A Fresh-cut Papaya Freshness Prediction Model Based on Partial Least Squares Regression and Support Vector Machine Regression

Overview

Journal Heliyon

Specialty Social Sciences

Date 2024 May 6

PMID 38707326

Authors

Liyan Rong

Yajing Wang

Yanqun Wang

Donghua Jiang

Jinrong Bai

Zhaoxia Wu

Lu Li

Tianyu Wang

Hui Tan

Affiliations

Soon will be listed here.

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.

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