基于可见/近红外高光谱的八角茴香与莽草无损鉴别

基于高光谱成像技术提出了一种八角茴香与其伪品莽草的快速鉴别方法。实验采集400~1000nm范围的高光谱数据，依据样本和背景像素点的光谱特征差异，选择850nm和450nm下的图像并进行差运算，结合阈值法去除背景信息，利用线性拉伸去除样本高度引入的阴影噪声像素点，再结合二值图像区域标记法从样本高光谱数据中自动提取其平均光谱数据；利用平均光谱数据，采用连续投影算法（Successive projections algorithm, SPA）选取了4个最优波长：533、617、665、807nm；基于最优波长下的光谱数据，建立了偏最小二乘判别（Partial least square discrimination analysis，PLSDA）模型，模型对鉴别八角和莽草的总体准确率为98.4%；利用所建多光谱模型对外部验证集数据进行预测，总体分类准确率为97.9%。利用常规图像处理技术同时对外部验证集数据进行处理，并对两种技术方法进行了比较，结果表明，依托高光谱成像技术建立的八角和莽草辨识的多光谱分析方法简单、高效，易于实现动态在线便携式检测。

Nondestructive Identification of Star Anise and Shikimmi by Visible/Near Infrared Hyperspectral Images

Based on hyperspectral imaging technique, an identification method of star anise and its counterfeit shikimmi was proposed. The hyperspectral data in the range of 400~1000nm were collected and analyzed. Firstly, according to the different spectral characteristics of samples and background pixels, images at 850nm and 450nm were selected and subtracted, and background information was removed by threshold method. Linear stretching method was further used to remove shadow noise pixels due to sample height. Combined with the region labeling method of binary image, the automatic extraction of average spectral data from sample hyperspectral data was realized. Then based on average spectral data, four optimal wavelengths were selected by successive projections algorithm (SPA), i.e., 533nm, 617nm, 665nm and 807nm. Based on the spectral data at the optimal wavelength, a partial least square discrimination analysis (PLSDA) model was established. The classification accuracy of star anise and shikimmi was 98.4%. Using the established multi-spectral model to predict the external validation set data, the overall classification accuracy was 97.9%, and the visualization results were good. Finally, the conventional image processing technology was also used to process the same external verification set data, and the results and advantages of the two methods were compared. The results showed that the multispectral analysis method based on hyperspectral imaging technique was simple, efficient and easy to realize dynamic on-line or portable detection applications. The proposed method can provide a theoretical basis for the development of on-line or portable detection instruments.