Visualization of vibrational spectroscopy for agro-food samples using t-Distributed Stochastic Neighbor Embedding. (August 2021)

• Record Type: Journal Article
• Authors: Luo, Na; Yang, Xinting; Sun, Chuanheng; Xing, Bin; Han, Jiawei; Zhao, Chunjiang
• Is Part Of: Food control. Volume 126(2021)
• Publication Date: 2021-08
• Subjects: Visualization -- Dimensionality reduction -- t-SNE -- Vibrational spectroscopy; Food -- Quality -- Periodicals; Food -- Analysis -- Periodicals; Food handling -- Periodicals; Food industry and trade -- Quality control -- Periodicals; Aliments -- Industrie et commerce -- Qualité -- Contrôle -- Périodiques; Aliments -- Qualité -- Périodiques; Aliments -- Analyse -- Périodiques; Hygiène alimentaire -- Périodiques; Food -- Analysis; Food handling; Food -- Quality; Periodicals; Electronic journals; 664.07
• DOI: 10.1016/j.foodcont.2020.107812
• Languages: English
• ISSNs: 0956-7135

Abstract

Abstract: Vibrational spectroscopy is an effective non-destructive technique, and it has been successfully applied in characteristics identification for agro-food samples. However, owing to the high dimensionality of spectral dataset, it is difficult to distinguish samples of different characteristics from observing the raw spectral. In this study, t-Distributed Stochastic Neighbor Embedding (t-SNE), an state-of-art method, was applied for visulization on the five vibrational spectroscopy data sets. The performances of t-SNE and the other reference methods (PCA and Isomap) were illustrated both from the differentiation ability in the 2-dimensional space and the accuracy of sequential classification model. For the former, t-SNE showed more satisfied visual discrimination results in 2-dimensional space and obtained better scores of clustering metrics, Silhouette Coefficient (0.59 average score compared to 0.24 achieved by PCA and 0.59 by Isomap) and Davies-Bouldin Index (1.51 average score compared to 2.58 achieved by PCA and 1.52 by Isomap). For the latter, two supervised classification models, k-nearest neighbor (KNN) and support vector machine (SVM), were constructed based on the new representations in 2-dimensional space, in both cases, the representations given by t-SNE outperformed the other methods in terms of accuracy (for KNN, 96% average accuracy compared to the 85% achieved by PCA and 92% by Isomap; for SVM, 96% average accuracy compared to the 86% achieved by PCA and 92% by Isomap). The results showed great potential of t-SNE for recognizing minute spectral differences between classes, and proved that t-SNE is an effective dimensionality reduction and visualization method, especially when complex and highly overlapping vibrational spectra are used for analysis. Highlights: The t-SNE algorithm is introduced into vibrational spectral data visualization. Vibrational spectroscopy was utilized to distinguish different categories of agro-food samples. Two clustering metrics were used to quantitatively assess visualization results. Two supervised classifiers were applied to analyze the new representations given by t-SNE.