Camel milk, esteemed for its high nutritional value, has long been a subject of interest. However, the adulteration of camel milk with cow milk poses a significant threat to food quality and safety. Fourier-transform infrared spectroscopy (FT-MIR) has emerged as a rapid method for the detection and quantification of cow milk adulteration. Nevertheless, its effectiveness in conveniently detecting adulteration in camel milk remains to be determined. Camel milk samples were collected from Alxa League, Inner Mongolia, China, and were supplemented with varying concentrations of cow milk samples. Spectra were acquired using the FOSS FT6000 spectrometer, and a diverse set of machine learning models was employed to detect cow milk adulteration in camel milk. Our results demonstrate that the Linear Discriminant Analysis (LDA) model effectively distinguishes pure camel milk from adulterated samples, maintaining a 100% detection rate even at cow milk addition levels of 10 g/100 g. The neural network quantitative model for cow milk adulteration in camel milk exhibited a detection limit of 3.27 g/100 g and a quantification limit of 10.90 g/100 g. The quantitative model demonstrated excellent precision and accuracy within the range of 10–90 g/100 g of adulteration. This study highlights the potential of FT-MIR spectroscopy in conjunction with machine learning techniques for ensuring the authenticity and quality of camel milk, thus addressing concerns related to food integrity and consumer safety.

The objective of the study was to compare and reveal differences in basic chemical parameters, fatty acids, amino acids, and lipid quality indices of crossbred buffalo (swamp x river type) milk produced in summer and winter. The buffalo milk samples were collected in summer (Jul–Aug) and winter (Dec–Jan) from Hubei province, China. The samples were detected by using CombiFoss apparatus, gas chromatography, and an automated specialized amino acid analyzer. The results showed that the basic chemical parameters, fatty acid profiles, lipid quality indices, and amino acid profiles of crossbred buffalo milk differed between summer and winter. Specifically, summer buffalo milk exhibited a higher content of MUFA (monounsaturated fatty acids) and PUFA (polyunsaturated fatty acids) than winter buffalo milk. Summer buffalo milk had a lower content of major SFA (saturated fatty acids), a higher content of ω-3 and DFA (hypocholesterolemic fatty acids), a lower ω-6/ω-3 ratio, a higher value of 3 unsaturated fatty acid indices (C14, C16, C18), and a lower value of IA (index of atherogenicity) and IT (index of thrombogenicity) than winter buffalo milk. Additionally, 17 amino acids, including 8 EAA (essential amino acids) and 9 NEAA (non-essential amino acids) were higher in summer buffalo milk. These results indicated that summer buffalo milk was more health-beneficial than winter buffalo milk. Therefore, summer buffalo milk might be a desirable diet option for human nutrition and health. Our findings provide valuable information for the research and development of buffalo dairy products in China or other Asian countries.

Mastitis is one of the most predominant diseases with a negative impact on ranch products worldwide. It reduces milk production, damages milk quality, increases treatment costs, and even leads to the premature elimination of animals. In addition, failure to take effective measures in time will lead to widespread disease. The key to reducing the losses caused by mastitis lies in the early detection of the disease. The application of deep learning with powerful feature extraction capability in the medical field is receiving increasing attention. The main purpose of this study was to establish a deep learning network for buffalo quarter-level mastitis detection based on 3054 ultrasound images of udders from 271 buffaloes. Two data sets were generated with thresholds of somatic cell count (SCC) set as 2 × 10⁵ cells/mL and 4 × 10⁵ cells/mL, respectively. The udders with SCCs less than the threshold value were defined as healthy udders, and otherwise as mastitis-stricken udders. A total of 3054 udder ultrasound images were randomly divided into a training set (70%), a validation set (15%), and a test set (15%). We used the EfficientNet_b3 model with powerful learning capabilities in combination with the convolutional block attention module (CBAM) to train the mastitis detection model. To solve the problem of sample category imbalance, the PolyLoss module was used as the loss function. The training set and validation set were used to develop the mastitis detection model, and the test set was used to evaluate the network’s performance. The results showed that, when the SCC threshold was 2 × 10⁵ cells/mL, our established network exhibited an accuracy of 70.02%, a specificity of 77.93%, a sensitivity of 63.11%, and an area under the receiver operating characteristics curve (AUC) of 0.77 on the test set. The classification effect of the model was better when the SCC threshold was 4 × 10⁵ cells/mL than when the SCC threshold was 2 × 10⁵ cells/mL. Therefore, when SCC ≥ 4 × 10⁵ cells/mL was defined as mastitis, our established deep neural network was determined as the most suitable model for farm on-site mastitis detection, and this network model exhibited an accuracy of 75.93%, a specificity of 80.23%, a sensitivity of 70.35%, and AUC 0.83 on the test set. This study established a 1/4 level mastitis detection model which provides a theoretical basis for mastitis detection in buffaloes mostly raised by small farmers lacking mastitis diagnostic conditions in developing countries.

Colostrum is a vital performance for buffaloes and potentially functional foods in the future. Therefore, this study aimed to evaluate the difference between the parity of buffalo colostrum and mature milk. Twenty pregnant buffaloes (primiparous = 10; multiparous = 10) were assigned to the same diet prepartum and milking routine postpartum. Calves were separated from the dams immediately after birth and colostrum was harvested within 2 h, whilst mature milk was harvested at 7 days postpartum. The colostrum was analyzed for immunoglobulin G and milk composition as the mature milk. The results showed that there was a higher level of protein, solid not fat, and milk urea nitrogen (p < 0.05), with a tendency for higher total solids (p = 0.08) in primiparous buffaloes’ colostrum compared with multiparous. No parity effect was observed in colostrum immunoglobulin G, fat, lactose, and yields of colostrum and composition (p > 0.05). There was no difference in mature milk composition and yield by parity affected (p > 0.05). Compared with mature milk composition, colostrum had a higher content protein, total solids, solid not fat, and milk urea nitrogen (p < 0.05); however, fat and lactose were lower than that of mature milk (p < 0.05). For minerals, multiparous buffaloes’ colostrum had a higher concentration of Fe (p = 0.05), while the mature milk had higher concentrations of K and P compared with primiparous. Buffalo colostrum had higher concentrations of Na, Mg, Co, Fe, and K with a lower concentration of Ca relative to mature milk (p < 0.05). It was observed that parity affected colostrum characteristics rather than mature milk and caused subtle variations in minerals in colostrum and mature milk of buffaloes. As lactation proceeded, both milk composition and minerals in the milk changed drastically.

Buffalo milk is a dairy product that is considered to have a higher nutritional value compared to cow’s milk. Linoleic acid (LA) is an essential fatty acid that is important for human health. This study aimed to investigate and validate the use of Fourier transform mid-infrared spectroscopy (FT-MIR) for the quantification of the linoleic acid in buffalo milk. Three machine learning models were used to predict linoleic acid content, and random forest was employed to select the most important subset of spectra for improved model performance. The validity of the FT-MIR methods was evaluated in accordance with ICH Q2 (R1) guidelines using the accuracy profile method, and the precision, the accuracy, and the limit of quantification were determined. The results showed that Fourier transform infrared spectroscopy is a suitable technique for the analysis of linoleic acid, with a lower limit of quantification of 0.15 mg/mL milk. Our results showed that FT-MIR spectroscopy is a viable method for LA concentration analysis.

This research aimed to evaluate the effects of Capsicum oleoresin (CAP) supplementation on rumen fermentation in vivo and In vitro, and lactation performance in buffaloes. In the experiment in vitro, 2 × 5 factorial design was carried out according to two temperatures (normal temperature: 39 °C; hyperthermal temperature: 42 °C) and five CAP concentrations (0 mg/L; 2 mg/L; 20 mg/L; 200 mg/L; 2000 mg/L). In the experiment in vivo, four multiparous mid-lactating Mediterranean buffaloes (body weight: 640.08 ± 17.90 kg) were randomly allocated to four treatments according to 4 × 4 Latin square design for CAP supplementation in four dosages (0 mg/kg, 10 mg/kg, 20 mg/kg, or 40 mg/kg of dry matter). The experiment’s results In vitro showed that hyperthermal temperature affected all fermentation characteristics measured in this research. CAP decreased the pH, short-chain fatty acids concentration, and percentages of propionate, butyrate, isobutyrate, valerate, and caproate, while increasing the percentage of acetate and the ratio of acetate to propionate at normal temperature (p ≤ 0.05). In the experiment in vivo, CAP decreased the percentage of propionate and quadratically affected acetate percentage in rumen fluid (p ≤ 0.05). CAP reduced rectal temperature and respiratory rates (p ≤ 0.05) and tended to increase dry matter intake quadratically (p ≤ 0.10). For lactation performance, CAP increased milk yield and milk lactose yield (p ≤ 0.05), and tended to increase milk protein yield (p ≤ 0.10). In conclusion, CAP modified rumen fermentation characteristics in vivo and In vitro and had beneficial effects on lactation performance in buffaloes during summer.