This episode is a deep dive into proposed method for detecting adulterants in milk using a convolutional neural network (CNN) trained on images of milk evaporation patterns.
Adulterants, such as water, urea, ammonium sulfate, and oil, create unique patterns during evaporation that are analyzed by the CNN to classify milk samples as adulterated or unadulterated.
The study focuses on the effectiveness of different regularization schemes, including implicit regularization through data augmentation, for improving the CNN's accuracy. Results show that the model trained with implicit regularization achieves the best performance, demonstrating its potential for real-time detection of milk adulteration.

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