http://nopr.niscpr.res.in/handle/123456789/63168 2026-10-10T20:22:39Z http://nopr.niscpr.res.in/handle/123456789/65002 Title: Standardization of Frying Condition for Preparation of Chhena Jhilli: A Traditional Cheese-based Sweet of Odisha Authors: Nayak, Krishnananda; Bakhara, C K; Rayaguru, Kalpana; Bal, Lalit M; Pal, U S; Dash, S K Abstract: Study aimed to standardize the frying conditions and ingredient selection for preparing Chhena jhilli, a cheese-based deep-fried sweet. The research compared the quality parameters (colour, texture, sensory evaluation, and functional characteristics) of samples prepared with either semolina or refined wheat flour mixed with cottage cheese, fried under different temperature (160, 170 and 180℃) and time (3, 4, and 5 minutes) combinations. Results showed that as frying temperature and time increased, the hardness of the samples consistently increased, while moisture content decreased. An improvement in total colour change and overall acceptability was observed when the frying temperature increased from 160℃ to 170℃ but declined at 180℃. Functional properties, such as volume expansion ratio and oil absorption capacity, also increased with higher temperatures, though the increase was not significant between samples fried at 170℃ and 180℃. The analysis concluded that the optimal Chhena jhilli sample was prepared using a 5:1 cheese-to-semolina ratio, fried at 170℃ for 5 minutes, and dipped in 40°Brix sugar syrup. This sample achieved the highest sensory score (8.42) and closely resembled market samples in terms of colour, hardness, volume expansion, and moisture content. The study's insights are highly valuable for the food processing industry, providing a foundation for efficient, scalable, and high-quality Chhena jhilli production that meets both domestic and global demand. Page(s): 1275-1283 2024-12-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/65001 Title: Effects of Drying Methods on Different Characteristics of Chokeberry Authors: Ceylan, Cigdem Mustu; Cakmakoglu, Selma Kayacan; Bekiroglu, Hatice; Yaman, Mustafa; Akcicek, Alican; Sagdic, Osman; Karasu, Salih Abstract: This research was aimed to determine effect of four different drying techniques, namely Hot Air Drying (HAD), Vacuum Drying (VD), Ultrasound-Assisted Vacuum Drying (USVD), and Freeze Drying (FD) on drying time, total bioactive content, phenolic and anthocyanin profile, surface characteristic and color change of chokeberries. The novelty of this study is the first application of USVD in chokeberry drying. The drying times were recorded as 2100, 1380, and 1200 minutes for HAD, VD, and USVD, respectively, indicating that the application of ultrasound significantly reduced the drying time. Total Phenolic Content (TPC) value of the dried samples varied between 53.15 and 81.18 GAE/g. The individual phenolic and anthocyanin content were determined by HPLC methods. The phenolic profile of the chokeberries showed that protocatechuic acid, catechin, and chlorogenic acid were the major phenolic compounds. The protocatechuic acid value changed between 707.04 and 1126.49 mg/100 and FD showed highest protocatechuic acid value. The anthocyanin profile test showed that the cyanidin-3-O-galactoside was the most prevalent anthocyanin and its value was found as 27725–198674 mcg/100g. E value was used to determine effect of drying techniques on color change of the dried samples. E value was found as 8.07–11.98. SEM analysis was used to determine effect of drying on surface characteristic of chokeberries. The samples dried by FD and USVD showed more porous structure. This study concluded that USVD emerged as a promising alternative to VD and HAD due to its shorter drying time, higher retention of bioactive compounds, and better color preservation. Page(s): 1284-1294 2024-12-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/65000 Title: Prediction based Multiobjective Solution of Economic Emission and Load Dispatch for Solar Integrated Power Systems Authors: Mishra, Sarat Kumar; Mishra, Sudhansu Kumar; Upadhyay, Prabhat Kumar; Jha, Rakesh Chandar Abstract: In present day power systems, the conventional thermal generating stations are being interconnected with solar photovoltaic sources to reduce the running cost along with environmental emission. For proper load dispatch, short term forecasting of electric load is essential to avoid overloads, surges and instability because of varying demand. In this work, Improved Multi-Objective Teaching Learning-Based Optimization (IMOTLBO) algorithm has been developed for effective Economic Emission and Load Dispatch (EELD). Here, the predicted load of a real time load center on a test Solar Integrated System (SIS) has been utilized to obtain Pareto solution, considering cost and emission as two objectives. The performance of the proposed IMOTLBO algorithm is compared with four other MOEAs, namely, Non-dominated Sorting Genetic Algorithm-II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), Modified Multiobjective Cat Swarm Optimization (MMOCSO) and Multi-Objective Differential Evolution with Recursive Distributed Constraint Handling (MODE-RDC). For efficient prediction of the expected electrical load demand, an efficient single layer low complexity neural network i.e. Functional Link Artificial Neural Network (FLANN) model is considered. The weights of FLANN model are optimized by utilizing four different algorithms; one derivative based i.e. Least Mean Squares (LMS), and three others heuristic algorithms, namely Particle Swarm Optimization (PSO), Jaya and TLBO. To compare the performance of the proposed TLBO based FLANN models with the other three models, the Root Mean Square Error (RMSE) has been considered as the performance index. The dominance of the proposed FLANN-TLBO models over others is investigated by conducting non-parametric statistical testing. Page(s): 1295-1305 2024-12-01T00:00:00Z http://nopr.niscpr.res.in/handle/123456789/64999 Title: GCN based Bio-Inspired Classifier for Autism Spectrum Disorder Authors: Christina, J; Sweetlin, J Dhalia; Singh, J Amar Pratap Abstract: Autism spectrum disorder is a diverse neurological state with long-lasting and in most instances lifetime implications for individuals. The early identification and intervention are crucial in mitigating the impact of this disorder, necessitating the development of an objective diagnostic method. This study proposes a novel diagnostic approach that utilizes the data extracted from resting state Functional Magnetic Resonance Imaging (rs-fMRI) and critical phenotypic data of each individual. Recursive feature elimination with Grey Wolf Optimization (GWO) is employed for identifying the optimal attributes from the fMRI data. The selected attributes are then inputted into a Graph Convolution Network (GCN) along with the demographic and basic clinical information for categorization purposes. By utilizing a bioinspired optimization algorithm, the likelihood of identifying the optimal feature subset is enhanced. The study compares the performance of the GCN obtained from the GWO feature selection using both the wrapper and filter approaches. The feature set selected through the GWO wrapper approach demonstrates improved accuracy, achieving 73.86%, along with an AUC of 0.817 when inputted into the Graph Convolution Network. These detections emphasise the significance of an objective and accurate ASD diagnosis method with a limited feature set. Page(s): 1306-1316 2024-12-01T00:00:00Z