Abstract
This study introduces a spectral-recognition method based on sparse representation. The proposed method, the linear regression sparse classification (LRSC) algorithm, uses different classes of training samples to linearly represent the prediction samples and to further classify them according to residuals in a linear regression model. Two kinds of spectral data with completely different physical properties were used in this study. These included infrared spectral data and laser-induced breakdown spectral (LIBS) data for Tegillarca granosa samples polluted by heavy metals. LRSC algorithm was employed to recognize the two classes of data, and the results were compared with common spectral-recognition algorithms, such as partial least squares discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), artificial neural network (ANN), random forest (RF), and support vector machine (SVM), in terms of recognition rate and parameter stability. The results show that LRSC algorithm is not only simple and convenient, but it also has a high recognition rate.
Highlights
Method for Spectral RecognitionPengchao Ye 1 , Guoli Ji 1,2 , Lei-Ming Yuan 3, * , Limin Li 3, *, Xiaojing Chen 3 , Fatemeh Karimidehcheshmeh 1 , Xi Chen 3 and Guangzao Huang 3
Spectral detection methods such as infrared spectra, laser-induced breakdown spectra (LIBS), andRaman spectra are widely used in the fields of food safety [1], environmental monitoring [2], and medical diagnosis [3] as fast, convenient, and green methods
It contained sample data for five classes of Tegillarca granosa, four of which were polluted by lead (Pb), copper (Cu), cadmium (Cd), and zinc (Zn) heavy metals in varying concentrations, and one class consisting of healthy Tegillarca granosa
Summary
Pengchao Ye 1 , Guoli Ji 1,2 , Lei-Ming Yuan 3, * , Limin Li 3, *, Xiaojing Chen 3 , Fatemeh Karimidehcheshmeh 1 , Xi Chen 3 and Guangzao Huang 3. Xiamen Research Institute of National Center of Healthcare Big Data, Xiamen 361005, China. College of Mathematics, Physics and Electronic Information Engineering, Wenzhou University, Wenzhou. Featured Application: The linear regression sparse classification (LRSC) algorithm is presented in this work to recognize Tegillarca granosa samples on the dataset of infrared spectral data and laser-induced breakdown spectral (LIBS), and this classification method can be extended to other spectral data for the classification of different categories. The thought of sparse representation in this classification algorithm is used to screen the useful information with less dimensions, and it can be used to detect the outlier samples
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