基于可见-近红外光谱和多光谱成像技术的梨损伤检测研究

提出了利用可见-近红外光谱技术和多光谱成像技术检测鸭梨损伤随时间及程度变化的新方法.利用可见-近红外光谱技术,分别结合偏最小二乘(panial least squares,PLS)和最小二乘支持向量机(least squares-support vector machine,LS-SVM)方法对鸭梨受损程度和受损天数进行预测.结果表明,两种方法在鸭梨损伤后期对损伤程度的判别均具有较好的效果;LS-SVM方法对鸭梨轻度损伤的损伤天数的预测精度较高,但重度损伤天数的预测效果不如PLS方法.然后利用多光谱图像预测鸭梨受损天数.研究发现,利用LS-SVM建立的模型预测效果较稳定,预测结果相关系数均在0.85左右.说明利用可见-近红外光谱分析技术和多光谱成像技术能够快速无损地检测出鸭梨的损伤程度及时间,为鸭梨检测提供了一种新方法.

Detection of pear injury based on visible-near infrared spectroscopy and multispectral image

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.