Development of a Composite Pork Quality Index from Instrumental Traits and Its Hyperspectral Prediction.

Yiqi Rao, Yongzhe He, Siyao Huang, Qian You, Shuqi Tang, Hu Zhang, Liandong Luo, Xiaoyan Xu, Xingguo Tian

Abstract read

In one paragraph

Article in Foods (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Yiqi RaoThe National Center for Precision Machining and Safety of Livestock and Poultry Products Joint Engineering Research Center, College of Food Science, South China Agricultural University, Guangzhou 510642, China.

Yongzhe HeGuangdong Provincial Key Laboratory of Food Quality and Safety, College of Food Science, South China Agricultural University, Guangzhou 510642, China.

Siyao HuangCollege of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China.ORCID 0009-0001-5989-8631

Qian YouGuangdong Provincial Key Laboratory of Food Quality and Safety, College of Food Science, South China Agricultural University, Guangzhou 510642, China.

Shuqi TangCollege of Engineering, South China Agricultural University, Guangzhou 510642, China.

Hu ZhangCollege of Engineering, South China Agricultural University, Guangzhou 510642, China.

Liandong LuoThe National Center for Precision Machining and Safety of Livestock and Poultry Products Joint Engineering Research Center, College of Food Science, South China Agricultural University, Guangzhou 510642, China.

Xiaoyan XuGuangdong Provincial Key Laboratory of Food Quality and Safety, College of Food Science, South China Agricultural University, Guangzhou 510642, China.

Xingguo TianThe National Center for Precision Machining and Safety of Livestock and Poultry Products Joint Engineering Research Center, College of Food Science, South China Agricultural University, Guangzhou 510642, China.

Funding

Agricultural Science and Technology Major Project NK2022110602Guangdong Basic and Applied Basic Research Foundation 2024A1515010433Guangdong Modern Agricultural Industrial Technology System Poultry Innovation Team 2024CXTD20Guangdong Provincial Key Laboratory of Food Quality and Safety 2020B1212060059Guangdong Rural Science and Technology Specialist Team KTP20240198Guangzhou Municipal Science and Technology Program 2024E04J0254National Center for Precision Machining and Safety of Livestock and Poultry Products Joint Engineering Research Center 2016(2203)National Key Research and Development Program of China 2023YFF11042

6 · The paper itself

Abstract

Pork quality is multidimensional, but conventional evaluation methods are usually destructive, time-consuming, and unsuitable for rapid grading. In this study, longissimus dorsi samples from pigs weighing 30-150 kg were used to construct a composite quality index (CQI) from instrumental traits. Traits related to color, water-holding capacity, antioxidant capacity, and texture were measured, and 13 traits were retained after principal component analysis. The resulting CQI represents a dataset-dependent summary of instrumental quality traits rather than a validated measure of sensory eating quality. Hyperspectral images were collected from pork slices, and CQI was predicted using interval combination optimization (ICO) combined with extreme gradient boosting (XGBoost). In the fixed spectral-level comparison, the SNV-ICO-XGBoost model achieved an

Indexed as

composite quality indexhyperspectral imaginginstrumental traitsmachine learningpork quality

Identifiers

PMID42650586

DOI10.3390/foods15162892

PMCPMC13511944

What OpenQuestion holds

Textmetadata

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 OpenQuestion graph.