A large number of certified samples are usually required to build models in the quantitative analysis of complicated matrices in laser-induced-breakdown spectroscopy (LIBS). Because of differences among instruments, including excitation and collection efficiencies, a quantitative model made on one instrument is difficult to apply directly to other instruments. Each instrument requires a large number of samples to model, which is very labor intensive and will hinder the rapid application of the LIBS technique. To eliminate the differences in spectral data from different instruments and reduce the cost of building new models, a piecewise direct standardization method combined with partial least squares (PLS_PDS) is studied in this work. Two portable LIBS instruments with the same configuration are used to obtain spectral data, one of which is called a master instrument because its calibration model is directly built on a large number of labeled samples, and the other of which is called a slave instrument because its model is obtained from the master instrument. The PLS_PDS method is used to build a transfer function of spectra between the master instrument and slave instrument to reduce the spectral difference between two instruments, and thus one calibration model can adapt to different instruments. Results show that for multiple elemental analyses of aluminium alloy samples, the number of samples required for slave modeling was reduced from 51 to 14 after model transferring by PLS_PDS, and the quantitative performance of the slave instrument was close to that of the master instrument. Therefore, the model transfer method can obviously reduce the sample number of building models for slave instruments, and it will be beneficial to advance the application of LIBS.
On-stream analysis of the element content in ore slurry plays an important role in the control of the mineral flotation process. Therefore, our laboratory developed a LIBS-based slurry analyzer named LIBSlurry, which can monitor the iron content in slurries in real time. However, achieving high-precision quantitative analysis results of the slurries is challenging. In this paper, a weakly supervised feature selection method named spectral distance variable selection was proposed for the raw spectral data. This method utilizes the prior information that multiple spectra of the same slurry sample have the same reference concentration to assess the important weight of spectral features, and features selected by this prior can avoid over-fitting compared with a traditional wrapper method. The spectral data were collected on-stream of iron ore concentrate slurry samples during the mineral flotation process. The results show that the prediction accuracy is greatly improved compared with the full-spectrum input and other feature selection methods; the root mean square error of the prediction of iron content can be decreased to 0.75%, which helps to realize the successful application of the analyzer.
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