The microbial content of unexpired pasteurized milk from selected supermarkets in a developing country

Effect of different preservatives on the physicochemical characteristics and shelf stability of Rasmalai: A comparative study

Rasmalai is a very popular, delicious, and nutritious indigenous sweet dish in Indo-Pakistani civilization. It has a very short shelf life, i.e., up to 3 days. The study was designed to assess the effect of preservatives (potassium sorbate and calcium propionate) on the shelf stability of Rasmalai. Moreover, proximate composition and sensory evaluation of prepared Rasmalai were also carried out in the present study. In general, potassium sorbate and calcium propionate significantly increased the shelf life of Rasmalai. But treatment (R5) containing a combination of both potassium sorbate and calcium propionate (500 ppm each) improved its shelf life by up to 12 days by keeping good sensorial characteristics. The maximum total plate counts as well as yeast and molds were observed in control Rasmalai (without any preservatives) whereas minimum counts were found in R5 treatment containing a combination of both potassium sorbate and calcium propionate (500 ppm each). In conclusion, all the preservatives used in the present study were effective in enhancing the shelf life of Rasmalai but R5 treatment containing a combination of both potassium sorbate and calcium propionate (500 ppm each) was the most effective in enhancing shelf life without deleterious effect on sensorial characteristics.

Factors influencing somatic cell counts and bacterial contamination in unpasteurized milk obtained from water buffalo in Bangladesh

Little has been published on the factors influencing the safety and quality of milk derived from water buffalo in Bangladesh. This study aims to describe the milk hygiene parameters and milk chain characteristics of unpasteurized raw milk sold to consumers in order to improve milk hygiene. A quantitative study design evaluated somatic cell counts, total bacterial counts, and specific gram-negative (Enterobacteria) and gram-positive (staphylococci) pathogens in 377 aseptically collected milk samples. Samples were collected at multiple nodes along the buffalo milk value chain: 122 bulk tank milk samples were collected at the farm level, 109 milk samples at the middlemen level, and 111 milk samples at the milk collection centers. In addition, 35 samples were taken from various milk products at the retail level. It was found that progressively increasing somatic cell counts and bacterial counts, including potential pathogens, occurred along the milk chain. A seasonal increase in spring was found, varying based on the farming system (semi-intensive versus intensive). Other factors included water purity and cleanliness of containers, mixing buffalo and cow's milk, and the location of the water buffalo milk producer (coastal or river basin). This study demonstrated how improving udder health and milk hygiene along the water buffalo milk value chain would increase the safety and quality of water buffalo milk in the study area.

Quality assessment of raw and pasteurized milk in Gondar city, Northwest Ethiopia: A laboratory-based cross-sectional study

Milk is a complete and highly nutritious source of food for human beings. However, in many developing countries, including Ethiopia, the quality of milk products has become a major health concern for consumers, particularly for infants and children. Therefore, the aim of the present study was to assess the quality of raw and pasteurized milk marketed in Gondar city, Northwest Ethiopia. A laboratory-based cross-sectional study was conducted on 90 milk samples. The samples were chosen using a simple random sampling technique. For statistical analysis, ANOVA and the Pearson correlation coefficient were used. The specific gravity of pasteurized milk, farm milk, and milk vendors were found to be 1.021, 1.027, and 1.026, respectively. Farm milk, milk vendors, and pasteurized milk had fat contents of 3.38%, 3.22%, and 3.09%, respectively. The total bacterial count in pasteurized milk, farm milk, and milk vendors was found to be 7.08, 6.73, and 6.94 log₁₀ CFU/mL, respectively. In raw milk, hydrogen peroxide (7.7%), formalin (7.7%), and water (3.8%) were found, whereas in pasteurized milk, hydrogen peroxide (50%), formalin (50%), and water (19.8%) were found. Based on the findings of this study, the quality of both raw and pasteurized milk was found to be poor as per the milk quality standards. This may cause significant public health-related problems. Therefore, an appropriate intervention should be conducted to improve the quality of milk.

Using Bland-Altman Analysis to Identify Appropriate Clonogenic Assay Colony Counting Techniques

OBJECTIVE

Determine the interchangeability of various methods utilized for counting colonies in clonogenic assays.

METHODS

Clonogenic assays of 2 head and neck cancer cell lines were counted through 4 different counting modalities: Manual counting pen, via microscope, 1 publicly available automated algorithm, and a semiautomated algorithm presented by the authors. Each method counted individual wells (N = 24). Pen and microscopic counts were performed by 2 observers. Parameters included both low-growth (<150 colonies/well) and high-growth (>150 colonies/well) cell lines. Correlational and Bland-Altman analyses were performed using SPSS software.

RESULTS

Interobserver manual pen count correlation R2 value in both growth conditions was 0.902; controlling for only low-growth conditions decreased R2 to 0.660. Correlation of microscopic versus pen counts R2 values for observers 1 and 2 were 0.955 and 0.775, respectively. Comparing techniques, Bland-Altman revealed potential bias with respect to the magnitude of measurement (P < .001) for both observers. Correlation of microscopic counts for both interobserver (R2 = 0.902) and intraobserver (R2 = 0.916) were analyzed. Bland-Altman revealed no bias (P = .489). Automated versus microscopic counts revealed no bias between methodologies (P = .787) and a lower correlation coefficient (R2 = 0.384). Semiautomated versus microscopic counts revealed no bias with respect to magnitude of measurement for either observer (P = .327, .229); Pearson correlation was 0.985 (R2 = 0.970) and 0.965 (R2 = 0.931) for observer 1 and 2. Semiautomated versus manual pen colony counts revealed a significant bias with respect to magnitude of measurement (P < .001).

CONCLUSION

Counting with a manual pen demonstrated significant bias when compared to microscopic and semiautomated colony counts; 2 methods were deemed to be interchangeable. Thus, training algorithms based on manual counts may introduce this bias as well. Algorithms trained to select colonies based on size (pixels2) and shape (circularity) should be prioritized. Solely relying on Bland-Altman or correlational analyses when determining method interchangeability should be avoided; they rather should be used in conjunction.

Microbial Safety of Milk from Vending Machines in the Informal Settlements of Nairobi, Kenya

The microbial quality of milk is an important parameter in determining its safety. Processes such as pasteurization are meant to ensure milk is safe for consumption; however, postpasteurization activities could lead to milk contamination, hence threatening the health of consumers. This study sought to determine the microbial safety of milk obtained from vending machines that were located in Nairobi’s informal settlements of Kibra and Dagoretti North. 37 milk samples were collected both from the storage tanks and vending machines located in the study area using a cross-sectional design and tested for microbial safety. Data were then recorded in datasheets and analyzed using frequencies and t-test with the aid of Statistical Package for Social Sciences software. Milk samples tested had acceptable levels of pH with a mean of 6.75; however, the pH of samples from the vending machines was lower than that of samples from the storage tanks. Milk samples from the vending machines had high levels of total viable count (Log 4.7028 cfu/mL) with 81% of the samples having counts beyond the acceptable standard. High levels of Staphylococcus aureus (Log 2.5485 cfu/mL) were also detected in 51% of the samples. Escherichia coli was detected in 27% of the milk samples from the vending machines, while Salmonella spp. was not detected. There was a significant increase in the number of total viable counts ( $P\le 0.001$ ), Staphylococcus aureus ( $P\le 0.007$ ), and Escherichia coli ( $P\le 0.015$ ) in milk samples from the vending machines compared to samples from the storage tanks. Study findings show that milk from the vending machines in the selected informal settlements of Nairobi is highly contaminated and can pose a health risk to consumers if taken without any further heat treatment. Milk vending machine handlers should be trained on practices that safeguard the quality and safety of milk. Public awareness should also be created on the general safety of milk from vending machines to protect consumers from potential health risks resulting from drinking contaminated milk.

Antimicrobial Drug Resistance of Escherichia coli and Staphylococcus aureus Isolated from Milk and Milk Based Beverages of Dhaka City, Bangladesh

The increase of pathogens in milk is threatening for the human beings. This is an investigation on overall microbiological analysis of raw and pasteurized milk as well as the milk-based beverages and also determining the antibiotic resistance pattern of isolated Escherichia coli and Staphylococcus aureus. A total of 100 samples (raw milk, pasteurized milk, mattha, lassi and laban) were taken from various locations of the capital city of Bangladesh, Dhaka. Total Viable Count, Total Coliform Count and Yeast and Moulds Count were performed as the microbiological inspection of selected samples. E.coli and Staphylococcus aureus were identified by conducting morphological analysis, gram-staining and biochemical tests. Antibiotic resistance pattern of isolated Escherichia Coli and Staphylococcus aureus were also detected with 11 commonly used antibiotics by conducting disc-diffusion method, following the CLSI guideline. The TVC range was the highest in raw milk samples (3.8×104 – 4.1×108 cfu/ml), and the lowest in pasteurized milk samples (1.2×102 – 5.4×103 cfu/ml), while 70% raw milk and 10% pasteurized milk samples strains were above the acceptable limit of Food and Drug Administration (FDA). Thirty-six Escherichia coli and thirty-two Staphylococcus aureus were isolated from all the 100 milk and milk-based beverage samples. The isolated Escherichia coli strains were most resistant to Penicillin G (81.58%), Erythromycin (78.94%) and Ampicillin (73.68%), and isolated Staphylococcus aureus strains were most resistant to Penicillin G (90.62%), Ampicillin (81.25%) and Methicillin (71.87%). respectively. Public awareness is needed to reduce the redundant use of antibiotics.

Deep Learning and Transfer Learning for Automatic Cell Counting in Microscope Images of Human Cancer Cell Lines

In biology and medicine, cell counting is one of the most important elements of cytometry, with applications to research and clinical practice. For instance, the complete cell count could help to determine conditions for which cancer cells could grow or not. However, cell counting is a laborious and time-consuming process, and its automatization is highly demanded. Here, we propose use of a Convolutional Neural Network-based regressor, a regression model trained end-to-end, to provide the cell count. First, unlike most of the related work, we formulate the problem of cell counting as the regression task rather than the classification task. This allows not only to reduce the required annotation information (i.e., the number of cells instead of pixel-level annotations) but also to reduce the burden of segmenting potential cells and then classifying them. Second, we propose use of xResNet, a successful convolutional architecture with residual connection, together with transfer learning (using a pretrained model) to achieve human-level performance. We demonstrate the performance of our approach to real-life data of two cell lines, human osteosarcoma and human leukemia, collected at the University of Amsterdam (133 training images, and 32 test images). We show that the proposed method (deep learning and transfer learning) outperforms currently used machine learning methods. It achieves the test mean absolute error equal 12 (±15) against 32 (±33) obtained by the deep learning without transfer learning, and 41 (±37) of the best-performing machine learning pipeline (Random Forest Regression with the Histogram of Gradients features).

Determining the Age of Spoiled Milk from Dried Films Using Attenuated Reflection Fourier Transform Infrared (ATR FT-IR) Spectroscopy

Milk spoilage is an inevitable occurrence, which generates waste and can result in food poisoning. When milk spoils, the off-flavor and curdling are due to excessive proliferation of various bacteria which causes pH changes. Time, temperature, environment, and previous handling practice all affect the spoilage rate. There is a need for a fast reliable and accurate method that can identify in situ early spoilage of milk. Here we show the ability of attenuated total reflection Fourier transformed infrared spectroscopy (ATR FT-IR) in conjunction with multivariate data analysis to predict the age of milk. We found that dried films vastly increased the absorbance of important biomolecules within milk such as lipids, proteins, and sugars, compared to an unchanged milk sample. This allowed us to note the minor discrepancies that happened in spoilage. Spoilt milk was characterized by bands associated with increased lipids, proteins, and lactic acid and a decrease in carbohydrates. A semi-quantitative prediction model for milk spoilage at room temperature demonstrated ATR FT-IR spectroscopy can predict milk age with a root mean square error of prediction of approximately 14 h. The model showed poor performance in the first 40 h but the predictions improved significantly after this time. The experimental procedure proposed for detecting biomolecules within milk has the potential to improve common practice. Furthermore, the model would be a starting point for newer and improved methods to predict the spoilage date of milk, with potential commercial uses to reduce food waste and costs to the milk industry.

Microbiological, biochemical and organoleptic properties of fermented-probiotic drink produced from camel milk

The microbiological and biochemical changes occurred during the fermentation of camel milk inoculated by three selected bacterial starter, were investigated as well as the sensory evaluation of the product. Milk samples were collected from camel herds of southeastern of Iran. Chr. Hansen ABT-10 starter including Lactobacillus acidophillus, Biphidobacterum biphidum and Sterptococcus thermophilus in ratio of 0.50 g per 100 mL of camel milk was added. This fermented product was examined at the 0, 3^rd, 6^th and 9^th days for microbiological, biochemical and sensory evaluations. The results showed the number of starter bacteria was maintained at least 10⁶ CFU mL^-1 during nine test days. It was shown that it could be used as fermented-probiotic drink. The product did not show any microbial contamination. The acidity and protein amount of produced drink showed a significant (p < 0.05) increase in different test days. Fat, solids-not-fat and ash amount of the product showed significant differences at the ninths' test day compared to the zero test day (p < 0.05). Organoleptic properties of product including flavor, color, odor, consistency, mouth feel and overall acceptance were significantly improved (p < 0.05). Therefore, the produced fermented-probiotic drink, in addition to keep maintenance and increased nutritional quantity value, was accepted by consumers in terms of organoleptic properties and it could be used as a healthy and functional drink.

High-Throughput Method for Automated Colony and Cell Counting by Digital Image Analysis Based on Edge Detection

Counting cells and colonies is an integral part of high-throughput screens and quantitative cellular assays. Due to its subjective and time-intensive nature, manual counting has hindered the adoption of cellular assays such as tumor spheroid formation in high-throughput screens. The objective of this study was to develop an automated method for quick and reliable counting of cells and colonies from digital images. For this purpose, I developed an ImageJ macro Cell Colony Edge and a CellProfiler Pipeline Cell Colony Counting, and compared them to other open-source digital methods and manual counts. The ImageJ macro Cell Colony Edge is valuable in counting cells and colonies, and measuring their area, volume, morphology, and intensity. In this study, I demonstrate that Cell Colony Edge is superior to other open-source methods, in speed, accuracy and applicability to diverse cellular assays. It can fulfill the need to automate colony/cell counting in high-throughput screens, colony forming assays, and cellular assays.

Microbiological quality of pasteurized milk on expiration date in Tehran, Iran

The aim of this study was to determine the microbiological quality of pasteurized milk on expiration date in Tehran. Two hundred fifty-four samples collected using a simple randomized sampling method from March 2014 to January 2015 were tested for total microbial count, coliform count, and Escherichia coli contamination according to Iran's National Standards methods. Total microbial count, coliform count, and E. coli contamination exceeded the standard limits in 61.1% [>7.5 × 10(4) (4.88 log) cfu/mL], 24.4% [>10 (1 log) cfu/mL], and 8.7% of the samples, respectively. The mean total microbial count [7.1 × 10(7) (7.85 log) cfu/mL] was above the standard limit. Only 36.6% of the studied samples were in accordance with Iran's National Standard limits. Based on our results, it is necessary to improve the microbial quality of pasteurized milk in Iran.

Genetic characterization of antibiotic-resistant Staphylococcus aureus from milk in the North-West Province, South Africa

Food borne diseases are a major public health concern worldwide. Staphylococcus aureus is one of the potential food borne pathogens which causes nosocomial and community acquired infections. In the present study, 74 representative strains of S. aureus isolated and characterized in previous study from different milk samples were subjected to random amplified polymorphic DNA (RAPD) polymerase chain reaction (PCR) and enterobacterial repetitive intergenic consensus (ERIC)-PCR to generate fingerprints to determine the genetic relationships of the isolated strains. A total of 20 RAPD patterns were generated and the number of amplified fragments obtained ranged from 0 to 8 with molecular weight ranging from 250 to 2000 bp. A dendrogram based on fingerprinting pattern grouped isolates into twelve major clusters (I–XII). In the case of ERIC-PCR 9 banding patterns were obtained with amplicons ranging from 1 to 8 and band sizes ranging from 250 to 2000 bp. A total of four major clusters (I–IV) were observed in the dendrogram based on ERIC fingerprints. The discrete banding patterns obtained both from ERIC-PCR and RAPD-PCR showed remarkably the genetic diversity of S. aureus. The findings of this study indicate that raw, bulk and pasteurized milk in the North-West Province was contaminated with toxigenic and multi-drug resistant S. aureus strains. This emphasizes the need to implement appropriate control measures to reduce contamination as well as the spread of virulent S. aureus strains to reduce the burden of disease in humans.