A new quantitative index for the assessment of tomato quality using Vis-NIR hyperspectral imaging. (30th August 2022)

• Record Type: Journal Article
• Authors: Shao, Yuanyuan; Shi, Yukang; Qin, Yongdong; Xuan, Guantao; Li, Jing; Li, Quankai; Yang, Fengjuan; Hu, Zhichao
• Is Part Of: Food chemistry. Volume 386(2022)
• Publication Date: 2022-08-30
• Subjects: Tomato -- Hyperspectral imaging -- Comprehensive quality index -- Factor analysis -- Regression model; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2022.132864
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: The quality of tomatoes was predicted by hyperspectral imaging and chemometrics. 12 quality indexes of three varieties of tomato were measured in the study. A new comprehensive quality index (CQI) was proposed through factor analysis. The MLR model was established to predict the comprehensive quality of tomatoes. Abstract: The quality of tomatoes is usually predicted by measuring a single index, rather than a comprehensive index. To find a comprehensive index, visible and near infrared (Vis-NIR) hyperspectral imaging was used for capturing the images of three varieties of tomatoes, and twelve quality indexes were measured as the reference standards. The changing trends and correlations of different indexes were analyzed, and comprehensive quality index (CQI) was proposed through factor analysis. The characteristic wavelengths were selected by successive projection algorithm (SPA) based on the hyperspectral data, which was used to establish three regression models for CQI prediction. The result indicated that MLR achieved good performance with R V 2 = 0.87, RMSEV = 1.33 and RPD = 2.58. After that, spatial distribution map was generated to visualize the CQI in tomato fruit. This study indicated that the comprehensive quality of tomatoes can be predicted non-destructively based on hyperspectral imaging and chemometrics, determining the optimal harvesting period.