Departamento de Biofísica, Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Unidad Profesional Lázaro Cárdenas, Prolongación de Carpio y Plan de Ayala S/N, Col. Santo Tomás, CP.11340 Ciudad de México, Mexico.
Departamento de Ingeniería Bioquímica, Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Unidad Profesional Adolfo López Mateos, Av. Wilfrido Massieu Esq. Cda. Miguel Stampa s/n, C.P.07738 Ciudad de México, Mexico.
Talanta. 2019 May 15;197:264-269. doi: 10.1016/j.talanta.2019.01.033. Epub 2019 Jan 9.
Fourier transform mid-infrared (MID-FTIR) spectroscopy coupled with chemometric analysis was used to identify and quantify coumarin (CMR) and ethyl vanillin (EVA) adulterations in pure vanilla extracts. Forty samples adulterated with CMR (0.25-10 ppm) and forty with EVA (0.25-10%) were prepared from pure vanilla extracts and characterized by MID-FTIR spectroscopy to develop chemometric models. Additionally, six commercial vanilla samples were analyzed. A soft independent modeling of class analogy (SIMCA) model was developed to identify and classify the purity from EVA-adulterated or CMR-adulterated samples. Prediction models for CMR or EVA content were developed using the principal component regression (PCR), partial least squares with single y-variables (PLS1), and with multiple y-variables (PLS2) algorithms. Moreover, the predictions of the best quantification chemometric model were compared with the results of a high-performance liquid chromatography-diode array detector (HPLC-DAD) method to evaluate the accuracy of the prediction. The PLS1 algorithm had better performance using 3 and 8 factors for EVA and CMR, respectively. The SIMCA model showed 100% recognition and rejections rates. The results demonstrate that adulteration of pure vanilla with EVA and CMR could be successfully predicted by the developed technique.
傅里叶变换中红外（MID-FTIR）光谱结合化学计量学分析用于鉴定和定量纯香草提取物中香豆素（CMR）和乙基香兰素（EVA）的掺假。从纯香草提取物中制备了 40 个掺有 CMR（0.25-10 ppm）和 40 个掺有 EVA（0.25-10%）的样品，并通过 MID-FTIR 光谱进行了表征，以开发化学计量模型。此外，还分析了 6 个商业香草样品。开发了软独立建模的类模拟（SIMCA）模型，以识别和分类 EVA 掺杂或 CMR 掺杂样品的纯度。使用主成分回归（PCR）、单变量偏最小二乘（PLS1）和多变量偏最小二乘（PLS2）算法，建立了用于预测 CMR 或 EVA 含量的预测模型。此外，还将最佳定量化学计量模型的预测结果与高效液相色谱-二极管阵列检测器（HPLC-DAD）方法的结果进行了比较，以评估预测的准确性。PLS1 算法在使用 3 个和 8 个因素时，对 EVA 和 CMR 的性能分别更好。SIMCA 模型的识别和拒绝率均达到 100%。结果表明，该技术可成功预测纯香草提取物中 EVA 和 CMR 的掺假。
