# Machine Learning Allows Calibration Models to Predict Trace Element Concentration in Soil with Generalized LIBS Spectra

## TL;DR

This paper presents a machine learning-based calibration model using generalized LIBS spectra that effectively predicts trace element concentrations in soil, reducing matrix effects and experimental fluctuations for more accurate and soil-independent measurements.

## Contribution

The study introduces a novel soil-independent calibration model employing generalized spectra and machine learning, improving LIBS measurement accuracy across diverse soils.

## Key findings

- Achieved 5-6% relative error in calibration and prediction.
- Reduced matrix effects and experimental fluctuations.
- Validated model's soil independence and accuracy.

## Abstract

Calibration models have been developed for determination of trace elements, silver for instance, in soil using laser-induced breakdown spectroscopy (LIBS). The major concern is the matrix effect. Although it affects the accuracy of LIBS measurements in a general way, the effect appears accentuated for soil because of large variation of chemical and physical properties among different soils. The purpose is to reduce its influence in such way an accurate and soil-independent calibration model can be constructed. At the same time, the developed model should efficiently reduce experimental fluctuations affecting measurement precision. A univariate model first reveals obvious influence of matrix effect and important experimental fluctuation. A multivariate model has been then developed. A key point is the introduction of generalized spectrum where variables representing the soil type are explicitly included. Machine learning has been used to develop the model. After a necessary pretreatment where a feature selection process reduces the dimension of raw spectrum accordingly to the number of available spectra, the data have been fed in to a back-propagation neuronal networks (BPNN) to train and validate the model. The resulted soilindependent calibration model allows average relative error of calibration (REC) and average relative error of prediction (REP) within the range of 5-6%.

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Source: https://tomesphere.com/paper/1906.08597