Vision system and electronic tongue application to detect coffee adulteration with barley

Abstract

Time consuming and expensive methods have been applied for detection of coffee adulteration based on the literature. In the present work, an optical method (vision system) and the application of an electronic tongue is proposed to reveal the addition of barley in different proportion to coffee in ground and brewed forms. In a range of 1 to 80% (w/w) Robusta coffee was blended with roasted barley. Principal Component Analysis (PCA) accomplished on vision system image data showed a good discrimination of the adulterated samples. The results of Polar Qualifi cation System (PQS) data reduction method revealed even small differences in the right barley content order by point method approach. With Partial Least Squares (PLS) regression the amount of barley in Robusta was predicted with high R 2 (0.996) and relatively low RMSEP (~2%) values in case of vision system data processing. Considering electronic tongue measurements, PCA results showed a good discrimination of samples with higher barley concentration. Misclassifi cation was found in the low concentrated area by Lienar Discriminant Analgsis (LDA). To obtain an accurate model for barley content prediction in coffee, the most sensitive sensor signals were used to apply PLS regression successfully (R 2 =0.97, RMSEP=3.99% (w/w)).

Emerging Technologies Based on Artificial Intelligence to Assess the Quality and Consumer Preference of Beverages

“…Oblitas and Castro [70] designed a system to evaluate the color of roasted coffee beans by placing the sample in a Petri dish inside a chamber with a uniform lighting source, and recording it with a video camera to further process it using an algorithm written in Matlab ® , the obtaining values in the CIELab color scale. Várvölgyi et al [71] presented another technique to measure color using a charge-coupled device camera, 12 halogen lights, and a metal holder for the sample to obtain the hue of roasted coffee beans; however, the authors did not mention the software used to analyze the images. In a more recent study, Morais de Oliveira et al [72] reported a CV system to evaluate green Arabica coffee beans using a black chamber composed of white lighting, and a digital camera, which was fixed 40 cm above the sample.…”