The 21 metabolites of the bamboo group are: 1-(9H-pyridinyl(3,4-b)indol-1-yl)-1,4-butanediol, fusarium chromanone, dalfoplostin, fluorescein D2, zanthobisquinolone, Hv-NCC-1, β-humulene, pollenin B, acetylated coumarin, haldatin A glucoside, kodoxione, scleroporphyrin, jubanin A, 13-carboxyγ-tocopherol, 22-phenol, hydroxyerythrodiol, (R)-1-O-(bD-methylfuranosyl-(1-2)-bD-pyranoglucoside)-1,3-octanediol, kaempferol 3-sophoroside 7-glucuronide, 3,4-dihydroxy-4-(1-oxo-1H-isochromen-3-yl)butoxy)sulfonic acid, 3,4-dihydroxy-4-(1-oxo-1H-isochromen-3-yl)butan-2-yl)oxy)sulfonic acid, Armexifolin, Methylnori chexanthone.

[0068] The 20 volatile components of the bamboo-ined group are: 2-pentyl-furan, dichloromethane, cyclohexane, 3-ethyl-2,2-dimethyl-pentane, dimethyl sulfide, 2-methoxy-ethanol, 1-undecene, 1-((2-methyl-2-propenyl)oxy)-butane, tris(trimethylsilyl)borate, fluoropropylene, 2-methyl-1,3-butadiene, 2-fluoropropylene, 3,4-dimethyl-2-hexanone, (Z)-2-pentene, 1-methylbutyl-oxirane, 3-isopropenyl-5,5-dimethyl-cyclopentene, formic acid, 1,3-dioxazol-2-one, N-propyl-3,4-methylenedioxyamphetamine, octamethyl-cyclotetrasiloxane;

[0069] The 13 metabolites of the bamboo-not-eating group are: spermidine, jemaclostrobin, sphingosine (1+), 4-α-methyl-5-α-cholesterol-7-ene-3-one, dynorphin A (6-8), 1-(5-methyl-3-pyridyl)-1-decanone, 3-hydroxyundecylcarnitine, phenethylacetate; cycloharringtonol C, TG, 4-β-hydroxymethyl-4-α-methyl-5-α-cholesterol-7-ene-3-β-ol, cysteine, 2-(3,5-dihydroxy-4-methoxyphenyl)-4H-chrom-4-one.

[0070] Step S4 also includes quantitative analysis of differences in metabolite abundance among bamboo species.

[0071] Compared with the prior art, the present invention has the following beneficial effects:

[0072] 1. The present invention discovered for the first time the intrinsic mechanism by which giant pandas select bamboo species based on olfactory and taste chemical cues, and designed a feeding experiment. Using purge-and-trap gas chromatography and liquid chromatography techniques, the volatile components and metabolites of different groups of bamboo were accurately measured. Ultimately, through analysis, the specific volatile substances and metabolites of preferred, edible, and inedible bamboo were determined. This result can be used to quickly determine whether a selected bamboo is preferred by giant pandas.

[0073] 2. The matching method provided by the present invention has the advantages of shorter screening time and lower cost, is easier to operate in production practice, improves matching efficiency, and solves the problems of inaccurate matching results, time-consuming and labor-intensive matching work in the current captive giant panda bamboo source matching work. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 This is the Venn analysis diagram of the differential metabolites among the non-bamboo-eating group, edible bamboo group, and bamboo-liking group.

[0075] Figure 2 The classification of substances and metabolic pathways identified in the KEGG database; (a) represents the classification of identified substances; (b) represents the classification of metabolic pathways.

[0076] Figure 3 Classification map of bamboo metabolites annotated in HMDB v4.0.

[0077] Figure 4 The heatmap of the correlation of 10 bamboo samples; among them: BS1-6, RZ1-6 and SZ1-6 are the edible bamboo group; HC1-6, HW1-6, ZY1-6 and RC1-6 are the edible bamboo group; HZ1-6, LZ1-6 and AJ1-6 are the non-edible bamboo group; the redder the color, the higher the correlation.

[0078] Figure 5The PCA principal component analysis and linear discriminant analysis diagrams of 10 bamboo species; among them: (a) represents PCA principal component analysis; (b) represents linear discriminant analysis.

[0079] Figure 6 The cation Venn analysis and anion Venn analysis diagrams of 10 types of bamboo; among them: (a) represents the cation Venn analysis; (b) represents the anion Venn analysis.

[0080] Figure 7 Volcano plot summarizing the quantitative differences in metabolite abundance among different groups; where: (a) represents the difference between the edible bamboo group and the bamboo-preferring group; (b) represents the difference between the edible bamboo group and the bamboo-ined group; (c) represents the difference between the bamboo-ined group and the bamboo-preferring group; the x-axis shows the log2 fold change between groups; the y-axis shows the statistical test of the fold change (log10 p value), with higher values ​​indicating greater significance; each point in the plot represents a specific metabolite; the size of each point represents the VIP value, and the color indicates relative accumulation (red) or depletion (blue).

[0081] Figure 8 Figure 2 shows the distribution of metabolites with different quantitative abundances in pairwise comparisons among bamboo leaves of the bamboo-loving, bamboo-edible, and bamboo-inedible groups; the numbers represent the total number of identified cations and anions.

[0082] Figure 9 The abundance distribution diagram of metabolites among different bamboo types in KEGG pathways; among them: (a) represents the edible bamboo group and the non-edible bamboo group; (b) represents the edible bamboo group and the edible bamboo group; (c) represents the edible bamboo group and the non-edible bamboo group; the y-axis shows the secondary classification of KEGG metabolic / environmental pathways; the x-axis shows the number of metabolites assigned to each pathway.

[0083] Figure 10 Figure 2 KEGG pathway enrichment plots for metabolites with different abundances in pairwise comparisons among bamboo species; (a) represents the bamboo-loving group and the bamboo-ined group; (b) represents the bamboo-loving group and the edible bamboo group; (c) represents the edible bamboo group and the bamboo-ined group; the color gradient indicates the significance of the enrichment (darker = more significant); the significance levels are as follows: ***p(FDR)<0.001, **p(FDR)<0.01, p(FDR)<0.05.

[0084] Figure 11 Annotation and classification diagram of different metabolites in HMDB v4.0; among them: (a) represents the bamboo-loving group and the bamboo-ined group; (b) represents the bamboo-loving group and the edible bamboo group; (c) represents the edible bamboo group and the bamboo-ined group. DETAILED DESCRIPTION

[0085] The present invention will be further described below with reference to specific examples, but the present invention is not limited to the following examples.

[0086] Unless otherwise specified, the experimental methods used in the following examples are conventional methods.

[0087] Unless otherwise specified, the reagents, materials, instruments, etc. used in the following examples can be obtained from commercial sources.

[0088] In the following embodiments:

[0089] QP2010GC / MS equipment: Shimadzu Corporation, Japan.

[0090] Acquity UPLC: built-in HSS T3 column, with specifications: 100 mm × 2.1 mm, 1.8 μm, Waters, Milford, USA.

[0091] Q Exactive HF-X mass spectrometer: Thermo Fisher Scientific, Waltham, MA, USA.

[0092] Example 1: Selection of bamboo sources for captive giant pandas

[0093] The steps are as follows:

[0094] Step 1: Collect bamboo species samples and classify them;

[0095] (1) Bamboo sample collection dates: August 20, September 4, and September 10, 2020.

[0096] Ten bamboo species were collected from the Beijing Botanical Garden, including Bashan wood bamboo, Indocalamus chinensis, Sasa bamboo, Rongcheng bamboo, Huangcao bamboo, Huangwen bamboo, Zaoyuan bamboo, Anji golden bamboo, Red bamboo, and Lei bamboo.

[0097] Specific sampling steps: 50±10 cm long bamboo stem samples were cut from 1-2 year old bamboo and stored at -20°C; then each bamboo sample was divided into three parts, which were used for the randomized meal feeding experiment of giant pandas, PT-GC-MS detection and HPLC-MS detection respectively.

[0098] (2) Randomized meal feeding experiment

[0099] Selection of experimental animals:

[0100] Four healthy captive giant pandas were randomly selected from the Beijing Zoo for a randomized meal feeding experiment.

[0101] Mengda: 9 years old, male giant panda;

[0102] Meng Er: 9 years old, male giant panda;

[0103] Gugu: 23 years old, male giant panda;

[0104] Mengmeng: 16 years old, female giant panda.

[0105] Randomized meal feeding experiment:

[0106] Feeding time: Self-service feeding at 1:30 pm every day, repeated 4 times in the same time period.

[0107] Food supply: approximately 2 kg of each bamboo sample.

[0108] A randomized feeding method was used to observe the effects of 10 different bamboo samples on the giant pandas' eating behavior. The following feeding behavior data were recorded: bamboo species selection, feeding frequency, feeding amount, chewing time per bite, and food consumption per meal. Bamboo samples were weighed before and after each meal to determine consumption.

[0109] Table 1 Observation record of giant panda Gugu's preference for eating bamboo

[0110]

[0111] Table 2 Observation record of giant panda Meng Er's preference for eating bamboo

[0112]

[0113] Table 3 Observation record of giant panda Mengda's preference for eating bamboo

[0114]

[0115]

[0116] Table 4 Observation record of giant panda Mengmeng's preference for eating bamboo

[0117]

[0118] (3) Analysis and judgment

[0119] a. Based on Equation 1, the Forage Ratio selection index (Wi) was used for analysis. This statistical method determines whether a species prefers to eat based on the amount of resources in the environment and the amount of resources being utilized, which can effectively solve the problem of unequal sample sizes.

[0120] Equation 1: W i =O i / P i ;

[0121] Where:

[0122] W irepresents the feeding tendency on the i-th bamboo species;

[0123] O i represents the proportion of the i-th type of bamboo in the giant panda's food intake;

[0124] P i It represents the proportion of the i-th type of bamboo in the total input.

[0125] b. At the end of each experiment, the average amount of bamboo consumed per panda per group was calculated, without considering the differences between individual pandas. A G-test was performed on the significance of the differences in bamboo species selection according to Equation 2. The G-test was performed with the degrees of freedom df = n - 1 and χ 2 The corresponding probability P≤0.05 indicates that the giant panda is selective in its feeding on the bamboo in this group. The null hypothesis tested is that the giant panda is random in its feeding on the bamboo in this group.

[0126] The test statistic is:

[0127] Equation 2:

[0128] Where:

[0129] χ 2 : chi-square value with (n–1) degrees of freedom;

[0130] n: number of resource types;

[0131] u i : the weight of the i-th type of bamboo used by giant pandas for food;

[0132] U=∑u i ;

[0133] m i : the weight of the i-th type of bamboo put in;

[0134] M=∑m i .

[0135] In degrees of freedom df = n-1, χ 2 When the corresponding probability P≤0.05, the giant panda is selective for this group of bamboo samples.

[0136] (c) Calculate the confidence interval of Wi according to Equation 3;

[0137] Equation 3: Confidence interval of Wi = Wi ± ZaSwi;

[0138] Among them: Sw i =[(1-O i ) / UO i +(1-P i ) / MP i ] 1 / 2 ;

[0139] Swi: standard deviation of bamboo i’s feeding efficiency;

[0140] Za: quantile of the standard normal distribution. To reduce the probability of error, a Bonferroni correction is performed, and the value of the significance level α is adjusted to α / 2n, thereby obtaining the corresponding Za value.

[0141] (4) Classification of bamboo samples:

[0142] The confidence interval values ​​of Wi are used to determine the giant panda's feeding tendency towards each bamboo species, with P, A, and R representing preferred bamboo, non-edible bamboo, and edible bamboo, respectively:

[0143] The judgment is as follows:

[0144] When the lower limit of the Wi confidence interval is greater than 1, it indicates that the giant panda prefers this bamboo species (as indicated by a high probability of eating, a large amount of food eaten, or a large proportion of food eaten);

[0145] When the upper limit of the Wi confidence interval is <1, it means that the giant panda avoids the bamboo species (manifested by a low probability of eating, a small amount of food eaten, or a small proportion of food eaten);

[0146] When Wi is equal to 1, it means that the giant panda randomly selects the bamboo species.

[0147] Judgment result:

[0148] Among the 10 species of bamboo, the four giant pandas preferred three of them, namely Bashan bamboo, Indocalamus truncatula and Phyllostachys sylvestris, which were defined as the edible bamboo group.

[0149] In the absence of preferred bamboo species, giant pandas accepted four species, namely, early garden bamboo, Rongcheng bamboo, yellow groove bamboo, and yellow striped bamboo, which were defined as the edible bamboo group.

[0150] The remaining three bamboo species, namely Anji golden bamboo, thunder bamboo and red bamboo, are always rejected by giant pandas and are defined as the non-eating bamboo group.

[0151] Step 2: Analyze the volatile components of 10 bamboo species using purge and trap gas chromatography / mass spectrometry (PT-GC-MS);

[0152] (1) Purge and trap conditions

[0153] Degassing: High-purity nitrogen; Purge temperature: Room temperature or constant temperature; Purge flow rate: 40 ml / min; Purge time: 11 min; Preheat temperature for trap #10: 180°C; Desorption temperature: 190°C; Desorption time: 0.5 min; Bakeout temperature: 220°C; Bakeout time: 10 min; Sample heating temperature: 40°C, valve temperature: 110°C, transfer line temperature: 110°C. All other parameters were set according to the instrument's instruction manual.

[0154] (2) Gas chromatography conditions

[0155] Rtx502.2 capillary chromatographic column (60m×0.32mm×1.8μm); vaporization chamber temperature 220℃; injection mode: split injection (split ratio 5:1); programmed temperature: initial temperature 30℃ (hold for 5min), increase to 190℃ at 10℃ / min, without holding, then increase to 250℃ at 20℃ / min, hold for 3min; column head pressure 71.03kPa.

[0156] (3) Mass spectrometry conditions

[0157] Ion source: EI source; ion source temperature: 200°C; interface temperature: 220°C, electron multiplier voltage: 1.02 kV; scanning mode: full scan or selected ion scan (SIM); scanning speed: 660 μm.

[0158] (4) Experimental procedures

[0159] A. Wash the bamboo sample, let it air dry, and then put it into a DFT-50 Chinese medicine grinder to grind it into powder;

[0160] B. Take 2g of powdered sample into a 40ml VOC brown sample bottle, add 8ml of ultrapure water, and tighten the cap;

[0161] C. Heat in a water bath at 80°C for 30 minutes, and then analyze under the above-mentioned instrumental conditions to obtain volatile components; the adsorbent used to collect volatile components consists of equal masses of Tenax, silica gel, and activated carbon.

[0162] Step 3: Analyze the primary and secondary metabolites of 10 bamboo species using high performance liquid chromatography-mass spectrometry (HPLC-MS);

[0163] Six replicates were prepared for each bamboo species, making a total of 60 samples.

[0164] The specific steps are as follows:

[0165] (1) 50 mg of bamboo sample was mixed with 400 μL of a methanol / water mixture (methanol / water volume ratio of 4:1) in a 2 mL centrifuge tube. The methanol / water mixture contained 0.02 mg / mL of the internal standard L-2-chlorophenylalanine.

[0166] (2) The extract was then ground using grinding beads (6 min, −10°C, 50 Hz), followed by ultrasonic treatment (30 min, 5°C, 40 kHz), centrifugation (13,000 g, 15 min, 4°C), and the supernatant was collected;

[0167] (3) The supernatant was transferred to an Acquity UPLC device equipped with an HSS T3 column and fractionated at 40°C in mobile phase A (0.1% formic acid in 95:5 water / acetonitrile) and mobile phase B (0.1% formic acid in 47.5:47.5:5 acetonitrile / isopropanol / water); the fractions were automatically injected into a Q Exactive HF-X mass spectrometer equipped with an ESI source, and mass spectral data were collected in positive and negative ionization modes;

[0168] Chromatographic conditions:

[0169] The chromatographic column was an ACQUITY UPLC HSS T3 (100 mm × 2.1 mm id, 1.8 μm; Waters, Milford, USA); the mobile phase A was 95% water + 5% acetonitrile (containing 0.1% formic acid), and the mobile phase B was 47.5% acetonitrile + 47.5% isopropanol + 5% water (containing 0.1% formic acid). The injection volume was 2 μL, and the column temperature was 40°C.

[0170] Mass spectrometry conditions: the sample was ionized by electrospray ionization, and the mass spectrometry signals were collected in positive and negative ion scanning modes respectively; scan type (m / z): 70-1050; sheath gas flow rate: 50Arb; auxiliary gas flow rate: 13Arb; heater temperature: 425°C; capillary temperature: 325°C; spray voltage (+): 3500V; spray voltage (-): 3500V; S-Lens RF level: 50; normalized collision energy (eV): 20%, 40%, 60%; resolution (full MS): 60000; resolution (MS2): 7500.

[0171] (4) The raw mass spectrometry data were imported into the LC-MS / MS data analysis software ProgenesisQI (Waters, Milford, MA, USA) for baseline filtering, peak identification, integration, retention time correction, and peak alignment. A data matrix containing retention time, peak area, mass-to-charge ratio, and identification information was prepared for post-processing and confidence analysis. The peak features and spectral data were then used to screen public databases such as the Human Metabolome Database (http: / / www.hmdb.ca / ), Melt Protein G2 (https: / / metlin.scripps.edu / ), and LIPID MAPS (https: / / www.lipidmaps.org / ), as well as the Kyoto Encyclopedia of Genes and Genomes (KEGG).

[0172] Step 4: Analyze the correlation and determine the markers;

[0173] (1) Analysis of volatile components

[0174] PT-GC-MS analysis revealed that each bamboo species contained 27 to 41 volatile components, with a total of 87 volatile components, including: 21 alkenes, 20 alcohols, 18 alkanes, 7 ketones, 4 heterocyclic compounds, 3 acids, 2 aldehydes, 2 ethers, 3 esters, 1 phenol and 6 other compounds.

[0175] Table 5 Volatile components of 10 types of bamboo used as staple food for giant pandas

[0176]

[0177]

[0178]

[0179]

[0180]

[0181] The above results show that the most abundant volatile components in edible bamboo are alcohols, alkenes and alkanes, respectively; the most abundant volatile components in edible bamboo and inedible bamboo are alcohols, aromatic hydrocarbons and alkenes, respectively.

[0182] Table 6 Types and contents of volatile components in 10 species of bamboo used as staple food for giant pandas

[0183]

[0184] Note: Aromatic hydrocarbons (benzene); heterocyclic hydrocarbons (furan); A represents the number of components (species); B represents the relative content (%).

[0185] (2) Principal component analysis and Venn analysis of volatile components

[0186] Principal component analysis was performed on the bamboo-loving group, and two principal components were extracted, of which principal component 1 covered 63% of the volatile components; principal component analysis was performed on the edible bamboo group, and three principal components were extracted, of which principal component 1 covered 49% of the volatile components; principal component analysis was performed on the bamboo-ined group, and two principal components were extracted, of which principal component 1 covered 59% of the volatile components.

[0187] The volatile substances of the three groups of principal components 1 were analyzed by Venn. Figure 1As shown, 8 volatile components were shared among the three groups, 19 volatile components were shared between pairs of groups, and 48 volatile components were unique to each group. Of these 48 volatile components, 21 volatile components are found in bamboo that is preferred by pandas, producing sweet and fresh aromas; 7 volatile components are found in edible bamboo; and 20 volatile components are found in inedible bamboo, producing pungent or floral aromas that appear to deter giant pandas from eating.

[0188] The 21 volatile components (markers) unique to the bamboo group are: (1S)-1,7,7-trimethyl-bicyclohept-2-one, camphene, 1,7,7-trimethyl-tricycloheptane, sec-butyl ester-cyanate, (1R)-2,6,6-trimethylbicyclohept-2-ene, α-P-ene, 3-methyl-2-butanol, methylsilane, 2-methyl-1-butene, N,N,N,N-tetramethyl-methanediamine, (1S)-6,6-Dimethyl-2-methylene-bicycloheptane, 1-benzyl-3-amino-4-cyano-3-pyrroline, (S)-2-methyl-1-butanol, (R)-(-)-2-pentanol, 2-ethyl-furan, carbonyl sulfide, 2,2,4,4-tetramethyl-1,3-cyclobutanediol, ethanol, hexamethyldisiloxane, N-dimethylaminomethyl-tert-butylisopropylphosphine, 1,3-propylene glycol.

[0189] Table 7 Odor analysis of bamboo-loving group (21 unique volatile compounds)

[0190]

[0191]

[0192] The 20 unique volatile components unique to the non-edible bamboo group are: 2-pentyl-furan, dichloromethane, cyclohexane, 3-ethyl-2,2-dimethyl-pentane, dimethyl sulfide, 2-methoxy-ethanol, 1-undecene, 1-((2-methyl-2-propenyl)oxy)-butane, tris(trimethylsilyl)borate, fluoropropylene, 2-methyl-1,3-butadiene, 2-fluoropropylene, 3,4-dimethyl-2-hexanone, (Z)-2-pentene, 1-methylbutyl-oxirane, 3-isopropenyl-5,5-dimethyl-cyclopentene, formic acid, 1,3-dioxazol-2-one, N-propyl-3,4-methylenedioxyamphetamine, and octamethyl-cyclotetrasiloxane.

[0193] Table 8 Odor analysis of the bamboo-free group (20 unique volatile compounds)

[0194]

[0195] The conclusions of volatile component analysis are as follows:

[0196] Bamboo-loving group: mainly sweet, fruity, and pine-wood scents, such as α-P-ene, camphene, ethanol, etc. These smells are mild and attractive, which is in line with the giant panda's preference for natural plant aromas.

[0197] The bamboo-resistant group contained pungent, chemical solvents, and sulfur-smelling substances (such as dichloromethane and dimethyl sulfide). These odors significantly inhibited the giant pandas' appetite.

[0198] The key difference: The volatile substances in the bamboo-eating group are mostly related to natural sweet fragrance, while the bamboo-averse group is rich in pungent or corrupt smell compounds, which causes giant pandas to selectively refuse to eat.

[0199] (3) Identification of primary and secondary non-volatile metabolites

[0200] The data analysis software ProgenesisQI extracted 16,208 peaks in positive ion mode and 18,516 peaks in negative ion mode. Database screening identified 1,238 positive ions and 1,157 negative ions from various public metabolomics databases and KEGG.

[0201] Table 9 Metabolite identification information

[0202]

[0203] Twenty-two different classes of metabolites were identified in the KEGG database, the most diverse of which were amino acids, phospholipids, and carboxylic acids ( Figure 2 a). These metabolic pathways can be divided into 15 major types, the most representative of which are amino acid metabolism, secondary metabolism, and carbohydrate metabolism ( Figure 2 b).

[0204] Fourteen different classes of metabolites were identified in the HMDB and Lipidmaps databases, among which the most abundant were lipids and lipid-like molecules, phenylpropanoids and polyketides, and organooxygen compounds ( Figure 3 ).

[0205] (4) Correlation between metabolic profiles of different bamboo species

[0206] The abundance of metabolites in different samples was compared by correlative thermography to determine similarities within groups (preferred, edible, and inedible) and differences between groups.

[0207] like Figure 4 As shown in Figure 2, the heatmap of the 60 bamboo samples shows that, as expected, there is a strong correlation between the six replicates of each bamboo species. It also shows moderately high correlations within groups, such as between samples HW and ZY, but low correlations in other cases, such as between samples HW and RC.

[0208] In order to address the structure of the data, principal component analysis is used again to reduce the dimension, remove noise and redundancy, and transform multiple indicators into several independent comprehensive indicators that contain most of the original information.

[0209] (5) Determine the markers of the bamboo-loving, bamboo-edible, and bamboo-intolerant groups

[0210] The analysis identified two principal components, the first explaining 23.9% of the variation and the second explaining 14%. This divided the species into two main clusters, the first representing edible bamboos and the second representing edible and inedible bamboos. The overlapping confidence ellipses represent the distribution of the true samples with 95% confidence ( Figure 5 a). Applying linear discriminant analysis (LDA) to refine the responses revealed that the first linear discriminant explained 19.3% of the variation and the second explained 12.5% ​​of the variation, reflecting a cumulative contribution of 31.8%, thus better presenting the overall information of the sample ( Figure 5 b). The figure shows that the bamboo-preferring group (green) separated from a cluster containing the edible bamboo group (blue) and the non-edible bamboo group (yellow), the latter of which was better separated by LDA than PCA. Similarly, the bamboo-preferring group was well separated from the rest of the samples, but LDA better separated the edible samples from the non-edible samples, with only minimal overlap (as shown in LD2). Even so, these results suggest that the edible and non-edible bamboo groups share some metabolic similarities.

[0211] By comparing the metabolites of each bamboo species, we found three groups with a total of 1,210 compounds. We further determined the following:

[0212] Seven unique cations and 14 unique anions were identified in the bamboos of the edible bamboo group, which are the markers of the edible bamboo group;

[0213] Three unique cations and four unique anions were found in edible bamboo;

[0214] Eight unique positive ions and five unique negative ions were found in inedible bamboo. Figure 6 As shown in the Venn diagram.

[0215] Table 10 Metabolites specific to the bamboo-loving, bamboo-edible, and bamboo-non-eating groups

[0216]

[0217] Table 11 Analysis of unique metabolites of the bamboo-loving group

[0218]

[0219]

[0220] Table 12 Analysis of unique metabolites in the bamboo-free group

[0221]

[0222] The conclusions of metabolite analysis are as follows:

[0223] 1. Metabolites of bamboo group:

[0224] Cationic: Mainly sweet and woody aroma (such as acetylcoumarol), some may enhance nutritional value (such as 13'-carboxyγ-tocopherol).

[0225] Anions: Contains sweet glycosides (such as pollenin B) and antioxidants to improve palatability and nutrition.

[0226] Overall impact: Mild smell and rich nutrition, attracting giant pandas to eat.

[0227] 2. Do not eat bamboo group metabolites:

[0228] Cationic: Contains fishy smell (spermidine), pungent chemical smell (pyridine derivatives), and suppresses appetite.

[0229] Anions: bitter taste (cycloharringtonol C), sulfur taste (cysteine), significantly reduce palatability.

[0230] Overall impact: Unpleasant odors and toxic substances (such as bitter and metallic tastes) cause giant pandas to refuse to eat.

[0231] 3. Key Differences

[0232] Metabolites of the bamboo-loving group are mostly related to energy supply and antioxidant activity, and have a pleasant smell;

[0233] Metabolites of the non-bamboo group are rich in defensive compounds (bitter and pungent), which directly interfere with the giant panda's feeding choices.

[0234] Recommendation: During artificial mating, it is necessary to prioritize the detection and elimination of characteristic metabolites of the non-bamboo-eating group (such as cycloharringtonol C and cysteine), while ensuring that the content of sweet and fragrant substances (such as acetylcoumarol and glycosides) in the bamboo-eating group is sufficient to optimize the feeding efficiency of giant pandas.

[0235] (6) Quantitative analysis of differences in metabolite abundance among bamboo species

[0236] The present invention studied the quantitative differences in the abundance of cations and anions between the groups, using a three-fold difference threshold and a significance of p < 0.05. Figure 7 As shown in the volcano plot in , 1214 cations (152 identified) and 1644 anions (168 identified) were detected. The pairwise significant differences in abundance are shown in the following table. Figure 8 The heat map shows the clustering of metabolites with significant pairwise abundance differences, showing a clear opposite trend between the edible bamboo sample and the other two groups of samples, with less difference between edible and inedible bamboo shoots.

[0237] Table 13 Pairwise comparison analysis of differential metabolites

[0238]

[0239] (7) KEGG pathway studies related to metabolites with different abundances

[0240] We also investigated the KEGG pathways associated with metabolites that differed in abundance in pairwise comparisons between bamboo species and found that most metabolites were associated with the main categories “metabolism” and “environmental information processing.” Dividing these into secondary categories revealed similarities between the pairwise comparisons, but also some differences ( Figure 9 For example, although the top category in all three comparisons was "secondary metabolism," the relative importance of carbohydrate and amino acid metabolism was much higher in the preferred vs. edible comparison than in the other two pairings. The edible vs. inedible comparison also had fewer pathways associated with it than in the other two pairings. Overall, 30 of the 240 differential metabolites in the preferred vs. inedible comparison were assigned to nine KEGG pathways ( Figure 9 a), 35 of the 182 differential metabolites were assigned to 9 KEGG pathways in the comparison between edible and edible species ( Figure 9 b), 11 of the 68 differential metabolites were mainly assigned to 6 KEGG pathways in the comparison between edible and inedible plants ( Figure 9 c).

[0241] The enrichment of a KEGG pathway is the ratio of the number of metabolites enriched in the pathway to the number of metabolites annotated in the pathway. The main pathways enriched in edible and inedible bamboo samples were the biosynthesis of flavonoids, flavonoids and flavonols, phenylalanine, tyrosine, and tryptophan, and the metabolism of amino sugars, nucleotide sugars, ascorbic acid, and glyoxylate. The enrichment of flavonoid biosynthesis showed the greatest significance ( Figure 10 a). The same biosynthetic pathways were enriched in both edible and edible bamboo samples, with flavonoid biosynthesis showing the greatest significance, but amino sugar and nucleotide sugar metabolism being replaced by starch and sucrose metabolism ( Figure 10 b). When comparing edible and inedible bamboo samples, the only enriched pathways were the biosynthesis of flavonoids and flavonols and the metabolism of proline and arginine ( Figure 10 c).

[0242] The quantitative metabolic dataset was mapped to the Human Metabolome Database (HMDB) v4.0, which allowed the successful annotation of 197 / 240 differential metabolites in the comparison between bamboo-loving and bamboo-inedible groups, 150 / 182 in the comparison between bamboo-loving and bamboo-edible groups, and 58 / 68 in the comparison between bamboo-ined and bamboo-inedible groups ( Figure 11 In all three groups, the distribution of metabolites at the superclass, class, or subclass level was similar, with lipids and lipid-like molecules (~34-40%), phenylpropanoids and polyketides (~22-26%), organic oxygen compounds (~8-13%), and organic heterocyclic compounds (~7-10%) being the main components.

[0243] The present study also found that secondary metabolite biosynthesis was the most abundant pathway in all three pairwise comparisons, but carbohydrate and amino acid metabolism were also strongly represented. Carbohydrates and amino acids are major metabolites and important dietary components that indicate the nutritional properties of bamboo, so their correlation with flavonoid biosynthesis in inedible bamboo leaves is noteworthy, potentially contributing to the anti-nutritional property of bitterness. Increased flavonoid content and the amino acids phenylalanine, tyrosine, and tryptophan may enhance bamboo bitterness. Increased starch (metabolism and accumulation of sucrose, amino sugars, and nucleotide sugars) may make bamboo sweeter, while ascorbic acid and acetaldehyde metabolism may increase its sourness. Therefore, after being attracted to certain volatile components, giant pandas may consolidate their preference for nutritionally beneficial bamboo by consuming those that taste sweeter, less bitter, and less sour. The expansion of the domestic chicken bitter taste receptor gene family (TAS2R) in giant pandas may facilitate this process by improving taste perception.

[0244] Step 5: Determine the range of bamboo species to be selected;

[0245] The selected bamboo sources are tested for the above-mentioned markers to determine the bamboo species that are the food source of giant pandas.

[0246] Although the present invention has been described in detail above using general descriptions and specific embodiments, it will be apparent to those skilled in the art that modifications and improvements may be made based on the present invention. Therefore, such modifications and improvements, which do not depart from the spirit of the present invention, are intended to be within the scope of protection claimed herein.

Claims

1. A method for artificially selecting bamboo species for giant panda food sources, comprising the following steps: S1. Feed giant pandas different bamboo samples, record their eating behavior data and calculate confidence intervals. Based on the confidence interval values, divide the bamboo samples into a group that prefers to eat bamboo, a group that can eat bamboo, and a group that does not eat bamboo. S2. Separately, bamboo samples from the bamboo-loving and bamboo-non-eating groups were collected and powdered, and then heated after adding water. The volatile components were collected and tested. S3. Separately, bamboo samples corresponding to the bamboo-loving group and the bamboo-non-eating group were collected, powdered, extracted, and the extract components were tested to determine the metabolites. S4. Analyze the correlation between the volatile components, metabolites, and metabolic profiles of each group to determine the markers of the bamboo-loving and bamboo-averse groups; S5. Conduct marker testing on the bamboo species to be selected to determine the preferred bamboo species.

2. The method according to claim 1, wherein: In step S1, the types of bamboo samples include at least Bashan wood bamboo, Indocalamus truncatum, Sasa bamboo, Rongcheng bamboo, Huangcao bamboo, Huangwen bamboo, Zaoyuan bamboo, Anji golden bamboo, red bamboo, and Phyllostachys praecox; The selection requirements of the bamboo samples are as follows: 1-2 years old, feeding stem segment length is 50±10cm; The feeding method: random meal; The feeding conditions are as follows: feeding once a day, each time supplying 1-3 kg / head, for a total of 4 days; The eating behavior data include: type of bamboo eaten, eating frequency, amount of food eaten, chewing time per mouthful of food, and amount consumed per feeding.

3. The method according to claim 1 or 2, characterized in that: In step S1, the confidence interval is calculated according to the following operations: (1) Forage Ratio selection index analysis method was used to determine feeding tendency; The expression is: i =O i / P i ; Where: W i : Feeding tendency on the i-th bamboo species; O i : the proportion of the i-th type of bamboo in the giant panda's food intake; P i : the proportion of the i-th type of bamboo in the total amount of bamboo put in; (2) G-test was performed on the significance of the difference in bamboo species selection, with degrees of freedom df = n-1, χ 2 When the corresponding probability P≤0.05, the giant panda is selective for this group of bamboo samples; The expression is: Where: χ 2 : chi-square value with (n–1) degrees of freedom; n: number of resource types; u i : the weight of the i-th type of bamboo used by giant pandas for food; U=∑u i ; m i : the weight of the i-th type of bamboo put in; M=Σm i ; (3) Calculate W i confidence intervals to confirm whether giant pandas prefer or avoid specific bamboo species; The expression is: Confidence interval = Wi ± ZaSwi Where: Sw i =[(1-O i ) / UO i +(1-P i ) / MP i ] 1 / 2 ; Swi: standard deviation of bamboo i’s feeding utilization; Za: quantile of the standard normal distribution. To reduce the probability of error, a Bonferroni correction is performed and the significance level α is adjusted to α / 2n. The corresponding Za value is obtained accordingly. (4) According to W i The confidence interval values ​​of were used to determine the giant panda's feeding tendency on each bamboo species; The judgment is as follows: When W i If the lower limit of the confidence interval is >1, it is judged to be a bamboo-loving plant; W i If the upper limit of the confidence interval is <1, it is judged that the person does not eat bamboo; W i The confidence interval of is = 1, and it is judged to be edible bamboo.

4. The method according to any one of claims 1 to 3, characterized in that: In step S2, the mass volume ratio of the powder to water is 1 g:4 mL; The heating conditions are: heating at 80°C water bath temperature for 30 min; The volatile components are collected using an adsorbent; the adsorbent is composed of Tenax, silica gel and activated carbon in a mass ratio of 1:1:

1.

5. The method according to any one of claims 1 to 4, characterized in that: In step S2, the detection is performed by purge and trap gas chromatography / mass spectrometry; The conditions for the detection are: Degassing: high-purity nitrogen; purge temperature: room temperature or constant temperature; purge flow rate: 40ml / min; purge time: 11min; 10 # Trap preheating temperature: 180°C; desorption temperature: 190°C; desorption time: 0.5 min; baking temperature: 220°C; baking time: 10 min; sample heating temperature: 40°C, valve temperature: 110°C, transfer line temperature: 110°C; Gas chromatography conditions: Rtx502.2 capillary column; vaporizer temperature, 220°C; injection mode: split injection, split ratio 5:1; temperature program: initial temperature, 30°C, hold for 5 min, increase to 190°C at 10°C / min, no hold, then increase to 250°C at 20°C / min, hold for 3 min; column head pressure, 71.03 kPa; Mass spectrometry conditions: ion source: EI source; ion source temperature: 200°C; interface temperature: 220°C, electron multiplier voltage: 1.02 kV; scanning mode: full scan or selected ion scan; scanning speed: 660 amu / s; scanning range: 45-350 amu / s; scanning time: 1-27 min.

6. The method according to any one of claims 1 to 5, characterized in that: In step S3, the extraction is performed according to the following operations: adding an extractant to the powder, grinding, ultrasonicating, centrifuging, and taking a supernatant containing the extract; The extractant is an aqueous solution containing 0.02 mg / mL L-2-chlorophenylalanine and 75% methanol by volume; The mass ratio of the powder to the extractant is 1 mg:8 μL; The grinding conditions are: -10°C, 50 Hz, 6 min; The ultrasonic conditions are: 5°C, 40KHz, 30min; The centrifugal conditions are: 13000 g, 4° C., 15 min.

7. The method according to any one of claims 1 to 6, characterized in that: In step S3, the detection is performed by high performance liquid chromatography-mass spectrometry; The conditions for the detection are: Chromatographic conditions: The chromatographic column was an ACQUITY UPLC HSS T3; mobile phase A was 95% water + 5% acetonitrile containing 0.1% formic acid; mobile phase B was 47.5% acetonitrile containing 0.1% formic acid + 47.5% isopropanol + 5% water. The injection volume was 2 μL, and the column temperature was 40°C. Mass spectrometry conditions: The sample was electrospray ionized, and mass spectrometry signals were collected in positive and negative ion scan modes, respectively; scan type: 70-1050 m / z; sheath gas flow rate: 50 Arb; auxiliary gas flow rate: 13 Arb; heater temperature: 425°C; capillary temperature: 325°C; spray voltage: +3500 V; spray voltage: -3500 V; S-Lens RF level: 50; Normalized collision energy eV: 20%, 40%, 60%; resolution full MS: 60000; resolution MS2: 7500.

8. The method according to any one of claims 1 to 7, characterized in that: The 21 volatile components of the bamboo group are: (1S)-1,7,7-trimethyl-bicyclohept-2-one, camphene, 1,7,7-trimethyl-tricycloheptane, sec-butyl ester-cyanate, (1R)-2,6,6-trimethylbicyclohept-2-ene, α,α-olefin, 3-methyl-2-butanol, methylsilane, 2-methyl-1-butene, N,N,N,N-tetramethyl-methanediamine, (1S) -6,6-dimethyl-2-methylene-bicycloheptane, 1-benzyl-3-amino-4-cyano-3-pyrroline, (S)-2-methyl-1-butanol, (R)-(-)-2-pentanol, 2-ethyl-furan, carbonyl sulfide, 2,2,4,4-tetramethyl-1,3-cyclobutanediol, ethanol, hexamethyldisiloxane, N-dimethylaminomethyl-tert-butylisopropylphosphine, 1,3-propylene glycol; The 21 metabolites of the bamboo group are: 1-(9H-pyridinyl(3,4-b)indol-1-yl)-1,4-butanediol, fusarium chromanone, dalfoplostin, fluorescein D2, zanthobisquinolone, Hv-NCC-1, β-humulene, pollenin B, acetylcoumarol, haldatin A glucoside, kodoxione, scleroporphyrin, jubanin A, 13-carboxy-γ-tocopherol, 22-phenol, hydroxyerythrodiol, (R)-1-O-(b-methyl)-D- Furanosyl-(1-2)-bD-pyranoside)-1,3-octanediol, kaempferol 3-sophoroside 7-glucuronide, 3,4-dihydroxy-4-(1-oxo-1H-isochromen-3-yl)butoxy)sulfonicacid, 3,4-dihydroxy-4-(1-oxo-1H-isochromen-3-yl)butan-2-yl)oxy)sulfonic acid, Armexifolin, Methylnori chexanthone.

9. The method according to any one of claims 1 to 7, characterized in that: The 20 volatile components of the bamboo-ined group are: 2-pentyl-furan, dichloromethane, cyclohexane, 3-ethyl-2,2-dimethyl-pentane, dimethyl sulfide, 2-methoxy-ethanol, 1-undecene, 1-((2-methyl-2-propenyl)oxy)-butane, tris(trimethylsilyl)borate, fluoropropylene, 2-methyl-1,3-butadiene, 2-fluoropropylene, 3,4-dimethyl-2-hexanone, (Z)-2-pentene, 1-methylbutyl-oxirane, 3-isopropenyl-5,5-dimethyl-cyclopentene, formic acid, 1,3-dioxazol-2-one, N-propyl-3,4-methylenedioxyamphetamine, octamethyl-cyclotetrasiloxane; The 13 metabolites of the bamboo-not-eating group are: spermidine, jemaclostrobin, sphingosine (1+), 4-α-methyl-5-α-cholesterol-7-ene-3-one, dynorphin A (6-8), 1-(5-methyl-3-pyridyl)-1-decanone, 3-hydroxyundecylcarnitine, phenethylacetate; cycloharringtonol C, TG, 4-β-hydroxymethyl-4-α-methyl-5-α-cholesterol-7-ene-3-β-ol, cysteine, 2-(3,5-dihydroxy-4-methoxyphenyl)-4H-chrom-4-one.

10. The method according to any one of claims 1 to 9, characterized in that: Step S4 also includes quantitative analysis of differences in metabolite abundance among bamboo species.

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