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Commercial Instant Coffee Classification Using an Electronic Nose in Tandem with the ComDim-LDA Approach

  • Gustavo Yasuo Figueiredo Makimori
  • Evandro BonaEmail author
Article
  • 29 Downloads

Abstract

Coffee is an important commodity for Brazil and ensuring product quality is a priority. An electronic nose (E-nose), with seven MOS sensors, was used to analyze 53 samples of six different commercial instant coffees produced by the same industry. Thereafter, chemometric tools such as common dimension analysis (ComDim) and linear discriminant analysis (LDA) were applied to classify the samples. ComDim is an unsupervised multiblock analysis able to reduce large data dimensions from different tables. A block for each E-nose sensor with the first derivative of the transient signal was used as ComDim input. Four common dimensions (CDs) were necessary to represent the E-nose data, which accumulated a total variance of 99.86%. Salience tables indicate a relation in CD1 between sensors S1, S3, S5, S6, and S8. Sensors S7 and S9 have more influence on CD2. The scores from the first four CDs were used as input to construct LDA classifiers. All models reached a sensitivity and specificity of 100% in the leave-one-out cross-validation. Thus, the proposed approach was able to classify correctly the aromatic pattern of different commercial instant coffees.

Keywords

Multiblock analysis Chemometrics Smellprint Quality control Coffee 

Notes

Funding Information

This work is financially supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq).

Compliance with Ethical Standards

Conflict of Interest

Gustavo Yasuo Figueiredo Makimori declares that he has no conflict of interest. Evandro Bona declares that he has no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

Not applicable.

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Post-Graduate Program of Food Technology (PPGTA)Federal University of Technology – Paraná (UTFPR)Campo MourãoBrazil

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