Moros J, Llorca I, Cervera M L, Pastor A, Garrigues S, de la Guardia M
Department of Analytical Chemistry, Universitat de Valencia, Edifici Jeroni Muñoz, 50th Dr. Moliner 46100, Burjassot, Valencia, Spain.
Anal Chim Acta. 2008 Apr 21;613(2):196-206. doi: 10.1016/j.aca.2008.02.066. Epub 2008 Mar 7.
It has been evaluated the potential of near-infrared (NIR) diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) as a way for non-destructive measurement of trace elements at microg kg(-1) level in foods, with neither physical nor chemical pre-treatment. Predictive models were developed using partial least-square (PLS) multivariate approaches based on first-order derivative spectra. A critical comparison of two spectral pre-treatments, multiplicative signal correction (MSC) and standard normal variate (SNV) was also made. The PLS models built after using SNV provided the best prediction results for the determination of arsenic and lead in powdered red paprika samples. Relative root-mean-square error of prediction (RRMSEP) of 23% for both metals, arsenic and lead, were found in this study using 20 well characterized samples for calibration and 13 additional samples as validation set. Results derived from this study showed that NIR diffuse reflectance spectroscopy combined with the appropriate chemometric tools could be considered as an useful screening tool for a rapid determination of As and Pb at concentration level of the order of hundred microg kg(-1).
已评估近红外(NIR)漫反射红外傅里叶变换光谱法(DRIFTS)作为一种无需物理或化学预处理即可无损测量食品中微克每千克(μg kg⁻¹)级痕量元素的方法的潜力。基于一阶导数光谱,使用偏最小二乘(PLS)多元方法建立了预测模型。还对两种光谱预处理方法,即多元散射校正(MSC)和标准正态变量变换(SNV)进行了关键比较。使用SNV后建立的PLS模型在测定辣椒粉粉末样品中的砷和铅时提供了最佳预测结果。本研究中,使用20个特征明确的样品进行校准,并另外使用13个样品作为验证集,发现两种金属砷和铅的预测相对均方根误差(RRMSEP)均为23%。本研究结果表明,近红外漫反射光谱法结合适当的化学计量工具可被视为一种用于快速测定浓度约为数百微克每千克(μg kg⁻¹)水平的砷和铅的有用筛选工具。