ArticleData in brief2025
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.
Article in Data in brief, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Near-Infrared Spectroscopy-Based Discriminant Analysis for the Classification of Coffee Quality in Dry Parchment and Green Coffee.Molecules (Basel, Switzerland) · 2026Article
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4 authors.
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Abstract
A comprehensive dataset on FT-NIR spectra for green and roasted specialty coffee is critically needed to advance predictive models for sensory quality assessment. To address this need, the NIR spectra of green and roasted specialty coffee were obtained using cutting-edge FT-NIR spectroscopy, while sensory quality analysis for roasted coffee beans followed the standardized protocol validated by the Specialty Coffee Association (SCA). This approach was designed to establish a robust dataset for calibrating machine learning-based predictive models of coffee cup quality. FT-NIR spectra were acquired using a Spectrum Two N-FT-NIR Spectrometer equipped with a high-resolution Indium Gallium Arsenide (InGaAs) detector, operating in diffuse reflectance mode. Spectral data were collected over a wavelength range of 12,000 to 4,000 cm
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