Paramita Guha

A Rapid High-Performance Liquid Chromatography Photodiode Array Detection Method to Determine Phenolic Compounds in Mung Bean (Vigna radiata L.)

A fast method was developed for simultaneous detection and quantification of 12 phenolic compounds in mung bean, using reversed phase high-performance liquid chromatography. The method was optimized for mobile phase combination, elution gradient, detection wavelength, and solvent extraction. All the phenolic compounds (gallic acid, neochlorogenic acid, catechin, chlorogenic acid, caffeic acid, p-coumaric acid, t-ferulic acid, vitexin, isovitexin, myricetin, quercetin, and kaempferol) were eluted for 18 min and recovered within a limit as per International Council for Harmonization guidelines. The method showed good linearity with correlation coefficient of 0.998. The limit of detection and quantification of all the compounds ranges from 0.27 ± 0.01 to 3.65 ± 0.3µg/mL and 0.91 ± 0.1 to 12.17 ± 0.9µg/mL, respectively. Vitexin (28.10 ± 0.20 to 29.60 ± 0.6 mg/100 g raw material) and isovitexin (34.09 ± 0.14 to 36.83 ± 0.82 mg/100 g raw material) were the major phenolic compounds along with other phenolic compounds found in mung beans.

Coupled heat and moisture transfer of a single mung bean grain based on IR heating

Infrared (IR) heating is often used for the treatment of liquid and solid foods. IR treatment is known to enhance their shelf life by reducing moisture content and inactivating the microorganisms. Mung bean (a type of pulse from India) is a short season crop; suffers maximum storage loss when compared to other legume grains. The losses are due to moisture and temperature movements. Drying of grains is an important post-harvest operation. IR drying is advantageous over the conventional drying methods. In this paper, the drying of mung bean is considered. An experimental setup is developed to obtain the required moisture and temperature profiles. The equivalent model is simulated using COMSOL multiphysics software and the percentage error between the experimental and simulated models is calculated. Results of numerical implementation are presented and possible further extensions are identified.

Changes in rheological properties of wheat due to storage

BACKGROUND The present study aimed to investigate changes in the fundamental rheological properties of dough prepared from wheat grains stored for 6 months at 20 °C, at ambient temperature (temperature varied with time) and at 4 °C. Stress/shear rate ramp, oscillation and creep-recovery tests were performed to assess the changes in rheological properties as a result of storage. RESULTS Samples were observed to be non-Newtonian; thus, the Bingham model estimated the yield stress, which was maximum for the wheat stored under ambient conditions and minimum for wheat stored at 20 °C. The stress required to break the bonds was maximum for wheat-dough stored under ambient conditions and minimum for wheat stored at 20 °C. Wheat stored at 20 °C also had the highest maximum creep and recovery strain. The viscoelastic properties of the three wheat-dough samples were compared. CONCLUSION The results obtained in the present study show that the wheat-dough prepared under ambient conditions behaves as a rigid and stiff material. The dough prepared from wheat stored at 20 °C had the maximum elasticity.

Impact of infrared treatment on quality and fungal decontamination of mung bean (Vigna radiata L.) inoculated with Aspergillus spp

BACKGROUND Mung bean is a rich source of protein, carbohydrates and fiber content. It also exhibits a high level of antioxidant activity due to the presence of phenolic compounds. Aspergillus flavus and A. niger are the two major fungal strains associated with stored mung bean that lead to post-harvest losses of grains and also cause serious health risks to human beings. Thus there is a need to explore an economical decontamination method that can be used without affecting the biochemical parameters of grains. RESULTS It was observed that infrared (IR) treatment of mung bean surface up to 70 °C for 5 min at an intensity of 0.299 kW m-2 led to complete visible inhibition of fungal growth. Scanning electron microscopy revealed that surface irregularities and physical disruption of spores coat are the major reasons behind the inactivation of IR-treated fungal spores. It was also reported that IR treatment up to 70 °C for 5 min does not cause any negative impact on the biochemical and physical properties of mung bean. CONCLUSION From the results of the present study, it was concluded that IR treatment at 70 °C for 5 min using an IR source having an intensity of 0.299 kW m-2 can be successfully used as a method of fungal decontamination. The fungal spore population was reduced (approximately 5.3 log10 CFU g-1 reductions) without significantly altering the biochemical and physical properties of grains.

Prediction of properties of wheat dough using intelligent deep belief networks

Abstract In this paper, the rheological and chemical properties of wheat dough are predicted using deep belief networks. Wheat grains are stored at controlled environmental conditions. The internal parameters of grains viz., protein, fat, carbohydrates, moisture, ash are determined using standard chemical analysis and viscosity of the dough is measured using Rheometer. Here, fat, carbohydrates, moisture, ash and temperature are considered as inputs whereas protein and viscosity are chosen as outputs. The prediction algorithm is developed using deep neural network where each layer is trained greedily using restricted Boltzmann machine (RBM) networks. The overall network is finally fine-tuned using standard neural network technique. In most literature, it has been found that fine-tuning is done using back-propagation technique. In this paper, a new algorithm is proposed in which each layer is tuned using RBM and the final network is fine-tuned using deep neural network (DNN). It has been observed that with the proposed algorithm, errors between the actual and predicted outputs are less compared to the conventional algorithm. Hence, the given network can be considered as beneficial as it predicts the outputs more accurately. Numerical results along with discussions are presented.

Characterization of Chickpea Flour by Near Infrared Spectroscopy and Chemometrics

ABSTRACT Near infrared (NIR) spectrometry was used for the rapid characterization of quality parameters in desi chickpea flour (besan). Partial least square regression, principal component regression (PCR), interval partial least squares (iPLS), and synergy interval partial least squares (siPLS) were used to determine the protein, carbohydrate, fat, and moisture concentrations of besan. Spectra were collected in reflectance mode using a lab-built predispersive filter-based instrument from 700 to 2500 nm. The quality parameters were also determined by standard methods. The root mean square error (RMSE) for the calibration and validation sets was used to evaluate the performance of the models. The correlation coefficients for moisture, fat, protein, and carbohydrates in chickpea flour exceeded 0.96 using PLS and PCR models using the full spectral range. Wavelengths from 2100 to 2345 nm had the lowest RMSE for quality parameters by iPLS. The error was further decreased by 0.41, 0.1, and 1.1% for carbohydrates, fats, and proteins by siPLS. The NIR spectral regions yielding the lowest RMSE of prediction were 1620–2345 nm for carbohydrates, 1180–1590 nm and 1860–2094 nm for fat, and 1700–2345 nm for proteins. The study shows that chickpea flour quality parameters were accurately determined using the optimized wavelengths.

Intelligent Prediction of Properties of Wheat Grains Using Soft Computing Algorithms

In this paper, chemical properties of wheat dough are predicted using different soft computing tools. Here, back-propagation and genetic algorithm techniques are used to predict the parameters and a comparative study is made. Wheat grains are stored at controlled environmental conditions. The content of fat, moisture, ash, time and temperature are considered as inputs whereas protein and carbohydrate contents are chosen as outputs. The prediction algorithm is developed using back-propagation algorithm, number of layers are optimized and mean square errors are minimized. The errors are further reduced by optimizing the weights using Genetic Algorithm and again the outputs are obtained. The error between predicted and actual outputs is calculated. It has been observed that with back-propagation along GA model algorithm, errors are less compared to the simple back-propagation algorithm. Hence, the given network can be considered as beneficial as it predicts more accurately. Numerical results along with discussions are presented.