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Detection and quantification of adulteration in sandalwood oil through near infrared spectroscopy

机译:檀香油中掺假的近红外光谱检测与定量

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摘要

The confirmation of authenticity of essential oils and the detection of adulteration are problems of increasing importance in the perfumes, pharmaceutical, flavor and fragrance industries. This is especially true for 'value added' products like sandalwood oil. A methodical study is conducted here to demonstrate the potential use of Near Infrared (NIR) spectroscopy along with multivariate calibration models like principal component regression (PCR) and partial least square regression (PLSR) as rapid analytical techniques for the qualitative and quantitative determination of adulterants in sandalwood oil. After suitable pre-processing of the NIR raw spectral data, the models are built-up by cross-validation. The lowest Root Mean Square Error of Cross-Validation and Calibration (RMSECV and RMSEC % v/v) are used as a decision supporting system to fix the optimal number of factors. The coefficient of determination (R~2) and the Root Mean Square Error of Prediction (RMSEP % v/v) in the prediction sets are used as the evaluation parameters (R~2 = 0.9999 and RMSEP = 0.01355). The overall result leads to the conclusion that NIR spectroscopy with chemometric techniques could be successfully used as a rapid, simple, instant and non-destructive method for the detection of adulterants, even 1% of the low-grade oils, in the high quality form of sandalwood oil.
机译:香精油的真实性的确认和掺假的检测是在香水,制药,香料和香料行业中日益重要的问题。对于“增值”产品(如檀香油)尤其如此。此处进行了系统的研究,以证明近红外(NIR)光谱以及多元校准模型(例如主成分回归(PCR)和偏最小二乘回归(PLSR))作为定性和定量确定掺假者的快速分析技术的潜在用途在檀香油中。在对NIR原始光谱数据进行适当的预处理之后,通过交叉验证建立模型。交叉验证和校准的最低均方根误差(RMSECV和RMSEC%v / v)用作确定最佳因子数量的决策支持系统。预测集中的确定系数(R〜2)和预测的均方根误差(RMSEP%v / v)用作评估参数(R〜2 = 0.9999和RMSEP = 0.01355)。总体结果得出以下结论:采用化学计量技术的近红外光谱可以成功地用作快速,简单,即时且无损的高质量形式的掺假物检测方法,即使是低级油中的1%檀香油。

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