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Hot air drying kinetics of paddy grains during instant controlled pressure drop (ICPD) assisted parboiling process and its impact on the quality and micro-structural properties of milled rice were investigated. Among five mathematical models, Midilli model showed best fitted outcomes for prediction of adequate drying behavior. For the mapping of moisture ratio (MR) as a function of treatment pressure (TP), decompressed state duration (DD) and drying time (DT), artificial neural network (ANN) and adaptive neuro-fuzzy interface system (ANFIS) were applied. ANFIS model (5-5-5) with Gaussian membership function demonstrated best performance when contrasted with 3-5-1 ANN architecture. Effective diffusivity of the drying process varied from 2.8 × 10−09 to 7.0 × 10−09 m2/s with the increase of TP and DD. In comparison of quality parameters with the variation of TP and DD, positive impacts on head rice yield (HRY), redness (a*) and yellowness (b*) values and negative consequences on cooking time (CT) and brightness (L*) value were observed. The outcomes additionally uncovered that parboiled rice obtained at 0.6 MPa TP, indicated best quality in terms of improved process performance, HRY, CT, color and micro-structural properties.

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Effects of treatment pressure (TP) and treatment time (TT) on the degree of gelatinization (DG) and its impact on the quality attributes of instant controlled pressure drop (ICPD) treated parboiled rice were investigated. Water diffusion and gelatinization kinetics were established for controlling the quality attributes of parboiled rice. Fick's second law was implemented for the evaluation of water diffusion kinetics. DG was fitted with first order reaction kinetics, which showed a rising trend of reaction rate constant from 0.03 to 0.05 s−1 with the variation of TP. Simulation of gelatinization temperature front and its effect on the gelatinization kinetics were modeled by applying computational fluid dynamics (CFD) approach. Process modeling of DG as a function of TP, TT, and moisture content (MC) was accomplished on the basis of 3‐7‐1 artificial neural network (ANN) architecture. Parboiled rice treated at 0.6 MPa TP showed the best quality in terms of broken percentage, ease of cooking, micro‐structural characteristics, and improved cooking and pasting properties. Practical application Instant controlled pressure drop (ICPD) treated parboiling process is an innovative approach for producing rice with improved quality attributes. Investigation of water diffusion and gelatinization kinetics is a vital aspect in order to control the milling quality of ICPD treated parboiled rice. Further, mapping of gelatinization temperature front is also necessary in order to control DG with the variation of ICPD treatment pressure and time. Therefore, this study was conducted to enumerate the effects of water diffusion and gelatinization kinetics on the quality attributes of ICPD treated parboiled rice.

A novel approach of instant decompression‐induced swell drying also known as instant controlled pressure drop‐hot air dried (ICPD‐HAD) process was applied for the process performance and quality enhancement of dried banana slices. Using a hybrid approach of particle swarm embedded methodology, the process parameters treatment pressure (TP), treatment time (TT), and duration of decompressed State (DDS) were optimized for various responses including drying time (DT), browning index (BI), rehydration ratio (RR), and total antioxidant activity (TAA). The optimized process conditions were found to be 0.1 MPa of TP, 25 s of TT, and 12 s of DDS for a minimum DT of 225 min and, maximum BI, RR, and TAA, of 6.20%, 3.40%, and 98.78% respectively. The results also demonstrated that the dried banana samples produced by the ICPD‐HAD method exhibited superior quality in terms of minimal DT and BI and maximum TAA and RR compared to those produced by the vacuum and hot air drying techniques. Microstructural investigation demonstrating the superiority of ICPD‐induced samples provided additional evidence for the minimum DT requirement. Furthermore, the use of an artificial neural network was employed to investigate the drying kinetics of the novel instant decompression‐induced swell drying process. With R2 greater than 0.99 and a minimum mean square error (MSE) of 0.000131, the 2‐4‐1 artificial neural network (ANN) architecture performed admirably as a means of optimal simulation and robust control of the drying process.Practical applicationsInstant decompression‐induced swell drying also known as ICPD‐HAD process is a novel approach for the drying of banana sample with enhanced process performance and quality attires. It is common for banana drying to cause loss of total antioxidant activity and other important functional properties like color and texture. This issue can be solved and process performance can be enhanced with less energy use by employing an innovative swell drying technique triggered by instant decompression. Therefore, the dried banana samples made using this cutting‐edge technique will be advantageous from a health point of view. This method can be used by the food manufacturing sector to produce dried bananas of a higher quality and greater consumer acceptance.