Research Progress and Future Trends of Machine Learning in the Field of Food Flavor
, Xing TIAN1,2 (
)Abstract
With the continuous improvement of living standards, people are concerned about not only whether foods are tasty or not, but also the combination of health elements and good flavor. Food flavor components are not only important factors in sensory quality, but also key indicators of the nutritional level of foods. At present, the traditional methods to evaluate and predict food flavor components are time-consuming and labor-intensive, and unable to handle large amounts of data. In contrast, machine learning (ML), the core of artificial intelligence, has incomparable advantages over traditional analytical techniques in distinguishing differences and finding commonalities, and has found good application in the field of food flavor analysis. In this context, this paper focuses on the current research status of ML in the field of food flavor, and introduces the principles and advantages of commonly used ML methods, as well as their latest applications and prospects in food flavor prediction and regulation. It also focuses on the advantages and future trends of modern intelligent sensory evaluation techniques combined with ML in the field of food flavor analysis, with a view to providing new ideas and theoretical foundations for food flavor analysis and prediction.
Keywords
References
WEI G Z, DAN M L, ZHAO G H, et al. Recent advances in chromatography-mass spectrometry and electronic nose technology in food flavor analysis and detection[J]. Food Chemistry, 2023, 405(Pt A): 134814. DOI:10.1016/j.foodchem.2022.134814.
DELAHUNTY C M, EYRES G, DUFOUR J. Gas chromatography-olfactometry[J]. Journal of Separation Science, 2006, 29(14): 2107-2125. DOI:10.1002/jssc.200500509.
BOUYSSET C, BELLOIR C, ANTONCZAK S, et al. Novel scaffold of natural compound eliciting sweet taste revealed by machine learning[J]. Food Chemistry, 2020, 324: 126864. DOI:10.1016/j.foodchem.2020.126864.
JIMÉNEZ-CARVELO A M, GONZÁLEZ-CASADO A, BAGURGONZÁLEZ M G, et al. Alternative data mining/machine learning methods for the analytical evaluation of food quality and authenticity: a review[J]. Food Research International, 2019, 122: 25-39. DOI:10.1016/j.foodres.2019.03.063.
ACHARYA S, DANDIGUNTA B, SAGAR H, et al. Analyzing milk foam using machine learning for diverse applications[J]. Food Analytical Methods, 2022, 15(12): 3365-3378. DOI:10.1007/s12161-022-02379-z.
DENG Z W, WANG T, ZHENG Y, et al. Deep learning in food authenticity: recent advances and future trends[J]. Trends in Food Science & Technology, 2024, 144: 104344. DOI:10.1016/j.tifs.2024.104344.
KHODAYAR M, LIU G Y, WANG J H, et al. Deep learning in power systems research: a review[J]. CSEE Journal of Power and Energy Systems, 2021, 7(2): 209-220. DOI:10.17775/CSEEJPES.2020.02700.
VIEJO C G, TORRICO D D, DUNSHEA F R, et al. Development of artificial neural network models to assess beer acceptability based on sensory properties using a robotic pourer: a comparative model approach to achieve an artificial intelligence system[J]. Beverages, 2019, 5(2): 33. DOI:10.3390/beverages5020033.
BREIMAN L. Random forests[J]. Machine Learning, 2001, 45(1): 5-32. DOI:10.1023/A:1010933404324.
MEN H, SHI Y, FU S L, et al. Mining feature of data fusion in the classification of beer flavor information using E-tongue and E-nose[J]. Sensors, 2017, 17(7): 1656. DOI:10.3390/s17071656.
MU F L, GU Y, ZHANG J, et al. Milk source identification and milk quality estimation using an electronic nose and machine learning techniques[J]. Sensors, 2020, 20(15): 4238. DOI:10.3390/s20154238.
TIAN H X, LIU H, HE Y J, et al. Combined application of electronic nose analysis and back-propagation neural network and random forest models for assessing yogurt flavor acceptability[J]. Journal of Food Measurement and Characterization, 2020, 14(1): 573-583. DOI:10.1007/s11694-019-00335-w.
CHEN C C, HUSNY J, RABE S. Predicting fishiness off-flavour and identifying compounds of lipid oxidation in dairy powders by SPME-GC/MS and machine learning[J]. International Dairy Journal, 2018, 77: 19-28. DOI:10.1016/j.idairyj.2017.09.009.
CORTES C, VAPNIK V. Support-vector networks[J]. Machine Learning, 1995, 20(3): 273-297. DOI:10.1007/BF00994018.
SÁNCHEZ A V D. Advanced support vector machines and kernel methods[J]. Neurocomputing, 2003, 55(1): 5-20. DOI:10.1016/S0925-2312(03)00373-4.
ZUO R G, CARRANZA E J M. Support vector machine: a tool for mapping mineral prospectivity[J]. Computers & Geosciences, 2011, 37(12): 1967-1975. DOI:10.1016/j.cageo.2010.09.014.
YANG Z W, MIAO N, ZHANG X, et al. Employment of an electronic tongue combined with deep learning and transfer learning for discriminating the storage time of Pu-erh tea[J]. Food Control, 2021, 121: 107608. DOI:10.1016/j.foodcont.2020.107608.
DAI C X, HUANG X Y, LV R Q, et al. Analysis of volatile compounds of Tremella aurantialba fermentation via electronic nose and HS-SPME-GC-MS[J]. Journal of Food Safety, 2018, 38(6): e12555. DOI:10.1111/jfs.12555.
LI K, ONODA K, KUMAZAKI T. Modeling taste score of rice by data mining from physicochemical parameters[J]. AIP Conference Proceedings, 2017, 1807: 020027. DOI:10.1063/1.4974809.
SAVILLE R, KAZUOKA T, SHIMOGUCHI N N, et al. Recognition of Japanese sake quality using machine learning based analysis of physicochemical properties[J]. Journal of the American Society of Brewing Chemists, 2022, 80(2): 146-154. DOI:10.1080/03610470.2021.1939973.
ALTMAN N S. An introduction to kernel and nearest-neighbor nonparametric regression[J]. The American Statistician, 1992, 46(3): 175-185. DOI:10.1080/00031305.1992.10475879.
GUTIERREZ-OSUNA R. Pattern analysis for machine olfaction: a review[J]. IEEE Sensors Journal, 2002, 2: 189-202. DOI:10.1109/JSEN.2002.800688.
WU X H, ZHU J, WU B, et al. Classification of Chinese vinegar varieties using electronic nose and fuzzy Foley-Sammon transformation[J]. Journal of Food Science and Technology, 2020, 57(4): 1310-1319. DOI:10.1007/s13197-019-04165-y.
HUANG G B. What are extreme learning machines? filling the gap between Frank Rosenblatt’s Dream and John von Neumann’s Puzzle[J]. Cognitive Computation, 2015, 7(3): 263-278. DOI:10.1007/s12559-015-9333-0.
DING S F, XU X Z, RU N. Extreme learning machine and its applications[J]. Neural Computing and Applications, 2013, 25: 549-556. DOI:10.1007/s00521-013-1522-8.
YANG W N, ZHOU W, LIAO W H, et al. Identification and quantification of concurrent control chart patterns using extreme-point symmetric mode decomposition and extreme learning machines[J]. Neurocomputing, 2015, 147: 260-270. DOI:10.1016/j.neucom.2014.06.068.
YU L, DAI W, TANG L. A novel decomposition ensemble model with extended extreme learning machine for crude oil price forecasting[J]. Engineering Applications of Artificial Intelligence, 2016, 47: 110-121. DOI:10.1016/j.engappai.2015.04.016.
MAO W T, HE L, YAN Y J, et al. Online sequential prediction of bearings imbalanced fault diagnosis by extreme learning machine[J]. Mechanical Systems and Signal Processing, 2017, 83: 450-473. DOI:10.1016/j.ymssp.2016.06.024.
FAN B B, ZHU R G, HE D Y, et al. Evaluation of mutton adulteration under the effect of mutton flavour essence using hyperspectral imaging combined with machine learning and sparrow search algorithm[J]. Foods, 2022, 11(15): 2278. DOI:10.3390/foods11152278.
ZUO E G, SUN L, YAN J Y, et al. Rapidly detecting fennel origin of the near-infrared spectroscopy based on extreme learning machine[J]. Scientific Reports, 2022, 12(1): 13593. DOI:10.1038/s41598-022-17810-y.
WANG Y T, YANG Z X, PIAO Z H, et al. Prediction of flavor and retention index for compounds in beer depending on molecular structure using a machine learning method[J]. RSC Advances, 2021, 11(58): 36942-36950. DOI:10.1039/d1ra06551c.
YANG Z F, XIAO R, XIONG G L, et al. A novel multi-layer prediction approach for sweetness evaluation based on systematic machine learning modeling[J]. Food Chemistry, 2022, 372: 131249. DOI:10.1016/j.foodchem.2021.131249.
VIEJO C G, FUENTES S, TORRICO D D, et al. Assessment of beer quality based on a robotic pourer, computer vision, and machine learning algorithms using commercial beers[J]. Journal of Food Science, 2018, 83(5): 1381-1388. DOI:10.1111/1750-3841.14114.
YU P G, LOW M Y, ZHOU W B. Development of a partial least squares-artificial neural network (PLS-ANN) hybrid model for the prediction of consumer liking scores of ready-to-drink green tea beverages[J]. Food Research International, 2018, 103: 68-75. DOI:10.1016/j.foodres.2017.10.015.
WANG P, TIAN H L, TAN F L, et al. Impact of commercial processing on volatile compounds and sensory profiles of flat peach juices by PLSR and BP network[J]. Journal of Food Processing and Preservation, 2020, 44: e14575. DOI:10.1111/jfpp.14575.
HUANG Y Q, KANGAS L J, RASCO B A. Applications of artificial neural networks (ANNs) in food science[J]. Critical Reviews in Food Science and Nutrition, 2007, 47(2): 113-126. DOI:10.1080/10408390600626453.
LI Y, FEI C H, MAO C Q, et al. Physicochemical parameters combined flash GC E-nose and artificial neural network for quality and volatile characterization of vinegar with different brewing techniques[J]. Food Chemistry, 2022, 374: 131658. DOI:10.1016/j.foodchem.2021.131658.
MATYUSHIN D D, SHOLOKHOVA A Y, BURYAK A K. A deep convolutional neural network for the estimation of gas chromatographic retention indices[J]. Journal of Chromatography A, 2019, 1607: 460395. DOI:10.1016/j.chroma.2019.460395.
CHANG Y T, HSUEH M, HUNG S P, et al. Prediction of specialty coffee flavors based on near-infrared spectra using machine-and deep-learning methods[J]. Journal of the Science of Food and Agriculture, 2021, 101(11): 4705-4714. DOI:10.1002/jsfa.11116.
QIU S S, WANG J. The prediction of food additives in the fruit juice based on electronic nose with chemometrics[J]. Food Chemistry, 2017, 230: 208-214. DOI:10.1016/j.foodchem.2017.03.011.
ZHOU L, ZHANG C, LIU F, et al. Application of deep learning in food: a review[J]. Comprehensive Reviews in Food Science and Food Safety, 2019, 18(6): 1793-1811. DOI:10.1111/1541-4337.12492.
PUERTA L, GONZALEZ C. Molecular descriptor to predict biological activity of analogues cocaine[M]. America: National Institute of Standards and Technology, 2020. DOI:10.13140/RG.2.2.34644.01923.
LEE C H, CHEN I T, YANG H C, et al. An AI-powered electronic nose system with fingerprint extraction for aroma recognition of coffee beans[J]. Micromachines, 2022, 13(8): 1313. DOI:10.3390/mi13081313.
KAEHLER A, BRADSKI G R. Learning OpenCV 3: Computer vsion in C++ with the openCV Library[M]. Sebastopol: O’Reilly Media, Inc., 2016. https://api.semanticscholar.org/CorpusID:61875646.
DI ROSA A R, LEONE F, CHELI F, et al. Fusion of electronic nose, electronic tongue and computer vision for animal source food authentication and quality assessment: a review[J]. Journal of Food Engineering, 2017, 210: 62-75. DOI:10.1016/j.jfoodeng.2017.04.024.
SCHWINDT V C, COLETTO M M, DÍAZ M F, et al. Could QSOR Modelling and machine learning techniques be useful to predict wine aroma?[J]. Food and Bioprocess Technology, 2023, 16(1): 24-42. DOI:10.1007/s11947-022-02836-x.
BI K X, ZHANG D, QIU T, et al. GC-MS Fingerprints profiling using machine learning models for food flavor prediction[J]. Process, 2019, 8(1): 23. DOI:10.3390/pr8010023.
GARG N, SETHUPATHY A, TUWANI R, et al. FlavorDB: a database of flavor molecules[J]. Nucleic Acids Research, 2018, 46(D1): D1210-D1216. DOI:10.1093/nar/gkx957.
WIENER A, SHUDLER M, LEVIT A, et al. BitterDB: a database of bitter compounds[J]. Nucleic Acids Research, 2012, 40(D1): D413-D419. DOI:10.1093/nar/gkr755.
FRITZ F, PREISSNER R, BANERJEE P. VirtualTaste: a web server for the prediction of organoleptic properties of chemical compounds[J]. Nucleic Acids Research, 2021, 49(W1): W679-W684. DOI:10.1093/nar/gkab292.
YANG Z F, XIAO R, XIONG G L, et al. A novel multi-layer prediction approach for sweetness evaluation based on systematic machine learning modeling[J]. Food Chemistry, 2022, 372: 131249. DOI:10.1016/j.foodchem.2021.131249.
LEE J, SONG S B, CHUNG Y K, et al. BoostSweet: learning molecular perceptual representations of sweeteners[J]. Food Chemistry, 2022, 383: 132435. DOI:10.1016/j.foodchem.2022.132435.
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.
BO W C, QIN D Y, ZHENG X, et al. Prediction of bitterant and sweetener using structure-taste relationship models based on an artificial neural network[J]. Food Research International, 2022, 153: 110974. DOI:10.1016/j.foodres.2022.110974.
DAGAN-WIENER A, NISSIM I, BEN ABU N, et al. Bitter or not? BitterPredict, a tool for predicting taste from chemical structure[J]. Scientific Reports, 2017, 7(1): 12074. DOI:10.1038/s41598-017-12359-7.
LIU Q, LUO D H, WEN T T, et al. In silico prediction of fragrance retention grades for monomer flavors using QSPR models[J]. Chemometrics and Intelligent Laboratory Systems, 2021, 218: 104424. DOI:10.1016/j.chemolab.2021.104424.
SUN Y N, ZHANG M, JU R H, et al. Novel nondestructive NMR method aided by artificial neural network for monitoring the flavor changes of garlic by drying[J]. Drying Technology, 2021, 39(9): 1184-1195. DOI:10.1080/07373937.2020.1821211.
SUN D W, ZHOU C Q, HU J, et al. Off-flavor profiling of cultured salmonids using hyperspectral imaging combined with machine learning[J]. Food Chemistry, 2023, 408: 135166. DOI:10.1016/j.foodchem.2022.135166.
BAHRAMPARVAR M, SALEHI F, RAZAVI S M A. Predicting total acceptance of ice cream using artificial neural network[J]. Journal of Food Processing and Preservation, 2014, 38: 1080-1088. DOI:10.1111/jfpp.12066.
SHANG L, LIU C N, TOMIURA Y, et al. Machine-learning-based olfactometer: prediction of odor perception from physicochemical features of odorant molecules[J]. Analytical Chemistry, 2017, 89(22): 11999-12005. DOI:10.1021/acs.analchem.7b02389.
BANERJEE R, TUDU B, BANDYOPADHYAY R, et al. A review on combined odor and taste sensor systems[J]. Journal of Food Engineering, 2016, 190: 10-21. DOI:10.1016/j.jfoodeng.2016.06.001.
LIU M, HAN X M, TU K, et al. Application of electronic nose in Chinese spirits quality control and flavour assessment[J]. Food Control, 2012, 26: 564-570. DOI:10.1016/j.foodcont.2012.02.024.
VIEJO C G, TONGSON E, FUENTES S. Integrating a low-cost electronic nose and machine learning modelling to assess coffee aroma profile and intensity[J]. Sensors, 2021, 21(6): 2016. DOI:10.3390/s21062016.
VIEJO C G, FUENTES S. Beer aroma and quality traits assessment using artificial intelligence[J]. Fermentation, 2020, 6: 56. DOI:10.3390/fermentation6020056.
WU D, CHENG Y, LUO D, et al. POP-CNN: predicting odor’s pleasantness with convolutional neural network[M]. IEEE Sensors Journal, 2019, 19(23): 11337-11345. DOI:10.1109/JSEN.2019.2933692.
EGGINK P M, MALIEPAARD C, TIKUNOV Y, et al. Prediction of sweet pepper (Capsicum annuum) flavor over different harvests[J]. Euphytica, 2012, 187(1): 117-131. DOI:10.1007/s10681-012-0761-6.
FUENTES S, TONGSON E, TORRICO D D, et al. Modeling pinot noir aroma profiles based on weather and water management information using machine learning algorithms: a vertical vintage analysis using artificial intelligence[J]. Foods, 2019, 9(1): 33. DOI:10.3390/foods9010033.
ZHU W, BENKWITZ F, KILMARTIN P A. Volatile-based prediction of sauvignon blanc quality gradings with static headspace-gas chromatography-ion mobility spectrometry(SHS-GC-IMS)and interpretable machine learning techniques[J]. Journal of Agricultural and Food Chemistry, 2021, 69(10): 3255-3265. DOI:10.1021/acs.jafc.0c07899.
LI B, LIU M, LIN F, et al. Marker-independent food identification enabled by combing machine learning algorithms with comprehensive GC × GC/TOF-MS[J]. Molecules, 2022, 27(19): 6237. DOI:10.3390/molecules27196237.
BOCCORH R K, PATERSON A. An artificial neural network model for predicting flavour intensity in blackcurrant concentrates[J]. Food Quality and Preference, 2002, 13(2): 117-128. DOI:10.1016/S0950-3293(01)00072-6.
LEONG Y X, LEE Y H, KOH C, et al. Surface-enhanced Raman scattering (SERS) taster: a machine-learning-driven multireceptor platform for multiplex profiling of wine flavors[J]. Nano Letters, 2021, 21(6): 2642-2649. DOI:10.1021/acs.nanolett.1c00416.
VIEJO C G, FUENTES S. Digital assessment and classification of wine faults using a low-cost electronic nose, near-infrared spectroscopy and machine learning modelling[J]. Sensors, 2022, 22(6): 2303. DOI:10.3390/s22062303.
MATYUSHIN D D, BURYAK A K. Gas chromatographic retention index prediction using multimodal machine learning[J]. IEEE Access, 2020, 8: 223140-223155. DOI:10.1109/ACCESS.2020.3045047.
TIEMAN D, BLISS P, MCINTYRE L, et al. The chemical interactions underlying tomato flavor preferences[J]. Current Biology, 2012, 22(11): 1035-1039. DOI:10.1016/j.cub.2012.04.016.
BARTOSHUK L, KLEE H. Better fruits and vegetables through sensory analysis[J]. Current Biology, 2013, 23(9): R374-R378. DOI:10.1016/j.cub.2013.03.038.
TIEMAN D, ZHU G T, RESENDE M F, et al. A chemical genetic roadmap to improved tomato flavor[J]. Science, 2017, 355: 391-394. DOI:10.1126/science.aal1556.
COLANTONIO V, FERRÃO L, TIEMAN D M, et al. Metabolomic selection for enhanced fruit flavor[J]. Proceedings of the National Academy of Sciences of the United States of America, 2022, 119(7): e2115865119. DOI:10.1073/pnas.2115865119.
FAN Z, HASING T, JOHNSON T S, et al. Strawberry sweetness and consumer preference are enhanced by specific volatile compounds[J]. Horticulture Research, 2021, 8(1): 66. DOI:10.1038/s41438-021-00502-5.
FERRÃO L F V, SATER H, LYRENE P, et al. Terpene volatiles mediates the chemical basis of blueberry aroma and consumer acceptability[J]. Food Research International, 2022, 158: 111468. DOI:10.1016/j.foodres.2022.111468.
VENKATASUBRAMANIAN V, MANN V. Artificial intelligence in reaction prediction and chemical synthesis[J]. Current Opinion in Chemical Engineering, 2022, 36: 100749. DOI:10.1016/j.coche.2021.100749.
COLEY C W, BARZILAY R, JAAKKOLA T S, et al. Prediction of organic reaction outcomes using machine learning[J]. ACS Central Science, 2017, 3(5): 434-443. DOI:10.1021/acscentsci.7b00064.
SEGLER M, WALLER M P. Neural-symbolic machine learning for retrosynthesis and reaction prediction[J]. Chemistry, 2017, 23(25): 5966-5971. DOI:10.1002/chem.201605499.
JELEN H, GACA A. Characterization of aroma compounds: structure, physico-chemical and sensory properties: from food to perception[J]. Flavour: From Food to Perception, 2016, 10: 126-153. DOI:10.1002/9781118929384.ch6.
TAKEOKA G R, BUTTERY R G, LING L C, et al. Odor thresholds of various unsaturated branched esters[J]. LWT-Food Science and Technology, 1998, 31(5): 443-448. DOI:10.1006/fstl.1998.0382.
REINECCIUS G. Flavor chemistry and technology, second edition[M]. Boca Raton: CRC Press, 2005: 520. DOI:10.1201/9780203485347.
BRENNA E, FUGANTI C, SERRA S. Enantioselective perception of chiral odorants[J]. Tetrahedron: Asymmetry, 2003, 14(1): 1-42. DOI:10.1016/S0957-4166(02)00713-9.
京公网安备11010802044758号