A Normalized Difference Spectral Recognition Index for Azurite Pigment

Hyperspectral technology is a nondestructive, fast, and reliable method for the detection and restoration of relics. Most of the band characteristics of mineral pigment are concentrated between 2200 and 2400 nm, and these data are expensive to obtain (the required imaging sensor is expensive). We are pursuing a hyperspectral index mean that can effectively distinguish pigments in shorter band ranges to achieve high application value that is much less expensive. In this study, based on the spectral features of azurite at 400–1500 nm, we created an azurite normalized difference spectral index (ANDSI) through feature band selection, derivation of characteristic formulae, and discrimination analysis. Reflectivity bands at 458, 806, and 1373 nm were selected to build the ANDSI. Azurite was compared with 25 other common pigments and it was found that the discrimination values between azurite and the other pigments exceeded 0.88 (where values >0.5 indicate discriminable pigments), demonstrating that the ANDSI is suitable for detecting azurite.

A Sparse Classification Based on a Linear Regression Method for Spectral Recognition

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.

A laser-induced fluorescent detector for pesticide residue detection based on the spectral recognition method

In the present study, a laser-induced fluorescent (LIF) detector was developed for pesticide residue detection based on a microfluidic-based fluorescent sensor array (MFSA).

Research on a spectral recognition method for on-line measurement of COD in dyeing wastewater based on SIMCA

A novel instrument and a method using multi-wavelength spectra combined with a pattern recognition method (SIMCA) were developed and evaluated.