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Utilization of Near Infrared (NIR) Spectrometry for Detection of Glass in the Waste-based Fuel

机译：利用近红外（NIR）光谱法检测废料燃料中的玻璃

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This paper presents the results of experimental measurements and multivariate statistical modeling concerning detection of soda-lime glass using near infrared (NIR) spectrometry technique. The purpose is to test if the glass is quantitatively detectable in a waste-based material and to assess what method of spectral data pretreatment is the most suitable in order to develop prediction models. The experiments were performed on six test samples containing a specific amount of glass distributed in background material. Pretreatment methods such as normalization and first and second derivatives were applied on the acquired absorbance spectral data. Principal component analysis (PCA) was employed in order to describe the relationship between pretreated data and the amount of glass in the test samples. Subsequently, principal component regression (PCR) was utilized for the development of prediction models. The results from the models show strong correlation between the pretreated data and the glass content. The most promising results were obtained from the model based on 1st derivative pretreatment when only absorbance spectral data from selected wavelengths are included.
机译：本文介绍了使用近红外线（NIR）光谱技术的实验测量和多变量统计建模的结果和多元统计学建模。目的是测试玻璃是否在废料的材料中定量检测，并评估光谱数据预处理的方法是最适合开发预测模型的方法。在含有在背景材料中分布的特定量玻璃的六种测试样品进行实验。预处理方法如归一化和第一和第二衍生物被应用于所获得的吸光光谱数据。采用主成分分析（PCA）以描述预处理数据与试样中玻璃量之间的关系。随后，利用主成分回归（PCR）来开发预测模型。该模型的结果显示出预处理数据和玻璃含量之间的强烈相关性。当仅包括来自所选波长的吸光度谱数据时，从基于第一衍生物预处理的模型获得最有希望的结果。

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