Soil Microbial Ecology and Environmental Toxicology Laboratory, Department of Zoology, University of Delhi, New Delhi, Delhi 110007, India.
Food and Environmental Protection Laboratory, International Atomic Energy Agency, Vienna International Centre, PO Box 100, 1400 Vienna, Austria.
Spectrochim Acta A Mol Biomol Spectrosc. 2021 Jan 5;244:118822. doi: 10.1016/j.saa.2020.118822. Epub 2020 Aug 11.
Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy integrated with chemometrics was effectively applied for the rapid detection and accurate quantification of fried mustard oil (FMO) adulteration in pure mustard oil (PMO). PMO was adulterated with FMO in the range of 0.5-50% v/v. Principal component analysis (PCA) elucidated the studied adulteration using two components with an explained variance of 97%. The linear discriminant analysis (LDA) was adopted to classify the adulterated PMO samples with FMO. LDA model showed 100% accuracy initially, as well as when cross-validated. To enhance the overall quality of models, characteristic spectral regions were optimized, and principal component regression (PCR) and partial least square regression (PLS-R) models were constructed with high accuracy and precision. PLS-R model for the 2nd derivative of the optimized spectral region 1260-1080 cm showed best results for prediction sample sets in terms of high R and residual predictive deviation (RPD) value of 0.999 and 31.91 with low root mean square error (RMSE) and relative prediction error (RE %) of 0.53% v/v and 3.37% respectively. Thus, the suggested method can detect up to 0.5% v/v of adulterated FMO in PMO in a short time interval.
衰减全反射傅里叶变换红外(ATR-FTIR)光谱结合化学计量学方法,可有效用于快速检测和准确量化纯芥菜油(PMO)中油炸芥菜油(FMO)的掺假情况。PMO 中 FMO 的掺假范围为 0.5-50% v/v。主成分分析(PCA)用两个解释方差为 97%的成分阐明了所研究的掺假情况。采用线性判别分析(LDA)对掺有 FMO 的 PMO 样品进行分类。LDA 模型最初以及交叉验证时的准确率均为 100%。为了提高模型的整体质量,优化了特征光谱区域,并构建了具有高精度和高精确度的主成分回归(PCR)和偏最小二乘回归(PLS-R)模型。对于优化光谱区域 1260-1080 cm 的二阶导数的 PLS-R 模型,在预测样本集方面表现出最佳结果,其相关系数(R)和剩余预测偏差(RPD)值高达 0.999 和 31.91,其均方根误差(RMSE)和相对预测误差(RE%)分别为 0.53% v/v 和 3.37%。因此,该方法可以在短时间内检测到 PMO 中高达 0.5% v/v 的掺假 FMO。