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Predictive Modeling of Thiol Changes in Raw Ground Pork as Affected by 13 Plant Extracts-Application of Arrhenius, Log-logistic and Artificial Neural Network Models.

ABSTRACT: In this study, predictive models of protein oxidation, expressed as the content of thiol groups (SH), in raw ground pork were established and their accuracy was compared. The SH changes were monitored during, maximum, 11 days of storage at five temperature levels: 4, 8, 12, 16, and 20 °C. The effect of 13 plant extracts, including spices such as allspice, black seed, cardamom, caraway, cloves, garlic, nutmeg, and onion, and herbs such as basil, bay leaf, oregano, rosemary, and thyme, on protein oxidation in pork was studied. The zero-order function was used to described SH changes with time. The effect of temperature was assessed by using Arrhenius and log-logistic equations. Artificial neural network (ANN) models were also developed. The results obtained showed very good acceptability of the models for the monitoring and prediction of protein oxidation in raw pork samples. High average R² coefficients equal to 0.948, 0.957, and 0.944 were obtained for Arhhenius, log-logistic and ANN models, respectively. Multiple linear regression (MLR) was used to assess the influence of plant extracts on protein oxidation and showed oregano as the most potent antioxidant among the tested ones in raw ground pork.

SUBMITTER: Muzolf-Panek M

PROVIDER: S-EPMC8229620 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:UnlabelledRaw sausages are perishable foodstuffs; reducing their salt content raises questions about a possible increased spoilage of these products. In this study, we evaluated the influence of salt reduction (from 2.0% to 1.5% [wt/wt]), in combination with two types of packaging (modified atmosphere [50% mix of CO2-N2] and vacuum packaging), on the onset of spoilage and on the diversity of spoilage-associated bacteria. After 21 days of storage at 8°C, spoilage was easily observed, characterized by noticeable graying of the products and the production of gas and off-odors defined as rancid, sulfurous, or sour. At least one of these types of spoilage occurred in each sample, and the global spoilage intensity was more pronounced in samples stored under modified atmosphere than under vacuum packaging and in samples with the lower salt content. Metagenetic 16S rRNA pyrosequencing revealed that vacuum-packaged samples contained a higher total bacterial richness (n = 69 operational taxonomic units [OTUs]) than samples under the other packaging condition (n = 46 OTUs). The core community was composed of 6 OTUs (Lactobacillus sakei, Lactococcus piscium, Carnobacterium divergens, Carnobacterium maltaromaticum, Serratia proteamaculans, and Brochothrix thermosphacta), whereas 13 OTUs taxonomically assigned to the Enterobacteriaceae, Enterococcaceae, and Leuconostocaceae families comprised a less-abundant subpopulation. This subdominant community was significantly more abundant when 2.0% salt and vacuum packaging were used, and this correlated with a lower degree of spoilage. Our results demonstrate that salt reduction, particularly when it is combined with CO2-enriched packaging, promotes faster spoilage of raw sausages by lowering the overall bacterial diversity (both richness and evenness).ImportanceOur study takes place in the context of raw meat product manufacturing and is linked to a requirement for salt reduction. Health guidelines are calling for a reduction in dietary salt intake. However, salt has been used for a very long time as a hurdle technology, and salt reduction in meat products raises the question of spoilage and waste of food. The study was conceived to assess the role of sodium chloride reduction in meat products, both at the level of spoilage development and at the level of bacterial diversity, using 16S rRNA amplicon sequencing and raw pork sausage as a meat model.

Dataset Information

Predictive Modeling of Thiol Changes in Raw Ground Pork as Affected by 13 Plant Extracts-Application of Arrhenius, Log-logistic and Artificial Neural Network Models.

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