Image processing based acrylamide detection from fried potato chip images using continuous wavelet transform

Fast foods like potato chips and French fries are very common and easily available. The preparation process of such food items is a detrimental factor in deciding if the item is suitable for consumption. Identification of presence of toxic substances like acrylamide in potato chips through conventional methods are time consuming and destructive and needs trained manpower. In the proposed work, an automatic image processing based technique is proposed to detect presence of acrylamide in fried potato chips. The potato chip area is segmented from its background followed by extraction of discriminatory features in the continuous wavelet transform domain using Morlet wavelet. The discriminatory features are analysed strategically and fed to LOOCV based Support Vector Machine classifier to identify presence of acrylamide in the potato chips. The proposed method has an accuracy of 98.33% with 100% specificity. Convincing results and fast computational time indicates that the proposed work can be used for development of non-destructive real-time applications for food quality monitoring.