Evidence map›Paper›PMID 42006649›Full record

ArticleFood chemistry: X2026

Quantitative analysis and visualization of live spotted seabass (

Shuai Che, Ang Li, Huan Wang, Changting An, Xiang Fang, Qing Wang, Qiyou Tao, Yun Li, Changlin Liu, Shufang Liu and 1 more

Erratum issuedAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Food chemistry: X, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Shuai CheState Key Laboratory of Mariculture Biobreeding and Sustainable Goods, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China.
Ang LiState Key Laboratory of Mariculture Biobreeding and Sustainable Goods, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China.
Huan WangState Key Laboratory of Mariculture Biobreeding and Sustainable Goods, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China.
Changting AnState Key Laboratory of Mariculture Biobreeding and Sustainable Goods, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China.
Xiang FangFujian Minwell Industrial Co., Ltd., Ningde 355200, China.
Qing WangFujian Minwell Industrial Co., Ltd., Ningde 355200, China.
Qiyou TaoSouth China Sea Fisheries Research institute, Chinese Academy of Fishery Science, Guangzhou 510300, China.
Yun LiOcean University of China, Qingdao 266003, China.
Changlin LiuState Key Laboratory of Mariculture Biobreeding and Sustainable Goods, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China.
Shufang LiuState Key Laboratory of Mariculture Biobreeding and Sustainable Goods, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China.
Zhimeng ZhuangState Key Laboratory of Mariculture Biobreeding and Sustainable Goods, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao 266071, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Flesh texture is one of the most important quality traits determining consumer satisfaction and perception of fish. Traditional methods for measuring flesh texture characteristics present several limitations. This study developed a rapid, efficient and non-invasive approach based on hyperspectral imaging (HSI) technology for evaluating texture profile analysis (TPA) parameters in live spotted seabass. Three types of surface hyperspectral data (skin with scales, SWS; skin without scales, SOS; and reversed skin without scales, RSOS) were collected from the dorsal region of 300 live fish across 400-1000 nm wavelength range. After applying five spectral pre-processing methods, five machine learning algorithms, including partial least squares regression (PLSR), least square support vector machine regression (LS-SVR), random forest (RF), convolutional neural network (CNN) and back propagation artificial neural network (BP-ANN), were performed to construct optimal prediction models for seven TPA parameters. Gumminess was predicted most effectively, with the RF model achieving a prediction set coefficient of determination (R

Indexed as

Hyperspectral imagingLive spotted seabassMachine learningTexture visualizationTPA parameters

Identifiers

PMID42006649
PMCPMC13091131

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.