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首页> 外文期刊>Talanta: The International Journal of Pure and Applied Analytical Chemistry >Prediction of coumarin and ethyl vanillin in pure vanilla extracts using MID-FTIR spectroscopy and chemometrics
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Prediction of coumarin and ethyl vanillin in pure vanilla extracts using MID-FTIR spectroscopy and chemometrics

机译:使用中FTIR光谱和化学物质的纯香草提取物预测香豆素和乙基香草蛋白

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摘要

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.
机译:傅里叶变换中红外(中FTIR)光谱与化学计量分析偶联用于鉴定和定量纯香草提取物中的香豆素(CMR)和乙基香草蛋白(EVA)掺杂物。用纯香兰提取物制备纯于CMR(0.25-10ppm)和40%(0.25-10%)掺杂的40个样品,其特征在于中间-FTIR光谱,以开发化学计量模型。另外,分析了六个商业香草样品。开发了一种级别类比(SIMCA)模型的软独立建模,以识别和分类EVA掺假或CMR掺假样品的纯度。使用主成分回归(PCR),具有单个Y变量(PLS1)的部分最小二乘和多个Y变量(PLS2)算法,开发了CMR或EVA内容的预测模型。此外,将最佳量化化学计量模型的预测与高性能液相色谱 - 二极管阵列检测器(HPLC-DAD)方法的结果进行了比较,以评估预测的精度。 PLS1算法分别使用3和8个因素进行了更好的EVA和CMR的性能。 SIMCA模型显示100%的识别和拒绝率。结果表明,通过开发的技术可以成功预测与EVA和CMR的纯香兰掺杂。

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