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In order to improve the optical measurement accuracy, a KOPLS-based spectrum analysis method for multi-component substances is proposed for the nonlinear model between the spectrum and the measured component. This method uses a kernel matrix to convert orthogonal irrelevant items into a high-dimensional space. Through iterative calculation and elimination, a nonlinear regression model between the spectral signal and the concentration matrix is established. It ensures the high computational efficiency of the algorithm and solves the problem of low non-linear regression accuracy in traditional algorithms. This method realizes high-precision spectral analysis of multi-component. Through experiments, the predicted concentration values of whole blood samples are compared under different algorithms. The experimental results show that the KOPLS algorithm greatly improves the prediction accuracy of the concentration of multi-component substances. The experiment proves that this method has strong engineering application value in multi-component measurement instruments.