Evidence map›Paper›PMID 38707326›Full record

ArticleHeliyon2024

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

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Liyan RongCollege of Food Science, Shenyang Agricultural University, Shenyang, Liaoning 110866, China.
Yajing WangCollege of Food Science, Shenyang Agricultural University, Shenyang, Liaoning 110866, China.
Yanqun WangCollege of Food Science, Shenyang Agricultural University, Shenyang, Liaoning 110866, China.
Donghua JiangCollege of Food Science, Shenyang Agricultural University, Shenyang, Liaoning 110866, China.
Jinrong BaiCollege of Food Science, Shenyang Agricultural University, Shenyang, Liaoning 110866, China.
Zhaoxia WuCollege of Food Science, Shenyang Agricultural University, Shenyang, Liaoning 110866, China.
Lu LiCollege of Food Science, Shenyang Agricultural University, Shenyang, Liaoning 110866, China.
Tianyu WangCollege of Food Science, Shenyang Agricultural University, Shenyang, Liaoning 110866, China.
Hui TanCollege of Food Science, Shenyang Agricultural University, Shenyang, Liaoning 110866, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Electronic noseElectronic tongueFresh-cut papayaFreshness classificationModel regressionPredictive analysis

Identifiers

PMID38707326
PMCPMC11068816

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.