{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T21:43:49Z","timestamp":1772919829636,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,3,24]],"date-time":"2023-03-24T00:00:00Z","timestamp":1679616000000},"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>Chemically pure plastic granulate is used as the starting material in the production of plastic parts. Extrusion machines rely on purity, otherwise resources are lost, and waste is produced. To avoid losses, the machines need to analyze the raw material. Spectroscopy in the visible and near-infrared range and machine learning can be used as analyzers. We present an approach using two spectrometers with a spectral range of 400\u20131700 nm and a fusion model comprising classification, regression, and validation to detect 25 materials and proportions of their binary mixtures. one dimensional convolutional neural network is used for classification and partial least squares regression for the estimation of proportions. The classification is validated by reconstructing the sample spectrum using the component spectra in linear least squares fitting. To save time and effort, the fusion model is trained on semi-empirical spectral data. The component spectra are acquired empirically and the binary mixture spectra are computed as linear combinations. The fusion model achieves very a high accuracy on visible and near-infrared spectral data. Even in a smaller spectral range from 400\u20131100 nm, the accuracy is high. The visible and near-infrared spectroscopy and the presented fusion model can be used as a concept for building an analyzer. Inexpensive silicon sensor-based spectrometers can be used.<\/jats:p>","DOI":"10.3390\/s23073441","type":"journal-article","created":{"date-parts":[[2023,3,27]],"date-time":"2023-03-27T03:01:14Z","timestamp":1679886074000},"page":"3441","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Detection of Plastic Granules and Their Mixtures"],"prefix":"10.3390","volume":"23","author":[{"given":"Roman-David","family":"Kulko","sequence":"first","affiliation":[{"name":"Technologie Campus Grafenau, Technische Hochschule Deggendorf, 94481 Grafenau, Germany"}]},{"given":"Alexander","family":"Pletl","sequence":"additional","affiliation":[{"name":"Technologie Campus Grafenau, Technische Hochschule Deggendorf, 94481 Grafenau, Germany"}]},{"given":"Andreas","family":"Hanus","sequence":"additional","affiliation":[{"name":"Sesotec GmbH, Regener Stra\u00dfe 130, 94513 Sch\u00f6nberg, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6029-4424","authenticated-orcid":false,"given":"Benedikt","family":"Elser","sequence":"additional","affiliation":[{"name":"Technologie Campus Grafenau, Technische Hochschule Deggendorf, 94481 Grafenau, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2963","DOI":"10.1080\/10408398.2020.1862045","article-title":"Advances in infrared spectroscopy combined with artificial neural network for the authentication and traceability of food","volume":"62","author":"Liang","year":"2022","journal-title":"Crit. 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