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

Feng Hu,Kai Bian,Pengcheng Yan,Rongying Dai,Mengran Zhou

doi:10.1109/access.2019.2933453

Abstract

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.

Highlights

Laser-induced fluorescence (LIF) [1], [2] is a spectroscopic method in which an atom or molecule is excited to a higher energy level by the absorption of laser light followed by spontaneous emission of light [3]
We focus on the principal component analysis network (PCANet) recognition model with the optimized hyper-parameters
The characteristics of the fluorescence spectra are studied by the PCANet with two-stage cascade principal component analysis (PCA), and the classification of liquor is realized by linear support vector machine (SVM)

Summary

Introduction

Laser-induced fluorescence (LIF) [1], [2] is a spectroscopic method in which an atom or molecule is excited to a higher energy level by the absorption of laser light followed by spontaneous emission of light [3]. It was first reported by Zare [4], [5] and coworkers in 1968. The construction of a fluorescence spectral classification model consists of the following steps: preprocessing [8], dimensionality reduction (feature extraction) [9] and classification [10]. The common spectral pretreatment methods include baseline correction [12], wavelet transform [13], moving average smoothing [14], Savitzky-Golay smoothing [15], Gaussian

Methods

Results

Conclusion