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

IF 1.3 Q4 FOOD SCIENCE & TECHNOLOGY Acta scientiarum polonorum. Technologia alimentaria Pub Date : 2023-06-30 DOI:10.17306/j.afs.1124
Wei Jiang, Yao Liu, Fu Qiao, Zhongyan Liu, Jianfang Xiong, Shaogeng Zeng
{"title":"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","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":"{\"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}","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

摘要

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。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Phase diagram combined with improved fuzzy support vector machine for rapid and nondestructive detection of Diarrhetic shellfish poisoning
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.
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
CiteScore
2.70
自引率
0.00%
发文量
70
期刊最新文献
Traditional fermented foods in Anatolia Effects of heating process on kinetic degradation of anthocyanin and vitamin C on hardness and sensory value of strawberry soft candy [pdf] Optimization of the osmotic dehydration process for improving the quality of vacuum-fried “Vinh Chau” shallots at the pilot stage [pdf] Traditional fermented foods in Anatolia [pdf] Physical characteristics and the effect of soaking on mineral concentration in rice varieties associated with human health risks
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1