Multi-component difference substance for detecting the authenticity of betel nut honey and its acquisition method and application

Abstract

The present invention provides a multi-component difference substance for detecting the authenticity of betel nut honey and a method for obtaining and using the same, and relates to the technical field of honey quality identification. Erucic acid amide, naringenin, raffinose, benzylformamide, 4-indole formaldehyde, and methoxyresorcinol are used as multi-component difference substances to identify the authenticity of betel nut honey, and the metabolites and relative contents in the betel nut honey component extract are determined by ultra-high performance liquid chromatography-mass spectrometry equipment, and the metabolite data of betel nut honey and the metabolite database of Chinese bee honey are used to perform multivariate statistical analysis to obtain key difference substances, and the SHapley Additive exPlanations method is combined to establish a method for jointly identifying the authenticity of betel nut honey by multi-component difference substances. The present invention helps to clarify the quality difference between betel nut honey and Chinese bee honey, has the advantages of being simple, fast, accurate and reliable, and provides a reliable method for authenticity identification and quality evaluation of betel nut honey.

Description

Technical Field

[0001] The invention relates to the technical field of honey quality identification, and in particular to a multi-component difference substance for detecting the authenticity of betel nut honey and an acquisition method and application thereof. Background Art

[0002] Betel nut, the dried seed of the betel nut fruit, is an important traditional Chinese medicine that has been used since ancient times.

[0003] Honey is a natural substance that is brewed by bees after collecting nectar, secretions or honeydew from plants and combining it with their own secretions. Betel nut honey refers to honey brewed by Chinese honey bees collecting nectar from betel nut flowers. It has the unique fragrance and taste of betel nut flowers and is a famous Hainan specialty.

[0004] Traditional food authenticity testing methods usually detect specific chemical components or indicators to determine the authenticity of honey. However, the steps of these methods are usually time-consuming and cumbersome, requiring professional laboratory equipment and technicians. Most importantly, this method is not suitable for specialty honeys that have market benefits and have not yet been fully developed. In contrast, multi-component differential analysis is a method based on omics technology and machine learning modeling, which has high-throughput analysis, comprehensive detection and data-driven analysis. This method is an emerging food authenticity testing method that surpasses traditional chemical analysis methods in efficiency and accuracy. However, to date, relevant technologies and methods for authenticity detection of betel nut honey have not been reported. Summary of the invention

[0005] In view of the shortcomings of the existing technology, the present invention provides a multi-component difference substance for detecting the authenticity of betel nut honey and a method for obtaining and applying the same. The method uses omics technology, database matching and machine learning technology to screen out the multi-component difference substances between betel nut honey and reported Chinese bee honey, thereby establishing a method for quickly identifying the authenticity of betel nut honey.

[0006] To achieve the above objectives, the technical solution of the present invention is implemented through the following technical solutions:

[0007] A multi-component differential substance for detecting the authenticity of betel nut honey, wherein the multi-component differential substance is erucamide, naringenin, raffinose, benzylformamide, 4-indolecarboxaldehyde and methoxyresorcinol.

[0008] The method for obtaining a multi-component difference substance for detecting the authenticity of betel nut honey comprises the following steps:

[0009] S1, preparing betel nut honey component extract;

[0010] S2. Use ultra-high performance liquid chromatography-mass spectrometry to perform non-targeted component identification on the betel nut honey component extract to obtain metabolite data of the betel nut honey component extract;

[0011] S3, preprocessing the metabolite data of the betel nut honey component extract obtained above, and matching them with the Chinese bee honey metabolite database;

[0012] S4. Betel nut honey was used as label 0 and the Chinese bee honey included in the Chinese bee honey metabolite database was used as label 1. Multivariate statistical analysis combined with machine learning methods were used to build a model. Based on the model evaluation results, the best model was selected for SHapleyAdditive exPlanations interpretability analysis to obtain the SHAP value. The Featureimportance score of the SHAP value>0.1 was used as the difference, and the multi-component difference was obtained by combining significance statistics.

[0013] Preferably, the method for preparing the betel nut honey component extract in step S1 comprises the following steps:

[0014] S1-1. Collect mature betel nut honey, accurately weigh 1-10 g of betel nut honey into a centrifuge tube, add 2-20 mL of water and vortex until mixed;

[0015] S1-2, continue to add 1-10 mL of methanol for extraction, centrifuge at 6000-14000 rpm for 10-30 min, and collect the supernatant;

[0016] S1-3. Filter the supernatant using a 0.22 μm filter membrane to obtain the betel nut honey component extract.

[0017] Preferably, the mature betel nut honey in step S1-1 is collected from March to June, and its maturity is judged by the beekeeper visually observing that the honey comb capping rate is greater than> 95%.

[0018] Preferably, the specific method of component identification in step S2 includes the following steps:

[0019] S2-1. Use 1-10 mL of methanol and 2-5 mL of pure water to activate the Agilent SPE column, load the betel nut honey component extract onto the Agilent SPE column, and rinse the column with 3-9 mL of water after loading.

[0020] S2-2, elute with methanol twice, 3-6 mL each time, collect the eluate, concentrate it with nitrogen, and reconstitute it with 1-3 mL of methanol to obtain the solution to be tested;

[0021] S2-3. Use ultra-high performance liquid chromatography-mass spectrometry to identify non-targeted components in the test fluid.

[0022] Preferably, the honey metabolite database in step S3 is established as follows:

[0023] ChinaHoney DB={variety:data}

[0024] {data:{metabolite_species:X i , relative_amount: Y i}

[0025] Where: i means there are i metabolites, among which X i is a list of common i metabolites, Y i is the relative content of the common metabolites.

[0026] Preferably, the preprocessing in step S3 includes denoising, alignment, RT correction, standardization and peak area extraction of the raw data; on this basis, the linear correlation between the features is measured based on the Pearson correlation coefficient, as shown in the following formula:

[0027]

[0028] Among them, ρX,Y represents the Pearson correlation coefficient of feature X and feature Y, cov(X,Y) represents the covariance of X and Y, σ X and σ Y represent the standard deviation of X and Y respectively;

[0029] Metabolites with high multicollinearity were removed according to ρX,Y>0.9;

[0030] The chi-square test was used to test whether there was a significant correlation between the variables of betel nut honey and Chinese bee honey, as follows:

[0031]

[0032] Among them, χ 2 represents the chi-square statistic, ij represents the observed frequency, E ij represents the expected frequency;

[0033] The chi-square test results were then combined with the SelectKBest method to obtain the top 20-45% of non-redundant metabolites.

[0034] Preferably, the specific operations in step S4 include the following steps:

[0035] S4-1. Modeling was performed using multivariate statistical analysis combined with machine learning methods, including principal component analysis (PCA) and classification models such as XGBoost, LightGBM, Random Forest, GaussianNB, and LinearDiscriminant.

[0036] S4-2. The model evaluation results are screened based on the evaluation parameters, including precision, recall, and accuracy. The calculation formula is as follows:

[0037]

[0038]

[0039]

[0040] Among them, TP stands for True Positive, TN stands for True Negative, FP stands for False Positive, and FN stands for False Negative.

[0041] S4-3. SHAP is calculated based on the permutations and combinations of metabolites. For each metabolite, its contribution in different feature sets is calculated, and considering the size of the metabolite set and the order of the features, the SHAP value of each metabolite is calculated as follows:

[0042]

[0043] Where N represents the total number of metabolites, S is the metabolite set, i represents the feature for which the SHAP value is to be calculated, and f(S) represents the prediction result of the model for the metabolite set S;

[0044] S4-4, according to the SHAP value Feature importance score>0.1 as the difference, respectively erucic acid amide, naringenin, raffinose, benzylformamide, 4-indolecarboxaldehyde, methoxyresorcinol.

[0045] The multi-component differential sample used to detect the authenticity of betel nut honey is applied to the authenticity identification of betel nut honey.

[0046] The present invention provides a multi-component difference substance for detecting the authenticity of betel nut honey and a method for obtaining the same and application thereof, which has the advantages of:

[0047] (1) The present invention identifies for the first time a multi-component metabolite method for authenticating betel nut honey, and studies the types and relative content differences of the metabolites.

[0048] (2) The present invention uses omics technology, database matching and machine learning technology to systematically and comprehensively analyze and screen out the multi-component differences between betel nut honey and reported Chinese honey. These differences can be used alone or in combination to detect the authenticity of betel nut honey.

[0049] (3) The present invention provides a rapid detection model for the authenticity of betel nut honey, which can be used in conjunction with high-throughput omics technology to achieve efficient identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 The principal component analysis diagram of betel nut honey and Chinese bee honey before and after feature processing in the embodiment of the present invention;

[0051] Figure 2 Schematic diagram of correlation analysis of metabolites of betel nut honey and Chinese honey bee after characteristic treatment in an embodiment of the present invention;

[0052] Figure 3 This is a schematic diagram of the confusion matrix results of distinguishing betel nut honey from Chinese bee honey based on machine learning modeling in an embodiment of the present invention;

[0053] Figure 4 This is a schematic diagram of SHAP analysis of multi-component metabolites in an embodiment of the present invention;

[0054] Figure 5 This is a box plot of the relative contents of multi-component metabolites of betel nut honey and Chinese bee honey in the embodiments of the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention is clearly and completely described below in combination with the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] Example:

[0057] Method for obtaining multi-component differentials for detecting the authenticity of betel nut honey:

[0058] (1) Betel nut honey was collected from the Hainan Qiongzhong Beekeeping Cooperative from March to June in 2022 and 2023. There were 36 samples in total, numbered BL1, BL2, BL3, BL4, BL5, BL6, BL7, BL8, BL9, BL10, BL11, BL12, BL13, BL14, BL15, BL16, BL17, BL18, BL19, BL20, BL21, BL22, BL23, BL24, BL25, BL26, BL27, BL28, BL29, BL30, BL31, BL32, BL33, BL34, BL35, BL36;

[0059] (2) Accurately weigh 5 g of the betel nut honey in a centrifuge tube, add 10 mL of deionized water and vortex until mixed; then, add 10 mL of methanol for extraction, centrifuge at 9000 rpm for 30 min, and then filter the supernatant using a 0.22 μm filter membrane to obtain a betel nut honey component extract;

[0060] (3) Take 5 mL of methanol and 5 mL of pure water to activate the Agilent SPE column, load the above betel nut honey component extract into the Agilent SPE column, and rinse the column with 3 mL of water after the loading is completed; further elute with methanol twice, 4 mL each time, collect the eluate for nitrogen blowing and concentration, and reconstitute with 1 mL of methanol; use ultra-high performance liquid chromatography-mass spectrometry equipment to extract the reconstituted betel nut honey component extract for non-targeted component identification, and obtain the metabolite data of the betel nut honey component extract, wherein the detection method of the ultra-high performance liquid chromatography-mass spectrometry equipment is as follows:

[0061] The chromatographic column was EclipsePlus C18 RRHD (3.0 mm×150 mm, 1.8 μm), the mobile phase was 0.1% formic acid-5 mmol / L ammonium formate aqueous solution (C) and 0.1% formic acid-5 mmol / L ammonium formate methanol solution (B); the gradient elution program was: 1 min, 99% C; 3 min, 99% C; 15 min, 10% C; 25 min, 1% C; 30 min, 99% C; 35 min, 99% C, the flow rate was 0.25 mL / min, and the injection volume was 0.2 μL.

[0062] Full scan MS and secondary MS modes were performed: the main mass spectrometry parameters were: sheath gas flow rate was set to 50Arb; aux gas flow rate was set to 12Arb; capillary temperature: 330℃; full MS resolution, 70000; AGC target for full MS was 3e6; MS / MS resolution, 17500; AGC target forMS / MS, 1e5; MS / MS isolation window, 4.0m / z; NCE, 30; spray voltage, 3.5kV (negative) or 4.0kV (positive).

[0063] (4) Data processing methods: peak alignment, peak extraction, and normalization;

[0064] (5) Establishment of a database of Chinese honey metabolites:

[0065] ChinaHoney DB={variety:data}

[0066] {data:{metabolite_species:X i , relative_amount: Y i}

[0067] Where: i means there are i metabolites, among which X i is a list of common i metabolites, Y i is the relative content of the common metabolites.

[0068] (6) The betel nut (BL) honey was used as label 0, and the Chinese bee honey included in the Chinese bee honey (ZF) metabolite database was used as label 1. The metabolites shared by the two were matched, and the metabolites were highly collinear and feature optimized. The results are as follows: Figure 1 As shown;

[0069] After feature processing, a total of 23 metabolites were obtained. The screened metabolites can better distinguish the differences between betel nut honey and Chinese honey, and the first two principal component analyses (PCA) can better explain the global changes (PCA1+PCA2>50%).

[0070] Correlation heat map analysis was performed on 23 metabolites, and the results are as follows Figure 2 As shown, it can be clearly seen that after feature processing, the correlation between metabolites is significantly reduced, which helps to improve the subsequent modeling quality;

[0071] (6) Five machine learning methods (Random Forest, GaussianNB, Linear Discriminant, KNN, and Decision Tree) were used to build models, and 5-fold cross validation was used to evaluate the accuracy of the model. The evaluation parameters were as follows:

[0072] Including precision, recall, and accuracy, the calculation formula is as follows:

[0073]

[0074]

[0075]

[0076] Among them, TP stands for True Positive, TN stands for True Negative, FP stands for False Positive, and FN stands for False Negative.

[0077] Table 1 below and Figure 3 The data show the classification performance of five machine learning models in betel nut honey and Chinese bee honey. It can be clearly seen that the Random Forest model based on 23 metabolites can 100% identify betel nut honey and Chinese bee honey.

[0078] Table 1 Performance of five machine learning models in the classification of betel nut honey and Chinese bee honey

[0079] Model Accuracy Recovery rate Accuracy RandomForest 100.00％ 100.00％ 100.00％ GaussianNB 97.50％ 100.00％ 97.78％ LinearDiscriminant 92.78％ 100.00％ 93.78％ KNN 85.83％ 100.00％ 87.33％ DecisionTree 95.00％ 100.00％ 91.78％

[0080] The best model (Random Forest) was selected for SHapleyAdditive exPlanations interpretability analysis to obtain SHAP values. The SHAP value feature importance score > 0.1 was used as the difference, and a total of 12 key metabolite compounds were obtained. The results are as follows Figure 4 As shown;

[0081] We further plotted the box plot of the relative content of metabolites in betel nut honey and Chinese bee honey to verify whether there were significant differences in these metabolites between the two. Figure 5 As shown; there are 6 different substances that can significantly distinguish betel nut honey from Chinese bee honey, namely erucic acid amide, naringenin, raffinose, benzylformamide, 4-indolecarboxaldehyde, and methoxyresorcinol.

[0082] Taken together, these results suggest that the screened multicomponent metabolites can be used to identify the authenticity of betel nut honey.

[0083] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for distinguishing betel nut honey from other bee honeys, characterized in that: Six differentiators were used for differentiation, and the six differentiators were erucamide, naringenin, raffinose, benzylformamide, 4-indolecarboxaldehyde, and methoxyresorcinol.

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