期刊
JOURNAL OF FOOD ENGINEERING
卷 116, 期 1, 页码 45-49出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.jfoodeng.2012.11.014
关键词
Insect; Vegetable soybean; Statistical feature; Hyperspectral transmittance imaging; Support vector data description
资金
- National Natural Science Foundation of China [60805014]
- Natural Science Foundation of Jiangsu Province (China) [BK2011148]
- Postdoctoral Science Foundation of China [2011M500851]
- Fundamental Research Funds for the Central Universities [JUSRP21132]
- 111 Project [B12018]
Insects in vegetable soybean products pose potential hazard to consumers, thus making the food industry liable for economic losses. The objective of the current study is to develop a hyperspectral imaging technique for detecting insect-damaged vegetable soybeans. Hyperspectral transmission images were acquired from normal and insect-damaged vegetable soybeans over the spectral region between 400 nm and 1000 nm for 100 vegetable soybean pods (225 beans). Four statistical image features (minimum, maximum, mean, and standard deviation) were extracted from the images for classification and given as input to a discriminant classifier. The support vector data description (SVDD) classifier achieved 100% calibration accuracy. SVDD achieved 97.3% and 87.5% accuracies for normal and insect-damaged samples, respectively, with a 95.6% overall classification accuracy, for the investigated independent test samples. Therefore, the hyperspectral transmittance technique can discriminate insect-damaged vegetable soybeans. (c) 2012 Elsevier Ltd. All rights reserved.
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