Artificial intelligence-enabled analysis methods and their applications in food chemistry

被引:0
作者
Gu, Chunyan [1 ]
Wang, Gang [1 ]
Zhuang, Weihua [1 ]
Hu, Jie [1 ]
He, Xun [2 ]
Zhang, Liang [3 ]
Du, Zhao [1 ]
Xu, Xuemei [4 ]
Yin, Minggang [4 ]
Yao, Yongchao [1 ]
Sun, Xuping [2 ]
Hu, Wenchuang [1 ,3 ]
机构
[1] Sichuan Univ, West China Hosp, Precis Med Translat Res Ctr, Frontiers Sci Ctr Dis Related Mol Network,Dept Lab, Chengdu, Peoples R China
[2] Sichuan Univ, West China Hosp, Ctr High Altitude Med, Chengdu, Peoples R China
[3] One Chip Biotechnol Co LTD, Chengdu, Peoples R China
[4] Zigong First Peoples Hosp, Dept Blood Transfus, Zigong, Peoples R China
关键词
Food chemistry; artificial intelligence; machine learning; food safety; food quality control; CLASSIFICATION; QUALITY;
D O I
10.1080/10408398.2025.2521648
中图分类号
TS2 [食品工业];
学科分类号
0832 ;
摘要
Food chemistry is a science that studies the composition, properties, and changes of food at the chemical and molecular levels, as well as their relationships to human health. With the rapid advancement of artificial intelligence (AI) technology, the field of food chemistry has undergone significant transformation, and new development opportunities have emerged. AI provides efficient, precise, and intelligent solutions for food analysis. This review examines the integration of AI technologies with conventional analytical methodologies in food chemistry, focusing on recent advancements in their applications. It elaborates on AI-driven approaches in spectroscopic analysis, chromatography, mass spectrometry, and sensor technology, highlighting their transformative potential in food quality control, identification of bioactive constituents, contaminant detection, nutritional analysis, and novel ingredient design. Through specific case studies, the review demonstrates how AI enhances analytical efficiency and accuracy, providing innovative solutions for future research and practical applications in food chemistry.
引用
收藏
页数:22
相关论文
共 103 条
[51]   Deep learning-based characterization and redesign of major potato tuber storage protein [J].
Luo, Xuming ;
Cao, Lijuan ;
Yu, Langhua ;
Gao, Meng ;
Ai, Ju ;
Gao, Dongli ;
Zhang, Xiaopeng ;
Lucas, William John ;
Huang, Sanwen ;
Xu, Jianfei ;
Shang, Yi .
FOOD CHEMISTRY, 2024, 443
[52]   Simultaneous detection of mixed pesticide residues based on portable Raman spectrometer and Au@Ag nanoparticles SERS substrate [J].
Ma, Lixin ;
Han, En ;
Yin, Limei ;
Xu, Qian ;
Zou, Caixia ;
Bai, Junwen ;
Wu, Wei ;
Cai, Jianrong .
FOOD CONTROL, 2023, 153
[53]   Deep learning accurately predicts food categories and nutrients based on ingredient statements [J].
Ma, Peihua ;
Zhang, Zhikun ;
Li, Ying ;
Yu, Ning ;
Sheng, Jiping ;
McGinty, Hande Kucuk ;
Wang, Qin ;
Ahuja, Jaspreet K. C. .
FOOD CHEMISTRY, 2022, 391
[54]   Application of deep learning for image-based Chinese market food nutrients estimation [J].
Ma, Peihua ;
Lau, Chun Pong ;
Yu, Ning ;
Li, An ;
Sheng, Jiping .
FOOD CHEMISTRY, 2022, 373
[55]   Hyperspectral Microscopy Technology to Detect Syrups Adulteration of Endemic Guindo Santo and Quillay Honey Using Machine-Learning Tools [J].
Machuca, Guillermo ;
Staforelli, Juan ;
Rondanelli-Reyes, Mauricio ;
Garces, Rene ;
Contreras-Trigo, Braulio ;
Tapia, Jorge ;
Sanhueza, Ignacio ;
Jara, Anselmo ;
Lamas, Ivan ;
Troncoso, Jose Max ;
Coelho, Pablo .
FOODS, 2022, 11 (23)
[56]   Digitalisation and Artificial Intelligence for sustainable food systems [J].
Marvin, Hans J. P. ;
Bouzembrak, Yamine ;
Van der Fels-Klerx, H. J. ;
Kempenaar, Corne ;
Veerkamp, Roel ;
Chauhan, Aneesh ;
Stroosnijder, Sanne ;
Top, Jan ;
Simsek-Senel, Gorkem ;
Vrolijk, Hans ;
Knibbe, Willem Jan ;
Zhang, Lu ;
Boom, Remko ;
Tekinerdogan, Bedir .
TRENDS IN FOOD SCIENCE & TECHNOLOGY, 2022, 120 :344-348
[57]   Comparative Study of Several Machine Learning Algorithms for Classification of Unifloral Honeys [J].
Mateo, Fernando ;
Tarazona, Andrea ;
Maria Mateo, Eva .
FOODS, 2021, 10 (07)
[58]   Application of Artificial Intelligence in Food Industry-a Guideline [J].
Mavani, Nidhi Rajesh ;
Ali, Jarinah Mohd ;
Othman, Suhaili ;
Hussain, M. A. ;
Hashim, Haslaniza ;
Abd Rahman, Norliza .
FOOD ENGINEERING REVIEWS, 2022, 14 (01) :134-175
[59]   Ripeness Classification of Bananas Using an Artificial Neural Network [J].
Mazen, Fatma M. A. ;
Nashat, Ahmed A. .
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 2019, 44 (08) :6901-6910
[60]   Machine learning prediction of the degree of food processing [J].
Menichetti, Giulia ;
Ravandi, Babak ;
Mozaffarian, Dariush ;
Barabasi, Albert-Laszlo .
NATURE COMMUNICATIONS, 2023, 14 (01)