FTIR结合DD-SIMCA和二维相关光谱的核桃产地判别分析  

FTIR spectroscopy combined with DD-SIMCA and Two-dimensional correlation spectroscopy(2DCoS)for judgement about walnut origins

作　　者：王永波 李洪艳 张想芬 温卫华 杨瑞[1] WANG Yongbo;LI Hongyan;ZHANG Xiangfen;WEN Weihua;YANG Rui(College of Forestry,Guizhou University,Guiyang 550025,Guizhou,China)

机构地区：[1]贵州大学林学院,贵阳550025

出　　处：《果树学报》2023年第1期155-168,共14页Journal of Fruit Science

基　　金：贵州省科技计划项目(黔科合支撑[2020]1Y011号);贵州省林业科研项目(黔林科合[2022]22号,黔林科合[2020]14号);贵州大学实验室开放项目。

摘　　要：【目的】建立一种基于傅里叶变换红外光谱(FTIR)的简便、快速的核桃产地鉴别方法。【方法】使用KBr压片法采集4类不同产地(新疆、贵州、四川、云南)的核桃仁(n=120)和核桃壳(n=80)的中红外(4000~400 cm^(-1))光谱信息,经S.G.平滑+MSC方法预处理后,进行化学计量学建模和2DCoS分析。【结果】各产地核桃仁中红外光谱的主要吸收峰在表征蛋白质的1649 cm^(-1)和1539 cm^(-1)处存在显著差异(VIP>1.0)。SVM比PLS-DA模型取得了更好的识别效果,其对核桃壳和核桃仁样本的最佳产地判别正确率分别为100%和97%。DD-SIMCA法对核桃仁样本的判别灵敏度和特异性均达到100%,高于SIMCA的87%分类正确率。各产地核桃仁样本的二维同步谱图存在差异,表明该法可以用于核桃产地的分类判别。【结论】FTIR光谱结合DD-SIMCA化学计量学方法或2DCoS分析技术可以实现对核桃产地的高效识别。【Objective】This work was to develop rapid and facile methods for the determination and classification of the walnut geographical origin based on kernel and shell samples by Fourier transform infrared spectroscopy(FTIR).【Methods】A total of 120 in-shelled walnut samples were collected from4 areas of China,including Xinjiang,Guizhou,Sichuan and Yunnan.The walnuts were split into 120kernel samples and 80 shell samples.After freeze-drying and oven-drying respectively,samples were numbered to maintain traceability throughout the process and were stored in a sealed bag for use.These samples were investigated under solid-state conditions in KBr pellets utilizing FTIR spectra in the range of 4000-400 cm^(-1).Several mathematical pre-treatment methods including baseline correction,SavitskyGolay(S.G.)smoothing,standard normal variate(SNV)and multiplicative scatter correction(MSC)were tested in the original spectral matrix.Then,the principal component analysis(PCA)was performed on the whole dataset for qualitative analysis of the spectra.Subsequently,four different classification models were applied to the FTIR data.Partial least squares discriminant analysis(PLS-DA),and support vector machine(SVM)were used to identify and categorize the spectra data from walnut kernel samples and shell samples.The class modeling techniques for soft independent modeling by class analogy(SIMCA)and data-driven soft independent modeling of class analogy(DD-SIMCA)were evaluated by using walnut kernel samples data.The DD-SIMCA was chosen because it was a powerful class-modeling technique to test whether a sample was a target category or not,to assign an unknown sample to a class(target category)or not(outlier).The DD-SIMCA model was established on the PCA decomposition of each class separately.The efficiency of such chemometrics models was evaluated in terms of correct-prediction percent,sensitivity and specificity.Besides,the synchronous two-dimensional correlation spectrum(2DCoS)maps based on temperature perturbations of the walnut kern

关 键 词：核桃产地 傅里叶变换红外光谱 数据驱动型簇类独立软模式分类 二维相关光谱 

分 类 号：S664.1[农业科学—果树学]