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

来源：

CRITICAL REVIEWS IN FOOD SCIENCE AND NUTRITION | 2025年

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

页数：22

共 103 条

[1] Rapid and ultrasensitive detection of thiram and carbaryl pesticide residues in fruit juices using SERS coupled with the chemometrics technique [J].

Adhikari, Samir ;

Joshi, Rahul ;

Joshi, Ritu ;

Kim, Minjun ;

Jang, Yudong ;

Tufa, Lemma Teshome ;

Gicha, Birhanu Bayissa ;

Lee, Jaebeom ;

Lee, Donghan ;

Cho, Byoung-Kwan .

FOOD CHEMISTRY, 2024, 457

[2] Principles and recent advances in electronic nose for quality inspection of agricultural and food products [J].

Ali, Maimunah Mohd ;

Hashim, Norhashila ;

Abd Aziz, Samsuzana ;

Lasekan, Ola .

TRENDS IN FOOD SCIENCE & TECHNOLOGY, 2020, 99 (99) :1-10

[3] Electronic nose coupled with artificial neural network for classifying of coffee roasting profile [J].

Astuti, Suryani Dyah ;

Wicaksono, Ihsan Rafie ;

Soelistiono, Soegianto ;

Annissa, Perwira ;

Permatasari, Dyah ;

Yaqubi, Ahmad Khalil ;

Susilo, Yunus ;

Putra, Cendra Devayana ;

Syahrom, Ardiyansyah .

SENSING AND BIO-SENSING RESEARCH, 2024, 43

[4] Artificial Intelligence to Improve the Food and Agriculture Sector [J].

Ben Ayed, Rayda ;

Hanana, Mohsen .

JOURNAL OF FOOD QUALITY, 2021, 2021

[5] Secure Identification, Traceability and Real-Time Tracking of Agricultural Food Supply During Transportation Using Internet of Things [J].

Bhutta, Muhammad Nasir Mumtaz ;

Ahmad, Muneer .

IEEE ACCESS, 2021, 9 :65660-65675

[6] GC-MS Fingerprints Profiling Using Machine Learning Models for Food Flavor Prediction [J].

Bi, Kexin ;

Zhang, Dong ;

Qiu, Tong ;

Huang, Yizhen .

PROCESSES, 2020, 8 (01)

[7] Random forests [J].

Breiman, L .

MACHINE LEARNING, 2001, 45 (01) :5-32

[8] Toward the Development of Combined Artificial Sensing Systems for Food Quality Evaluation: A Review on the Application of Data Fusion of Electronic Noses, Electronic Tongues and Electronic Eyes [J].

Calvini, Rosalba ;

Pigani, Laura .

SENSORS, 2022, 22 (02)

[9] Prediction of specialty coffee flavors based on near-infrared spectra using machine- and deep-learning methods [J].

Chang, Yu-Tang ;

Hsueh, Meng-Chien ;

Hung, Shu-Pin ;

Lu, Juin-Ming ;

Peng, Jia-Hung ;

Chen, Shih-Fang .

JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE, 2021, 101 (11) :4705-4714

[10] Health-Aware Food Recommendation Based on Knowledge Graph and Multi-Task Learning [J].

Chen, Yi ;

Guo, Yandi ;

Fan, Qiuxu ;

Zhang, Qinghui ;

Dong, Yu .

FOODS, 2023, 12 (10)

← 1 2 3 4 5 6 7 8 9 10 →