Work

Large dataset on Fourier transform near infrared (FT-NIR) spectroscopy of green and roasted specialty coffee: Preprocessed infrared spectra and sensory scores for machine learning-based quality monitoring

Year: 2025

Type: data-paper

Abstract: , and 64 accumulated scans per sample. The dataset includes both raw and preprocessed FT-NIR spectra, incorporating baseline correction, Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), as well as first and second derivatives to enhance spectral interpretation. One of the main strengths of this dataset is its ability to facilitate non-destructive quality assessment of roasted coffee using both green and roasted FT-NIR spectra. Furthermore, it also enables the prediction of roasted coffee sensory quality based on green coffee spectra, potentially reducing the need for roasting during quality evaluation and streamlining batch screening processes. This dataset enhances research on coffee quality, chemical marker identification, and roasting optimization, supporting both scientific and industrial applications. The dataset is structured into Excel files, systematically organized by processed samples and their replicates, providing a valuable framework for further analysis, model development, and calibration of multivariate statistical models.

Source: Data in Brief

DOI: 10.1016/j.dib.2025.111609

Authors: Andrés F. Bahamón-Monje, Ever M. Morales-Angulo, Gentil A. Collazos-Escobar, Nelson Gutiérrez-Guzmán

Institutions: Universidad Surcolombiana, Universitat Politècnica de València

Language: English

Topic: Spectroscopy and Chemometric Analyses

Subfield: Analytical Chemistry

Field: Chemistry

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,220

Funders: Sistema General de Regalías de Colombia, Universidad Surcolombiana