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Study on the Impact of Multicollinearity Among Components on Near-Infrared Spectroscopy Quantitative Models
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摘要: 在近红外光谱结合化学计量学方法进行定量分析中，变量之间存在的多重共线性是影响光谱模型效果的一个关键问题。探究了组分含量间的多重共线性对化学计量学定量模型的影响。设计维生素B6（低浓度）和维生素B1（高浓度）含量之间分别呈强相关性和弱相关性的两个体系，以维生素B6为目标待测组分，利用体系的近红外光谱信息结合偏最小二乘法建立组分含量的预测模型。结果表明，当体系中存在与目标组分具有强相关性的其它较高浓度共存组分时，模型可以借助该共存组分信息实现对较低浓度的目标组分更为准确地预测，从而提高了模型对目标组分定量分析的精确度。通过应用于检测含维生素B6和B1组分的市售口服液，进一步验证了组分含量之间存在强相关的多重共线性能够提高近红外光谱模型的定量预测能力。该研究结论具有很强的理论和实际应用价值，可以将其应用到复杂混合物体系组分的同时定量分析中。Abstract: In quantitative analysis using near-infrared spectroscopy combined with chemometric methods, multicollinearity among variables is a key issue affecting the performance of spectral models. This study investigates the impact of multicollinearity between component concentrations on chemometric quantitative models. Two systems were designed with strong and weak correlations between the concentrations of vitamin B6 (low concentration) and vitamin B1 (high concentration), respectively. Using vitamin B6 as the target component, prediction models for component concentrations were established using near-infrared spectral information combined with partial least squares regression. The results show that when there are coexisting components with high concentrations strongly correlated to the target component in the system, the model can utilize information from these coexisting components to achieve more accurate predictions of the lower-concentration target component, thereby improving the precision of quantitative analysis for the target component. The application of this approach to the detection of commercially available oral solutions containing vitamins B6 and B1 further verified that strong multicollinearity between component concentrations can enhance the quantitative predictive ability of near-infrared spectral models. The conclusions of this study have significant theoretical and practical application value and can be applied to the simultaneous quantitative analysis of components in complex mixture systems.
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图 3 维生素B6浓度的实际值和预测值的线性关系图
Figure 3. Linear relationship between actual and predicted values of vitamin B6 concentration
图 4 实际药物样本中维生素B6浓度的实际值和预测值的线性关系图
Figure 4. Linear relationship between actual and predicted values of vitamin B6 concentration in actual drug samples
表 1 维生素B6的预测结果
Table 1. Prediction results for vitamin B6
目标组分 溶液体系 最佳主成分数 校正集 预测集 RPD $ {R}_{c}^{2} $ RMSEC $ {R}_{p}^{2} $ RMSEP 维生素B6 维生素B6单组分溶液 2 0.9540 1.2369 0.8988 1.4997 3.0489 强相关性双组分溶液 5 0.9898 0.6101 0.9676 1.0272 4.6435 弱相关性双组分溶液 5 0.9492 1.2657 0.8969 2.1114 2.7562 
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表 2 实际药物样本的预测结果
Table 2. Prediction results for actual drug samples
目标组分 最佳主成分数 校正集 预测集 RPD $ {R}_{c}^{2} $ RMSEC $ {R}_{p}^{2} $ RMSEP 维生素B6 5 0.9624 1.2435 0.9348 1.6019 2.8418
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