{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T02:12:44Z","timestamp":1775182364304,"version":"3.50.1"},"reference-count":23,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T00:00:00Z","timestamp":1715817600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2022YFB4703500"],"award-info":[{"award-number":["2022YFB4703500"]}]},{"name":"National Key R&amp;D Program of China","award":["62203234"],"award-info":[{"award-number":["62203234"]}]},{"name":"National Natural Science Foundation of China","award":["2022YFB4703500"],"award-info":[{"award-number":["2022YFB4703500"]}]},{"name":"National Natural Science Foundation of China","award":["62203234"],"award-info":[{"award-number":["62203234"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Preprocessing plays a key role in Raman spectral analysis. However, classical preprocessing algorithms often have issues with reducing Raman peak intensities and changing the peak shape when processing spectra. This paper introduces a unified solution for preprocessing based on a convolutional autoencoder to enhance Raman spectroscopy data. One is a denoising algorithm that uses a convolutional denoising autoencoder (CDAE model), and the other is a baseline correction algorithm based on a convolutional autoencoder (CAE+ model). The CDAE model incorporates two additional convolutional layers in its bottleneck layer for enhanced noise reduction. The CAE+ model not only adds convolutional layers at the bottleneck but also includes a comparison function after the decoding for effective baseline correction. The proposed models were validated using both simulated spectra and experimental spectra measured with a Raman spectrometer system. Comparing their performance with that of traditional signal processing techniques, the results of the CDAE-CAE+ model show improvements in noise reduction and Raman peak preservation.<\/jats:p>","DOI":"10.3390\/s24103161","type":"journal-article","created":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T06:44:31Z","timestamp":1715841871000},"page":"3161","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Denoising and Baseline Correction Methods for Raman Spectroscopy Based on Convolutional Autoencoder: A Unified Solution"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-0426-6200","authenticated-orcid":false,"given":"Ming","family":"Han","sequence":"first","affiliation":[{"name":"Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin 300350, China"},{"name":"Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University, Tianjin 300350, China"},{"name":"Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, China"},{"name":"Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen 518083, China"}]},{"given":"Yu","family":"Dang","sequence":"additional","affiliation":[{"name":"Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin 300350, China"},{"name":"Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University, Tianjin 300350, China"},{"name":"Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, China"},{"name":"Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen 518083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9664-4534","authenticated-orcid":false,"given":"Jianda","family":"Han","sequence":"additional","affiliation":[{"name":"Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin 300350, China"},{"name":"Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University, Tianjin 300350, China"},{"name":"Tianjin Key Laboratory of Intelligent Robotics, Nankai University, Tianjin 300350, China"},{"name":"Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen 518083, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.aca.2011.03.002","article-title":"A review on the fabrication of substrates for surface enhanced Raman spectroscopy and their applications in analytical chemistry","volume":"693","author":"Fan","year":"2011","journal-title":"Anal. Chim. Acta"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1002\/(SICI)1097-4555(199702)28:2\/3<111::AID-JRS87>3.0.CO;2-Z","article-title":"Biomedical applications of Raman spectroscopy","volume":"28","author":"Lawson","year":"1997","journal-title":"J. Raman Spectrosc."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.orggeochem.2008.01.005","article-title":"Raman spectroscopy as a tool for the non-destructive identification of organic minerals in the geological record","volume":"39","author":"Edwards","year":"2008","journal-title":"Org. Geochem."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Gardiner, D.J. (1989). Practical Raman Spectroscopy, Springer.","DOI":"10.1007\/978-3-642-74040-4"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhao, R.-M., and Cui, H.-M. (2015, January 18\u201320). Improved Threshold Denoising Method Based on Wavelet Transform. Proceedings of the 7th International Conference on Modelling, Identification and Control (ICMIC), Sousse, Tunisia.","DOI":"10.1109\/ICMIC.2015.7409352"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1063\/1.4822961","article-title":"Savitzky-Golay smoothing filters","volume":"4","author":"Press","year":"1990","journal-title":"Comput. Phys."},{"key":"ref_7","first-page":"1862","article-title":"Baseline Correction of UV Raman Spectrum Based on Improved Piecewise Linear Fitting","volume":"40","author":"Man","year":"2020","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3759","DOI":"10.1039\/C8AY01089G","article-title":"Algorithm for optimal denoising of Raman spectra","volume":"10","author":"Barton","year":"2018","journal-title":"Anal. Methods"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, X., Bai, Y., Ma, Y., He, P., Tang, Y., and Lv, X. (2023). Denoising of Raman Spectra Using a Neural Network Based on Variational Mode Decomposition, Empirical Wavelet Transform, and Encoder-Bidirectional Long Short-Term Memory. Appl. Sci., 13.","DOI":"10.3390\/app132112046"},{"key":"ref_10","first-page":"1553","article-title":"A Denoising Method Based on Back Propagation Neural Network for Raman Spectrum","volume":"42","author":"Wang","year":"2022","journal-title":"Spectroscopy Spectr. Anal."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Barton, S., Alakkari, S., O\u2019Dwyer, K., Ward, T., and Hennelly, B. (2021). Convolution network with custom loss function for the denoising of low SNR Raman spectra. Sensors, 21.","DOI":"10.3390\/s21144623"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Pan, L., Pipitsunthonsan, P., Zhang, P., Daengngam, C., Booranawong, A., and Chongcheawchamnan, M. (2020, January 12\u201313). Noise Reduction Technique for Raman Spectrum Using Deep Learning Network. Proceedings of the 2020 13th International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, China.","DOI":"10.1109\/ISCID51228.2020.00042"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"104317","DOI":"10.1016\/j.chemolab.2021.104317","article-title":"Adversarial nets for baseline correction in spectra processing","volume":"213","author":"Liu","year":"2021","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Schmidt, M.N., Alstr\u00f8m, T.S., Svendstorp, M., and Larsen, J. (2019, January 12\u201317). Peak Detection and Baseline Correction Using a Convolutional Neural Network. Proceedings of the ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK.","DOI":"10.1109\/ICASSP.2019.8682311"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1177\/0003702819888949","article-title":"Single-Step Preprocessing of Raman Spectra Using Convolutional Neural Networks","volume":"74","author":"Wahl","year":"2020","journal-title":"Appl. Spectrosc."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"627746","DOI":"10.3389\/fgene.2020.627746","article-title":"CDAE: A cascade of denoising autoencoders for noise reduction in the clustering of single-particle cryo-EM images","volume":"11","author":"Lei","year":"2021","journal-title":"Front. Genet."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2179","DOI":"10.1007\/s40747-021-00428-4","article-title":"Methods for image denoising using convolutional neural network: A review","volume":"7","author":"Ilesanmi","year":"2021","journal-title":"Complex Intell. Syst."},{"key":"ref_18","first-page":"2022009","article-title":"Fault diagnosis of rolling bearings based on generative adversarial network and convolutional denoising auto-encoder","volume":"2","author":"Gu","year":"2022","journal-title":"J. Adv. Manuf. Sci. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"10909","DOI":"10.1007\/s00521-020-05638-4","article-title":"Feature learning using convolutional denoising autoencoder for activity recognition","volume":"33","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Vincent, P., Larochelle, H., Bengio, Y., and Manzagol, P.-A. (2008, January 5\u20139). Extracting and Composing Robust Features with Denoising Autoencoders. Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland.","DOI":"10.1145\/1390156.1390294"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1177\/0003702818811688","article-title":"An adaptive and fully automated baseline correction method for Raman spectroscopy based on morphological operations and mollification","volume":"73","author":"Chen","year":"2019","journal-title":"Appl. Spectrosc."},{"key":"ref_23","unstructured":"L\u00fc, M.L. (2017). Research on Baseline Correction and Noise Suppression Techniques in Raman Spectroscopy. [Master\u2019s Thesis, University of Electronic Science and Technology of China]."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/10\/3161\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:43:17Z","timestamp":1760107397000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/10\/3161"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,16]]},"references-count":23,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["s24103161"],"URL":"https:\/\/doi.org\/10.3390\/s24103161","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,16]]}}}