Periódicos
URI permanente desta comunidadehttps://thoth.dti.ufv.br/handle/123456789/3352
Navegar
2 resultados
Resultados da Pesquisa
Item Detection of adulterated coffee by fourier-transform infrared (FTIR) spectroscopy associated with sensory analysis(Universidade Federal de Lavras, 2022-06-09) Barrios-Rodriguez, Yeison Fernando; Devia-Rodriguez, Yenny; Gutierrez-Guzmán, NelsonBecause of its huge economic value, coffee has been the target of adulteration worldwide. Given the successful application of spectroscopic methods in detecting adulterants, this study aimed to employ attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) to detect adulterants in roasted coffee samples and compare the results with that of sensory analysis. In this study, twelve coffee samples were intentionally adulterated with varying concentrations, i.e., 10%, 30%, and 50%, of corn, beans, sawdust, and coffee husk. These adulterated samples were compared with one un-adulterated coffee sample and four roasted and ground commercially available coffee samples; spectral readings of caffeine and chlorogenic acid (CGA) standards were performed for reference. The sensory analysis was performed by 17 tasters who were trained by a Q-grader. The infrared (IR) spectra (FTIR) data were processed by multiplicative signal correction (MSC) and subjected to a principal component analysis (PCA), along with the results of the sensory analysis. The combination of sensory analysis and IR spectrum allowed to differentiate samples of adulterated coffee and unadulterated coffee by PCA, with an explanation of 79% variance. The results demonstrated that the wavenumbers associated with CGA and caffeine contribute significantly in distinguishing adulterated coffee samples.Item Estimation of percentage of impurities in coffee using a computer vision system(Departamento de Engenharia Agrícola - UFCG, 2022-01-14) Costa, Anderson G.; Silva, Eudócio R. O. da; Barros, Murilo M. de; Fagundes, Jonatthan A.The quality and price of coffee drinks can be affected by contamination with impurities during roasting and grinding. Methods that enable quality control of marketed products are important to meet the standards required by consumers and the industry. The purpose of this study was to estimate the percentage of impurities contained in coffee using textural and colorimetric descriptors obtained from digital images. Arabica coffee beans (Coffea arabica L.) at 100% purity were subjected to roasting and grinding processes, and the initially pure ground coffee was gradually contaminated with impurities. Digital images were collected from coffee samples with 0, 10, 30, 50, and 70% impurities. From the images, textural descriptors of the histograms (mean, standard deviation, entropy, uniformity, and third moment) and colorimetric descriptors (RGB color space and HSI color space) were obtained. The principal component regression (PCR) method was applied to the data group of textural and colorimetric descriptors for the development of linear models to estimate coffee impurities. The selected models for the textural descriptors data group and the colorimetric descriptors data group were composed of two and three principal components, respectively. The model from the colorimetric descriptors showed a greater capacity to estimate the percentage of impurities in coffee when compared to the model from the textural descriptors.