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大数据技术是指从各类数据中,通过不同分析方法和工具获取有价值信息的能力。人工智能技术是指研究、开发用于模拟、延伸和扩展人的智能的理论、方法、技术。大数据技术与人工智能技术相辅相成,大数据是人工智能的基石,人工智能是大数据应用的体现。食品科学大数据与人工智能则是将大数据技术、人工智能技术与食品科学交叉融合。将人工智能与大数据技术融合到传统食品科学领域中,可以创新食谱并智能推荐,追踪溯源食品,分析用户口味偏好,监控食品安全等,使得食品科学获得更大发展,并为人们提供更好的服务。本文阐释当前食品领域大数据与人工智能交叉融合发展现状,分析其面临的挑战,讨论可行性解决方案,展望食品科学大数据与人工智能技术未来发展趋势。本文通过抛砖引玉,旨在吸引更多的大数据和人工智能技术与食品科学相结合,从而更好地预见并服务于未来食品的发展。 相似文献
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以计算机、网络技术以及现代通信技术为代表的现代信息技术是当代科技发展的主要领域,以技术变革教育也势在必行。将人工智能、大数据、云计算、"互联网+"等现代信息技术和教育教学进行深度融合发展,以实现教育领域的"中国梦"。 相似文献
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Figueroa-González I Quijano G Ramírez G Cruz-Guerrero A 《Journal of the science of food and agriculture》2011,91(8):1341-1348
Owing to their health benefits, probiotics and prebiotics are nowadays widely used in yogurts and fermented milks, which are leader products of functional foods worldwide. The world market for functional foods has grown rapidly in the last three decades, with an estimated size in 2003 of ca US$ 33 billion, while the European market estimation exceeded US$ 2 billion in the same year. However, the production of probiotics and prebiotics at industrial scale faces several challenges, including the search for economical and abundant raw materials for prebiotic production, the low-cost production of probiotics and the improvement of probiotic viability after storage or during the manufacturing process of the functional food. In this review, functional foods based on probiotics and prebiotics are introduced as a key biotechnological field with tremendous potential for innovation. A concise state of the art addressing the fundamentals and challenges for the development of new probiotic- and prebiotic-based foods is presented, the niches for future research being clearly identified and discussed. 相似文献
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常用消毒灭菌法及其机理与应用 总被引:3,自引:0,他引:3
介绍了采用消毒灭菌方法,有加热消毒法,紫外线辐射法和化学药剂消毒法。常用化学药剂有醛类、含氯消毒剂、醇类消毒剂以及高锰酸钾、生石灰等,阐释了消毒与灭菌两个概念的区别。 相似文献
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The objectives of the current study were to investigate the relationship between body condition score (BCS) and dairy form and changes in genetic parameters for BCS and dairy form within and across lactations and age. Body condition score and dairy form were obtained from the Holstein Association USA, Inc. Records were edited to include those cows classified between 24 and 60 mo of age and between 0 and 335 d in milk (DIM). A minimum of 20 daughters per sire and 15 cows per herd-classification visit were required. The dataset consisted of 135,178 records from 119,215 cows. Repeatability, multiple trait, and random regression models were used to analyze the data. All models included fixed effects for herd-classification visit, age within lactations 1, 2, and 3 or higher, and 5th-order polynomials for DIM. Random effects included sire and permanent environment for all models. Random regression models included age at classification nested within sire or DIM and lactation number nested within sire. Genetic variance for both BCS and dairy form was lowest in early lactation and highest in midlactation. Genetic correlations within and across lactations were high. The genetic correlation between DIM 0 in lactation 1 and DIM 305 in lactation 3 was estimated to be 0.77 for BCS and 0.60 for dairy form. The genetic correlation estimate between 30 mo of age at classification and 50 mo of age at classification was 0.94 for both dairy form and BCS. The repeatability models appeared to generate accurate evaluations for BCS or dairy form at all ages and stages of lactation. 相似文献
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