Molecular Recognition and Threshold Prediction Model of Bitterness in Natural Compounds

FENG Baolong; REN Haibin; DUAN Jiahui; ZHANG Housen; WEN Chunhui; BAI Xiaosen; GAO Fei; WANG Yutang

doi:10.13386/j.issn1002-0306.2021080020

Volume 43 Issue 4

Feb. 2022

Turn off MathJax

Article Contents

Abstract

References

Science and Technology of Food Industry > 2022 > 43(4): 24-32. > DOI: 10.13386/j.issn1002-0306.2021080020

FENG Baolong, REN Haibin, DUAN Jiahui, et al. Molecular Recognition and Threshold Prediction Model of Bitterness in Natural Compounds[J]. Science and Technology of Food Industry, 2022, 43(4): 24−32. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2021080020.

Citation:

PDF (3774 KB)

Molecular Recognition and Threshold Prediction Model of Bitterness in Natural Compounds

1.
Center for Education Technology Northeast Agricultural University, Harbin 150030, China
2.
Key Laboratory of Dairy Science, Ministry of Education, Northeast Agricultural University, Harbin 150030, China
3.
College of Food Science, Northeast Agricultural University, Harbin 150030, China
4.
Institute of Food Science and Technology, Chinese Academy of Agricultural Sciences, Beijing 100193, China

More Information

Received Date: August 03, 2021
Available Online: December 19, 2021

Graphical Abstract

Abstract

Abstract

It is important to identify the bitter substances in natural compounds and determine their bitterness threshold for finding out the bitter molecules that affect the flavor of food and developing some foods with unique flavors. Identifying bitter molecules and predicting the threshold of bitter molecules based on the quantitative structure-activity relationship is a low-cost and rapid method. This research used Molecular Operating Environment (MOE), Chemopy and Mordred to generate 2D molecular descriptor to establish bitterness molecular recognition models with Support Vector Machine (SVM) and Random Forests (RF) algorithms. This study used above descriptors to establish bitterness threshold prediction models with Partial Least Squares Regression (PLSR), Random Forests Regression (RFR), k-Nearest Neighbor Regression (kNNR), and Principle Component Regression (PCR) algorithms. The results showed that the MOE-RF model had the highest accuracy of 0.982, the ChemoPy-PLSR model had the best bitterness prediction effect with a coefficient of determination of 0.85 and a root mean square error of 0.43. The two models would be combined to predict whether the molecule has bitterness and the threshold of bitterness or not.
- bitter compounds,
- recognition,
- threshold,
- prediction,
- verification

FullText(HTML)

References (34)

References

[1]	代丽凤, 罗理勇, 罗江琼, 等. 植物苦味物质概况及其在食品工业的应用[J]. 中国食品学报,2020,20(11):305−318. [DAI L F, LUO L Y, LUO J Q, et al. General situation of plant bitter substances and their application in food industry[J]. Chinese Journal of Food Science,2020,20(11):305−318.
[2]	张璞, 张耀, 桂新景, 等. 基于经典人群口尝法和电子舌法的中药饮片水煎液苦度叠加规律研究[J]. 中草药,2021,52(3):653. [ZHANG P, ZHANG Y, GUI X J, et al. Study on the superimposition law of bitterness of Chinese herbal medicine decoction pieces based on the classic oral taste method and electronic tongue method[J]. Chinese Herbal Medicine,2021,52(3):653. doi: 10.7501/j.issn.0253-2670.2021.03.007
[3]	ZHOU W, DAI Y, MENG J, et al. Network pharmacology integrated with molecular docking reveals the common experiment-validated antipyretic mechanism of bitter-cold herbs[J]. J Ethnopharmacol,2021,274:114042. doi: 10.1016/j.jep.2021.114042
[4]	RODGERS S, GLEN R C, BENDER A. Characterizing bitterness: Identification of key structural features and development of a classification model[J]. Journal of Chemical Information & Modeling,2006,46(2):569−576.
[5]	JESSICA A, SASKIA P, MATHIAS D, et al. Supersweet—a resource on natural and artificial sweetening agents[J]. Nucleic Acids Research,2010,39(Database):377−382.
[6]	AYANA W, MARINA S, ANAT L, et al. BitterDB: A database of bitter compounds[J]. Nucleic Acids Research,2012,40(D1):D413−D419. doi: 10.1093/nar/gkr755
[7]	BANERJEE P. Bitter Sweet forest: A random forest based binary classifier to predict bitterness and sweetness of chemical compounds[J]. Front Chem,2018,6:93. doi: 10.3389/fchem.2018.00093
[8]	MARGULIS E, DAGAN A, IVES S, et al. Intense bitterness of molecules: Machine learning for expediting drug discovery[J]. Computational and Structural Biotechnology Journal,2021,19:568−576. doi: 10.1016/j.csbj.2020.12.030
[9]	JESÚS J, RICO P, MEDINA L. Analysis of a large food chemical database: Chemical space, diversity, and complexity[J]. F1000 Research,2018,7:993. doi: 10.12688/f1000research.15440.2
[10]	NEELANSH G, APUROOP S, RUDRAKSH T, et al. FlavorDB: A database of flavor molecules[J]. Nucleic Acids Research, 2018, 4(46): 1210-1216.
[11]	PANESAR S, KENNEDY F. Burdock (Ed.), Fenaroli’s Handbook of Flavor Ingredients, 5th ed., CRC Press, Boca Raton, FL, USA, ISBN 0-8493-3034-3[J]. Carbohydrate Polymers,2006,65(3):386.
[12]	YANLI W, BRYANT H, TIEJUN C, et al. PubChem BioAssay: 2017 update[J]. Nucleic Acids Research,2017,45:955−963. doi: 10.1093/nar/gkw1118
[13]	SANTIAGO V. Medicinal chemistry and the molecular operating environment (MOE): Application of QSAR and molecular docking to drug discovery[J]. Current Topics in Medicinal Chemistry,2008,8(18):1555−1572. doi: 10.2174/156802608786786624
[14]	DONG C, QING X, QIAN H, et al. ChemoPy: Freely available python package for computational biology and chemoinformatics[J]. Bioinformatics,2013,29(8):1092−1094. doi: 10.1093/bioinformatics/btt105
[15]	MORIWAKI H, TIAN Y, KAWASHITA N, et al. Mordred: A molecular descriptor calculator[J]. Journal of Cheminformatics,2018,10(1):4. doi: 10.1186/s13321-018-0258-y
[16]	VED A, DDDS F, PHGD D, et al. Scores selection via Fisher’s discriminant power in PCA-LDA to improve the classification of food data[J]. Food Chemistry,2021,363:130296. doi: 10.1016/j.foodchem.2021.130296
[17]	TEAM R. R: A language and environment for statistical computing[Z]. 3.5. 1. R Foundation for Statistical Computing, 2020.
[18]	WICKHAM H. Ggplot2: Elegant graphics for data analysis[Z]. Springer-Verlag New York, 2016.
[19]	LIAW A, MATTHEW W. Classification and regression by randomForest[Z]. R News, 2002: 2, 18-22.
[20]	MEYER D, DIMITRIADOU E, HORNIK K, et al. Misc functions of the department of statistics, probability theory group (Formerly: E1071), TU Wien[Z]. 2020: e1071.
[21]	SCHLIEP K, HECHENBICHLER K. Kknn: Weighted k-nearest neighbors[Z]. 2016.
[22]	VENABLES W, RIPLEY B. Modern applied statistics with s[Z]. New York: Springer, 2002.
[23]	TILLÉ Y, MATEI A. Sampling: Survey sampling[Z]. 2016.
[24]	FB S, WB N, RJ P. Random forest as one-class classifier and infrared spectroscopy for food adulteration detection[J]. Food Chemistry,2019,293:323−332. doi: 10.1016/j.foodchem.2019.04.073
[25]	PHILLIPS T, ABDULLA W. Developing a new ensemble approach with multi-class SVMs for Manuka honey quality classification[J]. Applied Soft Computing,2021,111:107710. doi: 10.1016/j.asoc.2021.107710
[26]	YONG L, LIAO S, JIANG S, et al. Fast cross-validation for kernel-based algorithms[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2020,42(5):1083−1096.
[27]	INTELMANN D, BATRAM C, KUHN C, et al. Three TAS2R bitter taste receptors mediate the psychophysical responses to bitter compounds of hops (Humulus lupulus L.) and beer[J]. Chemosensory Perception,2009,2(3):118−132. doi: 10.1007/s12078-009-9049-1
[28]	ANONYMOUS D. ISO 4120—Sensory analysis—methodology—triangle test (ISO 4120: 2004). German Version (EN ISO 4120: 2007)[S].
[29]	TUWANI R, WADHWA S, BAGLER G. BitterSweet: Building machine learning models for predicting the bitter and sweet taste of small molecules[J]. Scientific Reports,2019,9(1):7155. doi: 10.1038/s41598-019-43664-y
[30]	郭兴峰, 魏芳, 周祥山, 等. 苦味肽的形成机理及脱苦技术研究进展[J]. 食品研究与开发,2017,38(21):207−211. [GUO X F, WEI F, ZHOU X S, et al. The formation mechanism of bitter peptides and the research progress of debittering technology[J]. Food Research and Development,2017,38(21):207−211. doi: 10.3969/j.issn.1005-6521.2017.21.041
[31]	毕继才, 崔震昆, 张令文, 等. 苦味传递机制与苦味肽研究进展[J]. 食品工业科技,2018,39(11):333−338. [BI J C, CUI Z K, ZHANG L W, et al. Bitterness transmission mechanism and research progress of bitter peptides[J]. Food Industry Science and Technology,2018,39(11):333−338.
[32]	BAYMAN E O, DEXTER F. Multicollinearity in logistic regression models[J]. Anesthesia & Analgesia,2021,133(2):362−365.
[33]	LUCIANA S, MARCUS T S, HAMILTON M I, et al. Quantitative elucidation of the structure–bitterness relationship of cynaropicrin and grosheimin derivatives[J]. Food Chemistry,2007,105(1):77−83. doi: 10.1016/j.foodchem.2007.03.038
[34]	ZHENG S Q, JIANG M Y, ZHAO C W, et al. E-Bitter: Bitterant prediction by the consensus voting from the machine-learning methods[J]. Frontiers in Chemistry,2018,6:82. doi: 10.3389/fchem.2018.00082

Supplements (0)

Cited By

Cited by

Periodical cited type(2)

1.	李杨，杨燕，王振南，吕慎金. 乳酸菌在畜禽营养与饲料中的应用. 饲料工业. 2024(05): 16-22 .
2.	任青霞，户行宇，张敏，周增佳，杨贞耐. 植物乳杆菌NMGL2的安全性评价及其产细菌素发酵条件优化. 中国食品学报. 2024(09): 184-193 .