Aquaculture requires precise non-invasive methods for biomass estimation. This research validates a novel computer vision methodology that uses a signature function-based feature extraction algorithm combining statistical morphological analysis of the size and shape of fish and machine learning to improve the accuracy of biomass estimation in fishponds and is specifically applied to tilapia (Oreochromis niloticus). These features that are automatically extracted from images are put to the test against previously manually extracted features by comparing the results when applied to three common machine learning methods under two different lighting conditions. The dataset for this analysis encompasses 129 tilapia samples. The results give promising outcomes since the multilayer perceptron model shows robust performance, consistently demonstrating superior accuracy across different features and lighting conditions. The interpretable nature of the model, rooted in the statistical features of the signature function, could provide insights into the morphological and allometric changes at different developmental stages. A comparative analysis against existing literature underscores the competitiveness of the proposed methodology, pointing to advancements in precision, interpretability, and species versatility. This research contributes significantly to the field, accelerating the quest for non-invasive fish biometrics that can be generalized across various aquaculture species in different stages of development. In combination with detection, tracking, and posture recognition, deep learning methodologies such as the one provided in the latest studies could generate a powerful method for real-time fish morphology development, biomass estimation, and welfare monitoring, which are crucial for the effective management of fish farms.

Meat products containing Vitamin D3 (VD3) are an innovative option that could contribute to reducing deficiencies in this micronutrient. Designing nanoemulsions that carry VD3 is the first step in developing functional meat products. Thereby, this study investigated the impact of food components on the nanoemulsion properties. A central composite design was used to study the effects of pea protein (PP, 0.5–2.5%), safflower oil (SO, 5–15%), and salt (0–0.5%) on the nanoemulsion stability (ζ-potential and particle size) and the VD3 retention. Also, the optimized nanoemulsion carrying VD3 was incorporated into a meat matrix to study its retention after cooking. The combination of food components in the optimized nanoemulsion were SO = 9.12%, PP = 1.54%, and salt content = 0.4%, resulting in the predicted values of ζ-potential, particle size, and VD3 retention of −37.76 mV, 485 nm, and 55.1%, respectively. The VD3 that was nanoencapsulated and included in a meat product remained more stable after cooking than the VD3 that was not encapsulated. If a meat product is formulated with 5 or 10% safflower oil, the stability of the nanoencapsulated VD3 is reduced. This research contributes to developing functional meat products carrying nanoencapsulated vitamin D3 in natural food-grade components.

Heterocyclic amines (HCAs) are compounds with carcinogenic potential formed during high-temperature processing of meat and meat products. Vegetables or their extracts with high antioxidant capacity can be incorporated into the meat matrix to reduce their formation, but it is necessary to find the optimal levels to achieve maximum inhibition without affecting the sensory properties. The objective of this study was to evaluate the effects of roselle extract (RE, 0–1%), potato peel flour (PP, 0–2%), and beef fat (BF, 0–15%) on the sensory properties and formation of HCAs in beef patties using response surface methodology. IQx, IQ, MeIQx, MeIQ, 4,8-DiMeIQx, and PhIP were identified and quantified by HPLC. Regression models were developed to predict sensory properties and HCAs’ formation. All models were significant (p < 0.05) and showed a R² > 0.70. Roselle extract and beef fat had a negative linear effect on the formation of the total HCAs, while PP had a positive linear effect. The optimal formula that minimizes the formation of HCAs included 0.63% RE, 0.99% PP, and 11.96% BF. RE and PP are foods that can be used as ingredients in low-fat beef patties to minimize the formation of HCAs without affecting their sensory properties.

Frankfurters are the most widespread type of emulsified meat product in the world. However, they could be considered unhealthy due to their high-fat content (15–30%). The meat industry wishes to change the perception of these products by reformulating them. One strategy is increasing the content of compounds considered beneficial for human health. Thus, the aim of this study was to determine the chemical, physico-chemical, and sensorial properties of Frankfurter-type sausages added with roselle (Hibiscus sabdariffa L.), extracts. Frankfurter-type sausages were made following a traditional formula. Three different formulations were prepared. The original mixture was used as a control sample (CS). The other samples were formulated by adding roselle extract at 4.08% (FRE4) and 8.17% (FRE8). For proximate analysis, no statistical differences were found between FRE4 and FRE8 and CS. However, the residual nitrite levels decreased from 88.41 mg NaNO₂/kg in CS to 86.31 and 69.82 mg NaNO₂/kg in FRE4 and FRE8, respectively. Regarding the sensory analysis of the frankfurters, CS and FRE4 samples generally scored significantly higher than FRE8 for all the parameters considered. This study suggests that the reformulation of Frankfurter-type sausages using roselle extracts is feasible and represents a viable alternative to improve the safety and the nutritional composition of the product.

Heterocyclic amines (HCA) are compounds formed by heating meat at a high temperature. Formation of HCA in meat products should be minimized by applying different approaches. The objective of this study was to investigate the effects of roselle extract (RE, 0%–1%), potato peel flour (PP, 0%–2%), and beef fat (BF, 0%–15%) on the formation of HCA in beef patties using response surface methodology. IQx, IQ, MeIQx, MeIQ, 4,8-DiMeIQx, and PhIP were identified and quantified by HPLC. Values from 0 to 18.09 ng IQx, 0.20 to 11.66 ng IQ, 0.29 to 3.93 ng MeIQx, 0.19 to 11.51 ng MeIQ, 0.65 to 1.72 ng 4,8-DiMeIQx, and 1.87 to 14.7 ng PhIP per gram of sample were obtained. All models were significant (p < 0.05), with no lack of fit (p > 0.05) and showed a determination coefficient >0.70. Roselle extract had a negative linear effect on the formation of the total HCA, while PP and BF had a positive quadratic effect. The optimal formula that minimizes the formation of HCA included 0.80% RE, 0.85% PP, and 7.54% BF. Roselle extract and potato peel are foods that can be used as ingredients to minimize the formation of HCA in beef patties.