Mousumi Ghosh
@ticollege.ac.in
Assistant Professor, TMSL
Techno Main Saltlake
EDUCATION
PhD in Food Process Engineering
RESEARCH INTERESTS
Lipid Chemistry, Powder materials, encalsulation
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Mousumi Ghosh, Rohit Upadhyay, Rakesh Kumar Raigar, and Hari Niwas Mishra
Wiley
Sourav Misra, Sitesh Kumar, Pooja Pandey, Shubham Mandliya, Mousumi Ghosh, Shubhangi Srivastava, and Dipendra Kumar Mahato
CRC Press
Mousumi Ghosh, Sreemoyee Chakraborty, Sourav Misra, Shubhangi Srivastava, Sourabh Bondre, Madhu Kamle, Pradeep Kumar, and Dipendra Kumar Mahato
CRC Press
Shubhangi Srivastava, Ashok Kumar Yadav, Mousumi Ghosh, Dipendra Kumar Mahato, Madhu Kamle, Pooja Pandey, Sreemoyee Chakraborty, and Pradeep Kumar
CRC Press
Mousumi Ghosh, Shubhangi Srivastava, Rakesh Kumar Raigar, and Hari Niwas Mishra
Springer Science and Business Media LLC
In this study, the multilayer perceptron (MLP) artificial neural networks (ANN) method was used to predict the various physiochemical attributes based on spray drying conditions for microencapsulated synergistic vegetable oil blend. This article also presents comparative studies between an MLP ANN and response surface methodology (RSM) in the modelling and prediction of quality attributes of microencapsulated oil blend. The MLP ANN was trained using experimental data comprising of inlet temperature and feed rate as input parameters with a set of quality attributes, viz. microencapsulation efficiency, peroxide value, moisture content, bulk density, colour, hygroscopicity and porosity as output responses with one hidden layer of three units. A good relationship was established between measured and predicted values with MLP topology. The final selected ANN model was compared to the RSM model for its modelling and predictive abilities based on performance indices, viz. RMSE, MAE and R 2 for each output responses. The developed neural network was able to predict efficiently different physico-chemical parameters studied for the microencapsulated vegetable oil blend with a R 2 values ranging between 0.75 and 0.98. The overall relative error during training (0.75) and testing (0.55) obtained was also satisfactory. Thus, MLP neural networking can be regarded as an efficient tool for the investigation, approximation and prediction of the microencapsulated characteristics of the vegetable oil blend.
Mousumi Ghosh, Rohit Upadhyay, Dipendra Kumar Mahato, and Hari Niwas Mishra
Springer Science and Business Media LLC
The thermal stability of ω-6 fatty acid-rich oils is a bewildering problem. The synergistic blends of sunflower (SO) (50–80%) and sesame oil (SEO) (20–50%) were optimized for improved thermal stability, better retention of antioxidants, and balanced ratio of ω-fatty acids (ω-6 and 9). The oil blends were thermally oxidized by Rancimat (temperature 100, 110, 120, and 130 °C; airflow rate 20 L h−1) for estimating the induction period (IP) and kinetic rate constant (k) of lipid oxidation. The oils were exhaustively characterized for thermal stability by thermogravimetry and differential scanning calorimetry. The temperature-dependent kinetics of lipid oxidation was described using Arrhenius equation (lnk vs. 1/T) and activated complex theory (lnk/T vs. 1/T). The calculated kinetic parameters, viz. activation energies, activation enthalpies, and entropies varied from 90.80 to 99.17, 87.58 to 95.94, − 33.28 to − 4.78 J mol−1 K−1, respectively (R2> 0.90, p < 0.05). The optimized blend (OB) consisted of 50.8 and 49.2% of SO and SEO, respectively, and showed the highest synergism (115%) and IP (100 °C) than SO (13.2 vs. 6.1 h). This could be attributed to lignans (6304 vs. 5289 mg kg−1)-induced thermal stability and effective retention of tocopherols (270 vs. 197 mg kg−1). OB possesses balanced composition of ω-fatty acids (ω-9, 34.5 vs. 28.7%; ω-6, 49 vs. 52%) and superior thermal stability (onset temperature, 387 vs. 212 °C; oil induction time, 21.6 vs. 15.7 min) than SO. It could be recommended over SO for culinary applications while ensuing thermal stability and nutritional benefits.
Mousumi Ghosh, Rohit Upadhyay, Dipendra Kumar Mahato, and Hari Niwas Mishra
Elsevier BV
Blended sunflower (SO) (50-80%) and sesame oils (SEO) (20-50%) were evaluated for thermo-oxidative stability (induction period, IP), oxidation kinetics (rate constant, k), synergy and shelf-life (25 °C) (IP25) using Rancimat (100, 110, 120, and 130 °C). The Arrhenius equation (ln k vs. 1/T) and activated complex theory (ln k/T vs. 1/T) were used to estimate activation energies, activation enthalpies and entropies, which varied from 92.05 to 99.17 kJ/mol, 88.83 to 95.94 kJ/mol, -35.58 to -4.81 J/mol K, respectively (R2 > 0.90, p < 0.05). Oil blend (OB) with 1:1 SO to SEO exhibited greatest synergy (115%), highest IP (100 °C) (13.2 vs. 6.1 h) and most extended IP25 (193 vs. 110 days) with a nutritionally stable composition of ω-fatty acids (ω9, 34.5 vs. 28.7%; ω6, 49 vs. 52%) compared with SO. Better retention of lignans (6205 vs. 3951 mg/kg) and tocopherols (332 vs. 189 mg/kg) were also noted in OB compared with SO alone.
Mousumi Ghosh, Chitra J Srivastava Shubhangi, and Hari Niwas Mishra
Oxford University Press (OUP)
J. Chitra, M. Ghosh, and H.N. Mishra
Elsevier BV
Abstract A rapid method for the quantification of cholesterol in the standardized dairy powders was developed using Fourier transform near-infrared (FT-NIR) spectroscopy coupled with appropriate chemometric techniques. Different spectral preprocessing methods were investigated for the partial least-squares (PLS) regression model development. The results showed that the second derivative PLS model in the spectral region of 6101.9–5446.2 cm −1 was the most robust with the best performance indicators (r 2 validation = 0.9998, RMSECV = 1.05 mg cholesterol/100 g, rank = 6 and RPD > 8). Functional band assignment of the major spectral peaks in the cholesterol spectrum was also possible. Statistical evaluation with the HPLC method proved that the developed NIR–chemometric method has good reproducibility and satisfactory accuracy profile. The comparable relative standard deviation (RSD) along with good precision accuracy (95.9–101%) of the proposed FT-NIR method, demonstrate its suitability for the rapid and routine analysis of cholesterol content in the dairy powders.