Human serum globulin (GLB), which contains various antibodies in healthy human serum, is of great significance for clinical trials and disease diagnosis. In this study, the GLB in human serum was rapidly analyzed by near infrared (NIR) spectroscopy without chemical reagents. Optimal partner wavelength combination (OPWC) method was employed for selecting discrete information wavelength. For the OPWC, the redundant wavelengths were removed by repeated projection iteration based on binary linear regression, and the result converged to stable number of wavelengths. By the way, the convergence of algorithm was proved theoretically. Moving window partial least squares (MW-PLS) and Monte Carlo uninformative variable elimination PLS (MC-UVE-PLS) methods, which are two well-performed wavelength selection methods, were also performed for comparison. The optimal models were obtained by the three methods, and the corresponding root-mean-square error of cross validation and correlation coefficient of prediction (SECV, RP,CV) were 0.813 g L−1 and 0.978 with OPWC combined with PLS (OPWC-PLS), and 0.804 g L−1 and 0.979 with MW-PLS, and 1.153 g L−1 and 0.948 with MC-UVE-PLS, respectively. The OPWC-PLS and MW-PLS methods achieved almost the same good results. However, the OPWC only contained 28 wavelengths, so it had obvious lower model complexity. Thus it can be seen that the OPWC-PLS has great prediction performance for GLB and its algorithm is convergent and rapid. The results provide important technical support for the rapid detection of serum.

中文翻译：

---

最佳伴侣波长组合法应用于人血清球蛋白的近红外光谱分析。

人血清球蛋白（GLB）在健康人血清中包含多种抗体，对临床试验和疾病诊断具有重要意义。在这项研究中，无需化学试剂即可通过近红外（NIR）光谱快速分析人血清中的GLB。采用最优伙伴波长组合（OPWC）方法选择离散信息波长。对于OPWC，通过基于二进制线性回归的重复投影迭代将多余的波长去除，结果收敛到稳定的波长数量。顺便说一下，从理论上证明了算法的收敛性。为了进行比较，还执行了移动窗口偏最小二乘法（MW-PLS）和蒙特卡洛非信息变量消除PLS（MC-UVE-PLS）方法，这是两种执行良好的波长选择方法。通过这三种方法获得了最佳模型，将OPWC与PLS组合使用的相应交叉验证均方根误差和预测相关系数（SECV，RP，CV）为0.813 g L-1和0.978。 PLS），使用MW-PLS分别为0.804 g L-1和0.979，使用MC-UVE-PLS分别为1.153 g L-1和0.948。OPWC-PLS和MW-PLS方法获得了几乎相同的良好结果。但是，OPWC仅包含28个波长，因此其模型复杂度明显较低。由此可见，OPWC-PLS对GLB具有很好的预测性能，其算法收敛迅速。该结果为快速检测血清提供了重要的技术支持。OPWC与PLS（OPWC-PLS）结合使用时，交叉验证和预测相关系数（SECV，RP，CV）的相应均方根误差分别为0.813 g L-1和0.978，以及0.804 g L-1和MW-PLS为0.979，MC-UVE-PLS为1.153 g L-1，0.948。OPWC-PLS和MW-PLS方法获得了几乎相同的良好结果。但是，OPWC仅包含28个波长，因此其模型复杂度明显较低。由此可见，OPWC-PLS对GLB具有很好的预测性能，其算法收敛迅速。该结果为快速检测血清提供了重要的技术支持。交叉验证和预测相关系数（SECV，RP，CV）的相应均方根误差分别为0.813 g L-1和0.978（OPWC与PLS（OPWC-PLS）组合），以及0.804 g L-1和MW-PLS为0.979，MC-UVE-PLS为1.153 g L-1，0.948。OPWC-PLS和MW-PLS方法获得了几乎相同的良好结果。但是，OPWC仅包含28个波长，因此其模型复杂度明显较低。由此可见，OPWC-PLS对GLB具有很好的预测性能，其算法收敛迅速。该结果为快速检测血清提供了重要的技术支持。OPWC-PLS和MW-PLS方法获得了几乎相同的良好结果。但是，OPWC仅包含28个波长，因此其模型复杂度明显较低。由此可见，OPWC-PLS对GLB具有很好的预测性能，其算法收敛迅速。该结果为血清的快速检测提供了重要的技术支持。OPWC-PLS和MW-PLS方法获得了几乎相同的良好结果。但是，OPWC仅包含28个波长，因此其模型复杂度明显较低。由此可见，OPWC-PLS对GLB具有很好的预测性能，其算法收敛迅速。该结果为快速检测血清提供了重要的技术支持。