Comparison of Spectroscopy-Based Methods and Chemometrics to Confirm Classification of Specialty Coffees

Abstract

The Specialty Coffee Association (SCA) sensory analysis protocol is the methodology that is used to classify specialty coffees. However, because the sensory analysis is sensitive to the taster’s training, cognitive psychology, and physiology, among other parameters, the feasibility of instrumental approaches has been recently studied for complementing such analyses. Spectroscopic methods, mainly near infrared (NIR) and mid infrared (FTIR—Fourier Transform Infrared), have been extensively employed for food quality authentication. In view of the aforementioned, we compared NIR and FTIR to distinguish different qualities and sensory characteristics of specialty coffee samples in the present study. Twenty-eight green coffee beans samples were roasted (in duplicate), with roasting conditions following the SCA protocol for sensory analysis. FTIR and NIR were used to analyze the ground and roasted coffee samples, and the data then submitted to statistical analysis to build up PLS models in order to confirm the quality classifications. The PLS models provided good predictability and classification of the samples. The models were able to accurately predict the scores of specialty coffees. In addition, the NIR spectra provided relevant information on chemical bonds that define specialty coffee in association with sensory aspects, such as the cleanliness of the beverage.

Spectrophotometry and chromatography analyses combined with chemometrics tools to differentiate green coffee beans into special or traditional

“…The literature is widely focused on the quality of the coffee bean, mainly in roasted coffee, about the species C. arabica and C. canephora or in the physical characteristics, and not on the special and traditional classification (Ferreira et al, 2021). As can be seen in some works in the literature: (i) Alonso-Salces et al ( 2009) evaluated the phenolic and methylxanthine composition in green coffee beans using multivariate analysis to determine the species and geographic origin; (ii) Mehari et al (2016) evaluated the phenolic composition of green coffee beans from Ethiopia to verify origin; (iii) Mehari et al (2019) using the profile of fatty acids in green coffee beans and chemometrics to classify the origin in Ethiopia; (iv) Mendes et al (2022) used infrared data in combination with chemometrics to evaluate the origin of green coffee beans within the state of Minas Gerais; (v) Belchior et al (2022) evaluated the creation of regression models on roasted coffee beans to assign quality indices for specialty coffees; (vi) Quan et al (2023) evaluated chemometric models to verify the species and origin of green coffee beans from Vietnam; and (vii) Santos et al (2023), who used UV-Vis spectroscopy data combined with single-class modeling on green coffee beans, to perform geographic authentication. Moreover, the focus becomes more evident with the recent bibliographical review by Chen et al (2023), which presents advances in targeted and non-targeted analyses for adulterant detection, species identification, and discrimination of geographic origin.…”

“…(2022) used infrared data in combination with chemometrics to evaluate the origin of green coffee beans within the state of Minas Gerais; (v) Belchior et al. (2022) evaluated the creation of regression models on roasted coffee beans to assign quality indices for specialty coffees; (vi) Quan et al. (2023) evaluated chemometric models to verify the species and origin of green coffee beans from Vietnam; and (vii) Santos et al.…”