Mancini Manuela, Taavitsainen Veli-Matti, Rinnan Åsmund
Department of Food Science, Faculty of Science, University of Copenhagen, Rolighedsvej 26, 1958 Frederiksberg C, Denmark; Department of Agricultural, Food and Environmental Sciences, Università Politecnica delle Marche, via Brecce Bianche 10, 60131 Ancona, Italy.
Lappeenranta University of Technology, Skinnarilankatu 34, 53850 Lappeenranta, Finland.
Waste Manag. 2024 Apr 15;178:321-330. doi: 10.1016/j.wasman.2024.02.033. Epub 2024 Mar 1.
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.
回收消费后废木材正成为一种比处置更具吸引力的选择。然而，其巨大的异质性要求对材料特性进行评估，以便确定最佳的回收方案和预期用途。事实上，废木材有多种木材用途/类型，同时存在不同程度的污染，并且可以根据其成分和质量等级分为不同类别。本研究使用手持式近红外光谱仪对一百多个废木材样本进行了测量并对其进行了表征。比较了三种分类方法，即K近邻法（KNN）、主成分分析 - 线性判别分析（PCA - LDA）和PCA - KNN，以开发根据最适合的再利用将废木材按质量类别分类的模型。此外，还研究了分类性能与样本光谱测量次数以及光谱测量平均值的关系。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在重复测量中取平均值时（分类准确率：90.9%）。近红外光谱结合化学计量学被证明是一种用于废木材实时分类的有前途的工具，可确保更准确和可持续的废木材管理。获取有关废木材材料质量和特性的实时信息有助于做出最佳回收方案的决策，提高其回收潜力。
