Recycling of post-consumer waste wood material is becoming an increasingly appealing alternative to disposal. However, its huge heterogeneity is calling for an assessment of the material characteristics in order to define the best recycling option and intended reuse. In fact, waste wood comes into a variety of uses/types of wood, along with several levels of contamination, and it can be divided into different categories based on its composition and quality grade. This study provides the measurement of more than a hundred waste wood samples and their characterisation using a hand-held NIR spectrophotometer. Three classification methods, i.e. K-nearest Neighbours (KNN), Principal Component Analysis – Linear Discriminant Analysis (PCA-LDA) and PCA-KNN, have been compared to develop models for the sorting of waste wood in quality categories according to the best-suited reuse. In addition, the classification performance has been investigated as a function of the number of the spectral measurements of the sample and as the average of the spectral measurements. The results showed that PCA-KNN performs better than the other classification methods, especially when the material is ground to 5 cm of particle size and the spectral measurements are averaged across replicates (classification accuracy: 90.9 %). NIR spectroscopy, coupled with chemometrics, turned out to be a promising tool for the real-time sorting of waste wood material, ensuring a more accurate and sustainable waste wood management. Obtaining real-time information about the quality and characteristics of waste wood material translates into a decision of the best recycling option, increasing its recycling potential.

中文翻译：

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使用近红外光谱法确定废木质材料最佳再利用的分类方法性能比较

消费后废弃木质材料的回收正成为一种越来越有吸引力的替代处理方式。然而，其巨大的异质性要求对材料特性进行评估，以确定最佳的回收选项和预期的再利用。事实上，废木材有多种用途/类型，以及多种污染程度，并且可以根据其成分和质量等级分为不同的类别。这项研究使用手持式近红外分光光度计对一百多个废木材样品进行了测量及其表征。比较了三种分类方法，即 K 最近邻 (KNN)、主成分分析 - 线性判别分析 (PCA-LDA) 和 PCA-KNN，以开发根据最佳质量类别对废木材进行分类的模型。适合重复使用。此外，还研究了分类性能作为样本光谱测量数量和光谱测量平均值的函数。结果表明，PCA-KNN 的性能优于其他分类方法，特别是当材料研磨至 5 厘米粒径且光谱测量值在重复次数中取平均值时（分类准确度：90.9%）。近红外光谱与化学计量学相结合，被证明是一种很有前途的废木材实时分类工具，可确保更准确和可持续的废木材管理。获取有关废弃木质材料的质量和特性的实时信息可以转化为最佳回收选项的决策，从而增加其回收潜力。