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首页> 外文期刊>Journal of the Chilean Chemical Society >SUPERVISED PATTERN RECOGNITION TECHNIQUES FOR CLASSIFICATION OF EUCALYPTUS SPECIES FROM LEAVES NIR SPECTRA
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SUPERVISED PATTERN RECOGNITION TECHNIQUES FOR CLASSIFICATION OF EUCALYPTUS SPECIES FROM LEAVES NIR SPECTRA

机译：用于从叶近红外光谱中分类真人鱼属物种的监督模式识别技术

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摘要

Three supervised pattern recognition methods (SPRM) were evaluated to discriminate between Eucalyptus globulus and Eucalyptus nitens species applying near infrared (NIR) spectroscopy on leaves. The methods used were k-nearest neighbor (KNN), soft modeling class analogy (SIMCA) and discriminant partial least squares (PLS-DA). First and second derivatives were used as transform techniques and mean-center (MC) and autoscaling (AS) as preprocessing techniques. The training set was constitued by 288 samples and 20 samples were used as validation set. A significant difference between the assayed methods was not observed, however best results for separation of classes and prediction rate were obtained when first derivative and MC were used for all the recognition pattern methods. Use of leaves and NIR spectroscopy avoids the destructive usual wood analysis in forest industries and facilities the fast classification of these species for forest applications.
机译：评估了三种监督模式识别方法（SPRM），以在叶片上应用近红外（NIR）光谱技术来区分小桉树和无花桉树。使用的方法是k最近邻（KNN），软建模类比（SIMCA）和判别偏最小二乘（PLS-DA）。一阶和二阶导数用作变换技术，均值中心（MC）和自动缩放（AS）作为预处理技术。训练集由288个样本构成，并且将20个样本用作验证集。没有观察到分析方法之间的显着差异，但是，当将一阶导数和MC用于所有识别模式方法时，可获得类别分离和预测率的最佳结果。叶片和近红外光谱的使用避免了林业工业中破坏性的常规木材分析，并避免了将这些树种快速分类以用于森林应用。

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