A least-squares support vector machine (LS-SVM) based on fractal analysis and CIELab parameters for the detection of browning degree on mango (Mangifera indica L.)

Authors:
Hong Zheng;Hongfei Lu
Affiliations:
College of Chemistry and Life Science, Zhejiang Normal University, Jinhua 321004, China;College of Chemistry and Life Science, Zhejiang Normal University, Jinhua 321004, China
Venue:
Computers and Electronics in Agriculture
Year:
2012

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Abstract

This paper introduces a least-squares support vector machine (LS-SVM) classifier to detect the degree of browning on mango fruits as a function of fractal dimension (FD) and L^*a^*b^* values. Our results showed that the best classification accuracy of browning degree was up to 100% using the LS-SVM classifier based on FD and L^*a^*b^* (@c=6.13, @s^2=9.36). However, the correct classification rates of 85.19% and 88.89% were achieved for the LS-SVM models based on FD (@c=1.13, @s^2=5.52) and based on L^*a^*b^* (@c=6.68, @s^2=2.44), respectively. Therefore, this study indicated the possibility of developing a potentially useful classification tool using the LS-SVM combined with FD and L^*a^*b^* values for classifying the degree of browning on mango fruits during processing, storage and distribution.