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Sinha, L. K.
- Effect of processing condition on the quality and beany flavour of soymilk
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Authors
Affiliations
1 Agro Produce Processing Division, Central Institute of Agricultural Engineering, Nabibagh, Berasia Road, Bhopal 462 038, IN
1 Agro Produce Processing Division, Central Institute of Agricultural Engineering, Nabibagh, Berasia Road, Bhopal 462 038, IN
Source
Current Science, Vol 109, No 6 (2015), Pagination: 1164-1171Abstract
Soymilk is a water extract of soybean and contains good-quality proteins, fat, minerals and phytochemicals. Regular use of soymilk enhances and protects human health. However, soymilk prepared by traditional method of cold-water grinding has a characteristic beany flavour which may not be acceptable to all consumers. This flavour could be minimized using appropriate processing technology. The present study shows that soymilk with almost negligible flavour could be produced using hot-water grinding and deodorization. Shelf-life of soymilk is about a week when it is pasteurized and stored in a refrigerator. The sensory quality parameters such as appearance, flavour, taste and overall acceptance of soymilk prepared by hot-water grinding followed by deodorization were good, indicating high consumer acceptance.Keywords
Beany flavour, deodorization, lipoxygenase, phytochemicals, soymilkFull Text
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- Advanced machine-learning approaches for landslide susceptibility map generation using remote sensing data and GIS
Abstract Views :42 |
Authors
Affiliations
1 Defence Geoinformatics Research Establishment, Defence Research and Development Organisation, Chandigarh 160 036, IN
2 School of Computer Engineering and Mathematical Science, Defence Institute of Advance Technology, Pune 411 025, IN
1 Defence Geoinformatics Research Establishment, Defence Research and Development Organisation, Chandigarh 160 036, IN
2 School of Computer Engineering and Mathematical Science, Defence Institute of Advance Technology, Pune 411 025, IN
Source
Current Science, Vol 127, No 9 (2024), Pagination: 1065-1075Abstract
Under the present Indian government initiative, all-weather roads are being taken up for four pilgrimage locations in the Uttarakhand state of India. The Rishikesh to Gangotri road axis is a major road used by local citizens and tourists. Rainfall and numerous anthropogenic activities become the primary reasons for landslide hazards in the area. An accurate Landslide Susceptibility Map (LSM) for any area is of paramount importance for the decision makers of land-use planning. The present study gives a comparative analysis of recent advanced algorithms, i.e. CatBoost, LightGBM and deep neural network topology for generating the LSM by following pixel-based. Fourteen causative factors along with landslide inventory of 154 locations are used for the study. LSM are generated based on JENKS natural break criteria using all the algorithms and their performance comparison is evaluated. Overall accuracy for train and test data, prediction accuracy, area under receiver operating characteristics (AUROC) score for test data, and computational time for model fit on train data; are the criteria used for performance evaluation of each algorithm. In this study, it is observed that LSM can be generated at considerably fast pace if CatBoost or LightGBM is used while deep neural network-based topology gives marginally better results on all other performance measure.Keywords
CatBoost, deep neural network, landslide susceptibility mapping, LightGBM.Full Text