RESUMO
The quality of beef products relies on the presence of a cherry red color, as any deviation toward brownish tones indicates a loss in quality. Existing studies typically analyze individual color channels separately, establishing acceptable ranges. In contrast, our proposed approach involves conducting a multivariate analysis of beef color changes using white-box machine learning techniques. Our proposal encompasses three phases. (1) We employed a Computer Vision System (CVS) to capture the color of beef pieces, implementing a color correction pre-processing step within a specially designed cabin. (2) We examined the differences among three color spaces (RGB, HSV, and CIELab*) (3) We evaluated the performance of three white-box classifiers (decision tree, logistic regression, and multivariate normal distributions) for predicting color in both fresh and non-fresh beef. These models demonstrated high accuracy and enabled a comprehensive understanding of the prediction process. Our results affirm that conducting a multivariate analysis yields superior beef color prediction outcomes compared to the conventional practice of analyzing each channel independently.
RESUMO
Insects are currently of interest due to their high nutritional value, in particular for the high concentration of quality protein. Moreover, it can also be used as an extender or binder in meat products. The objective was to evaluate grasshopper flour (GF) as a partial or total replacement for potato starch to increase the protein content of sausages and achieve good acceptability by consumers. GF has 48% moisture, 6.7% fat and 45% total protein. Sausages were analyzed by NIR and formulations with GF in all concentrations (10, 7, 5 and 3%) combined with starch (3, 5 and 7%) increased protein content. Results obtained for the sausages formulations with grasshoppers showed an increase in hardness, springiness, gumminess and chewiness through a Texture-Profile-Analysis. Moreover, a* and b* are similar to the control, but L* decreased. The check-all-that-apply test showed the attributes highlighted for sausages with GF possessed herbal flavor, brown color, and granular texture. The liking-product-landscape map showed that the incorporation of 7 and 10% of GF had an overall liking of 3.2 and 3.3, respectively, considered as "do not like much". GF can be used as a binder in meat products up to 10% substitution. However, it is important to improve the overall liking of the sausage.
RESUMO
BACKGROUND: Enterococci are lactic acid bacteria that can produce bacteriocins, which may offer an additional hurdle to control the growth of food-borne pathogens; moreover, these bacteriocins may have great potential as natural biopreservatives. The aim of this work was to characterize a bacteriocin-like inhibitory substance (BLIS) with antilisterial activity produced by an enterococcal strain. RESULTS: The bacteriogenic strain was isolated from Mexican fermented sausages and identified as Enterococcus faecium with 99% sequence similarity. Maximal activity was detected at 16 h, where bacterial growth was in middle of the stationary phase. The producer strain was not inhibited by its own antimicrobial peptide. BLIS showed a strong anti-Listeria activity and was inactivated by proteinase K. Heating (121 °C for 15 min) induced some inactivation, but thermotolerance was higher at acid pH values. The yield obtained with a pH-mediated purification process was 32.7%, showing a band with an estimated molecular weight of 3.5 kDa. Automated N-terminal Edman degradation showed the following sequence: YYGNGVTCGSHHCSVD. CONCLUSION: Biochemical characteristics of BLIS produced by E. faecium MXVK29 suggested that it belongs to Class IIa of the Klaenhammer classification and could be considered as a natural food preservative, although further studies need to be performed.
