Laser-induced breakdown spectroscopy as a reliable analytical method for classifying commercial cheese samples based on their cooking/stretching process

•A new LIBS method was proposed to discriminate the Kashar and processed cheese samples.•PLS-DA and PCA models are used to discriminate the samples based on elemental content.•The developed method with favorable sensitivity and selectivity is easy to implement with high predictive ability.•High-prec...

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Bibliographic Details
Published in:Food chemistry 2022-10, Vol.390, p.132946-132946, Article 132946
Main Authors: Sezer, Banu, Ozturk, Mustafa, Ayvaz, Huseyin, Apaydın, Hakan, Boyaci, Ismail Hakkı
Format: Article
Language:English
Subjects:
AAS
atomic absorption spectrometry
chemometrics
discriminant analysis
food chemistry
ICP-OES
least squares
LIBS
PCA
PLS-DA
prediction
principal component analysis
processed cheeses
variance
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Summary:•A new LIBS method was proposed to discriminate the Kashar and processed cheese samples.•PLS-DA and PCA models are used to discriminate the samples based on elemental content.•The developed method with favorable sensitivity and selectivity is easy to implement with high predictive ability.•High-precision quantification of elemental content of cheese samples. The present work evaluates the possibility of using laser-induced breakdown spectroscopy (LIBS) coupled with chemometric methods to classify cheese samples (namely Kashar cheese and processed cheese) based on their cooking/stretching process. Chemometric analysis of the data provided by LIBS and ICP-OES/AAS analyses made it possible to discriminate between the two cheese types regarding their elemental profiles. The principal component analysis model was able to discriminate the Kashar cheese with an explained variance of 97.02%. Furthermore, the partial least squares discriminant analysis model perfectly classified the Kashar samples with a prediction ability of 100%. Furthermore, calibration and validation models for Mg, Ca, Na, P, Zn, and K elements for both Kashar and processed cheese samples were developed using partial least square regression yielding high correlation coefficients and low root mean square errors. Overall, this study indicates that LIBS with chemometrics can be an easy-to-use and rapid monitoring system for cheese classification.
ISSN:0308-8146
1873-7072
DOI:10.1016/j.foodchem.2022.132946