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Thermal processing has been predominantly used in the food industry to improve food safety and shelf life. However, heat treatment induces detrimental effects like cooked flavor, texture change, and alteration in sensory attributes. These disadvantages encouraged the food industry to adopt non-thermal food processing technologies. Cold plasma is a promising non-thermal food processing method that uses charged, highly reactive gaseous molecules and species to inactivate contaminating microorganisms present in foods. Thus, it has attracted the attention of scientists globally. This review gives the reader an overview of cold plasma technology fundamentals and the detailed mechanism of interaction of reactive plasma species with the polyphenol compounds (simple phenolic acid, individual phenolic compounds, flavonoids, and anthocyanin) present in food. The impact of cold plasma on polyphenol compounds mainly depends on the food matrix and plasma process parameters, viz. voltage, feed gas, and treatment time. Among various polyphenols, flavonoids are degraded faster because of their high ability to scavenge plasma-generated free radicals. The reactive species cause oxidative degradation, double bond cleavage of polyphenol compounds, and aid in the extraction of phenolic compounds. The cold plasma technology has both positive and negative impacts on polyphenol concentration.

Cold plasma treatment of kiwifruit juice was studied in the domain of 18-30 kV of voltage, 2-6 mm of juice depth, and 6-10 min of treatment time using the response surface methodology (RSM). The experimental design utilized was a central composite rotatable design. The effect of voltage, juice depth, and treatment time on the various responses, namely peroxidase activity, color, total phenolic content, ascorbic acid, total antioxidant activity, and total flavonoid content, was examined. While modeling, the artificial neural network (ANN) showed greater predictive capability than RSM as the coefficient of determination (R 2 ) value of responses was greater in the case of ANN (0.9538-0.9996) than in RSM (0.9041-0.9853). The mean square error value was also less in the case of ANN than in RSM. The ANN was coupled with a genetic algorithm (GA) for optimization. The optimum condition obtained from ANN-GA was 30 kV, 5 mm, and 6.7 min, respectively.