Silva Adenilton C da, Soares Sófacles F C, Insausti Matías, Galvão Roberto K H, Band Beatriz S F, Araújo Mário César U de
Universidade Federal da Paraíba, Departamento de Química, Laboratório de Automação e Instrumentação em Química Analítica/Quimiometria (LAQA), Caixa Postal 5093, CEP 58051-970, João Pessoa, PB, Brazil.
Universidade Federal da Paraíba, Departamento de Química, Laboratório de Automação e Instrumentação em Química Analítica/Quimiometria (LAQA), Caixa Postal 5093, CEP 58051-970, João Pessoa, PB, Brazil; Departamento de Engenharia Química, Centro de Tecnologia (CT), Universidade Federal da Paraíba, 58051-900, João Pessoa, PB, Brazil.
Anal Chim Acta. 2016 Sep 28;938:53-62. doi: 10.1016/j.aca.2016.08.009. Epub 2016 Aug 20.
The two-dimensional linear discriminant analysis (2D-LDA) algorithm was originally proposed in the context of face image processing for the extraction of features with maximal discriminant power. However, despite its promising performance in image processing tasks, the 2D-LDA algorithm has not yet been used in applications involving chemical data. The present paper bridges this gap by investigating the use of 2D-LDA in classification problems involving three-way spectral data. The investigation was concerned with simulated data, as well as real-life data sets involving the classification of dry-cured Parma ham according to ageing by surface autofluorescence spectrometry and the classification of edible vegetable oils according to feedstock using total synchronous fluorescence spectrometry. The results were compared with those obtained by using the spectral data with no feature extraction, U-PLS-DA (Partial Least Squares Discriminant Analysis applied to the unfolded data), and LDA employing TUCKER-3 or PARAFAC scores. In the simulated data set, all methods yielded a correct classification rate of 100%. However, in the Parma ham and vegetable oil data sets, better classification rates were obtained by using 2D-LDA (86% and 100%), compared with no feature extraction (76% and 77%), U-PLS-DA (81% and 92%), PARAFAC-LDA (76% and 86%) and TUCKER3-LDA (86% and 93%).
二维线性判别分析（2D-LDA）算法最初是在面部图像处理的背景下提出的，用于提取具有最大判别能力的特征。然而，尽管2D-LDA算法在图像处理任务中表现出良好的性能，但它尚未应用于涉及化学数据的应用中。本文通过研究2D-LDA在涉及三维光谱数据的分类问题中的应用，填补了这一空白。该研究涉及模拟数据以及实际数据集，包括根据表面自发荧光光谱法对干腌帕尔马火腿进行老化分类，以及使用全同步荧光光谱法根据原料对食用植物油进行分类。将结果与未进行特征提取的光谱数据、U-PLS-DA（应用于展开数据的偏最小二乘判别分析）以及采用TUCKER-3或PARAFAC得分的LDA所获得的结果进行了比较。在模拟数据集中，所有方法的正确分类率均为100%。然而，在帕尔马火腿和植物油数据集中，使用2D-LDA（分别为86%和100%）获得的分类率优于未进行特征提取（分别为76%和77%）、U-PLS-DA（分别为81%和92%）、PARAFAC-LDA（分别为76%和86%）以及TUCKER3-LDA（分别为86%和93%）。
