Utilization of Porcine Livers through the Formation of Zn-Protoporphyrin Pigment Optimized by a Response Surface Methodology

There is a growing demand for clean-label products. This study aimed to obtain a food-grade coloring ingredient for meat products based on the formation of Zn-protoporphyrin from porcine livers, thus contributing to the development of nitrite-free products. First, the effects of sodium disulfite and acetic, ascorbic, and lactic acids on the formation of Zn-protoporphyrin and the total microbial count were studied. The combination of ascorbic and acetic acids resulted in a higher Zn-protoporphyrin content than acetic acid alone, and microbial levels were maintained (ca. 3 log CFU/mL). Second, a response surface methodology was used to maximize Zn-protoporphyrin while maintaining microbiological food standards. To that end, the effects of pH (4.2-5.4), incubation time (3-30 h), and temperature (25-50 °C) were studied. The selected conditions for Zn-protoporphyrin formation involved anaerobic incubation at pH 4.8 and 45 °C for 24 h. The safety was validated through challenge testing for relevant pathogens (Listeria monocytogenes, Salmonella spp., and Clostridium perfringens). A significant reduction (>6 log units) was observed in the selected conditions for L. monocytogenes and Salmonella, whereas C. perfringens spores remained at the inoculated levels. The optimized procedure is proven to be microbiologically safe, and may improve the color of nitrite-free meat products.

Rapid Microbial Quality Assessment of Chicken Liver Inoculated or Not With Salmonella Using FTIR Spectroscopy and Machine Learning

Chicken liver is a highly perishable meat product with a relatively short shelf-life and that can get easily contaminated with pathogenic microorganisms. This study was conducted to evaluate the behavior of spoilage microbiota and of inoculated Salmonella enterica on chicken liver. The feasibility of Fourier-transform infrared spectroscopy (FTIR) to assess chicken liver microbiological quality through the development of a machine learning workflow was also explored. Chicken liver samples [non-inoculated and inoculated with a four-strain cocktail of ca. 10³ colony-forming units (CFU)/g Salmonella] were stored aerobically under isothermal (0, 4, and 8°C) and dynamic temperature conditions. The samples were subjected to microbiological analysis with concomitant FTIR measurements. The developed FTIR spectral analysis workflow for the quantitative estimation of the different spoilage microbial groups consisted of robust data normalization, feature selection based on extra-trees algorithm and support vector machine (SVM) regression analysis. The performance of the developed models was evaluated in terms of the root mean square error (RMSE), the square of the correlation coefficient (R²), and the bias (B_f) and accuracy (A_f) factors. Spoilage was mainly driven by Pseudomonas spp., followed closely by Brochothrix thermosphacta, while lactic acid bacteria (LAB), Enterobacteriaceae, and yeast/molds remained at lower levels. Salmonella managed to survive at 0°C and dynamic conditions and increased by ca. 1.4 and 1.9 log CFU/g at 4 and 8°C, respectively, at the end of storage. The proposed models exhibited A_f and B_f between observed and predicted counts within the range of 1.071 to 1.145 and 0.995 to 1.029, respectively, while the R² and RMSE values ranged from 0.708 to 0.828 and 0.664 to 0.949 log CFU/g, respectively, depending on the microorganism and chicken liver samples. Overall, the results highlighted the ability of Salmonella not only to survive but also to grow at refrigeration temperatures and demonstrated the significant potential of FTIR technology in tandem with the proposed spectral analysis workflow for the estimation of total viable count, Pseudomonas spp., B. thermosphacta, LAB, Enterobacteriaceae, and Salmonella on chicken liver.