Unveiling the putative porcine myosin-based peptide markers for non-halal meat through chemometrics-assisted MRM-based proteomics

Handling massive proteomics datasets poses challenges due to assessing dataset quality and dealing with multiple dimensions of the dataset when establishing putative peptide markers. Therefore, this study aims to confirm putative porcine peptide markers for precision halal proteomics through chemometrics-assisted MRM-based proteomics. Chemometric data mining was employed to access the dispersion characteristics and normality of 509 commercial processed meat samples (beef, chicken, fish, and pork). All the samples displayed normal distributions and showed significant differences in the median. By employing chemometric principal component analysis, two significant dimensions were identified to select the putative porcine peptide markers. Out of 1204 identified peptides, two putative porcine peptide markers were critically selected: P25 and P68, derived from myosin-1. MRM acquisition was developed to verify the P25 and P68 for precision halal proteomics. Notably, only the MRM chromatogram of P68 showed a modified peptide peak. Nonetheless, the process of confirming putative porcine peptide markers from massive proteomics datasets is robust and reliable through chemometrics-assisted MRM-based proteomics for halal authentication in the context of meat speciation. It is recommended utilizing P25 as the peptide marker due to its purity and unmatched sequence with bovine, chicken, and fish based on the UniProtKB search.

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Publication Details

Journal
European Food Research and Technology
Published
2025-06-16
DOI
https://doi.org/10.1007/s00217-025-04814-x
Primary Topic
Identification and Quantification in Food
Type
article
Field-Weighted Citation Impact
0.00

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article

Unveiling the putative porcine myosin-based peptide markers for non-halal meat through chemometrics-assisted MRM-based proteomics

Syaiful Izwan Ismail, Muhamad Shirwan Abdullah Sani, Mohd Hafis Yuswan, Lai Kok Song et al.
European Food Research and Technology
Identification and Quantification in Food
article

Unveiling the putative porcine myosin-based peptide markers for non-halal meat through chemometrics-assisted MRM-based proteomics

Syaiful Izwan Ismail, Muhamad Shirwan Abdullah Sani, Mohd Hafis Yuswan, Lai Kok Song, Norazlina Ali
article en

Abstract

Handling massive proteomics datasets poses challenges due to assessing dataset quality and dealing with multiple dimensions of the dataset when establishing putative peptide markers. Therefore, this study aims to confirm putative porcine peptide markers for precision halal proteomics through chemometrics-assisted MRM-based proteomics. Chemometric data mining was employed to access the dispersion characteristics and normality of 509 commercial processed meat samples (beef, chicken, fish, and pork). All the samples displayed normal distributions and showed significant differences in the median. By employing chemometric principal component analysis, two significant dimensions were identified to select the putative porcine peptide markers. Out of 1204 identified peptides, two putative porcine peptide markers were critically selected: P25 and P68, derived from myosin-1. MRM acquisition was developed to verify the P25 and P68 for precision halal proteomics. Notably, only the MRM chromatogram of P68 showed a modified peptide peak. Nonetheless, the process of confirming putative porcine peptide markers from massive proteomics datasets is robust and reliable through chemometrics-assisted MRM-based proteomics for halal authentication in the context of meat speciation. It is recommended utilizing P25 as the peptide marker due to its purity and unmatched sequence with bovine, chicken, and fish based on the UniProtKB search.

European Food Research and TechnologyVol. 251(9)
Universiti Sains Islam Malaysia (MY), Higher Colleges of Technology (AE), Universiti Putra Malaysia (MY), International Islamic College (MY), International Islamic University Malaysia (MY)
Universiti Putra Malaysia
Zero hunger
Openalex Percentile: Top 87%
Identification and Quantification in Food
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