Mizoram University

Areca nut (AN) chewing is a major risk factor for oral cancer, partly due to carcinogenic areca nut-specific N-nitrosamines (ASNAs). While ASNAs are thought to form via nitrosation of areca nut-specific alkaloids (ASAs) by oral nitrite, their inherent presence in AN products remains unclear. We developed and validated an LC-HRMS method to quantify five ASNAs simultaneously. Thirty-eight AN samples, including raw materials (RMs) and commercial products (CPs) from India, China, and Taiwan, were analyzed. At least one ASNA was detected in all samples (0.39-242 pmol/g), with N-nitrosoguvacoline predominant, followed by N-nitrosoguvacine, 3-(N-nitrosomethylamino)propionitrile, and N-nitrosonipecotic acid; 3-(N-nitrosomethylamino)propanal was not detected. CPs contained higher ASNA levels than RMs, showing up to 133-fold and 272-fold increases in Gutkha and Yanguo, respectively. ASNA levels correlated with ASAs and nitrite (p < 0.001). These findings suggest that processing or additives enhance ASNA formation through nitrosating agents, emphasizing the need for monitoring to reduce potential risks.

Millet fermentation has gained significant attention due to its numerous health benefits, owing to its higher dietary fiber and gluten-free properties, making it suitable for celiacs, individuals with diabetes, and higher heart risk due to its low glycemic index and low-calorie content. This review critically highlights the current knowledge available about the potential smart technologies in millet fermentation, offering insights into food safety and quality concerns. A thorough literature search was conducted based on recent studies demonstrating the smart fermentation technologies in millet. The findings of this review suggested that the application of smart technologies in millet fermentation can improve precision and efficiency of fermentation process optimization, yield enhancement, and functional attributes. However, only limited applications of smart technologies were employed for millet fermentation, and more advanced and optimized technologies are yet to be explored and implemented for better outcomes. The current millet fermentation studies are mostly of concern towards the beneficial microorganisms, compromising the potential threats of pathogenic organisms if fermentation conditions are not maintained. Integrating advanced machine learning, modeling, and hazard analysis during the fermentation process would lead to attaining safety against contaminants, microbial contamination, toxins, and hazards in millet-based fermented products with assured quality.

Drought remains a significant threat to the humid tropical montane forests of the Eastern Himalaya. Nevertheless, existing monitoring frameworks often fail to capture the complex ecohydrological interactions of these sensitive ecosystems. This study introduces the Multivariate Water Stress-Vegetation Health Index (MWS-VHI), which integrates hydro-meteorological and biophysical variables, including rainfall, soil moisture, evapotranspiration, vegetation indices (NDVI and EVI), and land surface temperature. The index was scaled to a 0-100 range and coupled with Markov chain modelling to quantify its persistence, recurrence, and transition probabilities across defined ecosystem states. Analyses across five representative forest sites in Mizoram revealed strong resilience under high and very high vegetation health states, with gradual transitions between stress levels and no abrupt shifts between extreme drought and optimal conditions. Furthermore, sensitivity and Monte Carlo perturbation tests confirmed the robustness of the index, indicating that rainfall and soil moisture are the most significant parameters influencing forest growth in the region. The findings highlight that forests of Mizoram, while generally resilient, remain vulnerable to prolonged low-water-state conditions that recur less frequently but persist longer once established. The MWS-VHI framework provides a reliable, scalable tool for drought monitoring and forecasting in montane tropical forests. Its integration of vegetation health metrics with hydro-meteorological drivers provides actionable insights to support climate adaptation and mitigation, as well as forest management and biodiversity conservation measures in the region under future climate change scenarios.