Enhancing and predicting the microbial and oxidative stability of ostrich meat using chia mucilage–based edible coatings enriched with Lactococcus lactis LBM15 supernatant and SVR model

Articles in Press

Document Type : Original Research

Authors
Bahareh Goudarzi Shams Abadi 1
Mohammad Hojjati 1
Hossein Jooyandeh 1
Behrooz Alizadeh behbahani 1
Alireza Vasiee 2
Morteza Taki 3
1 Department of Food Science and Technology, Faculty of Animal Science and Food Technology, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, Islamic Republic of Iran.
2 Department of Food Safety and Quality Control, Research Institute of Food Science and Technology (RIFST), Mashhad, Islamic Republic of Iran.
3 Department of Agricultural Machinery and Mechanization Engineering, Faculty of Agricultural Engineering and Rural Development, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, Islamic Republic of Iran.
Abstract
Chia seed mucilage coatings enriched with cell-free supernatant (CFS) from Lactococcus lactis LBM15 effectively delayed spoilage of refrigerated ostrich meat for 10 days. Compared with the control, the 2% CFS coating notably limited microbial growth and oxidative spoilage: total viable counts increased to 9.50 ± 0.14 log CFU/g in the control but to only 6.80 ± 0.11 log CFU/g with the 2% CFS treatment. Similarly, peroxide values rose to 7.85 ± 0.13 meq O₂/kg in the control but remained at 3.10 ± 0.09 meq O₂/kg with the 2% CFS. Color retention was improved, with smaller declines in redness and a reduced overall color difference (ΔE). Sensory evaluations for odor, color, texture, and overall acceptance were also higher in coatings enriched with CFS. Meat slices were treated with a chia coating alone or with 1% or 2% CFS, along with an untreated control, and assessed for microbial counts, lipid oxidation, color metrics, texture, pH, moisture, and sensory attributes. Overall, combining chia mucilage with L. lactis CFS shows potential as a clean-label method to extend the shelf life of high-value ostrich meat. This study investigates the application of Support Vector Regression (SVR) to the simultaneous prediction of 12 key laboratory parameters. Model performance was evaluated using Mean Absolute Percentage Error (MAPE) and coefficient of determination (R²). Results demonstrated strong predictive performance, with test R² values ranging from 0.93 to 0.99 and MAPEs below 8% across all targets, confirming SVR’s effectiveness in modeling complex, nonlinear relationships in food-quality data.




 

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Articles in Press, Accepted Manuscript
Available Online from 14 January 2026