中文摘要：

为实现绿茶杀青叶料含水率的快速无损检测,基于可见-近红外光谱分析建立含水率的预测模型。使用FieldSpec 3型便携式地物光谱仪,采集192个杀青叶料样品的漫反射光谱信息,基于X-Y共生距离的样本划分算法SPXY,确定144个样本的校正集和48个样本的预测集。进行一阶微分和移动平滑滤波预处理后,采用相关系数法优选出11个特征波段,建立了含水率检测的偏最小二乘回归、主成分回归、人工神经网络及其组合的模型。结果表明,选用5个主成分的偏最小二乘回归模型最佳,其校正和预测模型的相关系数分别为0.990和0.819,均方根误差分别为0.011和0.037,预测含水率的平均相对误差为3.30%。

英文摘要：

To determine the moisture content in de-enzyming green tea leaves rapidly and nondestructively,prediction models were established based on visible-near infrared spectroscopy.Diffuse reflection spectra of 192 samples were collected with a portable field spectrometer（FieldSpec 3,ASD）,among which 144 samples were partitioned to a calibration set and 48 samples to a prediction set using the sample set partitioning method based on joint X-Y distance.11 sensitive bands were selected with correlation coefficient method,and then moisture content models of partial least squares and principal component regression,artificial neural network and their combination were established with the preprocessing methods of the first derivative and moving average filter.The model comparison showed that the prediction model of partial least squares regression was the best when 5 principal components were adopted.The calibration and prediction correlation coefficients were 0.990 and 0.819 respectively,and the root mean square errors of calibration and prediction were 0.011 and 0.037 respectively,and the mean error of predicted moisture content was 3.30%.