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Proceedings Paper

Food inspection using hyperspectral imaging and SVDD
Author(s): Faruk Sukru Uslu; Hamidullah Binol; Abdullah Bal
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Paper Abstract

Nowadays food inspection and evaluation is becoming significant public issue, therefore robust, fast, and environmentally safe methods are studied instead of human visual assessment. Optical sensing is one of the potential methods with the properties of being non-destructive and accurate. As a remote sensing technology, hyperspectral imaging (HSI) is being successfully applied by researchers because of having both spatial and detailed spectral information about studied material. HSI can be used to inspect food quality and safety estimation such as meat quality assessment, quality evaluation of fish, detection of skin tumors on chicken carcasses, and classification of wheat kernels in the food industry. In this paper, we have implied an experiment to detect fat ratio in ground meat via Support Vector Data Description which is an efficient and robust one-class classifier for HSI. The experiments have been implemented on two different ground meat HSI data sets with different fat percentage. Addition to these implementations, we have also applied bagging technique which is mostly used as an ensemble method to improve the prediction ratio. The results show that the proposed methods produce high detection performance for fat ratio in ground meat.

Paper Details

Date Published: 17 May 2016
PDF: 6 pages
Proc. SPIE 9864, Sensing for Agriculture and Food Quality and Safety VIII, 98640N (17 May 2016); doi: 10.1117/12.2223938
Show Author Affiliations
Faruk Sukru Uslu, Yildiz Technical Univ. (Turkey)
Hamidullah Binol, Yildiz Technical Univ. (Turkey)
Abdullah Bal, Yildiz Technical Univ. (Turkey)

Published in SPIE Proceedings Vol. 9864:
Sensing for Agriculture and Food Quality and Safety VIII
Moon S. Kim; Kuanglin Chao; Bryan A. Chin, Editor(s)

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