基础数据准确性对近红外光谱分析结果的影响

Effects of the Accuracy of Reference Data on NIR Prediction Results

基础数据的准确性是影响近红外光谱分析结果的一个重要因素。文章以人工配制的四组分混合物体系和实际的汽油校正集样本为例,通过人为增加基础数据误差的方法,研究了基础数据的准确性对近红外光谱分析结果的影响。结果表明,基础数据的准确性对近红外分析模型及其预测结果都有一定的影响,基础数据越准确,所建立模型的精度越高,其对未知样本的预测结果也越准确。对于精度相对较差测试方法提供的基础数据,通过大量样本的光谱分析和化学计量学统计处理,近红外方法有可能得到更精确的预测结果。

Reference data are indispensable to build near-infrared spectroscopy (NIR) calibration models. In the present paper, the effects of the accuracy of reference data on NIR calibration models and its prediction results were studied through two routine applications based on partial least square regression methods. The results indicate that the best NIR calibration statistics and the most accurate prediction results were aligned with the most accurate reference data. However, based on statistical analysis of numerous calibration samples, it is possible for NIR calibration models to obtain more accurate prediction results than the laboratory reference data used in the calibration sets. It is better to make less search for high accurate reference data and instead to introduce more calibration samples to improve the ruggedness of the calibration models.