The integration of near infrared spectroscopy (NIR) with machine learning techniques can be an adequate method for discrimination of wood species with commercial value. The aim of this study was to discriminate wood samples marketed as “Louros” from the Brazilian Amazon based on near-infrared spectroscopy and machine learning techniques. Samples of louro vermelho, louro branco, louro pimenta, louro preto, louro rosa, itauba, itauba amarela and preciosa were collected by members of two extractivist communities, Paraiso and Arimum, located in the “Green Forever” Extractivist Reserve in Pará state. Near-infrared spectra were obtained in the range 4000–10,000 \(\hbox {cm}^{-1}\), with resolution of \(4 \hbox { cm}^{-1}\), directly from sample surfaces oriented in the three anatomical sections: transverse, radial and tangential. This work tests three machine learning approaches—namely support vector machine (SVM), partial least squares-discriminant analysis (PLS-DA), and k-Nearest Neighbors (k-NN). The repeated k-fold cross validation method based on stratification and blocking was used to estimate the performance of the machine learning models. To build learning models, based on near infrared spectra, two situations were considered: (1) applying spectra from all wood sections and (2) using only spectra from one wood section. In general, mean spectra of “Louros” samples were similar. In all tests, models built with PLS-DA algorithm had accuracy and F1-Score superior to 97%. When analyzing PLS-DA applying spectra from only one wood section, tangential section had results slightly superior. Discriminative patterns can be obtained by near infrared spectra independent of anatomical section. The integration from NIR and PLS-DA was an adequate approach to recognize wood from “Louros” group.

将近红外光谱（NIR）与机器学习技术相集成可以成为区分具有商业价值的木材物种的适当方法。这项研究的目的是基于近红外光谱和机器学习技术，从巴西亚马逊市场上以“ Louros”商标销售的木材样品进行区分。卢罗·维梅略，卢罗·布朗科，卢罗·多香果，卢罗·普雷图，卢罗·罗莎，伊图巴，伊图巴·阿马雷拉和宝仕奥莎的样品是由位于帕拉州“永远永远的”萃取主义者保护区的两个提取主义者社区帕拉伊索和阿里穆姆采集的。获得的近红外光谱范围为4000–10,000 \（\ hbox {cm} ^ {-1} \），分辨率为\（4 \ hbox {cm} ^ {-1} \）直接从三个解剖部分（横向，径向和切向）中定位的样本表面获取。这项工作测试了三种机器学习方法-即支持向量机（SVM），偏最小二乘判别分析（PLS-DA）和k-最近邻居（k-NN）。基于分层和分块的重复k倍交叉验证方法用于估计机器学习模型的性能。为了建立基于近红外光谱的学习模型，考虑了两种情况：（1）应用所有木材部分的光谱，以及（2）仅使用一个木材部分的光谱。通常，“ Louros”样品的平均光谱相似。在所有测试中，使用PLS-DA算法构建的模型都具有较高的准确性，F1-Score优于97％。当仅使用一个木节的光谱分析PLS-DA时，切向节的结果稍好。可以通过独立于解剖部分的近红外光谱获得判别模式。NIR和PLS-DA的整合是识别“ Louros”集团木材的适当方法。