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.

LOD
RMSEP

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

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