Rapid detection of pea, soybean and chickpea protein in beef patties using chemometrics: comparing hand-held NIR and NIR hyperspectral imaging

Advanced, accurate and rapid technologies able to verify whether the products comply with food label claims are still required to avoid public health issues. The aim of this study was to evaluate the feasibility of point-based near infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) to determine if hand-made prepared burger samples contained vegetable protein in accordance with the legal threshold (3%) established by Spanish Royal Decree No. 474/2014. A total of 240 patties were fabricated, of which 60 contained pea (PP), 60 contained soybean (SP), and 60 chickpea protein (CP) at levels from 1 up to 6% (w/w). 60 hamburgers of Pure beef were included. Excellent results were achieved for identifying the type of protein added, using either partial least squares-discriminant analysis (PLS-DA) or linear discriminant analysis (LDA), with >90% of the samples in Test correctly classified. Based on protein inclusion, LDA discriminated 100% of the PP, SP and CP samples in Test with both NIR and HSI. Indeed, PLS-DA classified 100% of the PP and CP burgers using the NIR instrument. To manage double classification tasks, a hierarchical model classifier (HMC) was proposed for both NIR and HSI spectra, achieving classification rates ranging from 83.34% up to 100% in Test by combining LDA and PLS-DA models at the nodes. The results obtained in the study demonstrated the suitability of NIR spectroscopy in detecting low levels (1%) of vegetable protein flours added to beef burgers and; therefore, contributing to the disclosure of fraudulent practices in labelling at the food industry. • Pea, soybean and chickpea proteins added to meat matrixes can be detected by NIRS • NIRS is sensitive to minor levels (1% w/w) of plant protein in meat burgers • Excellent classification results were obtained for hand-held NIR and lab-based HSI • LDA outperformed PLS-DA for soybean detection using portable NIR and HSI camera • A screening method is proposed to test the legal limit for adding plant proteins

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Publication Details

Journal
Food Control
Published
2025-10-21
DOI
https://doi.org/10.1016/j.foodcont.2025.111793
Citations
1
Primary Topic
Meat and Animal Product Quality
Type
article
Field-Weighted Citation Impact
1.19

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article

Rapid detection of pea, soybean and chickpea protein in beef patties using chemometrics: comparing hand-held NIR and NIR hyperspectral imaging

K. Insausti, Silvia Arazuri Garín, Ainara López-Maestresalas, Sara León-Ecay
1 citations
Food Control
Meat and Animal Product Quality
1.19
article

Rapid detection of pea, soybean and chickpea protein in beef patties using chemometrics: comparing hand-held NIR and NIR hyperspectral imaging

K. Insausti, Silvia Arazuri Garín, Ainara López-Maestresalas, Sara León-Ecay
article en
1 citations

Abstract

Advanced, accurate and rapid technologies able to verify whether the products comply with food label claims are still required to avoid public health issues. The aim of this study was to evaluate the feasibility of point-based near infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) to determine if hand-made prepared burger samples contained vegetable protein in accordance with the legal threshold (3%) established by Spanish Royal Decree No. 474/2014. A total of 240 patties were fabricated, of which 60 contained pea (PP), 60 contained soybean (SP), and 60 chickpea protein (CP) at levels from 1 up to 6% (w/w). 60 hamburgers of Pure beef were included. Excellent results were achieved for identifying the type of protein added, using either partial least squares-discriminant analysis (PLS-DA) or linear discriminant analysis (LDA), with >90% of the samples in Test correctly classified. Based on protein inclusion, LDA discriminated 100% of the PP, SP and CP samples in Test with both NIR and HSI. Indeed, PLS-DA classified 100% of the PP and CP burgers using the NIR instrument. To manage double classification tasks, a hierarchical model classifier (HMC) was proposed for both NIR and HSI spectra, achieving classification rates ranging from 83.34% up to 100% in Test by combining LDA and PLS-DA models at the nodes. The results obtained in the study demonstrated the suitability of NIR spectroscopy in detecting low levels (1%) of vegetable protein flours added to beef burgers and; therefore, contributing to the disclosure of fraudulent practices in labelling at the food industry. • Pea, soybean and chickpea proteins added to meat matrixes can be detected by NIRS • NIRS is sensitive to minor levels (1% w/w) of plant protein in meat burgers • Excellent classification results were obtained for hand-held NIR and lab-based HSI • LDA outperformed PLS-DA for soybean detection using portable NIR and HSI camera • A screening method is proposed to test the legal limit for adding plant proteins

Food ControlVol. 181
Universidad Pública de Navarra (UPNA) (ES)
Universidad Pública de Navarra
Openalex Percentile: Top 19%
Meat and Animal Product Quality
1.19
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