[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118645-en":3,"doc-seo-118645-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":20,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},118645,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","How to feed emission spectra into machine learning models","This document explores the integration of emission spectra data into machine learning models, detailing the process from experimental acquisition to model deployment. It highlights that emission spectra represent a unique data type, and the presentation illustrates a workflow featuring spectroscopy and subsequent data processing. Key preprocessing steps include downscaling, normalization, array conversion, and importantly, data augmentation techniques such as noise addition. The document crucially notes that traditional time series data augmentation methods are restricted in this context due to the potential for inadvertent information loss or label alteration, citing limitations for NO shifting, NO inverting, and NO cropping. Machine learning applications in laser-induced breakdown spectroscopy (LIBS) are discussed, encompassing both classification and quantitative analysis (regression) using methods ranging from classical least squares to neural networks. The challenge of limited labeled spectral data is acknowledged, emphasizing the restrictions on conventional time series augmentation. The methodology presented aims to overcome these limitations to effectively leverage spectral data for advanced analytical tasks.","How to feed emission spectra into machine learning models C. H. Egerland, A. Lomashvili, E. Clave, K. Rammelkamp, S. Schröder, H.-W. Hübers Deutsches Zentrum für Luft-und Raumfahrt e.V., Institut für Optische Sensorsysteme, Berlin, Germany  \nEmission spectra are their  \nown kind of data type  \nFrom experiment to machine learning  \nLaser  \nRock  \n Spectrometer  \nProcessing  \n(1),(2),(3),(4),(5)  \nModel  \nFe 48%  \n Si 0.5% Mg 20%  \n...  \nPreprocessing  \n(1) Downscaling  \n(2) Normalization  \n(3) Array  \n[0.1, 0, 0, 0.195, ... , 0.65, 0]  \nData Augmentation  \n(5) Noise addition  \nNO shifting  \nNO inverting  \nNO cropping  \nMachine Learning Models  \nMachine learning for laser-induced breakdown spectroscopy involves classification as well as quantitative analysis (regression) of targets. Di ff erent methods are used from classical (partial) least squares to neural networks [1].  \nLabelled spectral data is limited, yet known data augmentation techniques of time series [2] are restricted because of the inadvertent information loss or label change [3,4].  \n[1] Boucher et al.,“A Study of Machine Learning Regression Methods for Major Elemental Analysis of Rocks Using Laser-Induced Breakdown Spectroscopy”, [https://doi.org/10.1016/j.sab.2015.02.003](https://doi.org/10.1016/j.sab.2015.02.003)[ ](https://doi.org/10.1016/j.sab.2015.02.003)[2] Wen et al.,“Time Series Data Augmentation for Deep Learning: A Survey”, [https://doi.org/10.24963/ijcai.2021/631](https://doi.org/10.24963/ijcai.2021/631)  \n[3] Anderson et al.,“Post-Landing Major Element Quantification Using SuperCam Laser Induced Breakdown Spectroscopy”, [https://doi.org/10.1016/j.sab.2021.106347](https://doi.org/10.1016/j.sab.2021.106347)  \n[4] Zorov et al.,“A Review of Normalization Techniques in Analytical Atomic Spectrometry with Laser Sampling: From Single to Multivariate Correction”, [https://doi.org/10.1016/j.sab.2010.04.009](https://doi.org/10.1016/j.sab.2010.04.009).  \nContact me:","cbCaihEdlNwYeNxK","https://ap.wps.com/l/cbCaihEdlNwYeNxK","pdf",332045,1,"English","en",105,"# Emission spectra are their own kind of data type\n## From experiment to machine learning\n## Preprocessing\n### Downscaling\n### Normalization\n### Array\n### Data Augmentation\n#### Noise addition\n#### NO shifting\n#### NO inverting\n#### NO cropping\n## Machine Learning Models","[{\"question\":\"What are the key steps in feeding emission spectra into machine learning models?\",\"answer\":\"The process involves acquiring spectra experimentally, followed by preprocessing steps like downscaling, normalization, array conversion, and data augmentation, before feeding the data into machine learning models for analysis.\"},{\"question\":\"What are the limitations of traditional time series data augmentation for emission spectra?\",\"answer\":\"Traditional time series augmentation techniques are restricted because they can lead to inadvertent information loss or label changes, and methods like shifting, inverting, or cropping are not suitable.\"},{\"question\":\"What types of analysis are performed using machine learning on laser-induced breakdown spectroscopy data?\",\"answer\":\"Machine learning for laser-induced breakdown spectroscopy is used for both classification tasks and quantitative analysis, which includes regression, employing various methods from classical techniques to neural networks.\"}]","How to feed emission spectra into machine learning models | 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are the key steps in feeding emission spectra into machine learning models?","Question",{"text":74,"@type":75},"The process involves acquiring spectra experimentally, followed by preprocessing steps like downscaling, normalization, array conversion, and data augmentation, before feeding the data into machine learning models for analysis.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What are the limitations of traditional time series data augmentation for emission spectra?",{"text":79,"@type":75},"Traditional time series augmentation techniques are restricted because they can lead to inadvertent information loss or label changes, and methods like shifting, inverting, or cropping are not suitable.",{"name":81,"@type":72,"acceptedAnswer":82},"What types of analysis are performed using machine learning on laser-induced breakdown spectroscopy data?",{"text":83,"@type":75},"Machine learning for laser-induced breakdown spectroscopy is used for both classification tasks and 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