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Abstract
This article uses infrared spectroscopy data from the research article titled "Data on roasted coffee with specific defects analyzed by infrared-photoacoustic spectroscopy and chemometrics" [1], in which they made mixtures of defective and healthy coffee beans using the Coffea Arabica (Arabica) and Coffea canephora (Robusta) species in different proportions. The free software R: A Language and Environment for Statistical Computing and ChemoSpec library package were used to make a principal component analysis and a model-based clustering of the infrared spectra data, it was possible to identify groups and trends in the data through PCA and a predictive model was obtained that managed to classify the samples into five classes.
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