| 光谱学与光谱分析 |
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| Detection of Pear Injury Based on Visible-Near Infrared Spectroscopy and Multispectral Image |
| CAO Fang1，WU Di1，ZHENG Jin-tu2，BAO Yi-dan1*，WANG Zun-yi3，HE Yong1 |
1. College of Biosystems Engineering and Food Science，Zhejiang University，Hangzhou 310029，China
2. Ningbo Technology Extension Center for Forestry and Specialty Forest Products, Ningbo 315010, China
3. Department of Scientific Research, Zhejiang Wangli University, Ningbo 315100, China |
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Abstract A new approach to detect the injury degree and time of pear based on visible-near infrared spectroscopy and multispectral image has been proposed. Firstly, visible-near infrared spectroscopy combined with partial least squares (PLS) and least squares-support vector machine (LS-SVM) was used for pear injury degree and time prediction. The result indicated that these two methods both have good performances in predicting pear injury degree in the late period. The LS-SVM method is more accurate in predicting the injury time of light pear injury, but its overall result of injury time prediction is not as good as that for the PLS method. Then, the multispectral image was used to predict the time of pear injury. The result shows that for different degrees of pear injury, the prediction models based on LS-SVM have a better performance with correlation coefficients around 0.85. The result of this study can be used to detect the injury degree and time of pear rapidly and non-destructively, and provide a new approach to pear classification.
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Received: 2010-06-16
Accepted: 2010-09-18
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Corresponding Authors:
BAO Yi-dan
E-mail: ydbao@zju.edu.cn
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