传感器驱动系统与人工智能相结合,用于乳制品生产的质量监测和 HACCP

IF 5.4 Q1 CHEMISTRY, ANALYTICAL
Roberto Dragone , Gerardo Grasso , Giorgio Licciardi , Daniele Di Stefano , Chiara Frazzoli
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引用次数: 0

摘要

保持良好的牛奶质量标准仍然是奶牛场主面临的一项挑战,这就需要一种既能适应环境又能降低生产成本的快速控制系统。为了在乳品链中实现质量监控和可追溯性,一个名为 BEST 的类似于危险分析和关键控制点的远程诊断(传感器驱动)系统获得了专利。BEST 每天都在奶牛场进行测试,以机器学习方法为基础,识别牛奶生产中异常情况(安全和质量)的新的可靠指标。在四个月的传感分析中获得的数据库通过人工智能算法进行了统计研究,以识别异常值。事实证明,BEST 能够在整个牛群数据库中发现具有特定特征的奶牛。尤其是,人工智能突出显示了唯一一头来自不同品种的奶牛、唯一一头最近分娩的奶牛以及牛群中唯一一头接受过 Micospectone® (林可霉素 + Spectinomycin)药物治疗的奶牛。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sensors driven system coupled with artificial intelligence for quality monitoring and HACCP in dairy production

The maintenance of good milk quality standards is still a challenge for dairy farmers that requires a rapid control system that is compatible with both the environment and production cost. A patented Hazard Analysis and Critical Control Points-like remote diagnostic (sensor driven) system named BEST was implemented to enable both quality monitoring and traceability in the dairy chain. BEST was daily tested in a dairy farm to identify new reliable indicators of anomalies (safety and quality) in milk production based on a Machine-Learning approach. The database obtained in four months of sensoristic analysis was subjected to a statistical study with AI algorithm to identify outliers. BEST proved ability to spot cows with specific characteristics in the whole herd's database. In particular, AI highlighted the sole cow from a different breed, the only cow that recently gave birth and the only cow in the herd that received treatment with the drug Micospectone® (Lincomycin + Spectinomycin).

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来源期刊
Sensing and Bio-Sensing Research
Sensing and Bio-Sensing Research Engineering-Electrical and Electronic Engineering
CiteScore
10.70
自引率
3.80%
发文量
68
审稿时长
87 days
期刊介绍: Sensing and Bio-Sensing Research is an open access journal dedicated to the research, design, development, and application of bio-sensing and sensing technologies. The editors will accept research papers, reviews, field trials, and validation studies that are of significant relevance. These submissions should describe new concepts, enhance understanding of the field, or offer insights into the practical application, manufacturing, and commercialization of bio-sensing and sensing technologies. The journal covers a wide range of topics, including sensing principles and mechanisms, new materials development for transducers and recognition components, fabrication technology, and various types of sensors such as optical, electrochemical, mass-sensitive, gas, biosensors, and more. It also includes environmental, process control, and biomedical applications, signal processing, chemometrics, optoelectronic, mechanical, thermal, and magnetic sensors, as well as interface electronics. Additionally, it covers sensor systems and applications, µTAS (Micro Total Analysis Systems), development of solid-state devices for transducing physical signals, and analytical devices incorporating biological materials.
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