Rafael Fagnani

Cattle death due to pneumonia-related mortalities (PRM) is a major problem in beef cattle feedlots worldwide. However, compared to other major beef-producing countries, Brazil lacks sufficient data on these losses, having no information to define the optimal feeding duration for cattle. This study investigated PRM at three beef cattle feedlots in the major cattle production states of Brazil over a 24-month period. PRM was the second cause of cattle mortality at these feedlots and accounted for 20.2% (142/703) of all mortalities. Nelore cattle had higher chances (OR = 1.79, p= 0.006) to develop PRM as compared with crossbreed animals, while cattle maintained in feedlots from Mato Grosso were at elevated risk (OR = 1.74, p = 0.031) to develop PRM relative to those maintained in the feedlots from Minas Gerais. Logistic regression revealed that PRM risk decreased with time on feed, confirming that PRM are more likely (p= 0.0005) to be elevated during the earlier days of cattle on-feed at these feedlots. The occurrence of PRM was not influenced by the sex (p= 0.653) of the animal affected nor seasonal variations (p = 0.653). The estimated annual cost of PRM at these feedlots was 105,027 USD, with a projected national cost of 4.040 M USD. A statistical model projected that it may be profitable to maintain cattle in these feedlots for approximately 120-130 days, after which financial investment declines. These results demonstrate the importance of PRM to the local beef cattle industry and emphasize the need to investigate other factors that may influence reduced productivity in beef cattle feedlots in Brazil.

Tampering with raw milk by adding water is a prevalent form of food fraud driven by economic motives. Although the freezing point can detect this adulteration, Brazil and many other regions only mandate this analysis for bulk-tank samples, which can mask the fraud when raw milk is pooled. However, individual compositional analysis of milk from producers is conducted monthly. This study aimed to leverage compositional analysis combined with artificial intelligence to predict water addition. Between 2021 and 2023, 278 raw milk samples from a dairy plant were analyzed using mid-infrared spectroscopy to determine fat, protein, lactose, urea, ash, total solids, and non-fat solids. Simultaneously, these samples were assessed for freezing point, excluding samples with a freezing point above -0.555°CH from the analysis. Of the remaining 251 samples, the Naive Bayes classifier was applied to distinguish between samples with a freezing point greater than -0.530°CH (indicating water addition) and those between -0.531°CH and -0.555°CH (normal). The classifier was trained with 75% of the data, and its performance was evaluated for sensitivity and specificity. The Naive Bayes classifier demonstrated 8% sensitivity, 98% specificity, and 38% positive predictive value. Limitations included a small sample size and the low frequency (15%) of adulterated samples. Future studies with larger sample sizes could improve predictions. Despite its low sensitivity, this algorithm can assist dairy processing plants in identifying potential cases of water addition, aiding in the gradual elimination of such adulteration. Tampering with raw milk by adding water is a prevalent form of food fraud driven by economic motives. Although the freezing point can detect this adulteration, Brazil and many other regions only mandate this analysis for bulk-tank samples, which can mask the fraud when raw milk is pooled. However, individual compositional analysis of milk from producers is conducted monthly. This study aimed to leverage compositional analysis combined with artificial intelligence to predict water addition. Between 2021 and 2023, 278 raw milk samples from a dairy plant were analyzed using mid-infrared spectroscopy to determine fat, protein, lactose, urea, ash, total solids, and non-fat solids. Simultaneously, these samples were assessed for freezing point, excluding samples with a freezing point above -0.555°CH from the analysis. Of the remaining 251 samples, the Naive Bayes classifier was applied to distinguish between samples with a freezing point greater than -0.530°CH (indicating water addition) and those between -0.531°CH and -0.555°CH (normal). The classifier was trained with 75% of the data, and its performance was evaluated for sensitivity and specificity. The Naive Bayes classifier demonstrated 8% sensitivity, 98% specificity, and 38% positive predictive value. Limitations included a small sample size and the low frequency (15%) of adulterated samples. Future studies with larger sample sizes could improve predictions. Despite its low sensitivity, this algorithm can assist dairy processing plants in identifying potential cases of water addition, aiding in the gradual elimination of such adulteration.

Listeriosis is a zoonosis whose etiological agent is the bacterium Listeria monocytogenes. Milk and dairy products are considered excellent substrates for the development of L. monocytogenes. Using conventional PCR, L. monocytogenes was investigated in 102 samples of raw milk from bulk tanks of small farms. Also, microbiological analysis was performed in positive samples by PCR. The primers such as PRS, LM1/LM2, inlA, inlC, and inlJ were used for spatial analysis. PRS indicated a positivity of 20.58%, and inlJ and inlC primers indicated 21.57%. The primers inlA and LM1/LM2 were negative. Our findings indicate it was possible to achieve effective molecular detection of L monocytogenes in the tank milk samples evaluated, in detriment to microbiological culture, in which there was no isolation.

This study investigates the blue water footprint of milk production in the northern region of Paraná, Brazil, focusing on water consumption in dairy cattle farms. Despite Brazil's significant position in global milk production, the country lacks robust initiatives addressing water footprint concerns within its dairy sector. Thirteen dairy farms were assessed to determine water consumption patterns, revealing an average water footprint of 45.48 (L kg-1 ECM of milk year-1), encompassing direct water use for animal drinking and washing dairy machinery. Notably, the proportion of water allocated for services, predominantly milking and cleaning, contributed significantly to water wastage, representing 68% of the total water footprint. Correlation analyses showcased a negative relationship between milk productivity and water footprint values, highlighting the potential for increased productivity to reduce water consumption. Additionally, no significant difference was found in water footprint values between manual and mechanical milking methods, emphasizing the adaptability of smaller-scale farming approaches in aligning with water sustainability principles. Considering the importance of water availability in contextualizing water footprints, this study underscores the need for broader dissemination of the water footprint concept within the dairy sector in Brazil. Implementing strategies to enhance productivity and reduce water consumption, along with fostering awareness and water reuse practices, emerges as crucial steps towards sustainable water management in dairy farming systems. This study investigates the blue water footprint of milk production in the northern region of Paraná, Brazil, focusing on water consumption in dairy cattle farms. Despite Brazil's significant position in global milk production, the country lacks robust initiatives addressing water footprint concerns within its dairy sector. Thirteen dairy farms were assessed to determine water consumption patterns, revealing an average water footprint of 45.48 (L kg-1 ECM of milk year-1), encompassing direct water use for animal drinking and washing dairy machinery. Notably, the proportion of water allocated for services, predominantly milking and cleaning, contributed significantly to water wastage, representing 68% of the total water footprint. Correlation analyses showcased a negative relationship between milk productivity and water footprint values, highlighting the potential for increased productivity to reduce water consumption. Additionally, no significant difference was found in water footprint values between manual and mechanical milking methods, emphasizing the adaptability of smaller-scale farming approaches in aligning with water sustainability principles. Considering the importance of water availability in contextualizing water footprints, this study underscores the need for broader dissemination of the water footprint concept within the dairy sector in Brazil. Implementing strategies to enhance productivity and reduce water consumption, along with fostering awareness and water reuse practices, emerges as crucial steps towards sustainable water management in dairy farming systems.

Members of the family Enterobacteriaceae are often linked to foodborne outbreaks, including acute diarrhea, with diarrheagenic Escherichia coli (DEC) as the most common cause of this disease in low‐ and middle‐income countries. A cross‐sectional study was carried out to evaluate the sanitary conditions of ready‐to‐consume milk, involving 750 pasteurized milk samples. These samples were analyzed for the Enterobacteriaceae count, the presence of Salmonella spp., and the enumeration of E. coli , as well as checking for the presence of DEC‐specific virulence genes. Two molecular assays were used to detect DEC‐specific virulence genes ( eae, bfpA, aggR, ipaH, est, elt, stx1, and stx2 ). Overall, a significant noncompliance rate (5%) was identified in the Enterobacteriaceae counts, indicating postpasteurization contamination. Two genes ( ipaH and elt ) were detected in 14.28% of E. coli ‐positive samples, highlighting the need for improved methods to minimize postpasteurization contamination in dairy plants. This improvement could contribute to better food safety standards internationally. Additionally, further studies are necessary to understand the actual risk posed by these strains circulating in milk for immunocompromised individuals or those with immature immune systems. This underscores a global concern for vulnerable populations worldwide.

Milk is an essential and widely consumed food. In 2022, Brazil produced approximately 25 billion liters of milk for human consumption, and contamination by agrochemicals can have significant impacts on public health. This study analyzed organophosphate and carbamate pesticide residues, as well as its physicochemical and microbiological quality in pasteurized milk sold in Londrina and Arapongas, Brazil. From June to December 2017, 35 samples of pasteurized milk were obtained from the commercial market and analyzed by HPLC and physical-chemical and microbiological tests for freezing point, alkaline phosphatase, lactoperoxidase, and Enterobacteriaceae count. All the samples met the maximum residue limits for both classes of agrochemicals, indicating safe consumption and good agricultural practices. However, the potential effects of degradation byproducts on human health from milk consumption require further investigation. Regarding the physicochemical and microbiological quality, 9% of the samples showed post-pasteurization contamination and 3% indicated water addition. Thus, it is concluded that the biggest challenge for pasteurized milk production industries is to avoid post-pasteurization microbiological contamination, and better tools and auditing are necessary to prevent and detect residual water.

This research communication delineates the quality of pasteurized cow milk sold in Brazil from 2015 to 2020. A cross-sectional study was performed gathering 1749 samples, which were evaluated for microbiological and physicochemical parameters, including Salmonella spp., total and thermotolerant coliforms, freezing point, alkaline phosphatase and lactoperoxidase. The proportion of compliant and non-compliant samples was compared through the years and jurisdiction of the inspection services. Interactions between the design and response variables were assessed by log-linear analysis. Overall, a considerable non-conformity rate (12%) was found for at least one microbiological or physicochemical parameter. Post-pasteurization contamination by coliforms was the major challenge for dairy industries. Notably, the non-compliance rate for freezing point increased during the SARS-CoV-2 pandemic. In addition, the ability to comply was linked to the type of inspection service. Thus, it is suggested that the SARS-CoV-2 pandemic is affecting the dairy industries in Brazil, and we strengthen the need for more studies monitoring the quality of milk over the years, which could assist industries and regulatory agencies to ensure the compliance of pasteurized milk.

Goat milk presents interesting characteristics to consumers, but the production of this food faces several challenges that influence its quality. Process failures from milking to processing and commercialization can expose milk to contamination by pathogenic microorganisms, including the coagulase-positive Staphylococcus group. Although Staphylococcus aureus is the most relevant species in mastitis and food poisoning, other species in this group are also important, especially those related to food poisoning. This study aimed to identify the coagulase-positive Staphylococcus species in goat milk using biochemical tests, determine the prevalence of antimicrobial resistance using the disc diffusion test, and investigate enterotoxin-producing genes, sea, seb, sec, sed, see, seg, seh, and sei by multiplex PCR. A total of 384 coagulase-positive Staphylococcus strains obtained from raw goat milk collected from nine farms in Paraná during four seasons of the year were studied. Biochemical tests showed that 85.69% of the 384 strains were S. aureus, followed by 9.38% of S. intermedius, 4.17% of S. hyicus and 0.78% of S. delphini. For the antimicrobial resistance test, up to three strains of each species identified as coagulase-positive Staphylococci were chosen from each farm, with a total of 74 strains. Of these, 27% (20/74) showed resistance to at least one antibiotic, and among all penicillin-resistant strains, 45% (9/20) also presented resistance to tetracycline. In the search for staphylococcal enterotoxin-producing genes, 49 S. aureus strains were studied; among them, 40.81% (20/49) presented enterotoxin-producing genes. The highest prevalence was detected for the sec gene, which was present in 22.44% (11/49) of the strains, followed by the seh gene in 18.36% (9/49), and the see gene was detected in 4.08% (2/49). It was concluded that S. aureus was the predominant species in raw goat milk, with a high prevalence of penicillin- and tetracycline-resistant Staphylococci and a significant number of strains with staphylococcal enterotoxin-producing genes. The strains studied carried enterotoxin-producing genes involved in food poisoning in humans, indicating that goat milk is a possible source of contamination and, therefore, a potential danger to public health.

Considering the high prevalence of subclinical mastitis and its impacts on milk production, thematic studies are needed and can provide strategic data for its control. This study aimed at investigating the most frequent microorganisms associated with subclinical mastitis in dairy cows in the northwest State of Paraná, Brazil and its sensitivity to antibiotics. A total of 478 foremilk samples were tested. Streptococci were the most frequently genus observed (63%), followed by staphylococci (31%). Streptococcus mutans, S. sanguis, and S. uberis were the most frequently identified species. For Streptococcus spp., the antimicrobial resistance was higher for Ceftiofur, whereas for Staphylococcus spp. it was also possible to verify a high occurrence of resistance for Gentamicin + Amoxicillin. Our results strengthen the knowledge of the microbiological agent and antibiotic-resistance patterns of pathogens at regional level, targeting future strategies to reduce the environmental spread of pathogenic strains resistant to antibiotics.

Lactose is the main carbohydrate in milk, and its absorption occurs via enzymatic hydrolysis, generating glucose and galactose. Lactose intolerance is the reduction of intestinal hydrolysis capacity due to hypolactasia, which results in the need to consume dairy foods with low levels of this carbohydrate. β-galactosidase enzymes are used in dairy industries to hydrolyze lactose, thereby allowing intolerant consumers access to dairy products without the negative health implications. Alternative and official analytical methods are used to quantify the carbohydrate content resulting from enzymatic hydrolysis. The objective of this study was to evaluate the enzymatic hydrolysis of two distinct industrial enzymes produced by the microorganisms Bacillus licheniformis and Kluyveromyces lactis using three analytical methods: enzymatic method, cryoscopy, and high performance liquid chromatography (HPLC) using artificial intelligence to improve the control of the industrial processes. After adding the enzymes to skim milk, time kinetics was performed by collecting samples at time 0, every 10 min for 1 h, and every 30 min until the end of 5 h of hydrolysis. In 97% of the cases, a decrease in lactose concentration was observed by HPLC, followed by the deepening of the cryoscopic point. Glucose measurements by absorbance and HPLC quantification were correlated (r = 0.79; p < 0.01) but not concordant (p < 0.01). It was concluded that by means of artificial intelligence, it was possible to indirectly estimate lactose concentration using an algorithm that associates cryoscopy and glucose concentration.