Differentiation Between Organic and Non-Organic Apples Using Diffraction Grating and Image Processing—A Cost-Effective Approach

Nanfeng Jiang, Weiran Song, Hui Wang, Gongde Guo, Yuanyuan Liu

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

As the expectation for higher quality of life increases, consumers have higher demands for quality food. Food authentication is the technical means of ensuring food is what it says it is. A popular approach to food authentication is based on spectroscopy, which has been widely used for identifying and quantifying the chemical components of an object. This approach is non-destructive and effective but expensive. This paper presents a computer vision-based sensor system for food authentication, i.e., differentiating organic from non-organic apples. This sensor system consists of low-cost hardware and pattern recognition software. We use a flashlight to illuminate apples and capture their images through a diffraction grating. These diffraction images are then converted into a data matrix for classification by pattern recognition algorithms, including k-nearest neighbors (k-NN), support vector machine (SVM) and three partial least squares discriminant analysis (PLS-DA)- based methods. We carry out experiments on a reasonable collection of apple samples and employ a proper pre-processing, resulting in a highest classification accuracy of 94%. Our studies conclude that this sensor system has the potential to provide a viable solution to empower consumers in food authentication.
Original languageEnglish
Pages (from-to)1667
JournalSensors
Volume18
Issue number6
DOIs
Publication statusPublished - 23 May 2018

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