This paper is aimed to develop a hybrid model (PCA-ANN) to predict the extractive content in eucalyptus wood clones. The input variables to the virtual analyzer are planting parameters available from the forest inventory, therefore the prediction does not rely on laboratory analysis of the wood samples, affording a quick estimate of the extractive contents. This study further bridges the literature gap on the investigation of the cause of variability of extractive content in eucalyptus wood. The PCA-ANN was identified from experimental data to predict and monitor the extractive content, since laboratory measurements can take several days and become available only after wood processing. The experimental data contained information on ten species of eucalyptus clones from five regions in the extreme south of Bahia, Brazil. Principal Component Analysis (PCA) firstly assessed the impact of planting variables on the extractives content. The variability of the data was represented by eight principal components and the variables that mostly contribute to the extractive content are: potential acidity, iron, saturation of aluminum, magnesium, pH, base saturation, remaining phosphorus, zinc, manganese and copper. The artificial neural network (ANN) with the 8 principal components in the input layer showed that the PCA could effectively reduce the dimensionality of the data. For practical purposes, though, the ANN with 10 input variables and 16 neurons in the hidden layer, presenting an average relative deviation of 1.5%, is recommended. The prediction of the extractive content is essential to allow preventive management practices toward the improvement of yield and quality of the cellulosic pulp.

本文旨在建立一个混合模型（PCA-ANN），以预测桉木无性系中的提取物含量。虚拟分析仪的输入变量是可从森林清单中获得的种植参数，因此，该预测不依赖于对木材样品的实验室分析，从而可以快速估算出提取物的含量。这项研究进一步弥合了桉树木材中提取物含量变化原因调查的文献空白。从实验数据中识别出PCA-ANN可以预测和监测提取物含量，因为实验室测量可能需要几天的时间，并且仅在木材加工后才可用。实验数据包含来自巴西巴伊亚州最南端五个地区的十种桉树无性系的信息。主成分分析（PCA）首先评估种植变量对提取物含量的影响。数据的可变性由八个主要成分表示，并且对提取物含量影响最大的变量是：潜在酸度，铁，铝，镁的饱和度，镁，pH，碱饱和度，剩余的磷，锌，锰和铜。在输入层中具有8个主要成分的人工神经网络（ANN）表明，PCA可以有效地降低数据的维数。但是，出于实际目的，建议在隐藏层中使用10个输入变量和16个神经元的ANN，其平均相对偏差为1.5％。