Detection of fraud in ginger powder using an automatic sorting system based on image processing technique and deep learning. (September 2021)

• Record Type: Journal Article
• Authors: Jahanbakhshi, Ahmad; Abbaspour-Gilandeh, Yousef; Heidarbeigi, Kobra; Momeny, Mohammad
• Is Part Of: Computers in biology and medicine. Volume 136(2021)
• Publication Date: 2021-09
• Subjects: Food fraud -- Ginger powder -- Machine vision -- Deep learning -- Convolutional neural networks; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104764
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Ginger is a well-known product in the food and pharmaceutical industries. Ginger is one of the spices which are adulterated for economic gain. The lack of marketability of grade 3 chickpeas (small and broken chickpeas) and their very low price have made them a good choice to be mixed with ginger in powder form and sold in the market. Demand for non-destructive methods of measuring food quality, such as machine vision and the growing need for food and spices, were the main motives to conduct this study. This study classified ginger powder images to detect fraud by improving convolutional neural networks (CNN) through a gated pooling function. The main approach to improving CNN is to use a pooling function that combines average pooling and max pooling. The Batch normalization (BN) technique is used in CNN to improve classification results. We show empirically that the combining operation used increases the accuracy of ginger powder classification compared to the baseline pooling method. For this purpose, 3360 image samples of ginger powder were prepared in 7 categories (pure ginger powder, chickpea powder, 10%, 20%, 30%, 40%, and 50% fraud in ginger powder). Moreover, MLP, Fuzzy, SVM, GBT, and EDT algorithms were used to compare the proposed CNN results with other classifiers. The results showed that using batch normalization based on gated pooling, the proposed CNN was able to grade the images of ginger powder with 99.70% accuracy compared to other classifiers. Therefore, it can be said that the CNN method and image processing technique effectively increase marketability, prevent ginger powder fraud, and promote traditional methods of ginger powder fraud detection. Highlights: Adulteration is damaging to the economy, consumer confidence, and is a potential health risk. Machine vision system and deep learning are fast and robust techniques to detect adulteration in herbs and spices. The data augmentation technique is used to expand the existing data set for increasing the accuracy of proposed CNN network. We validated that the gated pooling gives better performance than baseline pooling (Average pooling). The best performance for the proposed CNN model based on gated pooling was achieved with 99.70% accuracy.