{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T08:12:45Z","timestamp":1775808765107,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,19]],"date-time":"2021-08-19T00:00:00Z","timestamp":1629331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To correctly assess the cleanliness of technical surfaces in a production process, corresponding online monitoring systems must provide sufficient data. A promising method for fast, large-area, and non-contact monitoring is hyperspectral imaging (HSI), which was used in this paper for the detection and quantification of organic surface contaminations. Depending on the cleaning parameter constellation, different levels of organic residues remained on the surface. Afterwards, the cleanliness was determined by the carbon content in the atom percent on the sample surfaces, characterized by XPS and AES. The HSI data and the XPS measurements were correlated, using machine learning methods, to generate a predictive model for the carbon content of the surface. The regression algorithms elastic net, random forest regression, and support vector machine regression were used. Overall, the developed method was able to quantify organic contaminations on technical surfaces. The best regression model found was a random forest model, which achieved an R2 of 0.7 and an RMSE of 7.65 At.-% C. Due to the easy-to-use measurement and the fast evaluation by machine learning, the method seems suitable for an online monitoring system. However, the results also show that further experiments are necessary to improve the quality of the prediction models.<\/jats:p>","DOI":"10.3390\/s21165595","type":"journal-article","created":{"date-parts":[[2021,8,19]],"date-time":"2021-08-19T09:58:06Z","timestamp":1629367086000},"page":"5595","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Use of Hyperspectral Imaging for the Quantification of Organic Contaminants on Copper Surfaces for Electronic Applications"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8622-917X","authenticated-orcid":false,"given":"Tim","family":"Englert","sequence":"first","affiliation":[{"name":"Robert Bosch GmbH, Automotive Electronics, Postfach 1342, 72703 Reutlingen, Germany"},{"name":"Institute of Electrochemistry, Ulm University, Albert-Einstein-Allee 47, 89081 Ulm, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9405-797X","authenticated-orcid":false,"given":"Florian","family":"Gruber","sequence":"additional","affiliation":[{"name":"Fraunhofer Institute for Material and Beam Technology IWS, Winterbergstra\u00dfe 28, 01277 Dresden, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jan","family":"Stiedl","sequence":"additional","affiliation":[{"name":"Robert Bosch GmbH, Automotive Electronics, Postfach 1342, 72703 Reutlingen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Simon","family":"Green","sequence":"additional","affiliation":[{"name":"Robert Bosch GmbH, Automotive Electronics, Postfach 1342, 72703 Reutlingen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7777-2306","authenticated-orcid":false,"given":"Timo","family":"Jacob","sequence":"additional","affiliation":[{"name":"Institute of Electrochemistry, Ulm University, Albert-Einstein-Allee 47, 89081 Ulm, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karsten","family":"Rebner","sequence":"additional","affiliation":[{"name":"Process Analysis and Technology PA & T, Reutlingen University, Alteburgstra\u00dfe 150, 72762 Reutlingen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wulf","family":"Gr\u00e4hlert","sequence":"additional","affiliation":[{"name":"Fraunhofer Institute for Material and Beam Technology IWS, Winterbergstra\u00dfe 28, 01277 Dresden, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/TDMR.2013.2293792","article-title":"Impact of NaCl contamination and climatic conditions on the reliability of printed circuit board assemblies","volume":"14","author":"Verdingovas","year":"2013","journal-title":"IEEE Trans. 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