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Assessment of freezing effects on soil requires estimating the soil freezing characteristic curve (SFCC)—the variation of unfrozen water content with temperature. The existing methods for obtaining SFCCs often involve either costly experiments or heuristic inference from water retention data. Here, we propose a pore‐morphology‐based method for simple and efficient estimation of the freezing characteristic curve of water‐saturated porous media, whereby the pore‐scale configurations of water and ice phases are simulated in a digital image of porous microstructure. Idealizing the pore space as a system of overlapping spherical pores, the method simulates the freezing process with the Gibbs‐Thomson equation that can consider freezing‐point depression—a decrease in the freezing point due to spatial confinement—based on thermodynamics. For validation, we apply the proposed method to estimate the SFCC of a field soil for which the experimental freezing characteristics data are available. Results show that even with a digital pore image extracted from a surrogate discrete‐element packing of the soil, the proposed method provides an SFCC very close to the experimental data. Key Points A pore‐morphology‐based method is proposed to estimate the freezing characteristic curve of water‐saturated porous media The Gibbs‐Thomson equation and the sphere insertion method are combined to incorporate freezing point depression The proposed method is validated against experimentally measured freezing characteristic data of a field soil

Controlled freezing point storage assisted by the electrostatic field has been proven to maintain the quality of fresh meat effectively. In this study, we evaluated the freshness variation of pork under controlled freezing point storage assisted by different high-voltage electrostatic field (HVEF) usage frequencies, including single-used HVEF (SHVEF), interval-used HVEF (IHVEF), and continuous-used HVEF (CHVEF). The pH value, total volatile basic nitrogen (TVB-N), total viable count (TVC), and bacterial community composition were determined. The results showed that the pH value in the three groups gradually decreased, while the TVB-N and TVC increased along with the growth of bacteria. The IHVEF and CHVEF treatments effectively delayed the decrease in pH value and significantly reduced the overall level of TVC and TVB-N in fresh pork at a later storage period. Bacterial community composition analysis showed that the dominant bacteria in all three treatments were Pseudomonas , Latilactobacillus , and Brochothrix , and HVEF treatment can significantly decrease their diversity and abundance. The functional analysis showed that HVEF treatment has influenced the pathways of amino acid metabolism, carbohydrate metabolism, and energy metabolism during controlled freezing point storage. In conclusion, the HVEF treatment has a significant ( p  < 0.05) inhibitory effect against dominant bacteria and enhanced the storage quality of fresh pork. These results could provide theoretical guidance for the possible application of HVEF technology in controlled freezing point preservation of meat.

Freezing, a technique extensively employed within the food industry, is considered to be among the most prevalent preservation methods. Conventional techniques of freezing have the potential to cause certain forms of quality degradation, such as harm to cellular structure, and heightened loss of moisture. Therefore, innovative techniques for freezing have been devised to mitigate the drawbacks. Certain naturally occurring biomaterials that possess environmentally friendly, sustainable, non-harmful, remarkably efficient properties and the freezing point regulators (such as antifreeze proteins) have been scientifically proven to manage the freezing and thawing cycle, thus demonstrating promising prospects for utilization in the realm of food and food-related sectors. The purpose of this review is to thoroughly investigate the advanced freezing methods, and the cryoprotective impact of antifreeze proteins (AFPs), emphasizing their function in the food freeze–thaw process. Moreover, this review highlights the advantages and challenges of AFP employment in food preservation. The characteristics of AFPs are derived from their ability to exhibit thermal hysteresis, alter the crystal morphology, and prevent the process of ice recrystallization. Hence, AFPs have been effectively utilized to maintain the quality of a diverse range of refrigerated and frozen food products as a potential cryoprotectant agent in food industry.

Abstract Trichloroacetic acid is known as one of the harmful disinfection byproducts with chlorine of tap water and is regulated according to legally binding standards in Japanese Drinking Water Quality Standards. We developed a high-purity trichloroacetic acid reference material, NMIJ CRM 4074-a, with certified purity as a traceability source of standard solution supplied under the Japan Calibration Service System (JCSS). As trichloroacetic acid is hygroscopic, water could be the main impurity. Although all impurities in the sample can be possibly detected by the freezing point depression method (FPD), it was unclear for trichloroacetic acid whether water was detected by FPD owing to evaporation of water from the sample during fusion. Therefore, we confirmed that water in trichloroacetic acid was detected as an impurity by FPD. The procedure was validated from an increment of purity by FPD due to reduction of water content and an agreement of purity by FPD with those by neutralization titrimetry (NT) and mass balance approach (MBA), both methods were based on different measurement principles from FPD. The certified value was determined to be (0.999 ± 0.003) kg kg−1 from the purity assay by FPD and NT, and uncertainties due to the homogeneity and stability of the CRM were included in the expanded uncertainty. The reliability of the certified value was verified by the agreement of purities by FPD, NT, and MBA.

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.

The greenhouse gas (GHG) emission mandate on jet fuel requires a gradual reduction in the fuel’s GHG emissions, up to 50%, by 2050. For this reason, the demand for bio-jet fuel blended with conventional petroleum-derived jet fuel will increase. In order to meet the quality requirement of blended fuels (ASTM D7566), modeling that can predict the correlation between properties is required. Our aim was to predict the low-temperature properties using the distillation profile results obtained from Simulated Distillation (SIMDIS) according to the carbon number and chemical compositions of bio-jet fuel through correlation and regression analysis. We used hydroprocessed ester and fatty acid (HEFA) bio-jet fuel and hydrocarbon reagents that included C8, C10, and C12 carbons and five main families of hydrocarbons for blended jet fuel. This study shows an overall trend for each component, indicating that the distilled volume fraction is more affected than the carbon number. In the case of the freezing point, by composition, n-paraffin and naphthene have regression coefficients of more than 0.85 for the 50% and 60% recovery temperatures, respectively. In terms of carbon number, the C8 sample has a significant regression coefficient for the 40% recovery temperature, and C10 has a significant regression coefficient for the initial boiling point (IBP) and 10% recovery temperature. In the case of kinematic viscosity, by composition, the regression coefficient is significant for the 20% to 40% recovery temperatures. For naphthene, the kinematic viscosity exhibited no relationship with carbon number. This information can be utilized to determine the blended ratio of bio-jet fuel and conventional jet fuel in newly certified or commercial applications.

Sustainable food security assumes the elimination of food resources adulteration that is already present on farms. This paper is focused on changes in physical and chemical properties of raw cow’s milk treated by the addition of water and NaCl. The main studied factor is the freezing point of milk, which is strongly influenced by the chosen treatment. Adulteration of milk by water can be detected by the changed freezing point of the milk, but this can be brought within the range of standardized limits by the addition of NaCl. Determining the concentration of chloride ions in milk by the titration method is a proxy for the added NaCl. The analysis of raw cow’s milk from 17 agricultural farms in Southwest Slovakia revealed a negative correlation between the content of chlorides and the freezing point. In another laboratory experiment, the differences in the milk freezing points were statistically significant in the samples treated with different amounts of NaCl. The relationship of chlorides and the freezing point to other milk components (minerals, lipids, proteins, solids-not-fat, lactose, pH, and milk acidity after Soxhlet–Henkel) were analysed, as well. The results showed that the chosen method of chlorides detection to identify the adulteration of milk, by added water and NaCl, was not effective due to the unstable composition of milk and uncertainty in measurements (the coefficient of determination was very low, R2 = 0.3022).

The study was conducted on the Erbil Dairy Herd of the Erbil city, to evaluate the effect of body condition score of Bokani dairy cow on milk composition. Milk samples were examined weekly for milk yield and milk fat, protein percentage, fat / protein ratio, lactose, solids not fat (SNF) and freezing point for sixty days. Body condition score of individual cows was recorded in a 1-5 scale. Milk samples were collected from individual cow. Samples collected from cows having similar body condition score were mixed together to make composite sample. The results revealed that the body condition score was affected milk yield and fat percentage significantly (P<0.01). This score was also influenced the percentage of milk protein, fat/protein ratio, lactose, solids-non-fat (SNF) and freezing point (P<0.05). In conclusion, the body condition score is an important indicator to predict the milk yield traits in dairy cows and can be used as a marker for milk yield and milk quality in dairy cows.

The NOAA/NESDIS/NCEI Daily Optimum Interpolation Sea Surface Temperature (SST), version 2.0, dataset (DOISST v2.0) is a blend of in situ ship and buoy SSTs with satellite SSTs derived from the Advanced Very High Resolution Radiometer (AVHRR). DOISST v2.0 exhibited a cold bias in the Indian, South Pacific, and South Atlantic Oceans that is due to a lack of ingested drifting-buoy SSTs in the system, which resulted from a gradual data format change from the traditional alphanumeric codes (TAC) to the binary universal form for the representation of meteorological data (BUFR). The cold bias against Argo was about −0.14°C on global average and −0.28°C in the Indian Ocean from January 2016 to August 2019. We explored the reasons for these cold biases through six progressive experiments. These experiments showed that the cold biases can be effectively reduced by adjusting ship SSTs with available buoy SSTs, using the latest available ICOADS R3.0.2 derived from merging BUFR and TAC, as well as by including Argo observations above 5-m depth. The impact of using the satellite MetOp-B instead of NOAA-19 was notable for high-latitude oceans but small on global average, since their biases are adjusted using in situ SSTs. In addition, the warm SSTs in the Arctic were improved by applying a freezing point instead of regressed ice-SST proxy. This paper describes an upgraded version, DOISST v2.1, which addresses biases in v2.0. Overall, by updating v2.0 to v2.1, the biases are reduced to −0.07° and −0.14°C in the global ocean and Indian Ocean, respectively, when compared with independent Argo observations and are reduced to −0.04° and −0.08°C in the global ocean and Indian Ocean, respectively, when compared with dependent Argo observations. The difference against the Group for High Resolution SST (GHRSST) Multiproduct Ensemble (GMPE) product is reduced from −0.09° to −0.01°C in the global oceans and from −0.20° to −0.04°C in the Indian Ocean.

This study deals with the mathematical modeling of crystallization kinetics occurring during batch production of the ice cream. The temperature decrease was recorded in-situ through a computerized wireless system. A robust pattern-recognition algorithm of the experimental cooling curves was developed to determine the initial freezing point. The theoretical freezing point was used to calibrate the whole time-temperature profile. Finally, a modified Gompertz’s function was used to describe the main steps of crystallization kinetics. Derivative analysis of the Gompertz’s function allowed to determine the time-temperature physical markers of dynamic nucleation, ice crystal growth and air whipping. Composition and freezing properties were used as input variables in multivariate analysis to classification purposes of the ice cream mixtures as a function of their ability to produce high-quality ice cream. The numerical analysis of the whole cooling curve was used to build predictive models of the ice cream quality indices.