PCANet: A Common Solution for Laser-Induced Fluorescence Spectral Classification

Laser-induced fluorescence (LIF) technology is an advanced optical detection method, which has the advantages of fast, high precision and nondestructive testing, and is widely used in many fields. The general pattern recognition method for fluorescence spectral classification is highly dependent on pretreatment and dimension reduction. Specific pretreatment and dimension reduction methods are required for specific substances. Deep learning, especially the convolutional neural network (CNN), has the advantage of low dependence on data preprocessing and dimensionality reduction process, which makes it perform well in spectral classification. However, learning a useful CNN model for classification depends crucially on the expertise of parameter tuning and some ad hoc tricks, which is not suitable for chemometrics researchers. This paper presents a novel chemometrics tool for fluorescence spectra, principal component analysis network (PCANet), and more specifically a PCANet model with the optimized hyper-parameters (only optimized once). A two-stage cascaded PCANet model is constructed based on the liquor dataset, and the hyper-parameters are optimized and determined, which can make PCANet recognition model with the highest accuracy. Comparing the CNN model with two convolutional layers, the PCANet model is less affected by the size of the input image and the number of samples in the training set. At the same time, the performance of the two models on the mine water dataset is analyzed, and PCANet has higher recognition accuracy. That is to say, the PCANet is more accurate than CNN in fluorescence spectral classification, and its ability to expand application is stronger than that of the CNN model. The successful application of PCANet model with the optimized hyper-parameters (only optimized once) in the liquor dataset and the mine water dataset has important reference significance for the classification of fluorescence spectra of other substances in the future.