Kamdhenu University

Fisheries management requires reliable baseline data on growth and condition parameters of exploited stocks. This study assessed the length-weight relationship (LWRs) and Fulton’s condition factor (K) of Exhippolysmata ensirostris (n = 901) and Solenocera crassicornis (n = 849) from Jafrabad, Gujarat (January 2024–December 2024). These two species were chosen for the study, as penaeid shrimps represent an important component of domestic and export fisheries, while non-penaeid shrimps form an important component of dried fish and traditional seafood markets along the Gujarat coast. Total lengths ranged from 3.37 to 10.00 cm with corresponding body weights of 0.12–5.97 g in E. ensirostris, whereas S. crassicornis exhibited total length range of 3.02–9.83 cm with weight varying between 0.12 and 7.92 g. The LWRs of E. ensirostris showed positive allometric growth (b = 3.14), with a strong model fit (r² = 0.89), and the estimated b value differed significantly from isometric growth (t = 3.90, p < 0.05). Similarly, S. crassicornis also exhibited positive allometric growth (b = 3.08), with a high coefficient of determination (r² = 0.95), and a statistically significant deviation from isometry (t = 3.17, p < 0.05). The K peaked during September–October for E. ensirostris and April–May for S. crassicornis, consistent with spawning periods. This study represents the first global report on the LWRs and K of E. ensirostris, based on field samples collected from Gujarat, India. It provides essential baseline data for the management, conservation, and future stock assessment of these commercially important species in the North-western Arabian Sea ecosystem.

An experiment was conducted to evaluate the effects of probiotic, prebiotic and synbiotic administration on blood enzyme profile, antioxidant activity and health status in neonatal Jaffarabadi buffalo calves. Twenty-four calves, randomized into four groups of six each-control (C), probiotic (T1), prebiotic (T2) and synbiotic (T3)- were selected at 8 days of age. All calves received restricted suckling plus a basal diet and pelleted concentrate as per ICAR (2013) standards. T1 calves were fed probiotics (L. sporogenes and S. cerevisiae, 5 g/day), T2 received prebiotics (mannan-oligosaccharides, 5 g/day) and T3 were given a synbiotic mix (2.5 g each of probiotic and prebiotic per day). Blood enzyme parameters and antioxidant activity were measured on days 0, 84 and 175 of the experiment. Calves were monitored daily for signs of illness. Enzyme levels-including Lactate Dehydrogenase, Alkaline Phosphatase, Aspartate Aminotransferase and antioxidant Superoxide Dismutase-were not significantly affected by feed additives. However, blood Catalase activity was significantly higher (p <= 0.05) in the treatment groups (T1, T2, T3: 54.4-57.9 ng/ml) compared to the control (51.46 ng/ml). Supplementation with probiotic (0%), synbiotic (33.33%) and prebiotic (50%) reduced the incidence of colibacillosis, pneumonia and gastroenteritis compared to control (66.67%). Pyrexia, illness rates were lowest in the probiotic (16.67%) and synbiotic (66.67%) groups, while higher in the prebiotic and control groups (83.33%). In conclusion, feed additives enhanced antioxidant activity and probiotic and synbiotic supplements helped reduce common illnesses in calves.

Microstructure is a fundamental determinant of the quality, functionality, and stability of dairy products, governing critical attributes such as texture, rheology, meltability, and shelf-life. Conventional compositional analyses are not sufficient to fully explain processing-induced changes and product defects, necessitating microscopic approaches. This review examines the role of microstructural analysis as a quality assessment and process optimization tool for several dairy products, including fluid milk, fermented products, cheese, ice cream, butter, traditional dairy products, and milk powders. Emphasis is placed on the application of complementary microscopy techniques, light microscopy, confocal laser scanning microscopy, scanning and transmission electron microscopy, and cryogenic imaging methods, to elucidate the spatial organization and interactions of casein networks, fat globules, aqueous phases, air cells, and crystalline components. The review discusses evidence linking microstructural features with macroscopic product properties, demonstrating influence of processing variables such as homogenization, heat treatment, fermentation, freezing, and ripening on the changes in structural organization and, consequently, product characteristics. Furthermore, it illustrate the utility of microscopy in defect and critical control point identification, and industrial troubleshooting, including syneresis in yoghurt, textural defects in cheese, recrystallization in ice cream, and poor rehydration of milk powders and establishes microscopy as an indispensable tool for dairy product design, and quality control, providing considerable potential for enhancing consistency, functionality, and processing efficiency for dairy industry.

ABSTRACT Aflatoxins (AFs) are known to be cancer causing substances recognized within milk along with the milk goods. Studies reported that AFs exhibit a significant degree of resistance to high‐temperature processes such as pasteurization as well as ultra‐high temperature (UHT) treatment, indicating that these thermal methods are insufficient for their complete elimination. For decades, food safety and security have been critical priorities on both national and international levels, emphasizing the importance of preventive measures to avoid food contamination. The contamination of milk and dairy products with AFs poses severe health risks, including liver cancer, kidney damage, cardiac complications, and, in extreme cases, sudden death. Therefore, detecting and reducing AF concentrations in milk and related products is essential for safeguarding public health. Advanced analytical methods such as thin‐layer chromatography (TLC), high‐performance liquid chromatography (HPLC), mass spectrometry (MS), and enzyme‐linked immunosorbent assay (ELISA) are widely employed for the detection of AFs in milk and dairy products. Strategies to reduce AF contamination include physical, chemical, and biological approaches. Physical methods such as thermal deactivation, ultraviolet (UV) light exposure, ionizing radiation, and solvent extraction are commonly applied to decrease AF levels.

Milk and dairy products spoil quickly, so predicting shelf life accurately matters for cutting waste and limiting financial losses. Conventional methods rely on laboratory testing and kinetic modelling, which are slow and cope badly when storage temperatures fluctuate. This review examines work published between 2024 and 2026, using a percentage-based analysis of secondary data to describe how artificial intelligence (AI), machine learning (ML), deep learning (DL) and the Internet of Things (IoT) have been applied to milk quality monitoring and shelf-life prediction. Spectroscopy was the most common sensing method, appearing in 62% of the sensing systems examined, and edge computing featured in 64% of the IoT frameworks. Machine learning dominated the modelling at 58%, with deep learning at 27% and hybrid designs the remaining 15%. About 72% of studies reported accuracies above 95%, indicating strong predictive performance against traditional techniques. AI was also linked to efficiency gains of roughly 20−10% and better spoilage prediction, though explainable AI appeared in only 18–22% of cases, so transparency remains a weak point. Overall, AI offers considerable value for milk quality monitoring and shelf-life prediction, provided transparency, dataset standardisation and industrial-scale validation are properly addressed.