title: [Detection of pear injury based on visible-near infrared spectroscopy and multispectral image].
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 5.85. 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.
authors.affiliation: College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310029, China. kathycf919@yahoo.com.cn
authors.forename: Fang
authors.initials: F
authors.lastname: Cao
authors.forename: Di
authors.initials: D
authors.lastname: Wu
authors.forename: Jin-Tu
authors.initials: JT
authors.lastname: Zheng
authors.forename: Yi-Dan
authors.initials: YD
authors.lastname: Bao
authors.forename: Zun-Yi
authors.initials: ZY
authors.lastname: Wang
authors.forename: Yong
authors.initials: Y
authors.lastname: He
mesh_terms: D005504:Food Analysis / Q000379:methods*; D005638:Fruit; D016018:Least-Squares Analysis; D031989:Pyrus; D019265:Spectroscopy, Near-Infrared; D060388:Support Vector Machine