Research progress of edible mushroom polysaccharide-metal trace element complexes

Metal trace elements are crucial for human health, and the complexes of edible mushroom polysaccharides with metal trace elements are currently a research hotspot in the field of food science. This article reviews the preparation methods, structural characterization, and physiological activities of edible mushroom polysaccharide-metal trace element complexes, including iron, selenium, and zinc. Research has shown that iron complexes obtained through Co-thermal synthesis of the FeCl3 method exhibit excellent antioxidant and anti-anemia functions; selenium complexes prepared via selenium-enriched cultivation significantly enhance immunological and anti-cancer properties; zinc complexes improve lipid-lowering, liver protection, and antioxidant capabilities. However, there is an imbalance in research among different metal elements, particularly with a high density of studies on selenium complexes. These studies provide a foundation for the future development of edible mushroom polysaccharide-metal trace element complexes.

Grifola frondosa polysaccharides: A review on structure/activity, biosynthesis and engineering strategies

Polysaccharides are the main polymers in edible fungi Grifola frondosa, playing a crucial role in the physiology and representing the healthy benefits for humans. Recent efforts have well elucidated the fine structures and biological functions of G. frondosa polysaccharides. The recently-rapid developments and increasing availability in fungal genomes also accelerated the better understanding of key genes and pathways involved in biosynthesis of G. frondosa polysaccharides. Herein, we provide a brief overview of G. frondosa polysaccharides and their activities, and comprehensively outline the complex process, genes and proteins corresponding to G. frondosa polysaccharide biosynthesis. The regulation strategies including strain improvement, process optimization and genetic engineering were also summarized for maximum production of G. frondosa polysaccharides. Some remaining unanswered questions in describing the fine synthesis machinery were also pointed out to open up new avenues for answering the structure-activity relationship and improving polysaccharide biosynthesis in G. frondosa. The review hopefully presents a reasonable full picture of activities, biosynthesis, and production regulation of polysaccharide in G. frondosa.

Review of technology advances to assess rice quality traits and consumer perception

The increase in rice consumption and demand for high-quality rice is impacted by the growth of socioeconomic status in developing countries and consumer awareness of the health benefits of rice consumption. The latter aspects drive the need for rapid, low-cost, and reliable quality assessment methods to produce high-quality rice according to consumer preference. This is important to ensure the sustainability of the rice value chain and, therefore, accelerate the rice industry toward digital agriculture. This review article focuses on the measurements of the physicochemical and sensory quality of rice, including new and emerging technology advances, particularly in the development of low-cost, non-destructive, and rapid digital sensing techniques to assess rice quality traits and consumer perceptions. In addition, the prospects for potential applications of emerging technologies (i.e., sensors, computer vision, machine learning, and artificial intelligence) to assess rice quality and consumer preferences are discussed. The integration of these technologies shows promising potential in the forthcoming to be adopted by the rice industry to assess rice quality traits and consumer preferences at a lower cost, shorter time, and more objectively compared to the traditional approaches.

The use of multispectral imaging for the discrimination of Arabica and Robusta coffee beans

Arabica coffee beans are sold at twice the price, or more, compared to Robusta beans and consequently are susceptible to economically motivated adulteration by substitution. There is a need for rapid, non-destructive, and efficient analytical techniques for monitoring the authenticity of Arabica coffee beans in the supply chain. In this study, multispectral imaging (MSI) was applied to discriminate roasted Arabica and Robusta coffee beans and perform quantitative prediction of Arabica coffee bean adulteration with Robusta. The Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) model, built using selected spectral and morphological features from individual coffee beans, achieved 100% correct classification of the two coffee species in the test dataset. The OPLS regression model was able to successfully predict the level of adulteration of Arabica with Robusta. MSI analysis has potential as a rapid screening tool for the detection of fraud issues related to the authenticity of Arabica coffee beans.