近红外高光谱图像的宁夏枸杞产地鉴别  

Geographical Origin Identification of Lycium Barbarum Using Near-Infrared Hyperspectral Imaging

作　　者：王磊[1,2] 覃鸿 李静[3] 张小波 于丽娜[1,2] 李卫军[1,2] 黄璐琦 WANG Lei;QIN Hong;LI Jing;ZHANG Xiao-bo;YU Li-na;LI Wei-jun;HUANG Lu-qi(Institute of Semiconductors,Chinese Academy of Sciences,Beijing 100083,China;Center of Materials Science and Optoelectronics Engineering&School of Microelectronics,University of Chinese Academy of Sciences,Beijing 100049,China;State Key Laboratory Breeding Base of Dao-di Herbs,National Resource Center for Chinese Materia Medica,China Academy of Chinese Medical Sciences,Beijing 100700,China;State Key Laboratory Breeding Base of Dao-di Herbs,China Academy of Chinese Medical Sciences,Beijing 100700,China)

机构地区：[1]中国科学院半导体研究所高速电路与神经网络实验室,北京100083 [2]中国科学院大学材料科学与光电子工程中心,微电子学院,北京100049 [3]中国中医科学院中药资源中心道地药材国家重点实验室培育基地,北京100700 [4]中国中医科学院道地药材国家重点实验室培育基地,北京100700

出　　处：《光谱学与光谱分析》2020年第4期1270-1275,共6页Spectroscopy and Spectral Analysis

基　　金：国家重大科学仪器设备开发专项(2014YQ470377);国家重点研发计划(2017YFC1701603);国家中医药管理局委托项目(GZY-KJS-2018-004);宁夏回族自治区重点研发计划(2017BY079)资助。

摘　　要：宁夏产地的宁夏枸杞属于《中华人民共和国药典》收录的道地药材,药用价值较高、消费者青睐度更高,然而优质宁夏枸杞的种植面积较小、产量较低、枸杞子市场以乱充好、以其他产地冲抵道地产区产品的现象频发。因此,建立快速有效的宁夏枸杞产地鉴别模型对监督市场具有重要的意义。日常的市场交易枸杞子的鉴定一般凭借经验,但是该方法误差较大,可信度较低。传统的理化实验鉴别周期较长,非专业人员无法操作。近些年一些学者研究发现不同产地的宁夏枸杞成分含量具有差异性,然而枸杞子样本较小、形状不规则、成分分布不均匀,近红外光谱鉴别通常需要碾碎成粉末然后采集光谱信息,无法做到无损批量地采集枸杞子数据来鉴别枸杞子产地。近红外高光谱图像结合了近红外光谱和图像,包含丰富的空间信息和光谱信息,可以实现无损批量地采集非均匀样本光谱信息。利用近红外高光谱图像对甘肃、青海、新疆、宁夏和内蒙5个产地的宁夏枸杞进行产地鉴别。使用近红外高光谱图像系统采集了1 650个样本数据之后,通过阈值分割、图像去噪等方法提取出感兴趣区域(region-of-interest,ROI)。对比了ZCA白化(zero-phase component analysis whitening)预处理方法和常用的标准化(normalization)预处理方法,实验结果表明ZCA白化预处理是一种有效的高光谱数据预处理方法,可以去除特征之间的相关性,提升产地鉴别模型的准确率。对预处理后的数据采用偏最小二乘降维(partial least squares based dimension reduction,PLSDR)降低模型复杂度,结果表明经过ZCA白化预处理后的数据可以由288维特征降低到4个主成分,使得去除相关性后的特征可以被更少的隐藏特征来表示,这样可以极大程度上降低模型复杂性。最后,将降维后的特征输入到不同的分类器中进行训练,包括支持向量机(support vectoLycium barbarum produced in Ningxia belongs to the genuine regional drugs contained in the Pharmacopoeia of the People’s Republic of China. Due to the small planting area, low yield, high medicinal value and high consumer preference, the market is filled with chaos, and the phenomenon of passing others origins off as Ningxia happens occasionally. Therefore, it is of considerable significance to establish a rapid and effective geographical origin identification model of Lycium barbarum to supervise the market. In the process of market transactions, discriminating origin of Lycium barbarum is often based on experience, which has much error and low credibility. The traditional physical and chemical experiment has a long identification cycle and can’t be operated by non-professionals. In recent years, some scholars have found that the content of Lycium barbarum in different producing areas is different. However, because of the small sample size, irregular shape and uneven distribution of components, the near-infrared spectroscopy identification technique often needed to smash Lycium barbarum to collect spectral information. Near-infrared hyperspectral image technology combined with near-infrared spectroscopy and image technology, which contains rich spatial information and spectral information, can achieve non-destructive acquisition of spectral information. In this research, near-infrared hyperspectral image technology was used to discriminate the geographical origin of Lycium barbarum samples, which were gathered from Gansu, Qinghai, Xinjiang, Ningxia and Inner Mongolia in China. After collecting the hyperspectral information of 1 650 samples by hyperspectral image system, the region of interest(ROI) was effectively extracted by threshold image segmentation and denoising. During the pretreatment process, the comparison between zero-phase component analysis(ZCA) whitening results and normalization results indicated that ZCA whitening was an effective spectral preprocessing method to remove correlation between fe

关 键 词：近红外 高光谱 宁夏枸杞 产地鉴别 ZCA白化 Softmax 

分 类 号：O657.3[理学—分析化学]