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Chemometric Fingerprinting of Vietnamese Instant Noodle Brands via ICP‐MS and Principal Component Analysis.

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Title: Chemometric Fingerprinting of Vietnamese Instant Noodle Brands via ICP‐MS and Principal Component Analysis.
Authors: Bui, Dinh Tri1 (AUTHOR), Tran, Ha Minh Duc1 (AUTHOR), Le, Van Nhan1 (AUTHOR) levannhan.na@gmail.com, Bui, Quang Minh1 (AUTHOR), Nguyen, Hoang Khanh1 (AUTHOR), Bui, Thi Quynh1 (AUTHOR), Hebishy, Essam1 (AUTHOR) ehebishy+eic@wiley.com
Source: Journal of Food Processing & Preservation. 11/10/2025, Vol. 2025, p1-9. 9p.
Subjects: Chemical fingerprinting, Trace element analysis, Elemental analysis, Food safety, Principal components analysis, Ramen
Geographic Terms: Vietnam
Abstract: The rapid expansion of Vietnam's instant noodle market, coupled with risks of adulteration and contamination, necessitates robust analytical methods for brand description and safety assessment. In this study, we applied inductively coupled plasma mass spectrometry (ICP‐MS) to quantify 14 macro‐ and trace‐element concentrations (e.g., Na, K, Ca, Mg, Fe, Zn, Cu, Cd, Pb, Hg) in 80 noodle samples from four leading Vietnamese brands (Gau Do, Omachi, Hao Hao, 3 Mien). One‐way ANOVA followed by Tukey's HSD revealed significant inter‐brand differences in elemental profiles. Principal component analysis (PCA) distilled the elemental dataset into two components explaining 93.6% of total variance: PC1 (48.8%) was driven by Na, Cr, Fe, and Ni, while PC2 (44.8%) contrasted macro‐minerals (Ca, K, Mg) against contaminants (Pb, Mo). The resulting observation plot produced four distinct, non‐overlapping clusters corresponding to each brand, demonstrating the discriminative power of elemental fingerprinting. Method validation confirmed high precision (RSD < 5%), accuracy (recoveries 92–106%), and low detection limits (LODs 0.005–0.02 mg/kg). A consumer health risk assessment for heavy metals showed all levels below international safety thresholds, though lead concentrations warrant caution. Our findings validate ICP‐MS/PCA as a rapid, reliable tool for food authentication. Future work should extend sampling, incorporate cross‐validation, and compare with complementary techniques such as FTIR and isotopic analysis. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Food Processing & Preservation 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.)
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  Data: Chemometric Fingerprinting of Vietnamese Instant Noodle Brands via ICP‐MS and Principal Component Analysis.
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  Data: The rapid expansion of Vietnam&#39;s instant noodle market, coupled with risks of adulteration and contamination, necessitates robust analytical methods for brand description and safety assessment. In this study, we applied inductively coupled plasma mass spectrometry (ICP‐MS) to quantify 14 macro‐ and trace‐element concentrations (e.g., Na, K, Ca, Mg, Fe, Zn, Cu, Cd, Pb, Hg) in 80 noodle samples from four leading Vietnamese brands (Gau Do, Omachi, Hao Hao, 3 Mien). One‐way ANOVA followed by Tukey&#39;s HSD revealed significant inter‐brand differences in elemental profiles. Principal component analysis (PCA) distilled the elemental dataset into two components explaining 93.6% of total variance: PC1 (48.8%) was driven by Na, Cr, Fe, and Ni, while PC2 (44.8%) contrasted macro‐minerals (Ca, K, Mg) against contaminants (Pb, Mo). The resulting observation plot produced four distinct, non‐overlapping clusters corresponding to each brand, demonstrating the discriminative power of elemental fingerprinting. Method validation confirmed high precision (RSD &lt; 5%), accuracy (recoveries 92–106%), and low detection limits (LODs 0.005–0.02 mg/kg). A consumer health risk assessment for heavy metals showed all levels below international safety thresholds, though lead concentrations warrant caution. Our findings validate ICP‐MS/PCA as a rapid, reliable tool for food authentication. Future work should extend sampling, incorporate cross‐validation, and compare with complementary techniques such as FTIR and isotopic analysis. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: &lt;i&gt;Copyright of Journal of Food Processing &amp; Preservation is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder&#39;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.&lt;/i&gt; (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1155/jfpp/9186048
    Languages:
      – Code: eng
        Text: English
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        PageCount: 9
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    Subjects:
      – SubjectFull: Chemical fingerprinting
        Type: general
      – SubjectFull: Trace element analysis
        Type: general
      – SubjectFull: Elemental analysis
        Type: general
      – SubjectFull: Food safety
        Type: general
      – SubjectFull: Principal components analysis
        Type: general
      – SubjectFull: Ramen
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      – SubjectFull: Vietnam
        Type: general
    Titles:
      – TitleFull: Chemometric Fingerprinting of Vietnamese Instant Noodle Brands via ICP‐MS and Principal Component Analysis.
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            NameFull: Bui, Dinh Tri
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            NameFull: Tran, Ha Minh Duc
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            NameFull: Bui, Quang Minh
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            NameFull: Nguyen, Hoang Khanh
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              Text: 11/10/2025
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              Y: 2025
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