Abstract
Identification of adulteration in food products is a challenging task. This paper presents a deep convolutional neural network (CNN)-based classification model for classification between adulterated and unadulterated turmeric powder. Metanil yellow is used as adulterant in this work. The consolidated results show that the presented CNN model can provide up to 98.5% classification accuracy. Therefore, the presented computer vision technique can be a possible alternative approach to adulterant identification in turmeric powder.
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Mandal, D., Chatterjee, A., Tudu, B. (2021). Convolution Neural Network-Driven Computer Vision System for Identification of Metanil Yellow Adulteration in Turmeric Powder. In: Hassanien, A.E., Bhattacharyya, S., Chakrabati, S., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 1286. Springer, Singapore. https://doi.org/10.1007/978-981-15-9927-9_14
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DOI: https://doi.org/10.1007/978-981-15-9927-9_14
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Online ISBN: 978-981-15-9927-9
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