New Chemometrics, Foodomics and A.I. Technology in Food Analysis
Main Article Content
Keywords
foodomics, chemometrics, artificial intelligence, food analysis, machine learning
Abstract
Against the backdrop of increasingly complex global food supply chains and growing consumer demands for food quality and safety, traditional targeted detection methods can no longer meet the needs of high-throughput, non-destructive and systematic food analysis. The integrated development of foodomics, chemometrics and artificial intelligence (AI) has emerged as a pivotal research direction in this field. This paper aims to systematically sort out the theoretical framework and technical evolution of the three technologies, summarise their integrated application scenarios, analyse core limitations and propose future development directions. Through a systematic literature review, this study elaborates on the underlying principles and technical logic of each technology, and compares their technical boundaries, performance characteristics and complementary relationships in typical food analysis tasks. The results demonstrate that the three technologies have significant theoretical complementarity and have formed mature application paths in food authenticity traceability, quality evaluation and flavour component analysis. Nevertheless, their integrated application is still constrained by bottlenecks, including uneven data quality, limited model generalisation and insufficient technical standardisation. This study clarifies the integrated development logic of relevant technologies and provides valuable references for food analysis research, industrial practice, food safety supervision and the high-quality development of the food industry.
References
- [1]Class, L.-C., Kuhnen, G., Rohn, S., & Kuballa, J. (2021). Diving deep into the data: A review of deep learning approaches and potential applications in foodomics. Foods, 10(8), 1803. https://doi.org/10.3390/foods10081803
- [2]Rocchetti, G., Montesano, D., Bordoni, A., & Capozzi, F. (2025). AI-powered advances in data handling for enhanced food analysis: From chemometrics to machine learning. Foods, 14(19), 3415. https://doi.org/10.3390/foods14193415
- [3]Cifuentes, A. (2022). Foodomics: Analytical opportunities and challenges. Journal of Agricultural and Food Chemistry, 70(11), 3357–3367. https://doi.org/10.1021/acs.jafc.1c08018
- [4]Zhang, S., Chen, J., Gao, F., et al. (2024). Foodomics as a tool for evaluating food authenticity and safety from field to table: A review. Foods, 14(1), 15. https://doi.org/10.3390/foods14010015
- [5]Garcia, M. et al. (2025). Techniques used in foodomics. Food Science & Nutrition.
- [6]Fraga, C. G. et al. (2024). Foodomics: A sustainable approach for the specific nutrition and diets for human health. Food Science & Nutrition.
- [7]Costa, C. et al. (2026). Sensors and mass spectrometry connection for food analysis: A systematic review of methodological synergies. Chemosensors.
- [8]Barabasi, A. L. et al. (2026). Quantitative food compounds enable dietary ontology referencing across 500 foods and human plasma. Analytical Chemistry.
- [9]Djoumbou-Feunang, Y. et al. (2024). Chemical multiverse and diversity of food chemicals. Journal of Chemical Information and Modeling.
- [10]Taylor, A. J. et al. (2023). Data-driven elucidation of flavor chemistry. Journal of Agricultural and Food Chemistry.
- [11]Rodriguez, J. et al. (2022). An overview on the application of chemometrics tools in food authenticity and traceability. Foods.
- [12]Omar, M. et al. (2025). Integration of spectroscopy and chemometric analysis for food authentication: A review. International Food Research Journal.
- [13]Li, Y. et al. (2023). Current application of advancing spectroscopy techniques in food analysis: Data handling with chemometric approaches. Foods.
- [14]Wang, X. et al. (2025). A comprehensive review on minimally destructive quality and safety assessment of agri-food products: Chemometrics-coupled mid-infrared spectroscopy. Foods.
- [15]Liu, H. et al. (2024). Rapid and nondestructive determination of oil content and distribution of potato chips using hyperspectral imaging and chemometrics. ACS Food Science & Technology.
- [16]Kelly, S. et al. (2022). Food authentication of almonds (Prunus dulcis Mill.). Fast origin analysis with laser ablation inductively coupled plasma mass spectrometry and chemometrics. Journal of Agricultural and Food Chemistry.
- [17]Diaconu, A. et al. (2025). Quantification of paprika adulteration using visible/near-infrared spectroscopy and machine learning. Journal of Food Science and Technology.
- [18]Almeida, M. et al. (2025). Rapid authentication of plant-based milk alternatives by coupling portable Raman spectroscopy with machine learning. Journal of AOAC INTERNATIONAL.
- [19]Deng, W. et al. (2022). UV fingerprinting approaches for quality control analyses of food and functional food coupled to chemometrics. Molecules.
- [20]Loutfi, A. et al. (2023). Review on sensor array-based analytical technologies for quality control of food and beverages. Sensors.
- [21]Alkanan, Z. T., Altemimi, A. B., & Hashemi, S. M. B. (2025). Exploring the frontiers of food science: A comprehensive review of advanced magnetic resonance applications in food analysis. Food Science & Nutrition. https://doi.org/10.1002/fsn3.70643
- [22]Nicolaï, B. et al. (2025). Comparison between chemometrics and machine learning for the prediction of macronutrients in cheese using imaging spectroscopy. Journal of Food Engineering.
- [23]Zhou, W. et al. (2026). Deep learning-based image recognition for food science and technology: End-to-end workflows and domain-specific solutions. Comprehensive Reviews in Food Science and Food Safety.
- [24]Zhang, L. et al. (2021). A portable NIR-system for mixture powdery food analysis using deep learning. Infrared Physics & Technology.
- [25]Wang, Q. et al. (2026). A review of high-throughput optical sensors for food detection based on machine learning. Sensors.
- [26]Moor, C. et al. (2026). The application of artificial intelligence in food fraud and adulteration analysis: A review. Critical Reviews in Food Science and Nutrition.
- [27]Spink, J. et al. (2026). The role of artificial intelligence in combating food fraud: A systematic literature review. Critical Reviews in Food Science and Nutrition.
- [28]Stella, S. et al. (2025). Revolutionizing fresh food quality control in supply chains with machine learning: Current advances and future challenges. Comprehensive Reviews in Food Science and Food Safety.
- [29]Kurek, M. et al. (2026). Deep learning models for shelf-life prediction and regulation of various foods: A systematic review. Journal of Food Science.
- [30]Kaur, R. et al. (2025). An XAI-enabled 2D-CNN model for non-destructive detection of natural adulterants in the wonder hot variety of red chilli powder. Sustainable Food Technology.
- [31]Singh, P. et al. (2026). Rapid detection of milk adulteration using AI-driven portable colorimetric spectroscopy. Scientific Reports.
- [32]Ke, Q., Zhang, J., Huang, X., Kou, X., & Zhang, D. (2026). Machine learning unveils three layers of food complexity. npj Science of Food.
- [33]Zhang, H. et al. (2025). Artificial intelligence and food flavor: How AI models are shaping the future and revolutionary technologies for flavor food development. Comprehensive Reviews in Food Science and Food Safety.
- [34]Van der Putten, P. et al. (2026). Practical guide for food scientists to build AI: Data, algorithms, and applications. Food Chemistry.
- [35]Wolfert, J. et al. (2025). Domain knowledge, just evaluation, and robust data standards are required to advance AI in food science. Trends in Food Science & Technology.
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