The effect of a pH-shifting and ultrasound combined process on the functional properties and structure of pea protein isolate (PPI) was investigated. PPI dispersions were adjusted to pH 2, 4, 10, or 12, treated by power ultrasound for 5min, and incubated for 1h before the sample pH was brought back to neutral. After treatment, water solubility, protein aggregate size, solution turbidity, surface hydrophobicity (Ho), free sulfhydryl content (SH), and sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) of the soluble PPI were determined. pH-shifting at pH 12 and ultrasound combined treatment (pH12-US) significantly improved protein properties, while property modification of the samples treated under acidic conditions was less pronounced. The pH12-US treated PPI had a solubility seven times higher than the control, reaching an average particle size of 45.2nm. In addition, the pH12-US treated PPI significantly improved Ho due to disulfide bonds disruption, and produced more protein sub-units than other treatments. The soluble PPI obtained through this process may be a promising emulsifier for the enrichment of fat-soluble nutrients in foods.

Big data analysis has found applications in many industries due to its ability to turn huge amounts of data into insights for informed business and operational decisions. Advanced data mining techniques have been applied in many sectors of supply chains in the food industry. However, the previous work has mainly focused on the analysis of instrument‐generated data such as those from hyperspectral imaging, spectroscopy, and biometric receptors. The importance of digital text data in the food and nutrition has only recently gained attention due to advancements in big data analytics. The purpose of this review is to provide an overview of the data sources, computational methods, and applications of text data in the food industry. Text mining techniques such as word‐level analysis (e.g., frequency analysis), word association analysis (e.g., network analysis), and advanced techniques (e.g., text classification, text clustering, topic modeling, information retrieval, and sentiment analysis) will be discussed. Applications of text data analysis will be illustrated with respect to food safety and food fraud surveillance, dietary pattern characterization, consumer‐opinion mining, new‐product development, food knowledge discovery, food supply‐chain management, and online food services. The goal is to provide insights for intelligent decision‐making to improve food production, food safety, and human nutrition.

Drying is one of the most prevalent methods to reduce water activity and preserve foods. However, it is also the most energy-intensive food processing unit operation. Although a number of drying methods have been proposed and tested for the purpose of achieving a time- and energy-efficient drying process, almost all current drying methods still rely on thermal energy to remove moisture from the product. In this study, a novel use of power ultrasound was explored for drying of apple slices without the application of heat. The non-thermal ultrasound contact drying (US-CD) was performed in the presence of an air stream (26-40 °C) flowing over product surface to remove mist or vapor produced by the ultrasound treatment. The effects of the non-thermal US-CD, hot-air drying (HAD), and freeze drying (FD) on the changes in rehydration ratio, pH, titratable acidity, water activity, color, glass transition temperature, texture, antioxidant capacity, total phenols, and microstructures of the samples were evaluated. The moisture content of the apple slices reached below 5% (w.b.) after 75-80 min of US-CD, which was about 45% less than that of the HAD method. The antioxidant capacity and total phenol contents of the US-CD samples were significantly higher than that of the AD samples. The non-thermal ultrasonic contact drying is a promising method which has the potential to significantly reduce drying time and improve product quality.