Discrimination of black walnut shell and pulp in hyperspectral fluorescence imagery using Gaussian kernel function approach

• Published in: Journal of food engineering Vol. 81; no. 1; pp. 108 - 117
• Main Authors: Jiang, Lu, Zhu, Bin, Rao, Xiuqin, Berney, Gerald, Tao, Yang
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.07.2007; Elsevier
• Subjects: automatic detection; Biological and medical sciences; Cross-validation; fluorescence; Food engineering; Food industries; food processing; food processing quality; Fruit and vegetable industries; Fundamental and applied biological sciences. Psychology; Gaussian Kernel; General aspects; hulls; hyperspectral imagery; postharvest technology; pulp; sorting; Support vector machine (SVM); Walnut pulp; Walnut shell; walnuts; Walnut pulp; Support vector machine (SVM); Walnut shell; Cross-validation; Gaussian Kernel; Nuts; Support; Fluorescence; Cross validation; Imagery; Pulp; Discrimination; Juglandaceae; Walnut; Dicotyledones; Angiospermae; Juglans nigra; Spermatophyta; Vector

Automated discrimination between walnut shell and pulp has become an imperative task in the walnut postharvest processing industry in the US. During the last several years, hyperspectral fluorescence imaging has been widely used to inspect agricultural products for quality and safety due to its full...