牛奶污染分类的DLC算法

Ankur Sisodia, Swati Vishnoi, Sachin Upadhyay, D. Chauhan
{"title":"牛奶污染分类的DLC算法","authors":"Ankur Sisodia, Swati Vishnoi, Sachin Upadhyay, D. Chauhan","doi":"10.1109/SMART55829.2022.10046932","DOIUrl":null,"url":null,"abstract":"As buyers and producers turn out to be more aware of the significance of safe and high-quality products, food quality has always been a significant issue on the global market. Milk contamination is a typical extortion. One of the most widely known methods is to expand water, which is difficult to detect with sophisticated logical methods. Even at a scientific science research facility, it is hard to assess the validity of obsolete products at the time of procurement, which is more uncommon, but more hazardous. In other words, milk adulteration testing methodologies are frantically needed by the dairy industry. A microfluidic channel is used to separate different types of milk tests using chronoamperometry. Intriguing strategies use only ten microliters of data and rely on microfluidic-based SVM Classification. An illustration is provided that demonstrates how fast tests can be used to distinguish five distinctive milk brands. According to its past experiences, the framework can arrange the example type in less than five minutes. Various features from different natures were examined and tested for the refreshment curl communication circuit model, such as size, abundance, stage. When five types of milk were utilized for milk newness grouping, the accuracy rate was as high as 96.7%, and when 2% fat milk was used for milk newness grouping, the accuracy rate was as high as 100%. In addition, SVD and boxplot analysis were utilized without affecting the grouping precision of two different methods for extracting includes, thereby decreasing the frequency of radio recurrence data transfer. It is proposed in this paper that DL20 can be calculated with three fundamental attributes, such as water content, time taken, and quantity of milk, and the results show different characterizations. Compared with other milks with high water contents, this milk has low lacto levels because it is blended in with some substance to show it is legitimately lactose.","PeriodicalId":431639,"journal":{"name":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"DLC Algorithm for Milk Contamination Categorization\",\"authors\":\"Ankur Sisodia, Swati Vishnoi, Sachin Upadhyay, D. Chauhan\",\"doi\":\"10.1109/SMART55829.2022.10046932\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As buyers and producers turn out to be more aware of the significance of safe and high-quality products, food quality has always been a significant issue on the global market. Milk contamination is a typical extortion. One of the most widely known methods is to expand water, which is difficult to detect with sophisticated logical methods. Even at a scientific science research facility, it is hard to assess the validity of obsolete products at the time of procurement, which is more uncommon, but more hazardous. In other words, milk adulteration testing methodologies are frantically needed by the dairy industry. A microfluidic channel is used to separate different types of milk tests using chronoamperometry. Intriguing strategies use only ten microliters of data and rely on microfluidic-based SVM Classification. An illustration is provided that demonstrates how fast tests can be used to distinguish five distinctive milk brands. According to its past experiences, the framework can arrange the example type in less than five minutes. Various features from different natures were examined and tested for the refreshment curl communication circuit model, such as size, abundance, stage. When five types of milk were utilized for milk newness grouping, the accuracy rate was as high as 96.7%, and when 2% fat milk was used for milk newness grouping, the accuracy rate was as high as 100%. In addition, SVD and boxplot analysis were utilized without affecting the grouping precision of two different methods for extracting includes, thereby decreasing the frequency of radio recurrence data transfer. It is proposed in this paper that DL20 can be calculated with three fundamental attributes, such as water content, time taken, and quantity of milk, and the results show different characterizations. Compared with other milks with high water contents, this milk has low lacto levels because it is blended in with some substance to show it is legitimately lactose.\",\"PeriodicalId\":431639,\"journal\":{\"name\":\"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)\",\"volume\":\"35 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SMART55829.2022.10046932\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SMART55829.2022.10046932","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

随着购买者和生产者越来越意识到安全和高质量产品的重要性,食品质量一直是全球市场上的一个重要问题。牛奶污染是典型的敲诈勒索。其中最广为人知的方法之一是膨胀水,这很难用复杂的逻辑方法来检测。即使是在科学研究设施,也很难在采购时对过期产品的有效性进行评估,这是更罕见的,但也更危险。换句话说,乳制品行业迫切需要牛奶掺假检测方法。微流控通道用于分离不同类型的牛奶测试,使用计时电流计。有趣的策略只使用10微升的数据,并依赖于基于微流体的支持向量机分类。举例说明,如何快速测试可以用来区分五个不同的牛奶品牌。根据以往的经验,该框架可以在不到五分钟的时间内安排示例类型。对不同性质的点心卷通信电路模型进行了大小、丰度、阶段等特征的检验。当使用5种牛奶进行牛奶新鲜度分组时,准确率高达96.7%,当使用2%脂肪的牛奶进行牛奶新鲜度分组时,准确率高达100%。此外,在不影响两种不同提取方法的分组精度的情况下,利用奇异值分解和箱线图分析,降低了无线电重复数据传输的频率。本文提出DL20可以用含水量、耗时、奶量三个基本属性来计算,结果表现出不同的表征。与其他含水量高的牛奶相比,这种牛奶的乳酸含量较低,因为它与一些物质混合在一起,表明它是合法的乳糖。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DLC Algorithm for Milk Contamination Categorization
As buyers and producers turn out to be more aware of the significance of safe and high-quality products, food quality has always been a significant issue on the global market. Milk contamination is a typical extortion. One of the most widely known methods is to expand water, which is difficult to detect with sophisticated logical methods. Even at a scientific science research facility, it is hard to assess the validity of obsolete products at the time of procurement, which is more uncommon, but more hazardous. In other words, milk adulteration testing methodologies are frantically needed by the dairy industry. A microfluidic channel is used to separate different types of milk tests using chronoamperometry. Intriguing strategies use only ten microliters of data and rely on microfluidic-based SVM Classification. An illustration is provided that demonstrates how fast tests can be used to distinguish five distinctive milk brands. According to its past experiences, the framework can arrange the example type in less than five minutes. Various features from different natures were examined and tested for the refreshment curl communication circuit model, such as size, abundance, stage. When five types of milk were utilized for milk newness grouping, the accuracy rate was as high as 96.7%, and when 2% fat milk was used for milk newness grouping, the accuracy rate was as high as 100%. In addition, SVD and boxplot analysis were utilized without affecting the grouping precision of two different methods for extracting includes, thereby decreasing the frequency of radio recurrence data transfer. It is proposed in this paper that DL20 can be calculated with three fundamental attributes, such as water content, time taken, and quantity of milk, and the results show different characterizations. Compared with other milks with high water contents, this milk has low lacto levels because it is blended in with some substance to show it is legitimately lactose.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信