Differentiation and quantification of bovine and pork gelatin using UPLC-QTOF and ATR-FTIR spectroscopy: Addressing challenges in mixed gelatin analysis and detection

The amino acid sequence dictates protein folding and spatial structure, essential for understanding their behavior. Gelatin, important in industry and biomedicine, demands accurate source authentication due to ethical and dietary concerns. This study explores differentiating and quantifying bovine and pork gelatin using UPLC-QTOF and ATR-FTIR spectroscopy. UPLC-QTOF identified distinct ions for pure bovine ( m / z + 962.1471, NRLHFFK) and pork gelatin (m/z + 653.0289, YNEVK) but struggled with mixed gelatins due to protein-protein interactions altering peptide ion profiles. ATR-FTIR was employed to leverage ethanol's differential effects on gelatin's amide bands for quantifying pork gelatin contamination in bovine gelatin. The method showed a strong linear correlation (R 2 = 0.9994) with contamination levels and amide band transmission, with detection and quantification limits of 0.85 and 2.85 mg/100 mg, respectively. It effectively identified pork gelatin in halal candy, with recovery rates from 50.05 % to 103.69 %, highlighting ATR-FTIR's potential for accurate gelatin analysis. • Robust method for verifying gelatin sources in complex food matrices using FTIR- ATR. • UPLC-QTOF identified distinct ions for pure gelatins but struggles in mixed gelatins. • Protein-ethanol interactions enhance differentiation between gelatin types. • Specific amino acid interactions with ethanol affect gelatin's spectral properties. • Effective quantification of pork contamination in bovine gelatin.

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Publication Details

Journal
Food Chemistry
Published
2024-11-08
DOI
https://doi.org/10.1016/j.foodchem.2024.141883
Citations
9
Primary Topic
Identification and Quantification in Food
Type
article
Field-Weighted Citation Impact
2.10
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article

Differentiation and quantification of bovine and pork gelatin using UPLC-QTOF and ATR-FTIR spectroscopy: Addressing challenges in mixed gelatin analysis and detection

Hassan M.A. Hassan, Usama Souka, Salma M. Hassan
9 citations
Food Chemistry
Identification and Quantification in Food
2.10
article

Differentiation and quantification of bovine and pork gelatin using UPLC-QTOF and ATR-FTIR spectroscopy: Addressing challenges in mixed gelatin analysis and detection

Hassan M.A. Hassan, Usama Souka, Salma M. Hassan
article en
9 citations

Abstract

The amino acid sequence dictates protein folding and spatial structure, essential for understanding their behavior. Gelatin, important in industry and biomedicine, demands accurate source authentication due to ethical and dietary concerns. This study explores differentiating and quantifying bovine and pork gelatin using UPLC-QTOF and ATR-FTIR spectroscopy. UPLC-QTOF identified distinct ions for pure bovine ( m / z + 962.1471, NRLHFFK) and pork gelatin (m/z + 653.0289, YNEVK) but struggled with mixed gelatins due to protein-protein interactions altering peptide ion profiles. ATR-FTIR was employed to leverage ethanol's differential effects on gelatin's amide bands for quantifying pork gelatin contamination in bovine gelatin. The method showed a strong linear correlation (R 2 = 0.9994) with contamination levels and amide band transmission, with detection and quantification limits of 0.85 and 2.85 mg/100 mg, respectively. It effectively identified pork gelatin in halal candy, with recovery rates from 50.05 % to 103.69 %, highlighting ATR-FTIR's potential for accurate gelatin analysis. • Robust method for verifying gelatin sources in complex food matrices using FTIR- ATR. • UPLC-QTOF identified distinct ions for pure gelatins but struggles in mixed gelatins. • Protein-ethanol interactions enhance differentiation between gelatin types. • Specific amino acid interactions with ethanol affect gelatin's spectral properties. • Effective quantification of pork contamination in bovine gelatin.

Food ChemistryVol. 464
Al Ain University (AE), United Arab Emirates University (AE)
Zero hunger
Openalex Percentile: Top 12%
Identification and Quantification in Food
2.10
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