LIBS-Based Chemometric and Machine-Learning Approach for Detecting Adulteration in Açaí Pulp

Abstract

Food adulteration remains a major challenge in the commercialization of high-value agricultural products, creating a demand for rapid and reliable analytical screening methods. In this study, Laser-Induced Breakdown Spectroscopy (LIBS) combined with chemometric and machine-learning approaches was investigated for the detection of adulterants in commercial açaí (Euterpe oleracea Mart.) pulp. Controlled adulteration was performed using wheat flour, corn starch, and a commercial stabilizer at mass fractions of 2%, 4%, 6%, and 10% (w/w). The acquired LIBS spectra were preprocessed through correlation-based outlier removal followed by Standard Normal Variate (SNV) normalization. Principal Component Analysis (PCA) was subsequently applied for dimensionality reduction, with approximately 25 principal components required to explain nearly 95% of the total spectral variance. Supervised classification was then performed using a Random Forest (RF) algorithm applied to the PCA-reduced data set in order to discriminate between pure and adulterated samples. The classification performance was found to depend strongly on the adulteration level, yielding accuracies of 75%, 69%, 44%, and 81.2% for the 2%, 4%, 6%, and 10% concentrations, respectively. The best performance was achieved for the 10% adulteration level, where all pure açaí samples were correctly identified. The lower performance observed at intermediate concentrations indicates increased spectral overlap between pure and adulterated samples under these conditions. Overall, the results demonstrate that LIBS combined with chemometric and machine-learning techniques constitutes a promising strategy for rapid elemental fingerprinting and screening of adulteration in açaí pulp. These findings reinforce the potential of spectroscopic–chemometric approaches as practical tools for food authentication and quality control in high-value agricultural products.