Institute of Chemistry and Instituto Nacional de Ciência e Tecnologia de Bioanalítica (INCTBio), State University of Campinas (Unicamp), Campinas, São Paulo, Brazil.
Anal Chim Acta. 2011 Aug 5;699(1):120-5. doi: 10.1016/j.aca.2011.05.003. Epub 2011 May 11.
The use of multivariate curve resolution (MCR) to build multivariate quantitative models using data obtained from comprehensive two-dimensional gas chromatography with flame ionization detection (GC×GC-FID) is presented and evaluated. The MCR algorithm presents some important features, such as second order advantage and the recovery of the instrumental response for each pure component after optimization by an alternating least squares (ALS) procedure. A model to quantify the essential oil of rosemary was built using a calibration set containing only known concentrations of the essential oil and cereal alcohol as solvent. A calibration curve correlating the concentration of the essential oil of rosemary and the instrumental response obtained from the MCR-ALS algorithm was obtained, and this calibration model was applied to predict the concentration of the oil in complex samples (mixtures of the essential oil, pineapple essence and commercial perfume). The values of the root mean square error of prediction (RMSEP) and of the root mean square error of the percentage deviation (RMSPD) obtained were 0.4% (v/v) and 7.2%, respectively. Additionally, a second model was built and used to evaluate the accuracy of the method. A model to quantify the essential oil of lemon grass was built and its concentration was predicted in the validation set and real perfume samples. The RMSEP and RMSPD obtained were 0.5% (v/v) and 6.9%, respectively, and the concentration of the essential oil of lemon grass in perfume agreed to the value informed by the manufacturer. The result indicates that the MCR algorithm is adequate to resolve the target chromatogram from the complex sample and to build multivariate models of GC×GC-FID data.
利用多元曲线分辨（MCR）算法，结合全二维气相色谱-火焰离子化检测（GC×GC-FID）获得的数据，建立并评估了多元定量模型。MCR 算法具有一些重要的特点，如二阶优势和通过交替最小二乘法（ALS）优化后每个纯组分的仪器响应的恢复。建立了一个使用校准集来定量迷迭香油的模型，该校准集仅包含迷迭香油和谷物酒精溶剂的已知浓度。得到了一个与从 MCR-ALS 算法获得的迷迭香油浓度和仪器响应相关的校准曲线，并将该校准模型应用于预测复杂样品（迷迭香油、菠萝香精和商业香水的混合物）中油的浓度。预测的均方根误差（RMSEP）和百分比偏差的均方根误差（RMSPD）值分别为 0.4%（v/v）和 7.2%。此外，还建立了第二个模型来评估该方法的准确性。建立了一个定量柠檬草精油的模型，并预测了验证集和真实香水样品中的浓度。得到的 RMSEP 和 RMSPD 分别为 0.5%（v/v）和 6.9%，并且香水中柠檬草精油的浓度与制造商提供的值一致。结果表明，MCR 算法能够从复杂样品中解析目标色谱，并建立 GC×GC-FID 数据的多元模型。
