http://localhost:8080/xmlui/handle/123456789/37 2026-10-26T07:35:23Z 2026-10-26T07:35:23Z RIFA THASNEEM OXANA RISON YUSRA JAWAD OGHA LAKSHMI C V DHANISH REHMAN DR. RAJESH G K, Guide http://localhost:8080/xmlui/handle/123456789/2471 2026-10-17T06:11:37Z 2026-01-01T00:00:00Z

DEVELOPMENT OF A REAL-TIME INTELLIGENT pH SENSITIVE COLOUR CHANGE FRESHNESS INDICATOR USING ANTHOCYANIN-BIOPOLYMER FILM FOR FOOD APPLICATIONS RIFA THASNEEM; OXANA RISON; YUSRA JAWAD; OGHA LAKSHMI C V; DHANISH REHMAN; DR. RAJESH G K, Guide Food spoilage represents a critical challenge in the global food supply chain, necessitating innovative solutions for real-time freshness monitoring. This study successfully developed a biodegradable intelligent packaging film that provides visual indication of food freshness through pH-dependent colour transitions, addressing the limitations of conventional passive packaging systems. Anthocyanins extracted from purple cabbage were selected as the natural colourimetric indicator due to their pH sensitivity, non-toxicity, biodegradability, and sustainability advantages over synthetic indicators. Anthocyanin extraction was optimized using ultrasound-assisted extraction (UAE) and microwave-assisted extraction (MAE) techniques, with UAE yielding significantly higher anthocyanin content (21.8 mg/L) compared to MAE (15.3 mg/L). The extracted anthocyanins exhibited excellent pH responsiveness across pH 1–14, displaying distinct colour transitions from red in acidic conditions to purple at neutral pH and yellow-green under alkaline conditions, confirming their suitability as natural colourimetric sensors. Biodegradable films were fabricated using carboxymethyl cellulose (CMC) and chitosan as polymer matrices through solvent casting methodology. Comprehensive characterization revealed that CMC-based films demonstrated superior properties: higher thickness (0.36 mm), enhanced flexibility (33 mm elongation), better colour uniformity, and superior pH-responsive behaviour compared to chitosan films. Advanced analytical techniques including UV-Visible spectroscopy, FTIR, XRD, DSC, and SEM confirmed successful anthocyanin incorporation with excellent structural integrity and thermal stability. Storage studies on fresh mackerel fish and paneer over 12 days in refrigerated condition demonstrated that the anthocyanin-incorporated CMC film exhibited progressive colour changes from purple (fresh) to dark red (intermediate) to green (spoiled), accurately correlating with microbiological analysis and Total Volatile Base Nitrogen (TVB-N) values, thereby confirming its effectiveness as an intelligent freshness indicator. This study established that anthocyanin-biopolymer films represent a promising sustainable alternative to synthetic packaging, effectively combining environmental responsibility with functional freshness monitoring capability for highly perishable foods.

2026-01-01T00:00:00Z Meera Krishna P M Anshitha Ismail Hanna Theslin K C Nithya K Fathima Ghilsha P http://localhost:8080/xmlui/handle/123456789/2470 2026-10-17T06:13:55Z 2026-01-01T00:00:00Z

STANDARDIZATION OF PROCESS PARAMETERS FOR IDLI BATTER FERMENTATION USING SELECTED STARTER CULTURE COMBINATIONS Meera Krishna P M; Anshitha Ismail; Hanna Theslin K C; Nithya K; Fathima Ghilsha P The present study was carried out to standardize the fermentation of idli batter using selected starter culture combinations, with the aim of achieving consistent fermentation and extending its refrigerated shelf life. To achieve this, five starter cultures, namely Lactobacillus delbrueckii subsp. bulgaricus, Lactobacillus plantarum,Lactobacillus fermentum, Lactococcus Iactis, and Saccharomyces cerevisiae, were selected based on their acidification ability, carbon dioxide production, enzymatic activity, and compatibility during mixed-culture fermentation. The selected starter cultures were evaluated in different combinations and inoculum concentrations, and Response Surface Methodology (RSM) using Design-Expert software was employed to optimize the starter culture combination, inoculum concentration, and fermentation time. The resulting idli batters were then evaluated for changes in physico-chemical properties, sensory characteristics, and microbiological quality at fermentation periods of 2, 4 and 6 hours. Based on the responses obtained at different fermentation periods and inoculum concentrations, 6 h was identified as optimum. Among the optimized formulations, LPF at inoculum concentrations of 0.3 mL and LP at 0.2 mL per 100 g batter (LPF 0.3 and LP 0.2 respectively) were selected for further evaluation. The optimized treatments were subsequently subjected to a refrigerated storage study. The fermented batter was packed in 80 μm laminated pouches and stored under refrigerated conditions. Quality evaluation was carried out on the 0th, 7th , 14th and 21st days of storage. Among the optimized treatments, LPF 0.3 mL/100 g batter showed the most desirable overall response. A gradual decrease in pH along with an increase in titratable acidity was observed, indicating the progress of controlled fermentation. The production of carbon dioxide by the starter cultures contributed to a reduction in bulk density and an increase in volume expansion of the batter, resulting in idlis with a soft, porous, and spongy texture. Compared with the control, the starter culture-treated samples showed better colour stability, controlled acidification, and higher sensory acceptability during storage. Among the different treatments, the optimized LPF consortium, applied as a centrifuged cell suspension at 0.3 ml/100g batter, retained satisfactory physico-chemical, microbiological and sensory quality for up to 14 days under refrigerated storage. The study indicates that the use of a suitable starter culture combination can help achieve controlled and consistent fermentation of idli batter while improving its quality and refrigerated shelf life.

2026-01-01T00:00:00Z AFNA HAMEEM H ABHIRAM M A SREENSNDANA P V Dr. ASHA JOSEPH, (Guide) http://localhost:8080/xmlui/handle/123456789/2421 2026-09-08T05:46:57Z 2026-09-08T00:00:00Z

EVAPOTRANSPIRATION ESTIMATION USING EMPIRICAL AND MACHINE LEARNING MODELS A COMPARATIVE ASSESSMENT OF ACCURACY AND TRANSFERABILITY AFNA HAMEEM H; ABHIRAM M A; SREENSNDANA P V; Dr. ASHA JOSEPH, (Guide) This study compared empirical and machine learning (ML) models for estimating reference evapotranspiration (ET₀) at two agroclimatically contrasting stations in Kerala: RARS Pattambi (inland, semi-arid) and the College of Agriculture, Vellayani (coastal, humid). Using thirty years (1995–2025) of daily meteorological data, the FAO-56 Penman–Monteith equation served as the benchmark. Five ML models - CNN, LSTM, CNN-LSTM,XGBoost, and Random Forest—were developed under four input configurations (full variable, temperature-based, mass transfer-based, and radiation-based) and compared against six empirical models, with accuracy assessed via R², RMSE, and MAE, and spatial transferability quantified through a transferability score. ML models consistently outperformed empirical approaches across all input categories.With complete inputs, all ML models achieved R² > 0.95, with XGBoost RF performing best. Under reduced inputs, radiation-based models performed strongest, with LSTM and XGBoost exceeding an R² of 0.87, while the empirical Makkink model also performed comparatively well, reflecting the strong physical link between solar radiation and ET₀ in Kerala's climate. Temperature-based models performed weakest overall. Transferability analysis showed that models trained at Pattambi transferred more effectively to Vellayani than the reverse, particularly for radiation-based inputs, where external validation exceeded internal accuracy. Mass transfer-based models exhibited the weakest cross-station transferability, reflecting differing humidity dynamics between the inland and coastal environments. Validation using 2025 data confirmed these patterns. Overall, ML models - particularly XGBoost and Random Forest with complete data, and LSTM/CNN-LSTM under radiation-based inputs - offer a reliable and transferable framework for ET₀ estimation. Radiation-based inputs are recommended for data-scarce settings, and the demonstrated inland-to-coastal transferability supports extending these models to ungauged locations, providing a scientific foundation for improved irrigation planning and water resource management across Kerala.

2026-09-08T00:00:00Z Aleena Biju Sourav P Navya Ankita Anand Abhishek, (Guide) S http://localhost:8080/xmlui/handle/123456789/2420 2026-09-08T05:04:14Z 2026-09-08T00:00:00Z

Development of a Learning Based Shele- Life Prediction Model for Kaju Katli Aleena Biju; Sourav P; Navya Ankita Anand; Abhishek, (Guide) S Kaju katli is a widely consumed traditional Indian confectionery prepared primarily from cashew nuts and sugar. Despite its popularity, the product has a limited shelf life due to physicochemical and microbiological deterioration during storage, including moisture loss, lipid oxidation, and microbial growth. Conventional shelf-life determination methods rely on repeated laboratory testing, which is time-consuming, labour-intensive, and expensive. This study aims to develop a machine learning-based approach for predicting the shelf life of kaju katli using quality parameters obtained through laboratory analysis. The work focuses on integrating food science with artificial intelligence to provide a rapid, reliable, and cost-effective alternative to conventional shelf-life estimation. The study evaluates key physicochemical and microbiological parameters, namely moisture content, water activity (aw), free fatty acid (FFA), standard plate count (SPC), and yeast and mould count (YMC), as indicators of product quality during storage. Experimental data collected from laboratory analyses are organized and enhanced through feature engineering techniques to improve dataset quality and predictive capability. A Random Forest regression model is developed to establish relationships between these quality attributes and the remaining shelf life of kaju katli. The model is trained and evaluated using standard performance metrics to ensure prediction accuracy and reliability. The proposed machine learning model is expected to accurately predict shelf life by capturing the complex interactions among physicochemical and microbiological parameters that influence product deterioration. The approach minimizes dependence on prolonged storage studies while providing a scientific basis for quality monitoring and decision-making. The findings are expected to demonstrate that moisture content, water activity, FFA, SPC, and YMC are significant predictors of shelf life and can effectively support intelligent food quality assessment. Overall, this research highlights the potential of machine learning in modern food engineering by offering an efficient tool for shelf-life prediction of traditional confectionery products. The developed model can help manufacturers optimize storage conditions, reduce food wastage, improve inventory management, and enhance consumer confidence by providing more accurate shelf-life estimates. Furthermore, the methodology can be extended to other traditional Indian sweets and similar food products, contributing to the advancement of intelligent food processing and quality assurance systems.

2026-09-08T00:00:00Z