A Fast and Inexpensive Chemometric-Assisted Method to Identify Adulteration in Acai (Euterpe oleracea) Using Digital Images

Abstract

This work is concerned with an analytical method for detecting acai adulteration based on digital image (DI) assisted by mean one-class classification (OCC) chemometric approaches, namely data-driven soft independent modeling of class analogy (DD-SIMCA) and one-class partial least squares (OC-PLS). In this study, two adulterants were considered, wheat flour and cassava. Digital images were acquired, in triplicate, using a webcam (WC040 MULTILASER of 5 Mp, 2G glass lenses with USB connection) in a closed wooden box with appropriate lighting and stored in JPEG format (24 bits) with a dimension of 2880 × 1620 pixels. For all the images, a central circular area was defined, used the working region to construct the frequency histograms in the color levels considering the standard RGB (red-green-blue), HSI (hue-saturation-intensity), and grayscale color models. Preliminary results obtained by principal component analysis (PCA) indicated the formation of two sample clusters (adulterated and unadulterated). On the other hand, the formation of sample clusters with respect to the type of adulterant (wheat and cassava) was not observed. OCC (DD-SIMCA and OC-PLS) models were built using eight and four factors, respectively, showing satisfactory fit. In the prediction of an external set of samples, the following results were obtained: error rate (ER) 2 and 31%, SEN 100% for both models, and specificity (SPE) 98.14 and 78.69 for DD-SIMCA and OC-PLS, respectively.