Near infrared‐based process analytical technology module for estimating gelatinization optimal point

This research article focusses on the implementation of process analytical technique for the rapid prediction of gelatinization points during the process of steaming of Komal Chawal, an indigenous rice variety from the state of Assam, using the technique of near-infrared spectroscopy. The experimentation involves soaking Komal Chawal at 60°C for 90 min, then steaming at 0, 0.05, 0.1, 0.15, and 0.2 MPa for different time viz. 3, 6, 9, 12, 15, and 18 min, simultaneously Near Infrared (NIR) spectra were also acquired after every treatment. The degree of gelatinization (DG) and moisture gain were measured, and their change pattern was studied using first-order kinetics and Fick's law of diffusion. For first-order rate equation, the goodness of fits were obtained with R2 values between .964 and .927. The partial least square model developed with 10 latent variables showed a training accuracy in terms of statistical metrics were: R2 of .882 and RMSE of 2.56. Thus, NIR-based estimation process had shown reliability for the prediction of the DG. Practical Applications Komal Chawal, an indigenous variety to the state of Assam with a GI tag. It has a unique property of no cooking required, processed by the method of parboiling. So, a rapid prediction method of optimal gelatinization point estimation during the process can save a lot of funds and energy, thus making it more efficient for mass production in industries for the commercialization of the variety.