Study of continuous flow ultrasonication to improve total phenolic content and antioxidant activity in sorghum flour and its comparison with batch ultrasonication

Ultrasonic technology was applied to release the phenolics bound with starch and protein matrix in order to enhance total phenolic content (TPC) and antioxidant activity (AA) of the sorghum flour. Both the continuous flow and batch ultrasonication were implied with independent variables such as flour to water ratio (FWR), ultrasonication intensity (UI), and ultrasonication time (UT) with an additional variable as flow rate (FR) in continuous flow ultrasonication. All the process variables showed a significant effect on the corresponding ultrasonication process. The optimal conditions for the continuous flow ultrasonication were a FWR of 10% w/v, an UI of 20 W/cm², an UT of 130 s, and 15 ml/s FR which produced a maximum values of 70.9 mg GAE/100 g dry matter (d.m.) for TPC and 143.9 µmol TE/100 g d.m. for AA. Regarding the batch ultrasonication, the maximum values were 65.6 mg GAE/100 g d.m. and 141.0 µmol TE/100 g d.m. for TPC and AA, respectively at optimum conditions of 10% w/v FWR, 30 W/cm² UI, and 200 s UT. When comparing with the batch ultrasonication, the continuous flow process saved 35% time and 33% of energy consumption to obtain comparatively higher TPC and AA of the sorghum flour. Ultrasonication improved free phenolic acid content by releasing bound phenolics in the sorghum flour. Impact of various process parameters on specific energy was analyzed during both the processes, and influence of energy on TPC and AA of the sorghum flour was also observed for the batch and continuous flow ultrasonication.

Parboiled Paddy Drying with Different Dryers: Thermodynamic and Quality Properties, Mathematical Modeling Using ANNs Assessment

The effect of hybrid infrared-convective (IRC), microwave (MIC) and infrared-convective-microwave (IRCM) drying methods on thermodynamic (drying kinetics, effective moisture diffusivity coefficient (D_eff), specific energy consumption (SEC)) and quality (head rice yield (HRY), color value and lightness) characteristics of parboiled rice samples were investigated in this study. Experimental data were fitted into empirical drying models to explain moisture ratio (MR) variations during drying. The Artificial Neural Network (ANN) method was applied to predict MR. The IRCM method provided shorter drying time (reduce percentage = 71%) than IRC (41%) and microwave (69%) methods. The D_eff of MIC drying (6.85 × 10⁻¹¹–4.32 × 10⁻¹⁰ m²/s) was found to be more than the observed in IRC (1.32 × 10⁻¹⁰–1.87 × 10⁻¹⁰ m²/s) and IRCM methods (1.58 × 10⁻¹¹–2.31 × 10⁻¹¹ m²/s). SEC decreased during drying. Microwave drying had the lowest SEC (0.457 MJ/kg) compared to other drying methods (with mean 28 MJ/kg). Aghbashlo’s model was found to be the best for MR prediction. According to the ANN results, the highest determination coefficient (R²) values for MR prediction in IRC, IRCM and MIC drying methods were 0.9993, 0.9995 and 0.9990, respectively. The HRY (from 60.2 to 74.07%) and the color value (from 18.08 to 19.63) increased with the drying process severity, thereby decreasing the lightness (from 57.74 to 62.17). The results of this research can be recommended for the selection of the best dryer for parboiled paddy. Best drying conditions in the study is related to the lowest dryer SEC and sample color value and the highest HRY and sample lightness.

Trends in Chemometrics: Food Authentication, Microbiology, and Effects of Processing

In the last decade, the use of multivariate statistical techniques developed for analytical chemistry has been adopted widely in food science and technology. Usually, chemometrics is applied when there is a large and complex dataset, in terms of sample numbers, types, and responses. The results are used for authentication of geographical origin, farming systems, or even to trace adulteration of high value-added commodities. In this article, we provide an extensive practical and pragmatic overview on the use of the main chemometrics tools in food science studies, focusing on the effects of process variables on chemical composition and on the authentication of foods based on chemical markers. Pattern recognition methods, such as principal component analysis and cluster analysis, have been used to associate the level of bioactive components with in vitro functional properties, although supervised multivariate statistical methods have been used for authentication purposes. Overall, chemometrics is a useful aid when extensive, multiple, and complex real-life problems need to be addressed in a multifactorial and holistic context. Undoubtedly, chemometrics should be used by governmental bodies and industries that need to monitor the quality of foods, raw materials, and processes when high-dimensional data are available. We have focused on practical examples and listed the pros and cons of the most used chemometric tools to help the user choose the most appropriate statistical approach for analysis of complex and multivariate data.