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A single screen-printed electrode in tandem with chemometric tools for the forensic differentiation of Brazilian beers.


ABSTRACT: In the present study a single screen-printed carbon electrode (SPCE) and chemometric techniques were utilized for forensic differentiation of Brazilian American lager beers. To differentiate Brazilian beers at the manufacturer and brand level, the classification techniques: soft independent modeling of class analogy (SIMCA), partial least squares regression discriminant analysis (PLS-DA), and support vector machines discriminant analysis (SVM-DA) were tested. PLS-DA model presented an inconclusive assignment ratio of 20%. On the other hand, SIMCA models had a 0 inconclusive rate but an sensitivity close to 85%. While the non-linear technique (SVM-DA) showed an accuracy of 98%, with 95% sensitivity and 98% specificity. The SPCE-SVM-DA technique was then used to distinguish at brand level two highly frauded beers. The SPCE coupled with SVM-DA performed with an accuracy of 97% for the classification of both brands. Therefore, the proposed electrochemicalsensor configuration has been deemed an appropriate tool for discrimination of American lager beers according to their producer and brands.

SUBMITTER: Mutz YS 

PROVIDER: S-EPMC8980006 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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A single screen-printed electrode in tandem with chemometric tools for the forensic differentiation of Brazilian beers.

Mutz Yhan S YS   do Rosario Denes D   Silva Luiz R G LRG   Galvan Diego D   Janegitz Bruno C BC   de Q Ferreira Rafael R   Conte-Junior Carlos A CA  

Scientific reports 20220404 1


In the present study a single screen-printed carbon electrode (SPCE) and chemometric techniques were utilized for forensic differentiation of Brazilian American lager beers. To differentiate Brazilian beers at the manufacturer and brand level, the classification techniques: soft independent modeling of class analogy (SIMCA), partial least squares regression discriminant analysis (PLS-DA), and support vector machines discriminant analysis (SVM-DA) were tested. PLS-DA model presented an inconclusi  ...[more]

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