基于高光谱技术的茶鲜叶含水率检测与分析

茶鲜叶含水率是茶叶加工业中衡量茶叶品质的一个重要指标。为了实现茶叶加工过程中茶鲜叶含水率的快速检测,本文提出了一种应用高光谱技术分析茶鲜叶含水率的无损检测方法。通过对茶鲜叶高光谱图像感兴趣区域光谱数据的提取,利用4种不同的算法对原始数据进行预处理,采用逐步回归分析法对预处理后的数据提取特征波长,并采用多元线性回归法、偏最小二乘回归建立特征波长和茶鲜叶含水率定量分析模型。研究结果表明,经过卷积平滑处理后的正交信号校正的预处理结合逐步回归分析法所建立的偏最小二乘回归茶鲜叶含水率预测效果最佳,模型校正集、交叉验证集和预测集的相关系数分别为0.8977、0.8342和0.7749,最小均方根误差分别为0.0091、0.0311和0.0371。由此可见,高光谱技术能有效的实现茶鲜叶含水率的检测,这为茶叶加工业中衡量茶叶品质提供了新的检测方法。

Detection and Analysis of Moisture Content in Fresh Tea Leaves Based on Hyperspectral Technology

Moisture content in fresh tea leaves is an important index influencing tea quality during processing. In order to rapidly detect moisture content in tea during processing, a nondestructive method was introduced in this paper. Firstly, hyperspectral image data were captured from the fresh tea leaves. Secondly, four kinds of algorithms were used to preprocess the original data. Thirdly, the characteristic wavelength was extracted using stepwise regression analysis. Finally, a quantitative analysis model of the characteristic wavelength and moisture content in the fresh tea leaves was developed by the multiple linear regression and partial least squares regression. Experimental results showed that the best predicted effect of the partial least squares regression was obtained by the pretreatment of orthogonal signal correction after convolution smoothing and stepwise regression analysis. The correlation coefficients of the model calibration set, cross-validation set and prediction set were 0.8977, 0.8342 and 0.7749, respectively. The minimum root mean square errors were 0.0091, 0.0311 and 0.0371, respectively. Thus, hyperspectral technology could effectively detect the moisture content in fresh tea leaves, which would be useful in detecting quality changes in tea processing industry.