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Food quality assessment is a critical aspect of food production and safety, ensuring that products meet both regulatory and consumer standards. Traditional methods such as sensory evaluation, chromatography, and spectrophotometry are widely used but often suffer from limitations, including subjectivity, high costs, and time-consuming procedures. In recent years, the development of electronic nose (e-nose) and electronic tongue (e-tongue) technologies has provided rapid, objective, and reliable alternatives for food quality monitoring. These bio-inspired sensing systems mimic human olfactory and gustatory functions through sensor arrays and advanced data processing techniques, including artificial intelligence and pattern recognition algorithms. The e-nose is primarily used for detecting volatile organic compounds (VOCs) in food, making it effective for freshness evaluation, spoilage detection, aroma profiling, and adulteration identification. Meanwhile, the e-tongue analyzes liquid-phase components and is widely applied in taste assessment, beverage authentication, fermentation monitoring, and contaminant detection. Both technologies are extensively used in the quality control of dairy products, meat, seafood, fruits, beverages, and processed foods. Their ability to provide real-time, non-destructive, and high-throughput analysis makes them valuable tools in the food industry. This review explores the principles, advantages, and applications of e-nose and e-tongue systems in food quality assessment. Additionally, it discusses emerging trends, including IoT-based smart sensing, advances in nanotechnology, and AI-driven data analysis, which are expected to further enhance their efficiency and accuracy. With continuous innovation, these technologies are poised to revolutionize food safety and quality control, ensuring consumer satisfaction and compliance with global standards.
Details
Food products;
Nondestructive testing;
Signal processing;
Food quality;
Contaminants;
Dairy products;
Scientific imaging;
Volatile organic compounds--VOCs;
Chromatography;
Pattern recognition;
Technology;
Seafood;
Electronic tongues;
Fermentation;
Food industry;
Data analysis;
Spoilage;
Quality control;
Electronic noses;
Algorithms;
Real time;
Liquid phases;
Food safety;
Sensory evaluation;
Artificial intelligence;
Data processing;
Food contamination & poisoning;
Standards;
Pattern recognition systems;
Nanotechnology;
Aroma;
Automation;
Food processing;
Monitoring;
Machine learning;
Mass spectrometry;
Quality assessment;
Spectrophotometry;
Odors;
Sensors;
Process controls;
Sensory perception;
Sensor arrays;
Coffee industry;
Microbiota;
Enzymes
; Mayakrishnan Gopiraman 3
; Kim Ick Soo 3
; Seong-Cheol, Kim 4 1 Department of Molecular Analytics, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Thandalam, Chennai 602105, Tamil Nadu, India; [email protected], School of Chemical Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea
2 Advanced Materials Laboratory, Research Department of Physics, MES KeVeeYam College, Valanchery, Malappuram 676552, Kerala, India; [email protected]
3 Nano Fusion Technology Research Group, Institute for Fiber Engineering and Science (IFES), Interdisciplinary Cluster for Cutting Edge Research (ICCER), Shinshu University, Tokida 3-15-1, Ueda, Nagano 386-8567, Japan; [email protected] (G.M.); [email protected] (I.S.K.)
4 School of Chemical Engineering, Yeungnam University, Gyeongsan 38541, Republic of Korea