APPLYING MACHINE LEARNING TO IDENTIFY COUNTERFEIT FOODS
DOI:
https://doi.org/10.37943/13TFMT6695Keywords:
machine learning, classification, counterfeit foods, image and text recognition, recognition methods, food fraud.Abstract
Currently, the shelves of shops and supermarkets are filled with food that people consume daily, with many products coming from abroad. However, are all these products useful for the human body, and do they meet the standards? In this article, we will talk about how to identify low-quality products using modern machine learning. Recognition and classification of images and text based on machine learning can be a key technology in the fight against low[1]quality food. Automatic image and text recognition and classification of product information enable end customers to identify counterfeit products accurately and quickly by comparing them to trained templates. However, it is clear that this does not apply to all food processing enterprises. In food production, low-quality and non-standard products are used to reduce the cost of the product. Manufacturers can change their products by replacing higher quality products with lower quality ones. They may use confusing terms on the label to mislead you. When buying and serving counterfeit products, consumers suffer in different ways. First, they may not be getting the nutrients they need, adulterated foods may not be safe for their health, and may also be an economic loss for consumers. We evaluate the technical feasibility of the components of this food fraud detection architecture using a real-world scenario, including machine learning models to distinguish multiple products from each other. It allows you to control the circulation of food products at the state level, thereby protecting the end consumer from purchasing low-quality and potentially dangerous goods. In this article, we used the MobileNetV2 model and multiclass classification and evaluated the model we received from different angles.
References
AL-Mamun, M., Chowdhury, T., Biswas, B., & Absar, N. (2018). Food Poisoning and Intoxication: A Global Leading Concern for Human Health. Food Safety and Preservation, 307–352. https://doi.org/10.1016/b978-0-12-814956-0.00011-1
Zeng, G. (2017, October). Fruit and vegetables classification system using image saliency and convolutional neural network. In 2017 IEEE 3rd Information Technology and Mechatronics Engineering Conference (ITOEC) (pp. 613-617). IEEE. https://doi.org/10.1109/ITOEC.2017.8122370
Banti, M. (2020). Food adulteration and some methods of detection, review. International Journal of Nutrition and Food Sciences, 9(3), 86–94. https://doi.org/10.11648/j.ijnfs.20200903.13
Zenkov. D. et al. (2019, January 1). DSpace at Saint Petersburg State University: Machine learning methods in the problem of image recognition. DSpace at Saint Petersburg State University: Machine Learning Methods in the Problem of Image Recognition. http://hdl.handle.net/11701/25909
Atienza, R. (2020). Advanced Deep Learning with TensorFlow 2 and Keras (2nd ed.). Packt Publishing. Retrieved from https://www.perlego.com/book/1388452/advanced-deep-learning-withtensorflow-2-and-keras-apply-dl-gans-vaes-deep-rl-unsupervised-learning-object-detection-andsegmentation-and-more-2nd-edition-pdf (Original work published 2020)
Ganegedara, T. (2018). Natural Language Processing with TensorFlow (1st ed.). Retrieved from https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwiKmYDNqcn-AhXrpYsKHUx8D94QFnoECA4QAQ&url=https%3A%2F%2Fpdfcoffee.com%2Fdownload%2Fnaturallanguage-processing-tensorflowpdf-4-pdf-free.html&usg=AOvVaw2qehScB8cUSrm4e8E_Ph9A
Wilson, J.N., & Ritter, G.X. (2000). Handbook of computer vision algorithms in image algebra (2nd ed.). CRC press. 145-153. https://doi.org/10.1201/9781420042382
Podstawka, E., Światłowska, M., Borowiec, E., & Proniewicz, L.M. (2007). Food additives characterization by infrared, Raman, and surface-enhanced Raman spectroscopies. Journal of Raman Spectroscopy: An International Journal for Original Work in all Aspects of Raman Spectroscopy, Including Higher Order Processes, and also Brillouin and Rayleigh Scattering, 38(3), 356-363. https://doi.org/10.1002/jrs.1653
Hemanth, D.J., & Smys, S. (Eds.). (2018). Computational vision and bio inspired computing (Vol. 28). Springer. https://doi.org/10.1007/978-3-319-71767-8
Aung, M.M., & Chang, Y.S. (2014). Traceability in a food supply chain: Safety and quality perspectives. Food control, 39, 172-184. https://doi.org/10.1016/j.foodcont.2013.11.007
Olaf Ronneberger, Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Lecture Notes in Computer Science, 9351, 234-241. https://doi.org/10.1007/978-3-319-24574-4_28
Uddin, S.M.K., Hossain, M.M., Chowdhury, Z.Z., & Johan, M.R. (2021). Detection and discrimination of seven highly consumed meat species simultaneously in food products using heptaplex PCR RFLP assay. Journal of Food Composition and Analysis, 100, 103938. https://doi.org/10.1016/j.jfca.2021.103938
Arcuri, E.F., El Sheikha, A.F., Rychlik, T., Piro-Métayer, I., & Montet, D. (2013). Determination of cheese origin by using 16S rDNA fingerprinting of bacteria communities by PCR–DGGE: Preliminary application to traditional Minas cheese. Food Control, 30(1), 1-6. https://doi.org/10.1016/j.foodcont.2012.07.007
Ouyang, Q., Zhao, J., & Chen, Q. (2014). Instrumental intelligent test of food sensory quality as mimic of human panel test combining multiple cross-perception sensors and data fusion. Analytica chimica acta, 841, 68-76. https://doi.org/10.1016/j.aca.2014.06.001
Zou, S., Chen, W., & Chen, H. (2020). Image classification model based on deep learning in internet of things. Wireless Communications and Mobile Computing, 2020, 1-16. https://doi.org/10.1155/2020/6677907
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