Citation: | SU Lijun, LI Jian, KONG Jianlei, et al. Progress in Research on Machine Learning for Studies on Food Flavor Analysis[J]. Science and Technology of Food Industry, 2024, 45(18): 19−30. (in Chinese with English abstract). doi: 10.13386/j.issn1002-0306.2024020165. |
[1] |
WEI G, DAN M, ZHAO G, et al. Recent advances in chromatography-mass spectrometry and electronic nose technology in food flavor analysis and detection[J]. Food Chemistry,2023,405:134814.
|
[2] |
PU D, SHAN Y, WANG J, et al. Recent trends in aroma release and perception during food oral processing:A review[J]. Critical Reviews in Food Science and Nutrition,2022,64(11):11−17.
|
[3] |
田怀香, 熊娟涓, 于海燕, 等. 果酒中香气化合物的生物转化与调控机制研究进展[J]. 食品科学,2022,43(19):36−47. [TING H X, XIONG J J, YU H Y, et al. Biotransformation and biological regulation mechanism of aroma compound in fruit wine:A review[J]. Food Science,2022,43(19):36−47.]
TING H X, XIONG J J, YU H Y, et al. Biotransformation and biological regulation mechanism of aroma compound in fruit wine: A review[J]. Food Science, 2022, 43(19): 36−47.
|
[4] |
ZENG X, LI H, JIANG W, et al. Phytochemical compositions, health-promoting properties and food applications of crabapples:A review[J]. Food Chemistry,2022,386:132789.
|
[5] |
AL-DALALI S, LI C, XU B. Insight into the effect of frozen storage on the changes in volatile aldehydes and alcohols of marinated roasted beef meat:Potential mechanisms of their formation[J]. Food Chemistry,2022,385:132629.
|
[6] |
WANG L, QIAO K, DUAN W, et al. Comparison of taste components in stewed beef broth under different conditions by means of chemical analyzed[J]. Food Science & Nutrition,2020,8(2):955−964.
|
[7] |
王铁龙, 许凌云, 杨冠山, 等. 智能感官分析技术在食品风味中的研究进展[J]. 食品安全质量检测学报,2023,14(8):37−43. [WAND T L, XU L Y, YANG G S, et al. Progress in research on intelligent sensory analysis for studies on food flavor[J]. Journal of Food Safety and Quality,2023,14(8):37−43.]
WAND T L, XU L Y, YANG G S, et al. Progress in research on intelligent sensory analysis for studies on food flavor[J]. Journal of Food Safety and Quality, 2023, 14(8): 37−43.
|
[8] |
WU D W. Novel techniques for evaluating freshness quality attributes of fish:A review of recent developments[J]. Trends in Food Science & Technology,2019,83:259−273.
|
[9] |
TSENG Y J, CHUANG P J, APPELL M. When machine learning and deep learning come to the big data in food chemistry[J]. ACS Omega,2023,8(18):15854−15864.
|
[10] |
林慧君, 王小磊, 田梦圆, 等. 机器学习及其流行病学应用[J]. 中华流行病学杂志,2021,42(9):1689−1694. [LIN H J, WANG X L, TIAN M Y, et al. Machine learning and its epidemiological applications[J]. Chinese Journal of Epidemiology,2021,42(9):1689−1694.]
LIN H J, WANG X L, TIAN M Y, et al. Machine learning and its epidemiological applications[J]. Chinese Journal of Epidemiology, 2021, 42(9): 1689−1694.
|
[11] |
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 aobotic pourer:A comparative model approach to achieve an artificial intelligence system[J]. Beverages,2019,5(2):33.
|
[12] |
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.
|
[13] |
BI K, ZHANG D, QIU T, et al. GC-MS fingerprints profiling using machine learning models for food flavor prediction[J]. Processes,2020,8(1):23.
|
[14] |
LI C, HUA Y, PAN D, et al. A rapid selection strategy for umami peptide screening based on machine learning and molecular docking[J]. Food Chemistry,2023,404:134562.
|
[15] |
YANG Z, XIAO R, XIONG G, et al. A novel multi-layer prediction approach for sweetness evaluation based on systematic machine learning modeling[J]. Food Chemistry,2022,372:131249.
|
[16] |
SON M, PARK T H. The bioelectronic nose and tongue using olfactory and taste receptors:Analytical tools for food quality and safety assessment[J]. Biotechnology Advances,2017,36(2):371−379.
|
[17] |
SONG C, WANG Z, LI H, et al. Recent advances in taste transduction mechanism, analysis methods and strategies employed to improve the taste of taste peptides[J]. Critical Reviews in Food Science and Nutrition, 2023:20−21.
|
[18] |
DANIELE, CAVANNA, SANDRO, et al. Iron mobility spectrometry coupled to gas chromatography:A rapid tool to assess eggs freshness[J]. Food Chemistry,2019,271:691−696.
|
[19] |
LV W, LIN T, REN Z, et al. Rapid discrimination of Citrus reticulata 'Chachi' by headspace-gas chromatography-ion mobility spectrometry fingerprints combined with principal component analysis[J]. Food Research International,2020,131:108985.
|
[20] |
杨正飞. 基于机器学习的多层次甜味预测系统的构建研究[D]. 长沙:中南林业科技大学, 2021. [YANG Z F. Study on construction of multi-layer sweet prediction system based on machine learning[D]. Changsha:Central South Univeisity of Forestry & Technology, 2021.]
YANG Z F. Study on construction of multi-layer sweet prediction system based on machine learning[D]. Changsha: Central South Univeisity of Forestry & Technology, 2021.
|
[21] |
田怀香, 郑国茂, 于海燕, 等. 气味与滋味间相互作用对食品风味感知影响研究进展[J]. 食品科学,2023,44(9):259−269. [TIAN H X, ZHENG G M, YU H Y, et al. Research progerss on the effect of the interaction between odor and taste on food flavor perception[J]. Food Science,2023,44(9):259−269.] doi: 10.7506/spkx1002-6630-20220515-194
TIAN H X, ZHENG G M, YU H Y, et al. Research progerss on the effect of the interaction between odor and taste on food flavor perception[J]. Food Science, 2023, 44(9): 259−269. doi: 10.7506/spkx1002-6630-20220515-194
|
[22] |
ZHAO W Z, SU L J, HUO S T, et al. Virtual screening, molecular docking and identification of umami peptides derived from Oncorhynchus mykiss[J]. Food Science and Human Wellness,2023,12(1):89−93.
|
[23] |
DANG Y, HAO L, ZHOU T, et al. Establishment of new assessment method for the synergistic effect between umami peptides and monosodium glutamate using electronic tongue[J]. Food Research International,2019,121:20−27.
|
[24] |
MEN H, SHI Y, FU S. 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
|
[25] |
TOKO K, HARA D, TAHARA Y, et al. Relationship between the amount of bitter substances adsorbed onto lipid/polymer membrane and the electric response of taste sensors[J]. Sensors,2014,14(9):16274−16286. doi: 10.3390/s140916274
|
[26] |
HAYASHI N, CHEN R, IKEZAKI H, et al. Evaluation of the umami taste intensity of green tea by a taste sensor[J]. Journal of Agricultural & Food Chemistry,2008,56(16):7384−7387.
|
[27] |
吴仕敏, 余勤艳, 朱佳, 等. 基于电子舌和代谢组学分析揉捻频率对工夫红茶品质的影响[J]. 食品科学,2023,44(6):301−310. [WU S M, YU Q Y, ZHU J, et al. Analysis of the effect of rolling speed on the congou black tea quality using electronic tongue and metabolomics[J]. Food Science,2023,44(6):301−310.]
WU S M, YU Q Y, ZHU J, et al. Analysis of the effect of rolling speed on the congou black tea quality using electronic tongue and metabolomics[J]. Food Science, 2023, 44(6): 301−310.
|
[28] |
YU Z, KANG L, ZHAO, W, et al. Identification of novel umami peptides from myosin via homology modeling and molecular docking[J]. Food Chemistry,2020,344:128728.
|
[29] |
李翔, 聂青玉, 许彦. 定量描述分析和高效液相色谱指纹图谱在茶叶品质评价中的应用研究现状[J]. 中国茶叶,2021,43(10):59−62. [LI X, NIE Q Y, XU, Y. Application research status of quantitative descriptive analysis and HPLC fingerprint in tea quality evaluation[J]. China Tea,2021,43(10):59−62.] doi: 10.3969/j.issn.1000-3150.2021.10.010
LI X, NIE Q Y, XU, Y. Application research status of quantitative descriptive analysis and HPLC fingerprint in tea quality evaluation[J]. China Tea, 2021, 43(10): 59−62. doi: 10.3969/j.issn.1000-3150.2021.10.010
|
[30] |
蒋希希, 裴斐, 赵立艳, 等. 草菇鲜味肽的分离鉴定及呈味特性分析[J]. 食品科学,2022,43(12):235−242. [JIANG X X, PEI F, ZHAO L Y, et al. The separation and identification of umami peptides from Straw Mushroom and the analysis of its taste characteristic[J]. Food Science,2022,43(12):235−242.] doi: 10.7506/spkx1002-6630-20211110-124
JIANG X X, PEI F, ZHAO L Y, et al. The separation and identification of umami peptides from Straw Mushroom and the analysis of its taste characteristic[J]. Food Science, 2022, 43(12): 235−242. doi: 10.7506/spkx1002-6630-20211110-124
|
[31] |
陈美丽, 唐德松, 龚淑英, 等. 绿茶滋味品质的定量分析及其相关性评价[J]. 浙江大学学报(农业与生命科学版),2014,40(6):670−678. [CHEN M L, TANG D S, GONG S Y, et al. Quantitative analysis and correlation evaluation on taste quality of green tea[J]. Zhejing University (Agric & LifeSci),2014,40(6):670−678.]
CHEN M L, TANG D S, GONG S Y, et al. Quantitative analysis and correlation evaluation on taste quality of green tea[J]. Zhejing University (Agric & LifeSci), 2014, 40(6): 670−678.
|
[32] |
唐雪平. 基于化学分析与机器学习的铁观音茶叶品质评价体系[D]. 泉州:华侨大学, 2020. [TANG X P. The quality evaluation system for tieguanyin tea based on chemical analysis and machine learning[D]. Quanzhou:Huaqiao University, 2020.]
TANG X P. The quality evaluation system for tieguanyin tea based on chemical analysis and machine learning[D]. Quanzhou: Huaqiao University, 2020.
|
[33] |
尚晓睿, 陈柔含, 邓波, 等. 高效液相色谱-紫外检测法同时测定水产品中9种呈味核苷酸[J]. 分析实验室,2024,25(3):59−62. [SHANG X R, CHEN R H, DENG B, et al. Simultaneous determination of 9 flavored nucleotides in aquatic products by high performance liquid chromatography-ultraviolet method[J]. Chinese Journal of Analysis Laboratory,2024,25(3):59−62.]
SHANG X R, CHEN R H, DENG B, et al. Simultaneous determination of 9 flavored nucleotides in aquatic products by high performance liquid chromatography-ultraviolet method[J]. Chinese Journal of Analysis Laboratory, 2024, 25(3): 59−62.
|
[34] |
林婷, 刘强欣, 闻佳钰, 等. 高效色谱法测定风味发酵乳中的三氯蔗糖[J]. 食品工业,2022,43(5):294−297. [LIN T, LIU Q X, WEN J Y, et al. Determination of sucralose in flavored fermented milk by high performance chromatography[J]. Food Technology,2022,43(5):294−297.]
LIN T, LIU Q X, WEN J Y, et al. Determination of sucralose in flavored fermented milk by high performance chromatography[J]. Food Technology, 2022, 43(5): 294−297.
|
[35] |
黎琪, 李晓敏, 姜德铭, 等. 高效液相色谱法检测熟制猪肉中呈味核苷酸[J]. 肉类研究,2022,36(3):26−31. [LI Q, LI X M, JIANG D M, et al. Determination of flavor-active nucleotides in cooked pork by high performance liquid chromatography[J]. Meat Research,2022,36(3):26−31.]
LI Q, LI X M, JIANG D M, et al. Determination of flavor-active nucleotides in cooked pork by high performance liquid chromatography[J]. Meat Research, 2022, 36(3): 26−31.
|
[36] |
梁敏华, 陈丽君, 李志溥, 等. 市售豉香型白酒品质及风味感官分析[J]. 食品安全质量检测学报,2024,15(3):80−88. [LIANG M H, CHEN L J, LI Z P, et al. Quality and flavor sensory analysis of commercially available chi-flavored liquor[J]. Journal of Food Safety and Quality,2024,15(3):80−88.]
LIANG M H, CHEN L J, LI Z P, et al. Quality and flavor sensory analysis of commercially available chi-flavored liquor[J]. Journal of Food Safety and Quality, 2024, 15(3): 80−88.
|
[37] |
AMY, LOUTFI, SILVIA, et al. Electronic noses for food quality:A review[J]. Journal of Food Engineering,2015,144:103−111. doi: 10.1016/j.jfoodeng.2014.07.019
|
[38] |
LU L, HU Z, HU X, et al. Electronic tongue and electronic nose for food quality and safety[J]. Food Research International,2022,162:112214. doi: 10.1016/j.foodres.2022.112214
|
[39] |
MAHDI, GHASEMI-VARNAMKHASTI, MORTAZA, et al. Electronic nose and electronic mucosa as innovative instruments for real-time monitoring of food dryers[J]. Trends in Food Science & Technology,2014,34(2):158−166.
|
[40] |
MAHDI, GHASEMI-VARNAMKHASTI, JESUS, et al. Electronic nose as an innovative measurement system for the quality assurance and control of bakery products:A review[J]. Engineering in Agriculture Environment & Food,2016,9(4):365−374.
|
[41] |
WAKHID S, SARNO R, SABILLA S I. The effect of gas concentration on detection and classification of beef and pork mixtures using E-nose[J]. Computers and Electronics in Agriculture,2022,195:106838. doi: 10.1016/j.compag.2022.106838
|
[42] |
MAREK G, DOBRZAŃSKI B, ONISZCZUK T, et al. Detection and differentiation of volatile compound profiles in roasted coffee arabica beans from different countries using an electronic nose and GC-MS[J]. Sensors (Basel, Switzerland),2020,20(7):2124. doi: 10.3390/s20072124
|
[43] |
YE T T, LIU J, WAN P, et al. Investigation of the effect of polar components in cream on the flavor of heated cream based on NMR and GC-MS methods[J]. LWT-Food Science and Technology,2021,155:112940.
|
[44] |
WANG Q, LIU K, LIU L, et al. Correlation analysis between aroma components and microbial communities in Wuliangye-flavor raw liquor based on HS-SPME/LLME-GC-MS and PLFA[J]. Food Research International,2020,140(47):109995.
|
[45] |
CHEN 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.
|
[46] |
HUANLU S, JIANBIN L. GC-O-MS technique and its applications in food flavor analysis[J]. Food Research International,2018,114:187−198. doi: 10.1016/j.foodres.2018.07.037
|
[47] |
THOMSEN M, MARTIN C, MERCIER F, et al. Investigating semi-hard cheese aroma:Relationship between sensory profiles and gas chromatography-olfactometry data[J]. International Dairy Journal,2012,26(1):41−49. doi: 10.1016/j.idairyj.2012.04.009
|
[48] |
PANG X, GUO X, QIN Z, et al. Identification of aroma-active compounds in Jiashi muskmelon juice by GC-O-MS and OAV calculation[J]. Journal of Agricultural and Food Chemistry,2012,60(17):4179−4185.
|
[49] |
SARHIR A S, SERKAN. Fingerprint of aroma-active compounds and odor activity values in a traditional Moroccan fermented butter "Smen" using GC-MS-Olfactometry[J]. Journal of Food Composition and Analysis,2021,96(1):103761.
|
[50] |
SHANG L, LIU C, 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
|
[51] |
WANG S, CHEN H, SUN B. Recent progress in food flavor analysis using gas chromatography–ion mobility spectrometry (GC–IMS)[J]. Food Chemistry,2020,315:126158. doi: 10.1016/j.foodchem.2019.126158
|
[52] |
YAO W, CAI Y, LIU D, et al. Analysis of flavor formation during production of Dezhou braised chicken using headspace-gas chromatography-ion mobility spec-trometry (HS-GC-IMS)[J]. Food Chemistry,2022,370:130989. doi: 10.1016/j.foodchem.2021.130989
|
[53] |
GAO C, WANG R, ZHANG F, et al. The process monitors of probiotic fermented sour cherry juice based on the HS-GC-IMS[J]. Microchemical Journal,2022,180:107537. doi: 10.1016/j.microc.2022.107537
|
[54] |
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
|
[55] |
SUN Z, ZHAO W, LI Y, et al. An exploration of pepino (Solanum muricatum) flavor compounds using machine learning combined with metabolomics and sensory evaluation[J]. Foods,2022,11(20):3248. doi: 10.3390/foods11203248
|
[56] |
ZENG X, CAO R, XI Y, et al. Food flavor analysis 4.0:A cross-domain application of machine learning[J]. Trends in Food Science & Technology,2023,138:116−125.
|
[57] |
KREMER J, PEDERSEN K S, IGEL C. Active learning with support vector machines[J]. Wiley Interdisciplinary Reviews:Data Mining and Knowledge Discovery,2014,4(4):313−326. doi: 10.1002/widm.1132
|
[58] |
ELBADAWI M, GAISFORD S, BASIT A W. Advanced machine-learning techniques in drug discovery[J]. Drug Discovery Today,2020,26(3):769−777.
|
[59] |
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
|
[60] |
MYLES A J, FEUDALE R N, LIU Y, et al. An introduction to decision tree modeling[J]. Journal of Chemometrics,2010,18(6):275−285.
|
[61] |
JI H, PU D, YAN W, et al. Recent advances and application of machine learning in food flavor prediction and regulation[J]. Trends in Food Science & Technology,2023,138:738−751.
|
[62] |
PAVLOV Y L. Random forests[J]. Karelian Centre Russian Acadscipetrozavodsk,1997,45(1):5−32.
|
[63] |
LI Y, FEI C, MAO C, 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
|
[64] |
TIAN H, LIU H, HE Y, et al. Correction to:Combined application of electronic nose analysis and backpropagation neural network and random forest models for assessing yogurt flavor acceptability[J]. Journal of Food Measurement and Characterization,2020,14(4):573−583.
|
[65] |
ALFEILAT H A A, HASSANAT A, LASASSMEH O, et al. Effects of distance measure choice on k-nearest neighbor classifier performance:A review[J]. Big Data,2019,7(4):221−248. doi: 10.1089/big.2018.0175
|
[66] |
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
|
[67] |
CHANG Y T, HSUEH M C, 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,2021,107(15):4705−4714.
|
[68] |
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
|
[69] |
YANG Z, 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(3):107608.
|
[70] |
YAMASHITA R, NISHIO M, DO R K G, et al. Convolutional neural networks:An overview and application in radiology[J]. Insights into Imaging,2018,9(4):611−629. doi: 10.1007/s13244-018-0639-9
|
[71] |
QI L, DU J, SUN Y, et al. Umami-MRNN:Deep learning-based prediction of umami peptide using RNN and MLP[J]. Food Chemistry,2023,405:134935. doi: 10.1016/j.foodchem.2022.134935
|
[72] |
GREENER J G, KANDATHIL S M, MOFFAT L, et al. A guide to machine learning for biologists[J]. Nature Reviews Molecular Cell Biology,2021,23(1):40−55. doi: 10.1038/s41556-020-00618-1
|
[73] |
GUNTUBOINA C, DAS A, MOLLAEI P, et al. Peptide BERT:A language model based on transformers for peptide property prediction[J]. The Journal of Physical Chemistry Letters,2023,14(46):10427−10434. doi: 10.1021/acs.jpclett.3c02398
|
[74] |
ZHANG J, YAN W, ZHANG Q, et al. Umami-BERT:An interpretable BERT-based model for umami peptides prediction[J]. Food Research International,2023,172:113142. doi: 10.1016/j.foodres.2023.113142
|
[75] |
LIU J B, SONG H L, LIU Y, et al. Discovery of kokumi peptide from yeast extract by LC-Q-TOF-MS/MS and sensomics approach[J]. Journal of the Science of Food and Agriculture,2015,95(15):3183−3194. doi: 10.1002/jsfa.7058
|
[76] |
ZHANG G Z, MOUMING. New insight into umami receptor, umami/umami-enhancing peptides and their derivatives:A review[J]. Trends in Food Science & Technology,2019,88:429−438.
|
[77] |
QI L, GAO X, PAN D, et al. Research progress in the screening and evaluation of umami peptides[J]. Comprehensive Reviews in Food Science and Food Safety,2022,21(2):1462−1490. doi: 10.1111/1541-4337.12916
|
[78] |
HIROYUKI M K. Efficient computational model for identification of antitubercular peptides by integrating amino acid patterns and properties[J]. FEBS Letters,2019,593(21):3029−3039. doi: 10.1002/1873-3468.13536
|
[79] |
HASAN M M, MANAVALAN B, KHATUN M S, et al. Prediction of S-nitrosylation sites by integrating support vector machines and random forest[J]. Molecular Omics,2019,15(6):451−458. doi: 10.1039/C9MO00098D
|
[80] |
CHAROENKWAN P, YANA J, NANTASENAMAT C, et al. iUmami-SCM:A novel sequence-based predictor for prediction and analysis of umami peptides using a scoring card method with propensity scores of dipeptides[J]. Journal of Chemical Information and Modeling,2020,60(12):6666−6678. doi: 10.1021/acs.jcim.0c00707
|
[81] |
PHASIT C, JANCHAI Y, NALINI S, et al. iBitter-SCM:Identification and characterization of bitter peptides using a scoring card method with propensity scores of dipeptides[J]. Genomics,2020,112(4):2813−2822. doi: 10.1016/j.ygeno.2020.03.019
|
[82] |
FRITZ F, PREISSNER R, BANERJEE P. Virtual taste:A web server for the prediction of organoleptic properties of chemical compounds[J]. Nucleic Acids Research,2021,49(1):679−684.
|
[83] |
BO W, QIN D, 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
|
[84] |
WU D, LUO D, WONG K Y, et al. POP-CNN:Predicting odor pleasantness with convolutional neural network[J]. IEEE Sensors Journal,2019,19(23):11337−11345. doi: 10.1109/JSEN.2019.2933692
|
[85] |
CARDOSO SCHWINDT V, COLETTO M M, DÍAZ MÓNICA 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
|
[86] |
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
|
[87] |
LIU Q, LUO D, WEN 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
|
[88] |
LEE B K, MAYHEW E J, SANCHEZ-LENGELING B, et al. A principal odor map unifies diverse tasks in olfactory perception[J]. Science,2023,381(6661):999−1006. doi: 10.1126/science.ade4401
|
[89] |
YU H, LIU S, ZHOU Z, et al. Impact of aging microbiome on metabolic profile of natural aging Huangjiu through machine learning[J]. Foods,2023,12(4):906. doi: 10.3390/foods12040906
|
[90] |
潘思慧. 贮藏过程中番茄成熟度的智能化检测方法研究[D]. 镇江:江苏大学, 2017. [PAN S H. Study on intelligent detection method of tomato ripenessduring storage[D]. Zhenjiang:Jiangsu University, 2017.]
PAN S H. Study on intelligent detection method of tomato ripenessduring storage[D]. Zhenjiang: Jiangsu University, 2017.
|
[91] |
孟连君. 基于挥发性成分指纹图谱的白酒储存时间及品质鉴别研究[D]. 无锡:江南大学, 2021. [MENG L J. Identification of storage time and quality of Baijiu based on profiling of volatiles[D]. Wuxi:Jiangnan University, 2021.]
MENG L J. Identification of storage time and quality of Baijiu based on profiling of volatiles[D]. Wuxi: Jiangnan University, 2021.
|
[92] |
MAJCHRZAK T, WOJNOWSKI W, PŁOTKA-WASYLKA J. Classification of polish wines by application of ultra-fast gas chromatography[J]. European Food Research and Technology,2018,244(8):1463−1471. doi: 10.1007/s00217-018-3060-1
|
[93] |
SABILLA S I, SARNO R, TRIYANA K, et al. Deep learning in a sensor array system based on the distribution of volatile compounds from meat cuts using GC–MS analysis[J]. Sensing and Bio-Sensing Research,2020,29:100371. doi: 10.1016/j.sbsr.2020.100371
|
[94] |
SUN D, ZHOU C, 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
|
1. |
解鹏,高岳,吴晨奇,崔保威,王芳,王慧,范娜. 不同种类原料肉对发酵香肠理化品质及风味特性的影响. 肉类研究. 2025(03): 29-34 .
![]() | |
2. |
左敏,纪慧卓,苏礼君,张玉玉,颜文婧,张青川,孔建磊. 人工智能在食品安全中的最新应用及进展. 中国食品学报. 2024(10): 1-13 .
![]() |