Phase diagram combined with improved fuzzy support vector machine for rapid and nondestructive detection of Diarrhetic shellfish poisoning

IF 1.3 Q4 FOOD SCIENCE & TECHNOLOGY
Wei Jiang, Yao Liu, Fu Qiao, Zhongyan Liu, Jianfang Xiong, Shaogeng Zeng
{"title":"Phase diagram combined with improved fuzzy support vector machine for rapid and nondestructive detection of Diarrhetic shellfish poisoning","authors":"Wei Jiang, Yao Liu, Fu Qiao, Zhongyan Liu, Jianfang Xiong, Shaogeng Zeng","doi":"10.17306/j.afs.1124","DOIUrl":null,"url":null,"abstract":"Background. The diarrhoeal shellfish poisoning (DSP) toxin is a powerful marine biological toxin. Eating DSP toxin-contaminated mussels will lead to serious gastrointestinal diseases. To this end, a method for the detection of DSP toxins using near-infrared reflectance spectroscopy combined with pattern recognition is proposed. Material and methods. In the range from 950−1700 nm, spectral data of healthy mussels and DSP-contaminated mussels were obtained. To select the optimal band subsets, a band selection algorithm based on model cluster analysis was applied. As distinguishing DSP toxin-contaminated mussels from healthy mussels is a classification problem of imbalanced data, an improved fuzzy support vector machine-based recognition method was proposed. The influence of the parameters of the band selection algorithm and the fuzzy support vector machine on the model performance was analyzed. Results. Compared with the traditional support vector machine, the proposed model has better performance in detecting DSP toxins and is not affected by the imbalance ratio. Its geometric mean value can reach 0.9886 and the detection accuracy can reach 98.83%. Conclusion. The results show that as an innovative, fast and convenient analytical method, near-infrared spectroscopy is feasible for the detection of DSP toxins in mussels.","PeriodicalId":7209,"journal":{"name":"Acta scientiarum polonorum. Technologia alimentaria","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Acta scientiarum polonorum. Technologia alimentaria","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.17306/j.afs.1124","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"FOOD SCIENCE & TECHNOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Background. The diarrhoeal shellfish poisoning (DSP) toxin is a powerful marine biological toxin. Eating DSP toxin-contaminated mussels will lead to serious gastrointestinal diseases. To this end, a method for the detection of DSP toxins using near-infrared reflectance spectroscopy combined with pattern recognition is proposed. Material and methods. In the range from 950−1700 nm, spectral data of healthy mussels and DSP-contaminated mussels were obtained. To select the optimal band subsets, a band selection algorithm based on model cluster analysis was applied. As distinguishing DSP toxin-contaminated mussels from healthy mussels is a classification problem of imbalanced data, an improved fuzzy support vector machine-based recognition method was proposed. The influence of the parameters of the band selection algorithm and the fuzzy support vector machine on the model performance was analyzed. Results. Compared with the traditional support vector machine, the proposed model has better performance in detecting DSP toxins and is not affected by the imbalance ratio. Its geometric mean value can reach 0.9886 and the detection accuracy can reach 98.83%. Conclusion. The results show that as an innovative, fast and convenient analytical method, near-infrared spectroscopy is feasible for the detection of DSP toxins in mussels.
相图结合改进模糊支持向量机快速无损检测腹泻性贝类中毒
背景。腹泻贝类中毒(DSP)毒素是一种强效的海洋生物毒素。食用被DSP毒素污染的贻贝会导致严重的胃肠疾病。为此,提出了一种结合模式识别的近红外反射光谱法检测DSP毒素的方法。材料和方法。在950 ~ 1700 nm范围内,获得了健康贻贝和受dsp污染贻贝的光谱数据。为了选择最优波段子集,采用了基于模型聚类分析的波段选择算法。针对DSP毒素贻贝与健康贻贝的识别是一个数据不平衡的分类问题,提出了一种改进的模糊支持向量机识别方法。分析了波段选择算法和模糊支持向量机参数对模型性能的影响。结果。与传统的支持向量机相比,该模型具有更好的DSP毒素检测性能,且不受不平衡比的影响。其几何平均值可达0.9886,检测精度可达98.83%。结论。结果表明,近红外光谱作为一种创新、快速、方便的分析方法,用于贻贝中DSP毒素的检测是可行的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
2.70
自引率
0.00%
发文量
70
×
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