Academic Journal
A Novel FULS‐TSVM‐Based Model for Non‐Destructive Detection of Heavy Metals in Mussels via Near‐Infrared Spectroscopy.
| Τίτλος: | A Novel FULS‐TSVM‐Based Model for Non‐Destructive Detection of Heavy Metals in Mussels via Near‐Infrared Spectroscopy. |
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| Συγγραφείς: | Li, Ming, Meng, Xiangli, Liu, Yao, Liu, Zhongyan |
| Πηγή: | Journal of Chemometrics; Oct2026, Vol. 40 Issue 10, p1-12, 12p |
| Θεματικοί όροι: | Near infrared spectroscopy, Machine learning, Mussels, Outliers (Statistics), Seafood poisoning, Nondestructive testing, Metal analysis |
| Περίληψη: | With the rising global demand for seafood, mussels have become an important commercial product. They are located in estuarine environments and are prone to heavy metal accumulation. This poses a threat to the safety of mussel products for human consumption. Traditional detection methods are accurate, but limited by complex sample pretreatment, high costs, and slow processing speed. Near‐infrared spectroscopy (NIRS) can achieve non‐destructive and rapid detection, but the detection results are easily disrupted by imbalanced sample datasets, individual outliers, and low‐concentration contamination in samples. To solve these issues, this study first applied a fuzzy universum least squares twin support vector machine (FULS‐TSVM) algorithm to the detection of heavy metals in mussels. By integrating universum data to incorporate prior distribution knowledge and assigning fuzzy memberships to handle outlier samples and class imbalance, this model effectively extracts weak spectral differences caused by heavy metals. This study focused on mussels contaminated by cadmium (Cd). We conducted comparative experiments in imbalanced datasets, with outlier samples, and under high and low contamination concentrations. We also expanded the test objects to lead (Pb) and zinc (Zn) contaminated mussels to verify the model's generalization ability. The results show that compared to LS‐TSVM, FLS‐TSVM, and ULS‐TSVM models, the FULS‐TSVM has overall better detection accuracy and robustness. It maintains reliable detection performance even in extreme imbalanced datasets, numerous outliers, and low‐concentration contamination scenarios. This research proves that combining NIRS technology with the FULS‐TSVM algorithm provides an efficient, low‐cost way to quickly screen heavy metal contamination in mussels. The method has great application value for daily safety monitoring of marine aquatic food. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Chemometrics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=08869383&ISBN=&volume=40&issue=10&date=20261001&spage=1&pages=1-12&title=Journal of Chemometrics&atitle=A%20Novel%20FULS%E2%80%90TSVM%E2%80%90Based%20Model%20for%20Non%E2%80%90Destructive%20Detection%20of%20Heavy%20Metals%20in%20Mussels%20via%20Near%E2%80%90Infrared%20Spectroscopy.&aulast=Li%2C%20Ming&id=DOI:10.1002/cem.70177 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Items | – Name: Title Label: Title Group: Ti Data: A Novel FULS‐TSVM‐Based Model for Non‐Destructive Detection of Heavy Metals in Mussels via Near‐Infrared Spectroscopy. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Li%2C+Ming%22">Li, Ming</searchLink><br /><searchLink fieldCode="AR" term="%22Meng%2C+Xiangli%22">Meng, Xiangli</searchLink><br /><searchLink fieldCode="AR" term="%22Liu%2C+Yao%22">Liu, Yao</searchLink><br /><searchLink fieldCode="AR" term="%22Liu%2C+Zhongyan%22">Liu, Zhongyan</searchLink> – Name: TitleSource Label: Source Group: Src Data: Journal of Chemometrics; Oct2026, Vol. 40 Issue 10, p1-12, 12p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Near+infrared+spectroscopy%22">Near infrared spectroscopy</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Mussels%22">Mussels</searchLink><br /><searchLink fieldCode="DE" term="%22Outliers+%28Statistics%29%22">Outliers (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Seafood+poisoning%22">Seafood poisoning</searchLink><br /><searchLink fieldCode="DE" term="%22Nondestructive+testing%22">Nondestructive testing</searchLink><br /><searchLink fieldCode="DE" term="%22Metal+analysis%22">Metal analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: With the rising global demand for seafood, mussels have become an important commercial product. They are located in estuarine environments and are prone to heavy metal accumulation. This poses a threat to the safety of mussel products for human consumption. Traditional detection methods are accurate, but limited by complex sample pretreatment, high costs, and slow processing speed. Near‐infrared spectroscopy (NIRS) can achieve non‐destructive and rapid detection, but the detection results are easily disrupted by imbalanced sample datasets, individual outliers, and low‐concentration contamination in samples. To solve these issues, this study first applied a fuzzy universum least squares twin support vector machine (FULS‐TSVM) algorithm to the detection of heavy metals in mussels. By integrating universum data to incorporate prior distribution knowledge and assigning fuzzy memberships to handle outlier samples and class imbalance, this model effectively extracts weak spectral differences caused by heavy metals. This study focused on mussels contaminated by cadmium (Cd). We conducted comparative experiments in imbalanced datasets, with outlier samples, and under high and low contamination concentrations. We also expanded the test objects to lead (Pb) and zinc (Zn) contaminated mussels to verify the model's generalization ability. The results show that compared to LS‐TSVM, FLS‐TSVM, and ULS‐TSVM models, the FULS‐TSVM has overall better detection accuracy and robustness. It maintains reliable detection performance even in extreme imbalanced datasets, numerous outliers, and low‐concentration contamination scenarios. This research proves that combining NIRS technology with the FULS‐TSVM algorithm provides an efficient, low‐cost way to quickly screen heavy metal contamination in mussels. The method has great application value for daily safety monitoring of marine aquatic food. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Journal of Chemometrics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/cem.70177 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 1 Subjects: – SubjectFull: Near infrared spectroscopy Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Mussels Type: general – SubjectFull: Outliers (Statistics) Type: general – SubjectFull: Seafood poisoning Type: general – SubjectFull: Nondestructive testing Type: general – SubjectFull: Metal analysis Type: general Titles: – TitleFull: A Novel FULS‐TSVM‐Based Model for Non‐Destructive Detection of Heavy Metals in Mussels via Near‐Infrared Spectroscopy. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Li, Ming – PersonEntity: Name: NameFull: Meng, Xiangli – PersonEntity: Name: NameFull: Liu, Yao – PersonEntity: Name: NameFull: Liu, Zhongyan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 08869383 Numbering: – Type: volume Value: 40 – Type: issue Value: 10 Titles: – TitleFull: Journal of Chemometrics Type: main |
| ResultId | 1 |