Daszykowski Michal, Kula Michal, Stanimirova Ivana
Institute of Chemistry, University of Silesia in Katowice, 9 Szkolna Street, 40-006 Katowice, Poland.
Foods. 2023 Sep 8;12(18):3377. doi: 10.3390/foods12183377.
This study demonstrates the rapid and cost-effective possibility of quantifying adulterant amounts (corn flour or corn starch) in ground and dried garlic samples. Prepared mixtures with different concentrations of selected adulterant were effectively characterized using Fourier-transform near-infrared reflectance spectra (FT-NIR), and multivariate calibration models were developed using two methods: principal component regression (PCR) and partial least squares regression (PLSR). They were constructed for optimally preprocessed FT-NIR spectra, and PLSR models generally performed better regarding model fit and predictions than PCR. The optimal PLSR model, built to estimate the amount of corn flour present in the ground and dried garlic samples, was constructed for the first derivative spectra obtained after Savitzky-Golay smoothing (fifteen sampling points and polynomial of the second degree). It demonstrated root mean squared errors for calibration and validation samples equal to 1.8841 and 1.8844 (i.e., 1.88% concerning the calibration range), respectively, and coefficients of determination equal to 0.9955 and 0.9858. The optimal PLSR model constructed for spectra after inverse scattering correction to assess the amount of corn starch had root mean squared errors for calibration and validation samples equal to 1.7679 and 1.7812 (i.e., 1.77% and 1.78% concerning the calibration range), respectively, and coefficients of determination equal to 0.9961 and 0.9873. It was also possible to discriminate samples adulterated with corn flour or corn starch using partial least squares discriminant analysis (PLS-DA). The optimal PLS-DA model had a very high correct classification rate (99.66%), sensitivity (99.96%), and specificity (99.36%), calculated for external validation samples. Uncertainties of these figures of merit, estimated using the Monte Carlo validation approach, were relatively small. One-class classification partial least squares models, developed to detect the adulterant type, presented very optimistic sensitivity for validation samples (above 99%) but low specificity (64% and 45.33% for models recognizing corn flour or corn starch adulterants, respectively). Through experimental investigation, chemometric data analysis, and modeling, we have verified that the FT-NIR technique exhibits the required sensitivity to quantify adulteration in dried ground garlic, whether it involves corn flour or corn starch.
本研究证明了对磨碎并干燥的大蒜样品中掺假物（玉米粉或玉米淀粉）含量进行快速且经济高效定量分析的可能性。使用傅里叶变换近红外反射光谱（FT-NIR）对具有不同选定掺假物浓度的制备混合物进行了有效表征，并使用两种方法建立了多元校准模型：主成分回归（PCR）和偏最小二乘回归（PLSR）。它们是针对经过最佳预处理的FT-NIR光谱构建的，并且在模型拟合和预测方面，PLSR模型通常比PCR表现更好。为估计磨碎并干燥的大蒜样品中玉米粉的含量而构建的最佳PLSR模型，是针对Savitzky-Golay平滑处理（15个采样点和二次多项式）后获得的一阶导数光谱构建的。它显示校准样品和验证样品的均方根误差分别为1.8841和1.8844（即在校准范围内为1.88%），决定系数分别为0.9955和0.9858。为评估玉米淀粉含量而对经过反向散射校正后的光谱构建的最佳PLSR模型，校准样品和验证样品的均方根误差分别为1.7679和1.7812（即在校准范围内分别为1.77%和1.78%），决定系数分别为0.9961和0.9873。使用偏最小二乘判别分析（PLS-DA）也能够区分掺有玉米粉或玉米淀粉的样品。针对外部验证样品计算得出，最佳PLS-DA模型具有非常高的正确分类率（99.66%）、灵敏度（99.96%）和特异性（99.36%）。使用蒙特卡洛验证方法估计的这些品质因数的不确定性相对较小。为检测掺假物类型而开发的单类分类偏最小二乘模型，对验证样品呈现出非常乐观的灵敏度（高于99%），但特异性较低（识别玉米粉或玉米淀粉掺假物的模型分别为64%和45.33%）。通过实验研究、化学计量学数据分析和建模，我们已经验证FT-NIR技术具有所需的灵敏度，能够对干燥磨碎大蒜中的掺假情况进行定量分析，无论掺假物是玉米粉还是玉米淀粉。
