A fresh-cut papaya freshness prediction model based on partial least squares regression and support vector machine regression

Liyan Rong; Yajing Wang; Yanqun Wang; Donghua Jiang; Jinrong Bai; Zhaoxia Wu; Lu Li; Tianyu Wang; Hui Tan

doi:10.1016/j.heliyon.2024.e30255

A fresh-cut papaya freshness prediction model based on partial least squares regression and support vector machine regression

Heliyon. 2024 Apr 26;10(9):e30255. doi: 10.1016/j.heliyon.2024.e30255. eCollection 2024 May 15.

Authors

Liyan Rong¹, Yajing Wang¹, Yanqun Wang¹, Donghua Jiang¹, Jinrong Bai¹, Zhaoxia Wu¹, Lu Li¹, Tianyu Wang¹, Hui Tan¹

Affiliation

¹ College of Food Science, Shenyang Agricultural University, Shenyang, Liaoning 110866, China.

Abstract

This study investigated the physicochemical and flavor quality changes in fresh-cut papaya that was stored at 4 °C. Multivariate statistical analysis was used to evaluate the freshness of fresh-cut papaya. Aerobic plate counts were selected as a predictor of freshness of fresh-cut papaya, and a prediction model for freshness was established using partial least squares regression (PLSR), and support vector machine regression (SVMR) algorithms. Freshness of fresh-cut papaya could be well distinguished based on physicochemical and flavor quality analyses. The aerobic plate counts, as a predictor of freshness of fresh-cut papaya, significantly correlated with storage time. The SVMR model had a higher prediction accuracy than the PLSR model. Combining flavor quality with multivariate statistical analysis can be effectively used for evaluating the freshness of fresh-cut papaya.

Keywords: Electronic nose; Electronic tongue; Fresh-cut papaya; Freshness classification; Model regression; Predictive analysis.