Qualitatively best results were obtained with µ " 50 and λ " 100 for KL-smooth-LR and λ " 100 for KL-smooth. We show the result of µ " 50 for KL-LR, which was especially selected to clarify the contribution of the low-rankness in comparison with KL-smooth-LR. Other parameters were µ " 10 and λ " 10 for EUC-smooth-LR and λ " 10 for EUC-smooth, µ " 10 for EUC-LR, 10 for the moving average, and σ " 10 for the 1D Gaussian smoothing, respectively. For the Euclidean distance, using λ ą 10 overly smoothed spectra. For the moving average and the 1D Gaussian smoothing, smoothing beyond parameters of 10 tend to produce overly smoothed spectra. The parameters were the same in evaluating the performance of the preliminary noise reductions on their own and the performance by using wPCA in the second step. Sensitivity to the parameters is also important, and we briefly describe the result below for KL-smooth-LR. The proposed method is not as sensitive to the weight for the smoothness of spectra, and we also obtained a similar result when λ " 10 and µ " 50 as λ " 100 and µ " 50. The proposed method was relatively sensitive to the weight on the low-rankness: when λ " 100, similarly reasonable results were obtained within the range of µ " 20 to µ " 80. False peaks appeared out of this range as when using KL-smooth. Although the proposed method requires a parameter tuning as the other preprocessing techniques also does, we consider it practical enough to be used as a noise reduction tool.