Roberto Dragone , Gerardo Grasso , Giorgio Licciardi , Daniele Di Stefano , Chiara Frazzoli
{"title":"Sensors driven system coupled with artificial intelligence for quality monitoring and HACCP in dairy production","authors":"Roberto Dragone , Gerardo Grasso , Giorgio Licciardi , Daniele Di Stefano , Chiara Frazzoli","doi":"10.1016/j.sbsr.2024.100683","DOIUrl":null,"url":null,"abstract":"<div><p>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).</p></div>","PeriodicalId":424,"journal":{"name":"Sensing and Bio-Sensing Research","volume":"45 ","pages":"Article 100683"},"PeriodicalIF":5.4000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2214180424000655/pdfft?md5=6efb7414916fabe1d36294e4aef6b6a5&pid=1-s2.0-S2214180424000655-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sensing and Bio-Sensing Research","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2214180424000655","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
引用次数: 0
Abstract
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).
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.