Spectroscopic and Chromatographic Fingerprints for Discrimination of Specialty and Traditional Coffees by Integrated Chemometric Methods

• Published in: Food analytical methods Vol. 13; no. 12; pp. 2204 - 2212
• Main Authors: Abreu, Marcela B., Marcheafave, Gustavo G., Bruns, Roy E., Scarminio, Ieda S., Zeraik, Maria L.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-12-2020; Springer Nature B.V
• Subjects: Analytical Chemistry; Chemistry; Chemistry and Materials Science; Chemistry/Food Science; Chlorogenic acid; Chromatography; Classification; Coffee; Coffeehouses; Dichloromethane; Discrimination; Ductility tests; Factor analysis; Fatty acids; Fingerprints; Food Science; Information processing; Integrated approach; Lipid metabolism; Lipids; Metabolites; Microbiology; Multivariate analysis; Organic acids; Sample preparation; Secondary metabolites; Spectrophotometry; Spectroscopy; Spectrum analysis; Statistical analysis; Statistical mixture design; UV-vis; Factor analysis; Infrared; Chromatography; Specialty coffee
• ISSN: 1936-9751; 1936-976X
• DOI: 10.1007/s12161-020-01832-1

Classification of specialty coffees is based mainly on sensory qualities that demand high training and onerous analysis of baristas. The consumption of specialty coffees has been growing owing to consumers’ preference for better quality products. This work reports an integrated approach of sample preparation (statistical mixture design), instrumental techniques (spectroscopy and chromatography), and multivariate analysis (exploratory factor analysis) to discriminate specialty and traditional roasted commercial coffees, previously classified by the SCA (Specialty Coffee Association) cupping method. Factor analysis indicated that the fingerprint spectra obtained with aqueous and dichloromethane extracts provided the most divergent metabolite information. Aqueous extract promoted better discrimination for specialty and traditional coffee. Among the techniques used, chromatographic separation and UV-vis spectrophotometry detected metabolites that discriminated the coffees. The mid-infrared fingerprint showed lower discrimination between coffees. Factor loadings indicated that secondary metabolites related to better beverage quality correlate with the specialty coffees. The main metabolites responsible for discriminations were chlorogenic acids, trigonelline, organic acids, lipids, and fatty acids. This study presents for the first time the differentiation of traditional and specialty commercial coffee, using different analytical techniques, through its metabolomic profiles integrated with chemometric tools. These results highlight an alternative method of metabolomic approach for specialty coffee classification.
Graphical Abstract