Search results for: N. S. Deora

Authors: Bharat Singh Deora, Sushma Satpute

Abstract:

Cloud computing is based on resource sharing. Like other resources which can be shareable, storage is a resource which can be shared. We can use collective resources of storage from different locations and maintain a central index table for storage details. The storage combining of different places can form a suitable data storage which is operated from one location and is very economical. Proper storage of data should improve data reliability & availability and bandwidth utilization. Also, we are moving the contents of one storage to other according to our need.

Keywords: cloud computing, cloud storage, Iaas, PaaS, SaaS

Procedia PDF Downloads 307

Authors: A. Deswal, N. S. Deora, H. N. Mishra

Abstract:

The aim of the present study was to develop a rapid method for electronic nose for online quality control of oat milk. Analysis by electronic nose and bacteriological measurements were performed to analyse spoilage kinetics of oat milk samples stored at room temperature and refrigerated conditions for up to 15 days. Principal component analysis (PCA), discriminant factorial analysis (DFA) and soft independent modelling by class analogy (SIMCA) classification techniques were used to differentiate the samples of oat milk at different days. The total plate count (bacteriological method) was selected as the reference method to consistently train the electronic nose system. The e-nose was able to differentiate between the oat milk samples of varying microbial load. The results obtained by the bacteria total viable counts showed that the shelf-life of oat milk stored at room temperature and refrigerated conditions were 20 hours and 13 days, respectively. The models built classified oat milk samples based on the total microbial population into “unspoiled” and “spoiled”.

Keywords: electronic-nose, bacteriological, shelf-life, classification

Procedia PDF Downloads 232

Authors: Madhuresh Dwivedi, Navneet Singh Deora, Aastha Deswal, H. N. Mishra

Abstract:

FT-NIR spectroscopy and electronic nose was used to study the kinetics of dough proofing. Spectroscopy was conducted with an optic probe in the diffuse reflectance mode. The dough leavening was carried out at different temperatures (25 and 35°C) and constant RH (80%). Spectra were collected in the range of wave numbers from 12,000 to 4,000 cm-1 directly on the samples, every 5 min during proofing, up to 2 hours. NIR spectra were corrected for scatter effect and second order derivatization was done to transform the spectra. Principal component analysis (PCA) was applied for the leavening process and process kinetics was calculated. PCA was performed on data set and loadings were calculated. For leavening, four absorption zones (8,950-8,850, 7,200-6,800, 5,250-5,150 and 4,700-4,250 cm-1) were involved in describing the process. Simultaneously electronic nose was also used for understanding the development of odour compounds during fermentation. The electronic nose was able to differential the sample on the basis of aroma generation at different time during fermentation. In order to rapidly differentiate samples based on odor, a Principal component analysis is performed and successfully demonstrated in this study. The result suggests that electronic nose and FT-NIR spectroscopy can be utilized for the online quality control of the fermentation process during leavening of bread dough.

Keywords: FT-NIR, dough, e-nose, proofing, principal component analysis

Procedia PDF Downloads 362

Authors: Madhuresh Dwivedi, Navneet Singh Deora, H. N. Mishra

Abstract:

In the context of bakery products, the gluten component of wheat has a crucial role in stabilizing the gas-cell and crumb structures, appearance, mouth feel and maintaining the rheological properties, thus the acceptability of these products. However, because of coeliac disease, some individuals cannot tolerate the protein gliadin present in the gluten fraction of wheat flour. Also termed as gluten-sensitive enteropathy, it is a common chronicle disorder in populations throughout the world with average prevalence of 0.37%. The safest way for celiac sufferers is to stay away from gluten-containing foods such as wheat, rye, barley as well as durum wheat, spelt wheat, and triticale. Thus, in view of the current increasing incidence of gluten intolerant sufferers (due to improved diagnostic procedures), the development of gluten-free cereal-based bakery products suitable for celiac patients represents a challenging and serious task, but also very demanding call for food technologists as well as for the bakers. The use of alternative cereal starches (like rice, soy, maize, potato and so on), gums, hydrocolloids, dietary fibres, alternative protein sources, prebiotics and combinations of them represent the most widespread approach used as replacement to mimic gluten in the manufacture of industrial processable gluten-free bakery products due to their structure-building and water binding properties.

Keywords: gluten-free, coeliac disease, alternative flour, hydrocolloid, crumb structure

Procedia PDF Downloads 248

Authors: Navneet Singh Deora, Aastha Deswal, H. N. Mishra

Abstract:

Pasta is one of the most widely consumed food products around the world. Rapid determination of the moisture content in pasta will assist food processors to provide online quality control of pasta during large scale production. Rapid Fourier transform near-infrared method (FT-NIR) was developed for determining moisture content in pasta. A calibration set of 150 samples, a validation set of 30 samples and a prediction set of 25 samples of pasta were used. The diffuse reflection spectra of different types of pastas were measured by FT-NIR analyzer in the 4,000-12,000 cm-1 spectral range. Calibration and validation sets were designed for the conception and evaluation of the method adequacy in the range of moisture content 10 to 15 percent (w.b) of the pasta. The prediction models based on partial least squares (PLS) regression, were developed in the near-infrared. Conventional criteria such as the R2, the root mean square errors of cross validation (RMSECV), root mean square errors of estimation (RMSEE) as well as the number of PLS factors were considered for the selection of three pre-processing (vector normalization, minimum-maximum normalization and multiplicative scatter correction) methods. Spectra of pasta sample were treated with different mathematic pre-treatments before being used to build models between the spectral information and moisture content. The moisture content in pasta predicted by FT-NIR methods had very good correlation with their values determined via traditional methods (R2 = 0.983), which clearly indicated that FT-NIR methods could be used as an effective tool for rapid determination of moisture content in pasta. The best calibration model was developed with min-max normalization (MMN) spectral pre-processing (R2 = 0.9775). The MMN pre-processing method was found most suitable and the maximum coefficient of determination (R2) value of 0.9875 was obtained for the calibration model developed.

Keywords: FT-NIR, pasta, moisture determination, food engineering

Procedia PDF Downloads 234