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

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

Liyan Rong  1 , Yajing Wang  1 , Yanqun Wang  1 , Donghua Jiang  1 , Jinrong Bai  1 , Zhaoxia Wu  1 , Lu Li  1 , Tianyu Wang  1 , Hui Tan  1
Affiliations

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

Liyan Rong et al. Heliyon. .

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.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Evolution of fresh-cut papaya during eight days of storage.
Fig. 2
Fig. 2
Changes in L* (A), a* (B), b* (C), and ΔE (D) of fresh-cut papaya during storage. Different letters indicate significant differences (p < 0.05).
Fig. 3
Fig. 3
HCA analysis of fresh-cut papaya (A) and photographs (B) of physicochemical indicators during storage.
Fig. 4
Fig. 4
Changes in E-tongue (A), and E-nose (B)response values of fresh-cut papaya during storage. PCA plot of E-tongue (C) and E-nose (D) of fresh-cut papaya during storage.
Fig. 5
Fig. 5
Correlation analysis of physicochemical quality of fresh-cut papaya, e-tongue, and e-nose responses (p < 0.05). The sizes of the circles in the figure are proportional to the correlation; the larger the area, the higher the correlation. The color of the circles indicates a positive or negative correlation, with red depicting a positive and blue depicting a negative correlation. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 6
Fig. 6
Actual and predicted values of PLSR (A, C) and SVMR (B, D) models for aerobic plate counts of fresh-cut papaya during storage based on E-tongue (A, B) and E-nose (C, D) dataset.
See this image and copyright information in PMC

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