Comparison of Calibration Curve Method and Partial Least Square Method

in the Laser Induced Breakdown Spectroscopy Quantitative Analysis

Open Access JCC

Figure 5. Predicted VS. Reference concentration (wt%) of

Pb.

3.3. The Advantage of PLS Method

In PLS method, the regression coefficient means the re-

lationship between all independent variable X a nd de-

pendent variable Y, it can be calculated in the corres-

ponding number of principal components. In Section 3.2,

independent variable X (LIBS spectra data) and depen-

dent variable Y (element concentration) are used to es-

tablish the PLS calibration model. The regression coeffi-

cient between spectra data and element concen tration of

Al is shown in the Figure 6. The da rk line of Figure 6

means the regression coefficient with ordinate on the

right side, the light line means the spectra intensity with

ordinate on the left side. The spectral wavelength range

is 350 - 400 nm in the Figure 6. It can be observed that

at wavelength 394.25 nm and 396.06 nm, the regress ion

coefficient is 1.82 × 10−4 and 2.99 × 10−4, that is a larger

value. It means at this wavelength, the element concen-

tration and the spectra data have great correlation in PLS

model. Access to NIST, line 394.4 nm and 396.15 nm is

the Al characteristic spectral line. So in PLS model, in-

dependent variables and dependent variables have great

correlation at element characteristic spectral line (small

differences between the above wavelengths are caused by

error of spectrometer). But the intensity of spectra at the

same wavelength is weak and unstable. So the CC me-

thod calibration model using single spectral line intensity

is not good enough .

At wavelength 352.29 nm there is a peak but not Al

characteristic spectral line, it does not work in the CC

method Al calibration model. The regression coefficient

at the same wavelength is −0.92 × 10−4, it means the

spectral intensity and Al concentration are negative cor-

relation at this wavelength in PLS method calibration

model.

Unlike CC method to select the individual characteris-

tic peak, PLS method is to establish the relationship be-

Figure 6. The regression coefficient and spectrum of Al.

tween the intensity of full spectrum and element concen-

tration. By changing the independent variable space to

build a new principal components coordinate syste m, PLS

method establish positive or negative correlation rela-

tionship between all the spectral line intensity and ele-

ment concentrations. Then by selecting the number of

principal components to reduce the dimension of raw

data, PLS method establish a more stable quantitative

calibration model. We believe that this is the reason why

PLS quantitative calibration model is more effective than

CC method.

4. Conclusion

In this paper, both partial least squares and the calibra-

tion curve method were used to make the quantitative

analysis of LIBS spectral data of lead brass. After estab-

lishing the PLS calibration model, we can quickly get the

results of all the elements concentration. Compared with

the CC method, PLS method is more suitable for the

complicated matrix, element content different alloy.

5. Acknowledgements

This work has been sup po rted by the Equipment Devel-

opment Programs of the Chinese Academy of Sciences

(Grant No. YZ201247), the National High-Tech Research

and Development Program of China (863 Program) (Grant

No. 2012AA04060 8) and the National Natural Science

Fund (Grant No. 61004131).

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