Jamwal Rahul, Kumari Shivani, Kelly Simon, Cannavan Andrew, Singh Dileep Kumar
Soil Microbial Ecology and Environmental Toxicology Laboratory, Department of Zoology, University of Delhi, New Delhi, Delhi, 110007, India.
Food and Environmental Protection Laboratory, International Atomic Energy Agency, Vienna International Centre, PO Box 100, 1400, Vienna, Austria.
Curr Res Food Sci. 2022 Mar 11;5:545-552. doi: 10.1016/j.crfs.2022.03.003. eCollection 2022.
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 R 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.
最近，初榨椰子油（VCO）已成为最受欢迎的食用油之一，因为它可用于烹饪、油炸以及作为食品、药品和化妆品中的添加剂。这些特性使初榨椰子油在消费者中需求很高，因此迫切需要建立一种可靠的方法来鉴定其地理来源。通过本研究，首次确定电感耦合等离子体质谱（ICP-MS）结合多元化学计量学可用于鉴定不同省份初榨椰子油样品的地理来源。主成分分析（PCA）和线性判别分析（LDA）能够区分和分类不同地理来源的初榨椰子油样品。此外，根据ICP-MS分析获得的元素浓度校准数据集建立了校准模型（主成分回归和偏最小二乘回归）。使用外部数据集建立预测模型，以预测未知样品的地理来源。PCR和PLS-R模型均成功地以高R值（0.999）和低RMSEP值（分别为0.074和0.075% v/v）预测了地理来源。总之，ICP-MS结合回归建模可作为鉴定不同省份初榨椰子油样品地理来源的优秀工具。整个技术最为合适，因为它具有高灵敏度，并且能够对单个食用油样品进行简便的多金属分析。
