Cleaner and faster method to detect adulteration in cassava starch using Raman spectroscopy and one-class support vector machine

Abstract

Due to food adulteration concerns, analytical assays are routinely performed in labs to evaluate and ensure food quality control. However, classical analytical methods used to acquire reliable results are lengthy and costly. Therefore, we aim to propose a new approach to detect adulterants in cassava starch in a clean, green, cheap, and quick way. Raman spectroscopy meets all these requirements and presents great potential to perform such routine analyses. Data treatment is also an important step in authentication problems, and we propose the use of one-class models to do so. One-class support vector machine (OC-SVM) and soft independent modelling by class analogy (SIMCA) were the two approaches to one-class classifiers assessed in this study. Cassava starch samples were modified in the lab with adulteration ranging from 0.5 to 50%, with adulterants such as wheat flour, sodium bicarbonate, and others. The two chemometric models were statistically compared and OC-SVM was found to outperform SIMCA, reaching higher values of sensitivity (87.1%), specificity (86.8%), and accuracy (86.9%) in the prediction of known data samples. This better performance also resulted in the possibility of detecting adulterations over 2% by OC-SVM, compared to only 5% by SIMCA.