Prospects of cold plasma in enhancing food phenolics: analyzing nutritional potential and process optimization through RSM and AI techniques.

Consumption of plant-based food is steadily increasing and follows an augmented trend owing to their nutritive, functional, and energy potential. Different bioactive fractions, such as phenols, flavanols, and so on, contribute highly to the nutritive profile of food and are known to have a sensitivity toward higher temperatures. This limits the applicability of traditional thermal treatments for plant products, paving the way for the advancement of innovative and non-thermal techniques such as pulsed electric field, microwave, ultrasound, cold plasma, and high-pressure processing. Among these techniques, cold plasma would be an operative choice in plant-based applications due to their higher efficacy, greenness, chemical exclusivity, and quality retention. The efficiency of the plasma process in ensuring the bioactive potential depends on several factors, such as feeding gas, input voltage, exposure time, pressure, and current flow. This review explains in detail the optimization of process parameters of the cold plasma technique, ensuring greater extractability or retention of total phenols and antioxidant potential. Response surface methodology (RSM) is one of the common techniques involved in the optimization of these course factors. It also covers the convention of artificial intelligence-based methods, such as artificial neural networks (ANN) and genetic algorithms (GA), in evaluating the data on process parameters. The review critically examines the strengths of each optimization tool in determining the optimal process parameters for maximizing phenol retention and antioxidant activity. The ascendancy of these techniques was mentioned in the studies regarding fruit, vegetables, and their products, and they can also be applied to other food products.