Abstract
Deep-fried carbohydrate-rich foods items such as potato chips and French fries are one of the most popular snack foods consumed across the globe. In the production of these carbohydrate-rich foods items, a compound known as acrylamide is formed which is carcinogen and mutagen as well. The conventional chemical-based methods for detection of the presence of acrylamide in the deep-fried carbohydrate-rich food items are a time-consuming, destructive process that requires skilled manpower. The present work proposes a deep learning-based computer vision framework for automatic detection of the presence of acrylamide in potato chip samples with and without transfer learning. The performance of proposed six-layer CNN (without transfer learning) has been compared with the performance of the other transfer learned-models, for the present classification task using fivefold cross-validation. Experimental results show that the proposed six-layer CNN classifies the acrylamide-positive and negative samples with an average f1 score of 0.9251, whereas with the transfer learning-based approach, best average f1 score of 0.9644 was achieved. In conclusion, the proposed methodology in the current work is well suited for the acrylamide detection problem and the proposed work also analyses the effectiveness of the transfer learning-based approach when compared with the approach without utilizing the concept of transfer learning.
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Maurya, R., Singh, S., Pathak, V.K. et al. Computer-aided automatic detection of acrylamide in deep-fried carbohydrate-rich food items using deep learning. Machine Vision and Applications 32, 79 (2021). https://doi.org/10.1007/s00138-021-01204-7
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DOI: https://doi.org/10.1007/s00138-021-01204-7