Dairy, Volume 5, Issue 2 (June 2024) – 3 articles

In this study, the nutritional composition and the chemical safety of Wagashi Gassirè (WG) cheese sold in southern Benin markets were assessed. For this purpose, 15 WG were analysed for fatty acids, essential minerals, and chemical hazards (dioxins, aflatoxin M1 (AFM1), biogenic amines, metals, antibiotic and pesticide residues). The risks related to arsenic, lead, aluminium, AFM1, histamine, and tyramine were calculated using the methods recommended by the European Food Safety Authority. Oleic, palmitic and stearic acids, calcium, and phosphorus were the main fatty acids and minerals detected. Lead (0.08 ± 0.06 mg/kg) and AFM1 (0.3 ± 0.0 µg/kg) were detected in all samples and exceeded the maximum level set by the international standard. Cadaverine and tyramine were the main biogenic amines found. No pesticide residues were detected using a multi-residue method targeting compounds. Residues of quinolones, tetracyclines, and colistin antibiotics were also detected. The calculated chronic exposure indicated no public health concern for the chemical contaminants targeted. Moreover, the average cancer risk related to AFM1 intake was 3 × 10⁻⁴ cases/10⁵ persons/year for the Benin population through WG consumption. This study contributes to the nutritional characterization of WG and identifies lead and AFM1 as the most relevant chemical hazards of this product. Full article

The article examines the main characteristics of white brine cheeses from different manufacturers and changes in their quality indicators. These characteristics include the active acidity, electrical conductivity, total dissolved solids, oxidation–reduction potential, and organoleptic assessment. In this context, the connection to biomimetics lies in the approach of integrating multiple sensory modalities, similar to how biological systems often use multiple senses to perceive and understand their environment. For this purpose, spectral, ultrasonic, and gas characteristics were used, from which informative indices were extracted, united at a later stage in a vector of features. Based on the classification, it was found that the optical characteristics of cheeses from different manufacturers overlap, thus making it possible to predict the main indicators for each type of cheese. The results show that the use of a multimodal approach combining features from different sensors contributes to a better understanding of the variations in cheese properties, while improving the predictive abilities of the created models. The obtained results give a clear idea of the quality of the cheese, thus enabling adequate decisions to be made during the production process. Full article

Replacement heifers are regularly weighed to assess their health. These data also predict the milk yield in their first lactation (L). The first derivative of the growth curve represents the weight change rate at a given time. It is interesting to use the higher-order derivatives of one biological process, such as growth, to predict the outcome of another process, like lactation. With 78 records of grazing heifers, machine learning was used to predict the L based on variables calculated during the rearing period, from 3 to 21 months of age, every 3 months: body weight (P), first (1D), and second derivative (2D) of an individually modeled Fourier function. Other variables were the age at effective insemination (AI) and the season of the year when the heifer was born (E). The average deviance of the fitted models represented the goodness of fit. The models were trained using 85% of the records, and the fit was evaluated using the remaining data. The deviance was lower for the models including both derivatives in comparison to the models where the derivatives were not included (p = 0.022). The best models predicted the L using data of heifers at six months of age (r² = 0.62) and the importance of the variables in the model was 35, 28, 21, and 16% for 1D, AI, 2D, and P, respectively. By utilizing this type of model, it would be possible to select and eliminate excess heifers early on, thereby reducing the financial and environmental costs. Full article