AI Article Synopsis
- * The camera setup employs a scanning Fabry-Pérot interferometer and records data to distinguish between twelve different plastic types when heated to 60 °C.
- * A nine-component principal component analysis combined with a k-nearest neighbors model achieves high accuracy, with a 90% micro precision and 99% macro specificity, successfully classifying most plastic types except for ABS.
Article Abstract
The long wave infrared regime of the electromagnetic spectrum has been used to identify and classify different types of plastics. This work presents an early-stage version of an industrial hyperspectral camera, which is based on a low-order scanning Fabry-Pérot interferometer and is sensitive to radiation with wavelengths from 8 µm to 15 µm. As a demonstration, the setup is used to record interferograms and distinguish between twelve types of common plastics heated to 60 °C. The plastic identification is based on a nine-component principal component analysis model combined with k-nearest neighbors. The resulting model shows a micro precision of 90 % and a macro specificity of 99 %, being able to classify all tested plastic types except ABS.
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| http://dx.doi.org/10.1016/j.saa.2024.125501 | DOI Listing |
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