基于近、中红外光谱法融合判定黑果枸杞产地及品质信息

doi:10.3964/j.issn.1000-0593(2020)12-3878-06

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摘要：黑果枸杞属药食同源植物，富含多糖、蛋白质、矿物质、花青素等生物活性物质，具有清除自由基、抗氧化、美容养颜及调节人体免疫系统的作用，引起国内外研究者的极大关注，备受消费者的追捧。我国幅员辽阔，黑果枸杞的种植主要分布于新疆、西藏、内蒙古、青海、宁夏等地，不同产地受海拔、日照、环境因素的影响所产黑果枸杞品质也不尽相同。针对市场上不同产地黑果枸杞产地信息标注混乱，品质参差不齐而导致市场混乱现象，利用近、中红外光谱技术结合化学计量学方法对黑果枸杞的产地品质信息进行区分。首先对所收集不同产地的5种，共计190个黑果枸杞样本进行近、中红外光谱采集及多糖含量的测定，利用主成分分析（PCA）对所采集的原始数据进行降维处理并采用偏最小二乘-支持向量机（LS-SVM）对其进行定性区分。结果显示，经PCA处理后的三维主成分得分图可明显地将黑果枸杞的光谱数据按照产地类型分为5大类，进一步采用LS-SVM对其进行处理，得出融合光谱与单一近、中红外光谱所建LS-SVM模型相比，融合光谱所建模型的预测能力优于单一一种光谱所建模型的预测能力，当主成分数为9时，近、中红外融合光谱的校正集识别率达到100%，预测集识别率达到99.17%。采用联合区间偏最小二乘（Si-PLS）对多糖含量进行定量预测，结果显示，近、中红外光谱融合后建立Si-PLS预测模型的校正集相关系数（R_c）为0.976 9，交互验证均方根误差（RMSECV）最小为2.08%，预测集的相关系数（R_t）达到0.967 0，均方根误差（RMSEP）为2.40%。另外用15个新的黑果枸杞样本对所建立最佳Si-PLS模型进行验证，验证模型的R_t和RMSEP分别为0.947 7和2.57%，结果证明研究所建最佳Si-PLS模型的鲁棒性好、精确度高。结合LS-SVM、Si-PLS的近、中红外融合光谱技术，可以精简、优化模型，达到快速、准确地识别黑果枸杞的产地品质信息的目的。

关键词：红外光谱；黑果枸杞；多糖；光谱融合；联合区间偏最小二乘

Abstract：Lycium ruthenicum Murr. is a kind of traditional food with abundant nutrition such as polysaccharides, proteins, minerals and anthocyanins. It has a long history used as medicinal and food plants in China, meanwhile it has functions of scavenging free radicals, anti-oxidation, beautifying and regulating the human immune system. Lycium ruthenicum Murr. is mainly distributed in Tibet, Xinjiang, Inner Mongolia, Qinghai and Ningxia and so on. Different kinds Lycium ruthenicum Murr. have different kinds of quality. All of that can be calculated to high altitude, big diurnal amplitude and environmental aspect in different regions. Thereby, with the increase of demand for black Goji berry, there are miscellaneous black Goji berry priced at different price in the market. In order to rapidly and efficiently deter minute geographical origin and categories in Lycium ruthenicum Murr., Near infrared (NIR) and Fourier transform infrared (FTIR) spectroscopy was employed with the help of chemometrics. Five kinds of Lycium ruthenicum Murr. were analyzed. The 175 Lycium ruthenicum Murr. can be classified into 5 groups. Least-squares support vector machine (LS-SVM) was first performed to calibrate the discri mination model to identify the geographical origins and categories of Lycium ruthenicum Murr. LS-SVM model based on the combination of two spectroscopies were superior to those from either FTIR or IR spectra and the recognition rate of LS-SVM reached up to 99.17%, which showed excellent generalization for identification results. Polysaccharide contents were closely related with the quality of Lycium ruthenicum Murr. Synergy interval partial least squares (Si-PLS) was applied to develop the prediction model of polysaccharide contents. The model was optimized by a leave-one-out cross-validation, and its performance was tested according to the root mean square error of the cross validation (RMSECV) and correlation coefficient (R_c) in the calibration set. Experimental results showed that the optimum results of the Si-PLS model were achieved as follow: RMSECV=2.08%, R_c=0.976 9 and root mean square error of prediction (RMSEP)=2.40%, and correlation coefficient (R_t)=0.967 0 in the prediction set. Finally, the robustness of the LS-SVM model obtained was checked with the 15 new samples that did not belong to the calibration set. And, the calibration model obtained during the work was applied and the calibration values were compared with the external validation values. Si-PLS model provided RMSEP and R_t were 0.947 7 and 2.57% in external validation The overall results sufficiently demonstrate that the spectroscopy coupled with chemometrics has the potential to distinguish Lycium ruthenicum Murr.

Key words：Infrared Spectroscopy; Lycium Ruthenicum Murr.; Polysaccharide; Data fusion; Synergy interval partial least squares

收稿日期: 2019-06-24 修订日期: 2019-10-19

中图分类号:

O657.3

基金资助: 国家重点研发计划项目（2018YFD0400800）, 国家自然科学基金项目（31671844）, 江苏省研究生科研与实践创新计划项目（KYCX19_1629）资助

通讯作者: 李艳肖，邹小波 E-mail: 5983459@qq.com; zou_xiaobo@ujs.edu.cn

作者简介: 李亚惠，1991年生，江苏大学食品与生物工程学院博士研究生 e-mail: yahui_lee@126.com

引用本文:

李亚惠，李艳肖，谭伟龙，孙晓霞，石吉勇，邹小波，张俊俊，蒋彩萍. 基于近、中红外光谱法融合判定黑果枸杞产地及品质信息[J]. 光谱学与光谱分析, 2020, 40(12): 3878-3883.
LI Ya-hui, LI Yan-xiao, TAN Wei-long, SUN Xiao-xia, SHI Ji-yong, ZOU Xiao-bo, ZHANG Jun-jun, JIANG Cai-ping. Rapid Identification and Evaluation of Lycium Ruthenicum Murr. by Near-Infrared and Fourier Transform Infrared Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(12): 3878-3883.

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[1] LIN Li, ZHANG Pei-si, JIN Ling, et al(林丽, 张裴斯, 晋玲, 等). China Pharmacy(中国药房), 2013，(47): 4493.
[2] Li Yahui, Zou Xiaobo, Shen Tingting, et al. Food Analytical Methods, 2016, 10(4): 1.
[3] GU Xuan, ZHANG Xiao-qin, SONG Xiao-na, et al(顾选, 张晓芹, 宋晓娜, 等). Chinese Materia Medica(中国中药杂志), 2014，(24): 4759.
[4] LOU Tao-tao, TUO Yang-ling, JIN Ling, et al(娄涛涛, 陀扬凌, 金玲, 等). China Pharmacy(中国药房), 2016，(9): 1245.
[5] SHI Ji-yong, ZOU Xiao-bo, ZHAO Jie-wen, et al(石吉勇, 邹小波, 赵杰文, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2015，(7): 210.
[6] LÜ Dong-mei, HUANG Yuan, WEN Hui, et al(吕冬梅, 黄原, 文慧, 等). Food Science(食品科学), 2015，(9): 248.
[7] GONG Xiao-yu, QIU Shuang-feng, PENG Wei, et al(巩晓宇, 邱双凤, 彭炜, 等). Evaluation and Analysis of Drug-Use in Hospitals of China(中国医院用药评价与分析), 2016，(7): 883.
[8] Shi Jiyong, Zhang Fang, Wu Shengbin, et al. Food Chemistry, 2019，(274): 925.
[9] WANG He-bin, BAI Hong-jin, WANG Jin-lei(汪河滨, 白红进, 王金磊). Acta Agriculturae Boreali-Occidentalis Sinica(西北农业学报), 2007，(1): 157，175.
[10] WANG Jian-hong, CHEN Xiao-qin, YUAN Hui(汪建红, 陈晓琴, 原惠). Journal of Xinjiang Normal University(新疆师范大学学报), 2009，(3): 78，91.
[11] SHI ji-yong, HU Xue-tao, ZHU Yao-di, et al(石吉勇, 胡雪桃, 朱瑶迪, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2016，(2): 245.
[12] Shi Jiyong, Hu Xuetao, Zou Xiaobo, et al. Food Chemistry, 2017，(229): 235.
[13] LIANG Long, FANG Gui-gan, WU Ting, et al(梁龙, 房桂干, 吴珽, 等). Journal of Instrumental Analysis(分析测试学报), 2016，(1): 101.
[14] Huang Lin, Zhao Jiewen, Chen Quansheng, et al. Food Chemistry, 2014, 145(7): 228.