Effect of lemon juice concentration on proximate composition of cheese produced from cow's milk

The quality and composition of cheese produced in Ethiopia may vary due to the quality and composition of the milk, method of manufacture, and the process passed from parents to children by observation and practical experiences. In view of this, the effect of lemon juice concentration on the proximate composition of cheese produced from cow’s milk was investigated. The treatment consisted of three lemon juice concentrations (20 mL, 30 mL, and 40 mL) and two milk sources (Menelik and Yigezu dairy farms). The experiment was laid out as a Complete Randomized Design (RCD) in factorial arrangements (2×3=6 treatments) and replicated three times per treatment. Proximate composition (moisture, ash, crude protein, and crude fat) of cheese samples were analyzed using standard methods and sensory quality (aroma, taste, color, and overall acceptability) of cheese were determined using a 10-member panel. Lemon juice concentration has a significant (P< 0.05) effect on proximate composition and sensory characteristics of cheese. The proximate composition result showed that the highest moisture content was 56.23% in the Menelik milk sample treated at 20 mL lemon juice concentration, ash, and crude protein content were highest, 2.88%, and 16.31% in the Menelik milk sample treated at 30 mL lemon juice concentration. A significantly high crude fat (24.99%) content was observed in cheese processed with a 20 mL lemon juice treated milk sample. So, significantly high protein (16.31%), crude fat (24.99%), and moisture (56.23%) contents were observed in cheese, processed using lemon juice. The sensory quality result showed that the highest overall acceptability was 6.00 in the Menelik milk sample treated at 30 mL lemon juice concentration. The sensory quality of the cheese sample has average values of aroma, taste, color, and overall acceptability of 5.12, 5.35, 6.00, and 5.48 on a 7-point hedonic scale, respectively, and was liked slightly. The study concluded that lemon juice concentration had a significant effect on the proximate composition and sensory quality of cheese.

Graphene oxide modified screen-printed electrode for highly sensitive and selective electrochemical detection of ciprofloxacin residues in milk

The monitoring of antibiotic residues in foodstuffs by using rapid detection method is essential for food safety. In this work, the electrochemical sensor was developed by modification of screen-printed carbon electrode with graphene oxide, and then the ciprofloxacin (CIP) was detected based on the complexation of CIP with Mn2+. On modified electrode, the anodic stripping peak current response of Mn2+ was prohibited in the presence of CIP, and a peak current response of the complex was occurred. Thus, the peak current response of the complexation peak was employed as the indicating signal for CIP determination, which was more sensitive than the direct electrochemical oxidation response of CIP. Parameters that affect the signal response have been investigated in method. Under the optimum conditions, the peak current of the complexation peak was linearly correlated with the CIP content in the milk sample solution at 1.0 to 8.0 μM, and the linear correlation coefficients (R2) was 0.994. The limits of detection (LOD) was 0.30 μM. Recoveries of CIP in milk sample were ranged from 81.0 to 95.4% with relative standard deviations (RSDs) below 4.6%. The method showed high selectivity and sensitive, good reproducibility, indicated that this method has potential to be applied in CIP residue analysis.

βeta-lactam and Tetracycline Antibiotic Residues in Cow Milk in the Constantine region, Algeria

The aim of the present study was to investigate the βeta-lactam and tetracycline antibiotic residues in cow milk samples. A total of 122 samples of cow milk were collected from raw milk collectors (109 samples) and from a reconstituted pasteurized milk sales clerk (13 samples) in the Constantine region, Algeria and examined using the ΒetaStar Combo screening kit (Neogen, USA). Results indicates that 13 samples (10.66%) were positive for antibiotics residues: 12 (9.84%) for βeta-lactams (ten (8.20%) raw and two (1.64%) pasteurized milk samples) and only one (0.82%) for tetracyclines in a raw milk sample. It is evident that the Algerian consumer is not sheltered from the danger of antibiotic residues in milk and these inhibitor residues should constitute a constant concern for the dairy industry in Algeria. A control programme should be established.

Effect of Sakura Block on Milk Production and Milk Quality of FH Cows in Late Lactation

This study aims to evaluate milk production, fat content, and protein content of Friesian Holland (FH) dairy cow's milk by giving Sakura block at the final lactation of FH cow. The design used was the Latin Square Design (LSD), with treatments of P0 (0 Sakura blocks), P1 (300 gr Sakura blocks), P2 (600 gr Sakura blocks), and P3 (900 gr Sakura blocks). This study involved four dairy cows for four periods of which period consisted of 10 treatment days, where the last 3 days of each period were the milk sample collection. Milk production and feed consumption were measured and weighed daily. The results showed that the treatment had a significant effect (P <0.05) on the consumption of dry matter ratio and no significant effect (P> 0.05) on milk production, fat content, and milk protein.

SEROPREVALENCE OF COXIELLA BURNETII IN BULK TANK MILKSAMPLES OFFERED FOR SALE IN ERZURUM

Coxiellaburnetii is an obligate intracellular Gram-negative bacterium that causes Q fever in humans. Detection of Coxiellaburnetii in raw milk sample is accomplished by polymerase chain reaction (PCR) or enzyme-linked immunosorbent assay (ELISA) methods. This study was aimed to determine the seroprevalence of coxiellaburnetii in bulk tank milk samples of 24 cattle and 8 goats, which were offered for sale in Erzurum, Turkey. Moreover, pH values were also measured to detect possible relation with pH value and C. burnetiiseropositivity of milk sample. C. burnetii was found positive in 16 (66.7%) of the 24 cattle bulk tank milk samples, and 1 (12.5%) of the 8 goat milk. No statistically significant relation was observed between pH values and positivity of cattle bulk tank milk samples. In conclusion, seropositivity of the milk samples were very high in Erzurum, Turkey. The raw milk sold in local market may cause shedding of Q fever. Therefore, the raw milk should be subjected to heat treatment before consumption to prevent possible transmission of Q fever disease.

Identifying Health Status in Grazing Dairy Cows from Milk Mid-Infrared Spectroscopy by Using Machine Learning Methods

The early detection of health problems in dairy cattle is crucial to reduce economic losses. Mid-infrared (MIR) spectrometry has been used for identifying the composition of cow milk in routine tests. As such, it is a potential tool to detect diseases at an early stage. Partial least squares discriminant analysis (PLS-DA) has been widely applied to identify illness such as lameness by using MIR spectrometry data. However, this method suffers some limitations. In this study, a series of machine learning techniques—random forest, support vector machine, neural network (NN), convolutional neural network and ensemble models—were used to test the feasibility of identifying cow sickness from 1909 milk sample MIR spectra from Holstein-Friesian, Jersey and crossbreed cows under grazing conditions. PLS-DA was also performed to compare the results. The sick cow records had a time window of 21 days before and 7 days after the milk sample was analysed. NN showed a sensitivity of 61.74%, specificity of 97% and positive predicted value (PPV) of nearly 60%. Although the sensitivity of the PLS-DA was slightly higher than NN (65.6%), the specificity and PPV were lower (79.59% and 15.25%, respectively). This indicates that by using NN, it is possible to identify a health problem with a reasonable level of accuracy.