Oxidation kinetics and ANN model for shelf life estimation of pearl millet ( Pennisetum glaucum L.) grains during storage

Abstract This work analyzed the impact of storage period and temperature (5, 25, and 45°C) on the rancidity indicators and oxidation kinetics of pearl millet grains stored for 120 days. The storage period and temperature had a significant ( p ≤ 0.05) effect on the free fatty acid (FFA), acid value (AV), and peroxide value (PV). An Arrhenius‐type equation was used to estimate the shelf life of grains derived from the oxidation kinetics data of the rancidity parameters. A pseudo‐zero‐order reaction model fit the kinetics of pearl millet grains better than the first‐order reaction model, and this model was also exploited to anticipate the shelf life of the grains during storage. Higher storage temperatures have a less shelf life as compared to the grains stored at a lower temperature. Similarly, the pearl millet grains stored at 45°C have a shelf life of 8.5 weeks, which was much lower than the shelf life of 15.7 weeks at 5°C, respectively. FFA, AV, and PV of pearl millet grains were estimated using artificial neural network modeling based on Levenberg–Marquardt's backpropagation algorithm. Low root mean square error (0.262) values and high R 2 (0.998) values indicate a good fit for the developed model. The developed shelf‐life prediction model has a positive significance for the assessment of quality deterioration of grains during storage. Novelty Impact Statement Storage temperature and time impacted the rancidity indicators, such as FFA, PV, and AV in pearl millet grain. These rancidity indicators can be modeled using oxidation kinetics and on Arrehenius equation and ANN modeling shelf life of the grains can be predicted.