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Selection of calibration set samples in determination of olive oil acidity by partial least squares-attenuated total reflectance-Fourier transform infrared spectroscopy

机译:通过偏最小二乘衰减全反射-傅里叶变换红外光谱法测定橄榄油酸度时选择校准样品

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摘要

A chemometric method has been applied for the determination of the free fatty acid (FFA) concentration in commercial olive oil samples of different types an origins by using Fourier transform infrared spectroscopy (FTIR) attenuated total reflectance (ATR) measurements. Different methods for selecting the training set, including hierarchical cluster analysis, were applied and compared. The prediction capabilities of partial least squares (PLS) multivariate calibration methods, net analyte signal (NAS) preprocessing followed by PLS or classical least squares (CLS) regression method of ATR-FTIR data were evaluated. Several aspects, like spectral range to be considered, different preprocessing alternatives (mean centering, multiplicative scattering correction, standard normal variate (SNV)), together with a critical evaluation of the calibration set were made on using the mean square error of cross-validation and prediction, as control parameters. Using a calibration set of 16 samples the properties of 28 samples were predicted with relative precision of triplicates of 0.017 wt.%. The mean different between predicted and actual values and the standard deviation of mean differences were -0.001 and 0.037 wt.%, respectively. The limit of detection (LOD), sensitivity and selectivity of the methodology developed were evaluated in terms of the net analyte signal, being found a limit of detection of 0.072 wt.%, a sensitivity value of 0.077 in terms of analytical signal per unit of concentration, being expressed that in wt.%, and a linear relationship (R~2=0.9963) between selectivity and FFA concentration (equivalent to 0.24% for a sample containing 1 wt.% of FFA).
机译:通过使用傅里叶变换红外光谱(FTIR)衰减全反射率(ATR)测量,已应用化学计量学方法测定不同类型和来源的商品橄榄油样品中的游离脂肪酸(FFA)浓度。应用并比较了选择训练集的不同方法,包括层次聚类分析。评估了偏最小二乘(PLS)多元校准方法,净分析物信号(NAS)预处理,PLS或ATR-FTIR数据的经典最小二乘(CLS)回归方法的预测能力。使用交叉验证的均方误差对几个方面(如要考虑的光谱范围),不同的预处理方案(均值居中,乘法散射校正,标准正态变量(SNV))以及对校准集的关键评估进行了评估和预测,作为控制参数。使用16个样品的校准集,以0.017 wt。%的一式三份的相对精度预测了28个样品的性能。预测值和实际值之间的平均差异以及平均差异的标准偏差分别为-0.001和0.037 wt。%。根据净分析物信号评估了开发方法的检测限(LOD),灵敏度和选择性,发现检测限为0.072 wt。%,单位为分析信号的灵敏度值为0.077。浓度,以重量%表示,以及选择性和FFA浓度之间的线性关系(R〜2 = 0.9963)(对于含有1重量%FFA的样品而言,相当于0.24%)。

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