Browsing by Author "Tian J"

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    The Application of Artificial Intelligence and Big Data in the Food Industry
    (MDPI (Basel, Switzerland), 2023-12-18) Ding H; Tian J; Yu W; Wilson DI; Young BR; Cui X; Xin X; Wang Z; Li W; Yılmaz MT
    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.
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    White mulberry leaf (Morus alba L.) infusion as a strategy to reduce starch digestibility: The influence of particle size of leaf powder
    (Elsevier B.V., 2024-10-30) Aumasa T; Apinanthanuwong G; Singh J; Kaur L; Tian J; Phongthai S; Tanongkankit Y; Issara U; Ogawa Y; Donlao N
    Mulberry leaf (Morus Alba L.) has been found in clinical trials to be effective in reducing diabetes in Asia. The powdered tea market is expanding in popularity due to its functional properties. This study aimed to examine the influence of different particle sizes of mulberry leaf powder (MLP) infusion on the digestibility of starch in cooked Japonica rice (cv. Koshihikari) and the bioaccessibility of phytochemicals. Dried mulberry leaf was pulverized and sieved into several particle sizes: 160 μm (MLP160), 250 μm (MLP250), 404 μm (MLP404), and 774 μm (MLP774). Through simulated in vitro digestion, we assessed starch hydrolysis (%SH), the kinetics of starch hydrolysis, estimated glycemic index (eGI), as well as total phenolic content (TPC) and total flavonoid content (TFC). The smaller particle size of MLP showed a greater reduction of eGI. Specifically, infusions prepared from MLP160 resulted in a reduction of 15 % in eGI for cooked grains and 3 % for slurries, respectively. The reduction in eGI was attributed to the interaction among flavonoids and digestive enzymes, demonstrating a concentration-dependent manner on enzyme inhibition effect. Pulverization significantly influenced the concentration of phytochemicals and their bioaccessibility in infusions. This study offers valuable insights into determining optimal particle sizes for MLP, considering both physical and functional characteristics as well as implications for the food industry. The results further suggest that MLP infusion holds promise as a functional beverage, potentially providing benefits in reducing postprandial hyperglycemia.