Identification and Quantification of Adulterants in Coffee (Coffea arabica L.) Using FT-MIR Spectroscopy Coupled with Chemometrics
| Autor: | Ofelia Gabriela Meza-Márquez, Mauricio Flores-Valdez, Guillermo Osorio-Revilla, Tzayhri Gallardo-Velázquez |
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| Rok vydání: | 2020 |
| Předmět: |
Health (social science)
Communication Coffea arabica Coffea arabica L coffee quality Context (language use) Plant Science Health benefits chemometric studies lcsh:Chemical technology Health Professions (miscellaneous) Microbiology Economic benefits Chemometrics Partial least squares regression Ground coffee Principal component regression Coffea arabica L. FT-MIR lcsh:TP1-1185 Food science FT-MIR Food Science Mathematics |
| Zdroj: | Foods, Vol 9, Iss 851, p 851 (2020) Foods |
| ISSN: | 2304-8158 |
| DOI: | 10.3390/foods9070851 |
| Popis: | Food adulteration is an illegal practice performed to elicit economic benefits. In the context of roasted and ground coffee, legumes, cereals, nuts and other vegetables are often used to augment the production volume; however, these adulterants lack the most important coffee compound, caffeine, which has health benefits. In this study, the mid-infrared Fourier transform spectroscopy (FT-MIR) technique coupled with chemometrics was used to identify and quantify adulterants in coffee (Coffea arabica L.). Coffee samples were adulterated with corn, barley, soy, oat, rice and coffee husks, in proportions ranging from 1–30%. A discrimination model was developed using the soft independent modeling of class analogy (SIMCA) framework, and quantitative models were developed using such algorithms as the partial least squares algorithms with one variable (PLS1) and multiple variables (PLS2) and principal component regression (PCR). The SIMCA model exhibited an accuracy of 100% and could discriminate among all the classes. The quantitative model with the highest performance corresponded to the PLS1 algorithm. The model exhibited an R2c: ≥ 0.99, standard error of calibration (SEC) of 0.39–0.82, and standard error of prediction (SEP) of 0.45–0.94. The developed models could identify and quantify the coffee adulterants, and it was considered that the proposed methodology can be applied to identify and quantify the adulterants used in the coffee industry. |
| Databáze: | OpenAIRE |
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