Honey bitter component identification system and application

Abstract

The invention provides a honey bitter component identification system and application, and relates to the technical field of honey quality identification. The system comprises a bitterness recognizer and a bitterness threshold recognizer, the bitterness recognizer formed based on various fingerprints such as ECFP-546, ECFP-18 and ECFP-149 is used for discriminating the bitterness and non-bitterness of honey components, and the bitterness threshold recognizer formed based on multi-structural characteristics such as MaxESateIndex, MaxPartial Charge, PEOEVSA8 and VSAEState4 is used for discriminating the lowest bitterness perception threshold of the components. Through the system, bitter substances in the honey can be quickly and accurately identified, and technical support is provided for geographical identification, authenticity identification and medicinal function mining of the honey.

Description

Technical Field

[0001] The invention relates to the technical field of honey quality identification, and in particular to a honey bitter component identification system and application. Background Art

[0004] In bitter taste identification, biological evaluation and biosensor are two major methods. Biological evaluation includes sensory evaluation and animal evaluation. Some bitter ingredients may be toxic, which is the main challenge of sensory evaluation. The main obstacle of animal evaluation method is that it cannot identify the relationship between animal refusal behavior and perception. Electronic tongue is one of the biosensor methods commonly used to evaluate compound perception. It is mainly based on a multi-sensor array to perceive the overall characteristic response signal of the sample, and simulate the sample for quantitative and qualitative analysis. The disadvantage of electronic tongue is that it responds to a variety of compounds and cannot identify specific bitter substances with high specificity like biological receptors. In addition, its output is converted into a response value and the minimum perceived concentration of bitter substances cannot be known. The above methods also face problems such as high cost, time-consuming and need for professional technical support. When faced with the complexity of thousands of compounds in honey, they are particularly incapable. Based on this situation, we propose a system for identifying bitter components in honey. The system consists of two parts: a bitterness identifier and a bitterness threshold identifier. It can directly predict bitter substances and their thresholds in honey based on the fingerprint characteristics and structural characteristics of the compounds. Summary of the invention

[0005] In view of the shortcomings of the existing technology, the present invention provides a honey bitter component identification system and application, which can quickly and accurately identify the bitter substances in honey, and provide technical support for the geographical identification, authenticity identification and medicinal function mining of honey.

[0006] To achieve the above objectives, the technical solution of the present invention is implemented through the following technical solutions: A honey bitter component identification system, the identification system consists of a compound number input module, an InChIKey encoding module, a bitterness recognition module and a content sorting module; the compound number input module is used to take the names of bitter molecules and non-bitter molecules as input conditions; the InChIKey encoding module is used to convert the names of bitter molecules and non-bitter molecules into InChIKey, then convert the InChIKey into ASCII code, and generate corresponding fingerprint features and structural features; the bitterness recognition module includes a bitterness identifier and a bitterness threshold identifier, the bitterness identifier discriminates the bitterness and non-bitterness of honey components according to a discrimination model composed of corresponding fingerprint features; the bitterness threshold identifier is used to discriminate the lowest bitterness perception threshold of the components; the content sorting module is used to sort the content and bitterness threshold of the compounds, so as to obtain key bitter components in honey.

[0007] Preferably, the discriminant model is composed of a single or combined hydrophobic extended fingerprint of ECFP-4, ECFP-6, ECFP-18, ECFP-114, ECFP-149, ECFP-210, ECFP-308, ECFP-309, ECFP-322, ECFP-499, ECFP-539, ECFP-546, ECFP-691, ECFP-857, ECFP-952, ECFP-1155, ECFP-1668, ECFP-1850, ECFP-2017, and ECFP-2041.

[0008] Preferably, the bitterness threshold identifier is a quantitative model composed of single or combined molecular surface area, charge structure and electronegativity structural parameters such as MaxESateIndex, MaxPartialCharge, PEOE_VSA8, and VSA_EState4.

[0009] The above system is applied to the identification and appraisal of bitter components in food.

[0010] Preferably, the system is applied to the identification of bitter components in honey.

[0011] Preferably, the system is applied to the identification of bitter components of Quercus balsamina honey.

[0012] Preferably, the bitter components of Quercus viridis honey identified by the system are kynurenic acid, 4-indolecarboxaldehyde, 2,4-dihydroxyquinoline, 8-hydroxyquinoline, indoline, 2,4-dimethylbenzaldehyde, cytidine, ethylmorphine, uniconazole, ketotifen, 7-methylxanthine, DL-3,4-dihydroxyphenyldiol, oleamide, erucamide, 4,2',6'-trihydroxy-4'-methoxy-3',5'-dimethyldihydrochalcone, and difenoconazole.

[0013] The present invention provides a system for identifying bitter components of honey and its application, which has the following advantages over the prior art: (1) The system of the present invention includes a bitter taste identifier and a bitter taste threshold identifier. The bitter taste identifier based on multiple fingerprints such as ECFP-546, ECFP-18, and ECFP-149 is used to distinguish the bitterness and non-bitterness of honey components, and the bitter taste threshold identifier based on multiple structural characteristics such as MaxESateIndex, MaxPartialCharge, PEOE_VSA8, and VSA_EState4 is used to distinguish the minimum bitter taste perception threshold of the components. The present invention is low-cost, fast, and can identify the bitterness of thousands of compounds in honey without the need for professional technical support; (2) The system developed based on the present invention can identify the bitter taste in Quercus balsamina honey and mine a variety of unreported bitter components; (3) The present invention provides technical support for the geographical identification, authenticity identification and medicinal functional component mining of specialty honey. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 The best bitterness recognition model and the best bitterness threshold model in Example 1 of the present invention; Figure 2 is the key ECFP4 fingerprint of the best bitter taste recognition model in Example 1 of the present invention; Figure 3 It is the key structural feature of the optimal bitterness threshold model in Example 1 of the present invention. DETAILED DESCRIPTION

[0015] 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.

[0016] Embodiment 1: Bitter ingredient identification system settings: The BitterDB open source database (https: / / bitterdb.agri.huji.ac.il / dbbitter.php) was searched to identify 1248 bitter molecules and 1418 non-bitter molecules (these molecules include alkaloids, flavonoids, polyphenols, terpenes, etc.). In the construction of the bitter identifier, Rdkit was used to read the SMILES of the above molecular set and calculate the ECFP fingerprint of each molecule with a radius of 2, namely ECFP4, which is a 2048 confusion list composed of 0s and 1s; In the construction of the bitterness threshold identifier, 153 molecules with bitterness thresholds (quinine, tannin, matrine, etc.) were used as data sets. These data sets came from the BitterDB open source database (https: / / bitterdb.agri.huji.ac.il / dbbitter.php). Molecular properties were used as descriptors, and the model was trained by reducing the number of features to prevent dimensionality and improve the generalization ability of the model. Specifically, the RDKit package MolecularDescriptorCalculato module was used to calculate 208 descriptors of the structural properties of all compounds. After removing all zero-valued and highly multicollinear features (>0.6), 39 structural features were screened out.

[0017] Use MaxESateIndex, MaxPartialCharge, PEOE_VSA8, VSA_EState4, etc. for training and modeling: In model training, the data set is divided into a training set and an independent validation set at an 8:2 ratio. The training set is trained using 10-fold cross validation, that is, the model is divided into 10 parts on average, 9 of which are selected for model training each time, and the remaining one is tested. Each algorithm is optimized using hyperparameters, and after the model is trained, it is tested on an independent validation set.

[0018] The accuracy of the best bitter taste identifier and bitterness threshold identifier is calculated using the confusion matrix. The results are as follows: Figure 1 As shown. Figure 1 A represents the confusion matrix result of the best bitter taste identifier, with an accuracy of 86% for bitter taste and 83% for non-bitter taste. Figure 1 B represents the accuracy of the best bitterness threshold identifier, and the best model’s goodness of fit (R) on the test set and validation set. 2 ) are both higher than 0.75. These results show that the constructed bitter taste identifier and bitter taste threshold identifier both show high accuracy.

[0019] Figure 2 and Figure 3They represent the key features within the optimal bitter taste identifier and bitter taste threshold identifier, respectively, which indicate that hydrophobic groups, molecular surface area, charge structure and electronegativity are crucial for bitter taste recognition.

[0020] Embodiment 2: The bitter components in Quercus truncatula honey were identified by the above system: The collection time of Quercus truncatum bitter honey was from April to May, and the collection years were 2021 and 2023, with a total of 24 samples; 5 g of the above Quercus truncatum bitter honey was accurately weighed into a centrifuge tube, and 10 mL of deionized water was added for vortex shaking until mixed; then, 10 mL of methanol was added for extraction, and centrifuged at 9000 rpm for 30 min, and then the supernatant was filtered through a 0.22 μm filter membrane to obtain the Quercus truncatum bitter honey component extract; Take 5 mL of methanol and 5 mL of pure water to activate the Agilent SPE column, load the above-mentioned Quercus chinensis honey 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 re-dissolve with 1 mL of methanol; use ultra-high performance liquid chromatography-mass spectrometry equipment to identify non-targeted components of the re-dissolved Quercus chinensis honey extract, and obtain the metabolite data of the Quercus chinensis honey extract, wherein the detection method of the ultra-high performance liquid chromatography-mass spectrometry equipment is as follows: The chromatographic column was EclipsePlus C18 RRHD (3.0 mm×150 mm, 1.8 μm), and the mobile phases were 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.

[0021] Full scan MS and secondary MS modes were performed: the main mass spectrometry parameters were: sheath gas flow rate was set to 50 Arb; aux gas flow rate was set to 12 Arb; capillary temperature: 330°C; full MS resolution, 70000; AGC target for full MS was 3e6; MS / MS resolution, 17500; AGC target for MS / MS, 1e5; MS / MS isolation window, 4.0 m / z; NCE, 30; spray voltage, 3.5 kV (negative) or 4.0 kV (positive).

[0022] Data processing methods: peak alignment, peak extraction, and normalization, a total of 2603 compounds were obtained.

[0023] The bitter taste recognition system developed by this patent identified 1,636 bitter substances from 2,603 ​​compounds. These bitter substances mainly come from alkaloids, shikimic acid, phenylpropionic acid and fatty acids.

[0024] A total of 20 high-content bitter substances were obtained through the sorting module, of which 4 bitter substances had been reported, and 16 compounds (kynuric acid, 4-indolecarboxaldehyde, 2,4-dihydroxyquinoline, 8-hydroxyquinoline, indoline, 2,4-dimethylbenzaldehyde, cytidine, ethylmorphine, uniconazole, ketotifen, 7-methylxanthine, DL-3,4-dihydroxyphenyldiol, oleamide, erucamide, 4,2',6'-trihydroxy-4'-methoxy-3',5'-dimethyldihydrochalcone, and difenoconazole) were identified as bitter substances for the first time. The results are shown in Table 1.

[0025] Table 1 Twenty bitter substances with high content in Quercus quinquefolia honey

[0026] In summary, the present invention overcomes the deficiencies of the prior art and can be used to quickly identify the bitter components of honey. In addition to being used for identifying the bitter components of honey, the present invention can also be used for identifying the bitter components of other foods.

[0027] 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 system for identifying bitter components of honey, characterized in that: The identification system consists of a compound number input module, an InChIKey encoding module, a bitter taste recognition module and a content ranking module; The compound number input module is used to use the names of bitter molecules and non-bitter molecules as input conditions; The InChIKey encoding module is used to convert the names of bitter molecules and non-bitter molecules into InChIKey, then convert the InChIKey into ASCII code, and generate corresponding fingerprint features and structural features; The bitterness recognition module includes a bitterness identifier and a bitterness threshold identifier. The bitterness identifier is used to distinguish the bitterness and non-bitterness of honey components according to a discrimination model formed by corresponding fingerprint features; the bitterness threshold identifier is used to distinguish the lowest bitterness perception threshold of the components; The content ranking module is used to sort the content and bitterness threshold of the compounds, so as to obtain the key bitter components in the honey.

2. The system according to claim 1, characterized in that The discriminant model is composed of single or combined hydrophobic extended fingerprints of ECFP-4, ECFP-6, ECFP-18, ECFP-114, ECFP-149, ECFP-210, ECFP-308, ECFP-309, ECFP-322, ECFP-499, ECFP-539, ECFP-546, ECFP-691, ECFP-857, ECFP-952, ECFP-1155, ECFP-1668, ECFP-1850, ECFP-2017, and ECFP-2041.

3. The system according to claim 1, characterized in that The bitterness threshold identifier is a quantitative model composed of single or combined molecular surface area, charge structure and electronegativity structure parameters of MaxESateIndex, MaxPartialCharge, PEOE_VSA8, VSA_EState4.

4. A system as claimed in any one of claims 1 to 3 for use in identifying bitter components in food.

5. The use according to claim 4, characterized in that: The system is applied to the identification of bitter components in honey.

6. The use according to claim 4, characterized in that: The system is applied to the identification of bitter components of Quercus balsamina honey.

7. The use according to claim 6, characterized in that: The bitter components of Quercus viridis honey identified by the system are kynurenic acid, 4-indolecarboxaldehyde, 2,4-dihydroxyquinoline, 8-hydroxyquinoline, indoline, 2,4-dimethylbenzaldehyde, cytidine, ethylmorphine, uniconazole, ketotifen, 7-methylxanthine, DL-3,4-dihydroxyphenyldiol, oleamide, erucamide, 4,2',6'-trihydroxy-4'-methoxy-3',5'-dimethyldihydrochalcone, and difenoconazole.

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