Impact of infrared treatment on quality and fungal decontamination of mung bean (Vigna radiata L.) inoculated with Aspergillus spp

Recent developments in applications of physical fields for microbial decontamination and enhancing nutritional properties of germinated edible seeds and sprouts: a review

Germinated edible seeds and sprouts have attracted consumers because of their nutritional values and health benefits. To ensure the microbial safety of the seed and sprout, emerging processing methods involving physical fields (PFs), having the characteristics of high efficiency and environmental safety, are increasingly proposed as effective decontamination processing technologies. This review summarizes recent progress on the application of PFs to germinating edible seeds, including their impact on microbial decontamination and nutritional quality and the associated influencing mechanisms in germination. The effectiveness, application scope, and limitation of the various physical techniques, including ultrasound, microwave, radio frequency, infrared heating, irradiation, pulsed light, plasma, and high-pressure processing, are symmetrically reviewed. Good application potential for improving seed germination and sprout growth is also described for promoting the accumulation of bioactive compounds in sprouts, and subsequently enhancing the antioxidant capacity under favorable PFs processing conditions. Moreover, the challenges and future directions of PFs in the application to germinated edible seeds are finally proposed. This review also attempts to provide an in-depth understanding of the effects of PFs on microbial safety and changes in nutritional properties of germinating edible seeds and a theoretical reference for the future development of PFs in processing safe sprouted seeds.

An Automated Image Processing Module for Quality Evaluation of Milled Rice

The paper demonstrates a low-cost rice quality assessment system based on image processing and machine learning (ML) algorithms. A Raspberry-Pi based image acquisition module was developed to extract the structural and geometric features from 3081 images of eight different varieties of rice grains. Based on features such as perimeter, area, solidity, roundness, compactness, and shape factor, an automatic identification system is developed to segment the grains based on their types and classify them by using seven machine learning algorithms. These ML models are trained using the images and are compared using different ML models. ROC curves are plotted for each model for quantitative analysis to assess the model’s performance. It is concluded that the random forest classifier presents an accuracy of 77 percent and is the best-performing model for the classification of rice varieties. Furthermore, the same algorithm is efficiently employed to determine the price of adulterated rice samples based upon the market price of individual rice.

Technologies for disinfection of food grains: Advances and way forward

Growing demand from the consumers for minimally processed and high-quality food products has raised the scientific quest for foods with improved natural flavours in conjunction with a restricted supplement of additives. In this context, achieving quality and safe food grains and the identification of suitable processing and disinfection technologies have also become the key issues. Microbial contamination is one of the major reasons responsible for the spoilage of food grains. Various sources of contamination such as air and water (both contaminated with dust and dirt), animals (insects, birds, rodents), environmental conditions (rainfall, drought, temperature), unhygienic handling, harvesting, processing equipment and improper storage conditions are responsible for the microbial spoilage of food grains. In order to maintain the food grains safe and un-contaminated, several food processing technologies have been explored and implemented, with the ultimate purpose of maintaining the safety, freshness and nutritional attributes of the food products. Among these technologies, microwave, radiofrequency, infrared, ohmic heating, novel drying methods along with non-thermal methods such as cold plasma, irradiation, ozonation and nanotechnology have attracted much attention because of considerable reduction in the overall processing time with minimum energy consumption. This review aims to discuss the advances involving the said technologies for controlling the microbial contamination of food grains in accordance with their inactivation. Current research status of the thermal and non-thermal emerging technologies for the preservation of food grains as well as perspectives for further research in this area are also elaborated in detail.