Assessment of geographical origin of virgin coconut oil using inductively coupled plasma mass spectrometry along with multivariate chemometrics

Abstract

Recently, Virgin coconut oil (VCO) has emerged as one of the most favorable edible oils because of its application in cooking, frying as well as additive used in food, pharmaceuticals, and cosmetic goods. These qualities have established VCO in high consumer demand and there is a great need of establishing a reliable method for the identification of its geographical origin. Through this present study, for the first time, it has been established that Inductively Coupled Plasma-Mass-Spectrometry (ICP-MS) combined with multivariate chemometrics can be used for the identification of the geographical origin of the VCO samples of various provinces. Principal Component Analysis (PCA), and Linear Discriminant Analysis (LDA) were able to differentiate and classify the VCO samples of different geographical origins. Further, calibration models (Principal Component Regression and Partial Least Square Regression) were developed on the calibration dataset of the elemental concentration obtained from the ICP-MS analysis. An external dataset was used to develop the prediction model to predict the geographical origin of an unknown sample. Both PCR and PLS-R models were successfully able to predict the geographical origin with a high R2 value (0.999) and low RMSEP value 0.074 and 0.075% v/v of prediction respectively. In conclusion, ICP-MS combined with regression modelling can be used as an excellent tool for the identification of the geographical origin of the VCO samples of various provinces. This whole technique is the most suitable as it has high sensitivity as well as provides easy multi-metal analysis for a single sample of edible oil.

Keywords

• Virgin coconut oil
• ICP-MS
• Multivariate chemometrics
• Regression model