基于近红外光谱的水蜜桃采摘期的鉴别方法

提出了一种利用近红外漫反射光谱技术结合光纤传感技术建立水蜜桃采摘期的鉴别方法.从无锡阳山镇的某大棚采摘了距最佳采摘期天数为3,2,1以及处于最佳采摘期的水蜜桃各48个,用近红外光谱仪对样品进行了光谱采集.对原始光谱进行平滑、一阶微分和多元散射校正预处理,采用主成分分析(PCA)结合偏最小二乘(PLS)法建立了水蜜桃采摘期的鉴别模型.研究显示:一阶微分和平滑组合预处理后的鉴别模型效果最好,校正集模型和预测集模型的决定系数分别为0.9279和0.9138;模型的内部交叉验证均方差(RMSECV)和预测均方根偏差(RMSEP)分别为0.3003和0.3349;水蜜桃样品校正集和预测集的鉴别正确率分别为95.13%和93.75%.结果表明:利用近红外漫反射光谱技术对水蜜桃采摘期的鉴别具有很好的应用前景.

Identification method of picking period of juicy peaches based on near infrared spectroscopy

A near infrared diffuse spectroscopy technology combined with optical fiber sensing technology is used to explore nondestructive testing methods to identify the picking period of juicy peaches. 48 each of juicy peaches of 3,2 and 1 days from the best picking period and the day in the best picking period from a greenhouse in Yangshan town of Wuxi. The samples are collected by near infrared spectrometer. Three pre-processing methods i. e. smoothing,first derivative and mutiplicative scatter correction are used. Identification models of picking period are developed by principal component analysis(PCA) and partial least square(PLS) regression. The results show that first derivative and smoothing combination preprocessing construct the best predicted model. The correlation coefficient of calibration and validation model are 0. 9279 and 0. 9138 respectively. The root mean square error of cross validation(RMSECV) and the root mean square error of prediction (RMSEP) are 0. 3003 and 0. 3349 respectively. The recognition rates in the calibration set and prediction set of juicy peaches are 95. 13% and 93. 75% respectively. Results shows that the method of using the near infrared spectroscopy technology to identify the picking period of juicy peaches has a very good application prospect.