Assessing and Quantifying the Surface Texture of Milk Powder Using Image Processing

The Application of Artificial Intelligence and Big Data in the Food Industry

Over the past few decades, the food industry has undergone revolutionary changes due to the impacts of globalization, technological advancements, and ever-evolving consumer demands. Artificial intelligence (AI) and big data have become pivotal in strengthening food safety, production, and marketing. With the continuous evolution of AI technology and big data analytics, the food industry is poised to embrace further changes and developmental opportunities. An increasing number of food enterprises will leverage AI and big data to enhance product quality, meet consumer needs, and propel the industry toward a more intelligent and sustainable future. This review delves into the applications of AI and big data in the food sector, examining their impacts on production, quality, safety, risk management, and consumer insights. Furthermore, the advent of Industry 4.0 applied to the food industry has brought to the fore technologies such as smart agriculture, robotic farming, drones, 3D printing, and digital twins; the food industry also faces challenges in smart production and sustainable development going forward. This review articulates the current state of AI and big data applications in the food industry, analyses the challenges encountered, and discusses viable solutions. Lastly, it outlines the future development trends in the food industry.

Self-assembly of three-dimensional liver organoids: virtual reconstruction via endocytosed polymer dots for refactoring the fine structure

In vitro culture of organoids holds considerable promise for the treatment of diseases or the provision of artificial organs. Traditional 2D differentiation from mesenchymal stem cells (MSCs) faces challenges in replicating the development of embryonic organs at the cellular level; conversely, the cultivation of 3D organoids exhibits potential for application. It is crucial for clinicians and technology researchers to acquire insights into organoid tissue differentiation, autonomous morphogenesis, as well as 3D assembly processes in vitro. In this investigation, novel 3D organoids capable of engendering complex liver-like tissues in vitro were cultured, and a class of high-luminance semiconductor polymer dots (Pdots) was employed to monitor the self-assembly process of 3D liver organoid tissues and cellular interaction and migration dynamics. Three-dimensional liver-bud (3D-LB) organoid tissues were derived through the interplay of induced MSCs, Wharton's Jelly, and human umbilical vein endothelial cells (HUVECs), and their structural characteristics were determined during the liver-bud organoid development; ultimately, the co-cultured organoid spatial cellular clusters resembling a truffle were successfully replicated. Utilizing R8-Pdots with remarkable resolution and biocompatibility, the structural elements of functional and vascularized organs derived from liver organoid tissues were adeptly reconstituted, and this investigation shall contribute to a further understanding of human hepato-developmental physiology and liver-disease modeling.

Application of Three-Dimensional Digital Photogrammetry to Quantify the Surface Roughness of Milk Powder

The surface appearance of milk powders is a crucial quality property since the roughness of the milk powder determines its functional properties, and especially the purchaser perception of the milk powder. Unfortunately, powder produced from similar spray dryers, or even the same dryer but in different seasons, produces powder with a wide variety of surface roughness. To date, professional panelists are used to quantify this subtle visual metric, which is time-consuming and subjective. Consequently, developing a fast, robust, and repeatable surface appearance classification method is essential. This study proposes a three-dimensional digital photogrammetry technique for quantifying the surface roughness of milk powders. A contour slice analysis and frequency analysis of the deviations were performed on the three-dimensional models to classify the surface roughness of milk powder samples. The result shows that the contours for smooth-surface samples are more circular than those for rough-surface samples, and the smooth-surface samples had a low standard deviation; thus, milk powder samples with the smoother surface have lower Q (the energy of the signal) values. Lastly, the performance of the nonlinear support vector machine (SVM) model demonstrated that the technique proposed in this study is a practicable alternative technique for classifying the surface roughness of milk powders.