Evidence map›Paper›PMID 42800453›Full record

ArticlePoultry science2026

Portable near-infrared spectroscopy combined with machine learning for rapid detection of broiler breast myopathies.

Yazavinder Singh, Silvia Magro, Arianna Goi, Jean Christophe Parisse, Angélique Travel, Cécile Berri, Massimo De Marchi, Carmen L Manuelian

Abstract read
In one paragraph

Article in Poultry science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Yazavinder SinghDepartment of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, Stecca 1, Legnaro, PD, 35020, Italy.
Silvia MagroDepartment of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, Stecca 1, Legnaro, PD, 35020, Italy.
Arianna GoiDepartment of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, Stecca 1, Legnaro, PD, 35020, Italy.
Jean Christophe ParisseLDC, Rue Saint-Laurent, Sablé sur Sarthe, 72302, France.
Angélique TravelITAVI, Unité recherche Avicole BP1, Nouzilly, 37380, France.
Cécile BerriINRAE, Université de Tours, BOA, Nouzilly, 37380, France.
Massimo De MarchiDepartment of Agronomy, Food, Natural resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, Stecca 1, Legnaro, PD, 35020, Italy. Electronic address: Massimo.demarchi@unipd.it.
Carmen L ManuelianGroup of Ruminant Research (G2R), Department of Animal and Food Sciences, Universitat Autònoma de Barcelona (UAB), Bellaterra, 08193, Spain. Electronic address: Carmen.Manuelian@uab.cat.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Growth-related breast myopathies are among the major meat quality defects encountered in modern broiler production, affecting breast meat quality and requiring affected fillets to be diverted to further processing. This study aimed to evaluate the feasibility of a pocket-sized near‑infrared (NIR) spectrometer for detecting white striping (WS), wooden breast (WB), and spaghetti meat (SM) in broiler breast meat. A total of 4579 broilers from a commercial crossbred and a broiler breeders line were sampled across 8 flocks and multiple slaughter days, and fillets were categorized by a trained evaluator for WS, WB, and SM severity. Breast surfaces were scanned five times at standardized anatomical locations using a portable spectrometer (740-1070 nm), and the spectra were averaged prior to chemometric analyses. After data cleaning, 4500 samples were retained, and partial least squares discriminant analysis (PLS‑DA) models (70% training set; 30% testing set) were developed and evaluated using balanced accuracy (BA), sensitivity, and specificity. Results revealed models that BA in the testing ranged from 50 to 78%, depending on the classification scenario. Models for SM and isolated WS or WB showed limited discrimination (≤56%), reflecting class imbalance and spectral overlap with concurrent myopathies. Intermediate discrimination was obtained for Non-defect (ND) vs WS and/or WB (75%), ND vs any defect (75%), WS severity (74%), and WB severity (73-74%). The highest BA values were achieved for models contrasting ND fillets with WS-associated myopathies (78%) or WB-associated myopathies (77%). For the ND vs any defect model, sensitivity and specificity were 67% and 82%, respectively, indicating a higher ability to identify ND fillets than defective fillets. Variable‑importance analysis showed defect‑specific spectral regions: 800-900 nm for SM (water‑related absorptions), 900-950 nm for WS (lipid‑related features), and 1000-1070 nm for WB (protein/collagen‑associated absorptions), reflecting underlying biochemical alterations. In conclusion, a pocket‑sized NIR spectrometer can serve as a practical, rapid first‑pass screening tool for detecting combined broiler breast myopathies at processing lines, although isolated and low‑prevalence defects remain challenging to classify reliably.

Indexed as

Chemometricsdegenerationmyopathyscreeningspectroscopy

Identifiers

PMID42800453
PMCPMC13636439

What Socratic holds

Textmetadata
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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.