Yang Ping, Zhou Ran, Zhang Wen, Tang Shisong, Hao Zhongqi, Li Xiangyou, Lu Yongfeng, Zeng Xiaoyan
Appl Opt. 2018 Oct 1;57(28):8297-8302. doi: 10.1364/AO.57.008297.
The problems of adulteration and mislabeling are very common in the food industry. Laser-induced breakdown spectroscopy (LIBS) coupled with chemometric methods has many intrinsic advantages on adulteration analysis of various materials. In this work, several chemometric algorithms, i.e., principal component analysis (PCA), decision tree (DT), random forest (RF), partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and support vector machine (SVM), were carried out assisted by LIBS technology to study the classification performances on rice geographic origins. A series of samples, including 20 kinds of rice samples from different geographic origins, was detected using LIBS with no pretreatment processes. For data analysis, PCA was employed to reduce the input variables, and to reduce the collinearity of LIBS spectral results as well. The results showed the classification accuracies of the mentioned chemometric algorithms of DT, RF, PLS-DA, LDA, and SVM with 89 input variables of 86.80%, 96.30%, 96.80%, 98.60%, and 99.20%, respectively. At the same time, the operation times of these algorithms were 3.81 s, 54.64 s, 3.63 s, 2.09 s, and 531.01 s, respectively. On the other hand, 30 principal components of input variables were also tested under the same conditions. The classification accuracies for the above algorithms were 81.60%, 98.00%, 95.70%, 98.40%, and 99.20%, respectively. The operation times were 2.01 s, 4.88 s, 3.67 s, 0.36 s, and 308.55 s, respectively. In addition, the five-fold cross-validation classification accuracies with 30 input variables for DT, RF, PLS-DA, LDA, and SVM were 83.75%, 97.95%, 94.75%, 98.35%, and 99.25%, respectively. As a result, LDA was demonstrated to be the best and most efficient tool for rice geographic origin classification assisted by LIBS with high accuracy and analytical speed, which has great potential for rapid identification of adulterated products in agriculture without use of any chemical reagent.
掺假和标签错误问题在食品行业非常普遍。激光诱导击穿光谱法(LIBS)结合化学计量学方法在各种材料的掺假分析方面具有许多内在优势。在这项工作中,在LIBS技术的辅助下,开展了几种化学计量学算法,即主成分分析(PCA)、决策树(DT)、随机森林(RF)、偏最小二乘判别分析(PLS-DA)、线性判别分析(LDA)和支持向量机(SVM),以研究对大米地理来源的分类性能。使用LIBS对一系列样本进行了检测,包括20种来自不同地理来源的大米样本,且未进行预处理。对于数据分析,采用PCA来减少输入变量,并降低LIBS光谱结果的共线性。结果表明,上述化学计量学算法(DT、RF、PLS-DA、LDA和SVM)在89个输入变量下的分类准确率分别为86.80%、96.30%、96.80%、98.60%和99.20%。同时,这些算法的运行时间分别为3.81秒、54.64秒、3.63秒。2.09秒和531.01秒。另一方面,在相同条件下还测试了30个输入变量的主成分。上述算法的分类准确率分别为81.60%、98.00%、95.70%、98.40%和99.20%。运行时间分别为2.01秒、4.88秒、3.67秒、0.36秒和308.55秒。此外,DT、RF、PLS-DA、LDA和SVM在30个输入变量下的五折交叉验证分类准确率分别为83.75%、97.95%)、94.75%、98.35%和99.25%。结果表明,LDA被证明是在LIBS辅助下进行大米地理来源分类的最佳且最有效的工具,具有高精度和分析速度,在不使用任何化学试剂的情况下,对于快速识别农业掺假产品具有巨大潜力。