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Chemometric methods for classification of clonal varieties of green coffee using Raman spectroscopy and direct sample analysis

Luna, Aderval S., da Silva, Arnaldo P., da Silva, Camila S., Lima, Igor C.A., de Gois, Jefferson S.
Subtropical plant science 2019 v.76 pp. 44-50
Bayesian theory, Raman spectroscopy, chemometrics, coffee beans, discriminant analysis, models
This work presents the development of methods for classification of clonal varieties of coffee using chemometrics coupled to Raman spectroscopy and direct sample analysis. Spectra were collected directly from each bean in triplicate, and the homogeneity of the sample’s surface was studied. The spectral range between 1200 and 1800 cm−1 is related to organic groups that are relevant for the discrimination of coffee. The main peaks were observed at about 1600 cm−1, 1630 cm−1, 1120 cm−1, and 1200 cm−1. All collected spectra were baseline-aligned, then the preprocessing mean centering (MC) or multiplicative scatter correction (MSC) were used. The original data matrix X was replaced by the PCA-scores matrix T before applying the classification methods. Linear discriminant analysis (LDA), mixture discriminant analysis (MDA), quadratic discriminant analysis (QDA), regularized discriminant analysis (RDA), partial least squares-discriminant analysis with Bayesian inference (PLS–DA), and soft independent modeling of class analogies (SIMCA) methods were applied and compared.Multiplicative scatter correction provided more accurate results when compared to MC, which may be attributed to the direct analysis of solid samples that requires correction for radiation scattering. Using MSC, the LDA correctly classified 98.7% of the samples, while MDA, RDA, QDA, PLS–DA, and SIMCA corrected classified 100% of the samples. On the other hand, using MC, correct classification of the samples was 62.7% for LDA, 70.7% for MDA, 62.7% for RDA, 62.7% for QDA, 61.3% for PLS–DA, and 97.3% for SIMCA.