LIBS Info: Element Analysis

Title	Authors	Material	Detector	Spectrometer	Software
A novel method to extract important features from laser induced breakdown spectroscopy data: application to determine heavy metals in mulberries	Di Wu, Yong He, Jingyu Wang, Liang Yang, Liuwei Meng, Lingxia Huang	Organic	Andor iStar ICCD	Mechelle 5000	Solis
Laser:	Nd:YAG 1064.0000nm 80.0000mJ NoneHz
Gate Delay:	4.000us
Gate Width:	16.000us
The authors demonstrate a novel machine learning analysis technique for Cu and Cr in Mulberries. Samples were dried, powdered, sieved and then pressed into pellets prior to analysis. A key outcome was that the analytical performance was significantly improved by not using the full spectra, but by a subset of the data.

Element	Detection Limit (ppm)	Wavelength (nm)	Other Wavelengths (nm)	Calibration Method	Calibration Range (ppm)	Notes

Element	RMSE (ppm)	Wavelength (nm)	Calibration Method	Notes
Cr	211.0000 ppm	425.4600	Univariate Calibration	Univariate calibration for comparison with ML result
Cr	92.0000 ppm	-10.0000	Machine Learning	SVM result, following extensive pixel/spectra feature reduction
Cu	306.0000 ppm	-10.0000	Machine Learning	RMSEP Result using a ML process involving SVM, and schemes to select import peaks/spectral featurs
Cu	714.0000 ppm	425.4600	Univariate Calibration	Univariate calibration result to demonstrate improvement obtained by ML solution