Groundnut oil is known as a good source of essential fatty acids which are significant in the physiological development of the human body. The oil is also recognized for its ability to reduce the risk of cardiovascular diseases. It is characterized by a strong pleasant aroma which contributes to its demand on the market which makes it highly appreciated and sells at a very good price. However, some groundnut oil producers have been suspected to produce groundnut oil by blending it with cheaper oils especially palm olein at different concentrations or by adding groundnut flavor to palm olein. Over the years, there have been several methods to detect adulteration in oils which are time-consuming and expensive.
Spectroscopy is one of the most commonly used instrumental methods, primarily chosen because of its rapid screening capabilities and non-destructive nature. Near infrared (NIR) and ultraviolet–visible (UV–Vis) spectroscopies are cheap and rapid methods for oil adulteration. This present study aimed to apply NIR and UV–Vis in combination with chemometrics to develop models for prediction and quantification of groundnut oil adulteration.
Using principal component analysis (PCA) scores, pure and prepared adulterated samples showed overlapping showing similarities between them. Linear discriminant analysis (LDA) models developed from NIR and UV–Vis gave an average cross-validation accuracy of 92.61% and 62.14% respectively for pure groundnut oil and adulterated samples with palm olein at 0, 1, 3, 5, 10, 20, 30, 40 and 50% v/v.
With partial least squares regression free fatty acid, color parameters, peroxide and iodine values could be predicted with R2CV’s (coefficient of cross validation)up to 0.8799 and RMSECV’s (Root Mean Square Error of Cross validation) which is a measure of model stability being lower than 3 ml/100 ml for NIR spectra and R2CV’s up to 0.81 and RMSECV’s lower than 4 ml/100 ml for UV–Vis spectra.For R2CV the closer the values are to 1 the better the model used and the farther the values are from 1 the poorer the model developed. Root Mean Square Errors ( RMSEs) measures the amount of error generated during the processing of the data. The closer the values are to zero the more stable the model is and vice versa. NIR spectra produced better models as compared to UV–Vis spectra.
Click on the link below to read the full article.
Comments