• Prediction of blown pack in va...

Treffer: Prediction of blown pack in vacuum-packaged beef based on microbiome profiles and supervised machine learning.

Title:
Prediction of blown pack in vacuum-packaged beef based on microbiome profiles and supervised machine learning.
Authors:
Kremer FS; Pelotas Federal University, Center of Technological Development, Laboratory of Bioinformatics, Campus Universitário, 96160-000 Capão do Leão, RS, Brazil., Rodrigues RDS; Federal University of Viçosa, Department of Veterinary Medicine, InsPOA - Laboratory of Food Inspection, Campus Universitário, 36570-900 Viçosa, MG, Brazil; Federal University of Viçosa, BIOAGRO - Institute of Biotechnology Applied to Agriculture, BIOMOLVET - Laboratory of Veterinary Molecular Biology, Campus Universitário, 36570-900 Viçosa, MG, Brazil., Omori WP; Neoprospecta Microbiome Technologies, Av. Luiz Boiteux Piazza, 1302, 88054-700 Florianópolis, SC, Brazil., de Oliveira RR; Neoprospecta Microbiome Technologies, Av. Luiz Boiteux Piazza, 1302, 88054-700 Florianópolis, SC, Brazil., de Oliveira GAS; Federal University of Viçosa, Department of Veterinary Medicine, InsPOA - Laboratory of Food Inspection, Campus Universitário, 36570-900 Viçosa, MG, Brazil; Federal University of Viçosa, BIOAGRO - Institute of Biotechnology Applied to Agriculture, BIOMOLVET - Laboratory of Veterinary Molecular Biology, Campus Universitário, 36570-900 Viçosa, MG, Brazil., Nero LA; Federal University of Viçosa, Department of Veterinary Medicine, InsPOA - Laboratory of Food Inspection, Campus Universitário, 36570-900 Viçosa, MG, Brazil; Federal University of Viçosa, BIOAGRO - Institute of Biotechnology Applied to Agriculture, BIOMOLVET - Laboratory of Veterinary Molecular Biology, Campus Universitário, 36570-900 Viçosa, MG, Brazil. Electronic address: nero@ufv.br.
Source:
International journal of food microbiology [Int J Food Microbiol] 2025 Nov 02; Vol. 442, pp. 111375. Date of Electronic Publication: 2025 Jul 31.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Elsevier Science Publishers Country of Publication: Netherlands NLM ID: 8412849 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-3460 (Electronic) Linking ISSN: 01681605 NLM ISO Abbreviation: Int J Food Microbiol Subsets: MEDLINE
Imprint Name(s):
Original Publication: Amsterdam : Elsevier Science Publishers, c1984-
MeSH Terms:
Food Packaging*/methods , Microbiota* , Bacteria*/classification , Bacteria*/genetics , Bacteria*/isolation & purification , Red Meat*/microbiology , Supervised Machine Learning*, Cattle ; Animals ; Vacuum ; Food Microbiology ; Food Storage ; Machine Learning ; Temperature
Contributed Indexing:
Keywords: Machine learning; Meat spoilage; Metataxonomic; NGS
Entry Date(s):
Date Created: 20250802 Date Completed: 20250904 Latest Revision: 20250904
Update Code:
20250905
DOI:
10.1016/j.ijfoodmicro.2025.111375
PMID:
40752116
Database:
MEDLINE

Weitere Informationen

The preservation of vacuum-packaged beef products is essential for maintaining shelf life. However, the occurrence of blown pack phenomenon, characterized by the expansion of packaging due to gas production by spoilage microorganisms, is still a challenge. In the present work, we demonstrate that microbiome analysis using next generation sequencing (NGS) and machine learning might be useful in the analysis, modeling and prediction of spoilage and blown pack in vacuum-packaged beef. Beef systems (n = 10) were vacuum-packed, stored at 4 and 15 °C, and their populations were monitored based on NGS at 0 h and 7, 14, 21 and 28 days. Our analysis allowed the prediction of blown pack based on information of the initial microbiome in beef and storage conditions, identification of the relationship of different bacteria genera associated with spoilage along with temperature, which were consistent with differential abundance analysis, and estimate the relationship of temperature and blown pack. Using SHAP (Shapley Additive Explanations) to interpret the XGBoost model, we identified temperature as the most influential factor in blown pack prediction when considering microbiome data from day zero. Additionally, SHAP analysis of Random Forest and XGBoost models based on OTU Spearman correlation and linear regression, computed about time, highlighted Peptoniphilus as the most important bacterial genus, followed by Hafnia and Peptostreptococcus. Additional studies might extend these methods for other types of meat, cuts and including additional storage conditions, allowing a better modeling of the dynamics in the microbiome associated with the blown pack phenomenon.
(Copyright © 2025 Elsevier B.V. All rights reserved.)

Declaration of competing interest 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.