TY - JOUR
T1 - Differentiation Between Organic and Non-Organic Apples Using Diffraction Grating and Image Processing—A Cost-Effective Approach
AU - Jiang, Nanfeng
AU - Song, Weiran
AU - Wang, Hui
AU - Guo, Gongde
AU - Liu, Yuanyuan
PY - 2018/5/23
Y1 - 2018/5/23
N2 - 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.
AB - 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.
U2 - doi.org/10.3390/s18061667
DO - doi.org/10.3390/s18061667
M3 - Article
VL - 18
SP - 1667
JO - Sensors
JF - Sensors
IS - 6
ER -