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A multi-residue method was developed and validated for the simultaneous determination of 39 and 33 chemical pesticides in rice using LC-MS/MS and GC-MS/MS, respectively. The coefficient of determination was 0.994 to 0.999 over the linear concentration range of 0.01 to 0.1 μg mL-1. The LOD and LOQ were in the range of 0.31 to 3.35 and 1.04 to 10.06 μgkg-1, repsectively. The mean recoveries at 0.01, 0.05, and 0.1 mgkg-1 were found within the range 70 to 120% with satisfactory precision (RSD < 15%). Measurement uncertainty (Uexp) values estimated at 0.05 mgkg-1 spiking level were found lower than 20 μgkg-1 for all tested pesticides. A rice and wheat powder check samples under the proficiency test were analyzed using a standardized method, and the Z score recorded within the acceptable range of −2 to +2. The proposed method was applied to analyze the pesticides in real samples collected from the market and quantified the residues. The developed method could be useful for monitoring 72 chemical pesticides in rice matrices and can be adopted for the pesticide monitoring program.

Radiofrequency (RF) heating was explored to induce accelerated ageing in rice to reduce the time of ageing. RF exposure time and moisture content of rice were set at five levels between 10 and 115 min and 9.5–18.5% respectively using central composite rotatable design. The values of different quality attributes of rice viz., volume expansion ratio (VER), water uptake (WU), gel consistency (GC), adhesiveness (ADH), cohesiveness (COH), amylose content (AC), reducing sugars (RS), free fatty acids (FFA), peak viscosity (PV), final viscosity (FV), and overall acceptability varied between 3.79 and 3.88, 269-281 g/100 g, 80–82 mm, (−) 0.35 - (−)1.12 N, 98.45–119.13, 19.22–19.95%, 0.17–0.21%, 1.9–3.4%, 290.35–349.59 RVU, 260.51–290.45 RVU, and 3–4.8 respectively. Models obtained for all the above quality parameters were highly significant (p ≤ 0.0006). RF treated rice differed significantly in various physico-chemical, cooking, textural and pasting properties as compared to fresh rice. WU, GC, FFA, COH, PV and FV increased significantly (p

Sweet orange (Citrus sinensis Osbeck) belongs to sub family Aurantoideae which is categorised under family Rutaceae. Peel is the main by-product obtained from sweet orange fruit juice processing industry. Peel is rich in many bioactive component, so to utilize this peel for industrial purpose it need to cover to stable form. So in present study sweet orange peel is dried in three drying technique: solar tunnel drying, hot air drying and dehumidified air drying and ground with three different grinding methods: hammer mill grinding at ambient temperature, hammer mill grinding with water cooling and hammer mill grinding with liquid nitrogen cooling. Ground sweet orange peel powder was examined for functional properties. Among nine combinations, dehumidified air dried and liquid nitrogen (LN2) cooled ground sample showed significantly good functional properties such as ascorbic acid (40.00 mg.100g-1), phenols (2.23 mg.g-1), total carotenoids (0.28 mg.g-1), total flavonoids (33.76 mg.g-1), radical scavenging activity (75.59 %), pH (6.26) and water activity (0.35).

The performance evaluation of Aloe vera gel extraction machine in terms of extraction efficiency, gel recovery, and extraction loss were evaluated at three level of roller speeds (11.40, 15.10, and 18.80 rpm) and roller clearance (4, 5, and 6 mm). The data obtained were used for development of artificial neural network (ANN) model. The roller speed, roller clearance, extraction efficiency, gel recovery, and extraction loss were used as the inputs and the gel extraction efficiency, gel recovery, and extraction loss were the output from the model. New feed forward neural network was used for the model building and trained using Bayesian regularization training algorithm. Optimum ANN architecture was obtained by trial and error method. The results showed that the correlation coefficient for calibration and validation was .9751 and .9982, respectively, the root mean square error for calibration and validations was .4746 and .1258, respectively, and the ANN efficiency for calibration and validation was .9507 and .9964, respectively, were recorded for ANN model 5-10-3. Results indicated that ANN can be used efficiently for the modeling of the process indices of the Aloe vera gel extraction machine. Practical applications Using a set of experimental data from Aloe vera gel extraction machine artificial neural network (ANN) model was developed to evaluate and predict the performance of Aloe vera gel extraction machine in terms of extraction efficiency, gel recovery, and extraction loss. The evaluation of ANN model with test data proved that model has very good accuracy. Using this model one can search for optimum operation conditions of Aloe vera gel extraction machine to maximize the efficiency and gel recovery and minimize the extraction loss.

Supercritical fluid extraction (SFE) has shown a great potential for the extraction and isolation of phytochemicals from various food samples as it minimizes sample handling, provides fairly clean Process extracts, SC-CO2 is the fluid most commonly used in SFE with several advantages. Supercritical fluid extraction was carried out at selected SC-CO2 pressures (100, 150 and 200 bar) and temperatures (40, 50 and 60 degreesC). The concentrations of food phyto-chemicals 'viz.', total phenols, total flavonoids, total carotenoids, total sterols and total tocopherols from 'Moringa oleifera. Lam' seed kernels were found to be 41.82 to 44.71 mg GAE/g, 14.82 to 18.25 mg RE/g, 15.45 to 17.06 ppm, 892.05 to 984.17 ppm, 80.27 to 92.26 ppm were highest at 200 bar pressure and 50 degreesCtemperature. All of these parameters were significantly (p