Calibration set selection method based on the “M + N” theory: application to non-invasive measurement by dynamic spectrum
Calibration set selection method based on the “M + N” theory: application to non-invasive measurement by dynamic spectrum
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DOI：
10.1039/c6ra19272f
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发表时间：
2016-11
期刊：
影响因子：
3.9
通讯作者：
Ling Lin;Zhang Qirui;Mei Zhou;Xu Sijia;Gang Li
中科院分区：
文献类型：
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作者：
Ling Lin;Zhang Qirui;Mei Zhou;Xu Sijia;Gang Li
选择合适的校准集方法对于稳健的定量模型非常重要，特别是对于血液成分的无创测量。偏最小二乘回归（PLSR）是利用光谱数据建立多元校准模型的最流行的回归方法之一。然而，PLSR 模型的成功取决于代表性集的可用性。 “M+N”理论为提高成分分析模型可靠性提供了新思路，其中M为成分信息，N代表外界干扰。本文提出了一种基于“M+N”理论的校准集选择新方法。对于 M 元素，该方法同时考虑目标成分和非目标成分。动态频谱（DS）是一种基于PPG的无创血液成分分析方法。在本研究中，我们应用了一种新的校准集选择方法，通过 PLSR 模型和 DS 方法来预测血红蛋白。总蛋白被视为非目标成分，是血液中仅次于血红蛋白的最重要成分。实验结果表明，与随机选择方法相比，新的选择方法可以显着提高模型精度。与仅考虑血红蛋白浓度分布的选择方法相比，新选择方法的相关系数提高了8.03%，RMSEP降低了15.41%。实验结果验证了所提出的校准集选择方法的性能，可以指导基于光谱的化学成分分析，提高预测性能。
An appropriate method for calibration set selection is very important for a robust quantitative model, especially for the non-invasive measurement of blood components. Partial least squares regression (PLSR) is one of the most popular regression methods for establishing multivariate calibration models with spectroscopic data. However, the success of the PLSR model depends on the availability of a representative set. The “M + N” theory provides a new idea for improving the model reliability of composition analysis, with M being the component information and N representing the outside disturbance. Herein, a new calibration set selection method based on “M + N” theory is proposed. For M elements, the method considers both the target and non-target components. Dynamic spectrum (DS) is a non-invasive blood composition analysis method based on PPG. In this study, we applied a new calibration set selection method for the prediction of hemoglobin by the PLSR model with the DS method. The total protein was regarded as the non-target component, which is the most important component in the blood after hemoglobin. The experimental results showed that compared with the random selection method, the new selection method can significantly improve the model accuracy. The correlation coefficient of the new selection method was increased by 8.03% and RMSEP was reduced by 15.41% than that of the selection method when only considering the hemoglobin concentration distribution. The experimental results verify the performance of the proposed calibration set selection method, which can guide the chemical composition analysis based on the spectrum to improve the prediction performance.