Abstract:
Groundnut paste safety and quality is of great concern to consumers due to potential contamination and adulteration which poses serious health risk. This study investigated the safety and quality of groundnut paste using wet chemistry standard method as well as develop a novel application of hand-held NIR spectrometry coupled with chemometrics for the examination of groundnut paste authenticity and quality in real time. Samples were collected within the major markets in the Central region (Mankessim, Kotokuraba, Twifo Praso, Swedru and Kasoa). The authenticity of groundnut paste was evaluated through a physicochemical analysis and fungi count were also determined. A handheld near-infrared spectrometer was used to predict the presence of cassava flour and roasted maize flour at different percentage purity. Among the pre-processing methods used to ensure the quality and accurately of the final analysis, standard normal variant (SNV) was found to be superior. Principal component analysis (PCA) was used to extract relevant information from the spectral data set and the results showed that groundnut paste samples of different categories could be clustered. The performance of the Support Vector Machine (SVM) model shows strong predictive capabilities, with R² values of 0.9751 for cassava flour and 0.9753 for roasted maize flour in the training phase, indicating that it explains a substantial portion of the variance in the data. Most of the groundnut paste samples examined showed low contamination of fungi ranging from 1.60 – 2.48 log10CFU/g. The current study showed that NIR spectroscopy can classify and determine groundnut paste adulterated with cassava flour and roasted maize flour.
