Microwave frequency-based sensor is a novel technology for insect detection in stored grains, that has advantages in terms of enhanced efficiency, easy installation, and reduced operation cost than conventional insect detection methods. The microwave sensor works on the principle of resonance. A shift in resonance frequency that occurs upon a disturbance or intervention (such as by insect movement in grain bulks) in the measuring cavity is used to detect the insect presence and movement. Microwave sensors with different operating frequencies: 1.2 GHz, 1.4 GHz and 1.8 GHz were tested to detect the Tribolium castaneum adult insects in stored wheat bulks. The peak resonating frequency for each sensor was evaluated before and after insect introduction into the wheat bulks. The shift in peak frequency was directly related to the presence of insects in the stored bulks. The Fast Fourier Transform (FFT) spectrum collected using spectrum analyzer during experiment was fed into MATLAB to develop simulation between the shift in resonance frequency and the presence of insects. The results indicated that the sensor had potential to detect even single insect in the stored wheat bulks.

Food safety of eggs, a good source of high‐quality protein and essential vitamins, is critical to ‎avoid the risk of foodborne illness for consumers. Cold Plasma has attracted global attention as an emerging green technology‎ for food safety. The primary objective of this study is to assess the effectiveness of cold plasma as a chemical-free and non-thermal approach for decontaminating egg surface‎ and investigate its potential as an alternative to the conventional method of washing eggs. For this aim, a cold plasma jet device using air as feed gas was utilized to decontaminate the surfaces of eggs inoculated with Escherichia coli bacteria.‎ The different operating variables for the device, including distance between the nozzle and egg surface (1, 2, and 3 cm), device power ‎‎(300, 350, and 400 W), gas flow rate (30, 32.5, and 35 L/min), and exposure time (20, 40, and 60 s)‎ were examined in the study. To evaluate the potential impact of cold plasma on egg quality, different properties such as eggshell thickness, specific gravity, albumen pH, Haugh unit, and yolk index were measured after treatment. The results showed that a maximum reduction of 1.5 log CFU/egg could be achieved after 60 s treatment of egg surface at 1cm distance by cold plasma device set at 400W power and 35 L/min air flow rate. Moreover, our results indicate that there were no significant differences in the quality of untreated eggs and those treated with cold plasma, suggesting that this method does not have any adverse impacts on egg quality.

Plant-based meat analogues (PBMA) have received widescale research interest, and several reports have documented novel structuring techniques and techno-functional ingredients. However, there is a dearth of research concerning their quality and safety. Presence of numerous plant-derived ingredients with diverse physicochemical properties causes quality changes during storage which hinders their palatability and commercialization. Most existing wet chemistry approaches for quality monitoring are laborious, destructive, and time-consuming. This study reports the use of a non-destructive optical method using visible-near-infrared (VNIR) (400-1000 nm) portable hyperspectral imaging (HSI) coupled with multivariate analysis to monitor and predict the major quality traits of plant-based meat burgers (PBMB) including color, moisture, pH, and textural properties. HSI and quality measurements of 10 different PBMB formulations prepared with ingredient concentrations (w/w) ranging from 10-30% textured vegetable protein and 5-25% pea protein were recorded during a 14-day storage period. Spectral profiles extracted from VNIR-HSI were pre-processed with Savitzky–Golay 1st derivative with 11 smoothing points and mean centering. Unsupervised Principal Component Analysis (PCA) model on pre-processed data classified 140 PBMB samples based on storage days, formulations, and explored 10 wavelengths significantly contributing to predict their quality. Partial Least Square Regression (PLSR) was conducted both in the pre-processed full spectral range and selected wavelengths, to predict the quality traits. The figures of merit of PLSR models yielded good prediction of pH, redness, moisture, and hardness with low error values. The results unravel the feasibility of VNIR-HSI as a real-time and non-invasive method for predicting potential chemical changes of PBMA during storage.