Journal
JOURNAL OF FOOD ENGINEERING
Volume 179, Issue -, Pages 11-18Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jfoodeng.2016.01.002
Keywords
Hyperspectral imaging; Strawberry; Ripeness classification; Texture features; Data fusion
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Funding
- National Key Foundation for Exploring Scientific Instrument of China [2014YQ47037703]
- Zhejiang Provincial Public Welfare Technology Research Projects [2014C32103]
- Natural Science Foundation of China [61273062]
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A hyperspectral imaging system covering two spectral ranges (380-1030 nm and 874-1734 nm) was applied to evaluate strawberry ripeness. The spectral data were extracted from hyperspectral images of ripe, mid-ripe and unripe strawberries. The optimal wavelengths were obtained from spectra of 441.1 -1013.97 and 941.46-1578.13 nm by loadings of principal component analysis (PCA). Pattern texture features (correlation, contrast, entropy and homogeneity) were extracted from the images at optimal wavelengths. Support vector machine (SVM) was used to build classification models on full spectral data, optimal wavelengths, texture features and the combined dataset of optimal wavelengths and texture features, respectively. SVM models using combined datasets performed best among all datasets. SVM models using datasets from hyperspectral images at 441.1-1013.97 nm performed better with classification accuracy over 85%. The overall results indicated that hyperspectral imaging could be used for strawberry ripeness evaluation, and data fusion combining spectral information and spatial information showed advantages in strawberry ripeness evaluation. (C) 2016 Elsevier Ltd. All rights reserved.
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