Feasibility study for the analysis of coconut water using fluorescence spectroscopy coupled with PARAFAC and SVM methods
Feasibility study for the analysis of coconut water using fluorescence spectroscopy coupled with PARAFAC and SVM methods
复制标题
使用荧光光谱结合 PARAFAC 和 SVM 方法分析椰子水的可行性研究
DOI：
10.1108/bfj-12-2019-0941
复制
发表时间：
2020-05-11
影响因子：
3.3
通讯作者：
Tan, Chin Ping
中科院分区：
文献类型：
--
作者：
Gu, Haiyang;Liu, Kaiqi;Tan, Chin Ping
采用并行因子分析（PARAFAC）和支持向量机（SVM）相结合的方法对椰子水的荧光光谱进行识别和区分。应用PARAFAC将三维激发发射矩阵数据约简为二维数据。在这项研究中，支持向量机被应用于区分六个商业椰子水品牌。使用PARAFAC方法提取荧光光谱中三个最大变化的数据作为SVM分类器的输入数据。通过三种支持向量机方法（Ga-SVM、PSO-SVM和Grid-SVM）实现了六个商业椰子水品牌的区分结果。在训练集、测试集和CV准确率上，最佳分类准确率分别为100.00%、96.43%和94.64%。上述结果表明，荧光光谱结合PARAFAC和SVM方法被证明是一种简单，快速的检测方法，椰子水和其他饮料。
Parallel factor analysis (PARAFAC) coupled with support-vector machine (SVM) was carried out to identify and discriminate between the fluorescence spectroscopies of coconut water brands.,PARAFAC was applied to reduce three-dimensional data of excitation emission matrix (EEM) to two-dimensional data. SVM was applied to discriminate between six commercial coconut water brands in this study. The three largest variation data from fluorescence spectroscopy were extracted using the PARAFAC method as the input data of SVM classifiers.,The discrimination results of the six commercial coconut water brands were achieved by three SVM methods (Ga-SVM, PSO-SVM and Grid-SVM). The best classification accuracies were 100.00%, 96.43% and 94.64% for the training set, test set and CV accuracy.,The above results indicate that fluorescence spectroscopy combined with PARAFAC and SVM methods proved to be a simple and rapid detection method for coconut water and perhaps other beverages.