Application of artificial intelligence in food quality control has been a new trend, bringing a complete change to the traditional way of food quality control, helping to shorten analysis and detection time and carrying out real time analysis due to nondestructive sample preparation, simpler operations because of using of sensors, collecting a large amount of information thanks to taking all measurement data... This article provides a preliminary view of identification, discrimination and classification of food samples based on using machine learning and deep learning models coupled with analytical data obtained from spectral measurements, using sensors instead of the nose (electronic- nose), camera and image analysis (computer vision) for food quality control purposes such as determining food freshness, authenticating origin as well as detecting adulteration of foods. The published studies show that the application of machine learning models especially in rapid analysis and sample-free analysis has great potential as an alternative to targeted analysis methods with specific analytes in samples.
food quality control, machine learning, deep learning, computer vision, adulteration
[1]. H. Sheikh, C. Prins, and E. Schrijvers, "Artificial Intelligence: Definition and Background," Research for Policy, pp. 15–41, 2023.
[2]. H. Anwar, T. Anwar, and S. Murtaza, "Review on food quality assessment using machine learning and electronic nose system," Biosensors and Bioelectronics: X, vol. 14, pp. 100365–100365, 2023.
[3]. J. Han, T. Li, Y. He, and Q. Gao, "Using Machine Learning Approaches for Food Quality Detection," Mathematical Problems in Engineering, vol. 2022, p. e6852022, 2022.
[4]. P. A. L. Pearce, B. A. Fuchs, and K. L. Keller, "The role of reinforcement learning and value-based decision-making frameworks in understanding food choice and eating behaviors," Frontiers in Nutrition, vol. 9, 2022.
[5]. L. Zhou, C. Zhang, F. Liu, Z. Qiu, and Y. He, "Application of Deep Learning in Food: A Review," Comprehensive Reviews in Food Science and Food Safety, vol. 18, no. 6, pp. 1793–1811, 2019.
[6]. T. Abass, E. O. Itua, T. Bature, and M. A. Eruaga, "Concept paper: Innovative approaches to food quality control: AI and machine learning for predictive analysis," World Journal of Advanced Research and Reviews, vol. 21, no. 3, pp. 823–828, 2024.
[7]. P. B. Pathare, U. L. Opara, and F. A.-J. Al-Said, "Colour Measurement and Analysis in Fresh and Processed Foods: A Review," Food and Bioprocess Technology, vol. 6, no. 1, pp. 36–60, 2012.
[8]. D. Obenland, S. Collin, B. Mackey, J. Sievert, K. Fjeld, and M. L. Arpaia, "Determinants of flavor acceptability during the maturation of navel oranges," Postharvest Biology and Technology, vol. 52, no. 2, pp. 156–163, 2009.
[9]. D. Sun and D. Wen, "Computer vision - An objective, rapid and non-contact quality evaluation tool for the food industry," Journal of Food Engineering, vol. 61, no. 1, pp. 1–2, 2004.
[10]. R. Sekar L, N. Ambika, V. Divya, and T. Kowsalya, "Fruit Classification System Using Computer Vision: A Review," International Journal of Trend in Research and Development (IJTRD), vol. 5, no. 1, 2018.
[11]. N. B. A. Mustafa, K. Arumugam, S. K. Ahmed, and Z. A. M. Sharrif, "Classification of fruits using Probabilistic Neural Networks - Improvement using color features," in TENCON 2011 - 2011 IEEE Region 10 Conference, Bali, Indonesia, pp. 264-269, 2011.
[12]. M. Khojastehnazhand, M. Omid, and A. Tabatabaeefar, "Development of a lemon sorting system based on color and size," African Journal of Plant Science, pp. 122-127, 2011.
[13]. D. M. Agha, "Animal Species Classification Using Just Neural Network," International Journal of Engineering and Information Systems (IJEAIS), vol. 7, no. 9, pp. 20–28, 2023, 2024.
[14]. T. R. Shultz, D. Mareschal, and W. C. Schmidt, "Modeling Cognitive Development on Balance Scale Phenomena," Machine Learning, vol. 16, no. 1/2, pp. 57–86, 1994.
[15]. J. Han, T. Li, Y. He, and Q. Gao, "Using Machine Learning Approaches for Food Quality Detection," Mathematical Problems in Engineering, vol. 2022, p. e6852022, 2022.
[16]. J. Han, T. Li, Y. He, and Q. Gao, "Using Machine Learning Approaches for Food Quality Detection," Mathematical Problems in Engineering, vol. 2022, p. e6852022, 2022.
[17]. V. Doğan, M. Evliya, L. N. Kahyaoglu, and V. Kılıç, "On-site colorimetric food spoilage monitoring with smartphone embedded machine learning," Talanta, vol. 266, no. 1, 2024.
[18]. L. G. Fahad, S. F. Tahir, U. Rasheed, H. Saqib, M. Hassan, and H. Alquhayz, "Fruits and Vegetables Freshness Categorization Using Deep Learning," Computers, Materials & Continua, EBSCOhost, 2022.
[19]. D. Saha and A. Manickavasagan, "Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review," Current Research in Food Science, vol. 4, pp. 28-44, 2021.
[20]. Y. Liu, S. Zhou, W. Han, W. Liu, Z. Qiu, and C. Li, "Convolutional neural network for hyperspectral data analysis and effective wavelengths selection," Analytica Chimica Acta, vol. 1086, pp. 46-54, 2019.
[21]. C. Xia, S. Yang, M. Huang, Q. Zhu, Y. Guo, and J. Qin, "Maize seed classification using hyperspectral image coupled with multi-linear discriminant analysis," Infrared Physics & Technology, vol. 103, 2019.
[22]. P. Baranowski, W. Mazurek, and J. Pastuszka-Woźniak, "Supervised classification of bruised apples with respect to the time after bruising on the basis of hyperspectral imaging data," Postharvest Biology and Technology, vol. 86, pp. 249-258, 2013.
[23]. A. Dacal-Nieto, A. Formella, P. Carrión, E. Vazquez-Fernandez, and M. FernándezDelgado, "Common Scab Detection on Potatoes Using an Infrared Hyperspectral Imaging System," Lecture Notes in Computer Science, pp. 303–312, 2011.
[24]. K. E. Washburn, S. K. Stormo, M. H. Skjelvareid, and K. Heia, "Non-invasive assessment of packaged cod freeze-thaw history by hyperspectral imaging," Journal of Food Engineering, vol. 205, pp. 64-73, 2017.
[25]. H. M. Lalabadi, M. Sadeghi, and S. A. Mireei, "Fish freshness categorization from eyes and gills color features using multi-class artificial neural network and support vector machines," Aquacultural Engineering, vol. 90, 2020.
[26]. A. Alaimahal, S. Shruthi, M. Vijayalakshmi, and P. Vimala, "Detection of Fish Freshness Using Image Processing," International Journal of Engineering Research & Technology, vol. 5, no. 9, 2018.
[27]. E. T. Yasin, I. A. Ozkan, and M. Koklu, "Detection of fish freshness using artificial intelligence methods, 2023.
[28]. D. R. Wijaya, N. F. Syarwan, M. A. Nugraha, D. Ananda, T. Fahrudin, and R. Handayani, "Seafood Quality Detection Using Electronic Nose and Machine Learning Algorithms With Hyperparameter Optimization," in IEEE Access, vol. 11, pp. 62484- 62495, 2023.
[29]. J. Tan and J. Xu, "Applications of electronic nose (e-nose) and electronic tongue (etongue) in food quality-related properties determination: A review," Artificial Intelligence in Agriculture, vol. 4, 2020.
[30]. X. Tian, J. Wang, and S. Cui, "Analysis of pork adulteration in minced mutton using electronic nose of metal oxide sensors," Journal of Food Engineering, vol. 119, no. 4, pp. 744–749, 2013.
[31]. S. M. Szkudlarz and H. H. Jeleń, "Detection of olive oil adulteration with rapeseed and sunflower oils using MOS electronic nose and SMPE-MS," Journal of Food Quality, vol. 33, no. 1, pp. 21–41, 2010.
[32]. M. Śliwińska, P. Wiśniewska, T. Dymerski, W. Wardencki, and J. Namieśnik, "Application of Electronic Nose Based on Fast GC for Authenticity Assessment of Polish Homemade Liqueurs Called Nalewka," Food Analytical Methods, vol. 9, no. 9, pp. 2670–2681, 2016.
[33]. S. Buratti, N. Sinelli, E. Bertone, A. Venturello, E. Casiraghi, and F. Geobaldo, "Discrimination between washed Arabica, natural Arabica and Robusta coffees by using near infrared spectroscopy, electronic nose and electronic tongue analysis," Journal of the Science of Food and Agriculture, vol. 95, no. 11, pp. 2192–2200, 2014.
[34]. Q. Wang, L. Li, W. Ding, et al., "Adulterant identification in mutton by electronic nose and gas chromatography-mass spectrometer," Food Control, vol. 98, pp. 431–438, 2019.
[35]. Z. Jandric, D. Roberts, N. Rathor, A. Abrahim, M. Islam, and A. Cannavan, "Assessment of fruit juice authenticity using UPLC-QTOF MS: A metabolomics approach," Food Chemistry, 2014.
[36]. M. E. Dasenaki and N. S. Thomaidis, "Quality and Authenticity Control of Fruit Juices-A Review," Molecules, vol. 24, no. 6, 2019.