Department of Applied Physics, University of Eastern Finland, 70210 Kuopio, Finland.
Sensors (Basel). 2020 Feb 17;20(4):1076. doi: 10.3390/s20041076.
Wood chips are extensively utilised as raw material for the pulp and bio-fuel industry, and advanced material analyses may improve the processes in utilizing these products. Electrical impedance spectroscopy (EIS) combined with machine learning was used in order to analyse heartwood content of pine chips and bark content of birch chips. A novel electrode system integrated in a sampling container was developed for the testing using frequency range 42 Hz-5 MHz. Three electrode pairs were used to measure the samples in x-, y- and z-direction. Three machine learning methods were used: K-nearest neighbor (KNN), decision tree (DT) and support vector machines (SVM). The heartwood content of pine chips and bark content of birch chips were classified with an accuracy of 91% using EIS from pure materials combined with a k-nearest neighbour classifier. When using mixed materials and multiple classes, 73% correct classification for pine heartwood content (four groups) and 64% for birch bark content (five groups) were achieved.
木屑广泛用作纸浆和生物燃料工业的原料，而先进的材料分析可以改进这些产品的利用过程。为了分析松木木屑的心材含量和桦木木屑的树皮含量，使用了结合机器学习的电阻抗谱（EIS）。为了进行测试，开发了一种集成在采样容器中的新型电极系统，其频率范围为 42 Hz-5 MHz。使用三个电极对在 x、y 和 z 方向测量样品。使用了三种机器学习方法：K-最近邻（KNN）、决策树（DT）和支持向量机（SVM）。使用纯材料结合 KNN 分类器的 EIS，松木木屑的心材含量和桦木木屑的树皮含量的分类准确率达到 91%。当使用混合材料和多个类别时，松木心材含量（四组）的正确分类率为 73%，桦木树皮含量（五组）的正确分类率为 64%。
