2026-10-12T23:04:18Z https://www.scidb.cn/oai

10.57760/sciencedb.sepu.00018 2026-07-06T14:35:40Z

2026-07-06 Dataset for Training and Validation of a High-Performance Liquid Chromatography Signal Denoising Model doi:10.57760/sciencedb.sepu.00018 en This dataset supports the study titled “Development of a deep learning-based model for denoising high performance liquid chromatography signals”. It was used for model training, performance evaluation, and real-sample validation. The dataset includes 500 pairs of clean and noisy simulated high-performance liquid chromatography signals, covering a broad signal-to-noise ratio range of 1–35 dB. Each signal contains 8,000 sampling points over a time range of 0–20 min. The dataset was divided into training, validation, and test subsets at a ratio of 6:2:2 for training and evaluating a wavelet-domain multi-scale convolutional neural network (MSCNN). In addition, real chromatographic signals were included to validate the denoising performance of the proposed model under instrumental noise and complex sample-matrix conditions. The dataset can be used for chromatographic signal denoising, peak-shape fidelity evaluation, overlapped-peak analysis, and intelligent preprocessing of chromatographic data. wavelet transform; signal denoising; deep learning; high performance liquid chromatography Jiao Yanke Song Xuexian zhang shuang qing yue Ma Xibo RESTRICTED dataset Science Data Bank