PDF(Pubmed)
Abstract：
Virgin coconut oil (VCO) is a functional food with important health benefits. Its economic interest encourages fraudsters to deliberately adulterate VCO with cheap and low-quality vegetable oils for financial gain, causing health and safety problems for consumers. In this context, there is an urgent need for rapid, accurate, and precise analytical techniques to detect VCO adulteration. In this study, the use of Fourier transform infrared (FTIR) spectroscopy combined with multivariate curve resolution-alternating least squares (MCR-ALS) methodology was evaluated to verify the purity or adulteration of VCO with reference to low-cost commercial oils such as sunflower (SO), maize (MO) and peanut (PO) oils. A two-step analytical procedure was developed, where an initial control chart approach was designed to assess the purity of oil samples using the MCR-ALS score values calculated on a data set of pure and adulterated oils. The pre-treatment of the spectral data by derivatization with the Savitzky-Golay algorithm allowed to obtain the classification limits able to distinguish the pure samples with 100% of correct classifications in the external validation. In the next step, three calibration models were developed using MCR-ALS with correlation constraints for analysis of adulterated coconut oil samples in order to assess the blend composition. Different data pre-treatment strategies were tested to best extract the information contained in the sample fingerprints. The best results were achieved by derivative and standard normal variate procedures obtaining RMSEP and RE% values in the ranges of 1.79-2.66 and 6.48-8.35%, respectively. The models were optimized using a genetic algorithm (GA) to select the most important variables and the final models in the external validations gave satisfactory results in quantifying adulterants, with absolute errors and RMSEP of less than 4.6% and 1.470, respectively.
摘要：
初榨椰子油（VCO）是一种具有重要健康益处的功能性食品。其经济利益鼓励欺诈者故意在VCO中掺入廉价和低质量的植物油以获取经济利益，给消费者带来健康和安全问题。在这种情况下，迫切需要快速，准确,和精确的分析技术来检测VCO掺假。在这项研究中,使用傅里叶变换红外（FTIR）光谱结合多变量曲线分辨率交替最小二乘（MCR-ALS）方法进行了评估，以验证VCO的纯度或掺假，参考低成本商业油如向日葵（SO），玉米（MO）和花生（PO）油。开发了两步分析程序，其中设计了初始控制图方法，以使用在纯油和掺假油的数据集上计算的MCR-ALS得分值来评估油样品的纯度。通过用Savitzky-Golay算法衍生化的光谱数据的预处理允许获得能够在外部验证中区分具有100%正确分类的纯样品的分类限制。下一步,使用具有相关性约束的MCR-ALS开发了三种校准模型，用于分析掺假的椰子油样品，以评估混合成分。测试了不同的数据预处理策略，以最佳地提取样品指纹中包含的信息。通过导数和标准正态变量程序获得的RMSEP和RE％值在1.79-2.66和6.48-8.35％的范围内获得最佳结果，分别。使用遗传算法（GA）对模型进行了优化，以选择最重要的变量，外部验证中的最终模型在量化掺假方面给出了令人满意的结果。绝对误差和RMSEP分别小于4.6%和1.470。
