Machine Learning Allows Calibration Models to Predict Trace Element Concentration in Soils with Generalized LIBS Spectra
| Autor: | Yuqing Zhang, Jin Yu, Zengqi Yue, Yi-Shuai Niu, Nicole Delepine-Gilon, Ye Tian, Chen Sun, Tianlong Zhang, Hua Li, Liang Gao |
|---|---|
| Přispěvatelé: | State Key Laboratory of Structural Analysis for Industrial Equipment (Dalian University of Technology), Ocean University of China: Qingdao, Shandong, Equipe Image - Laboratoire GREYC - UMR6072, Groupe de Recherche en Informatique, Image et Instrumentation de Caen (GREYC), Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU)-Normandie Université (NU)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU), The First Affiliated Hospital of Chongqing Medical University, Plasma spectroscopies, hyphenated methods & speciation, Institut des Sciences Analytiques (ISA), Institut de Chimie du CNRS (INC)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Shanghai Jiao Tong University [Shanghai] |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
FOS: Computer and information sciences Multivariate statistics Computer Science - Machine Learning Trace (linear algebra) Physics - Instrumentation and Detectors Calibration (statistics) lcsh:Medicine FOS: Physical sciences Feature selection Machine learning computer.software_genre Article Machine Learning (cs.LG) 03 medical and health sciences 0302 clinical medicine Dimension (vector space) [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] Approximation error [CHIM.ANAL]Chemical Sciences/Analytical chemistry Physics - Chemical Physics Range (statistics) lcsh:Science Mathematics Chemical Physics (physics.chem-ph) [PHYS]Physics [physics] Multidisciplinary Sensors business.industry lcsh:R Instrumentation and Detectors (physics.ins-det) Physics - Plasma Physics Plasma Physics (physics.plasm-ph) 030104 developmental biology Optical sensors Soil water lcsh:Q Artificial intelligence business computer 030217 neurology & neurosurgery |
| Zdroj: | Scientific Reports Scientific Reports, Nature Publishing Group, 2019, 9 (1), ⟨10.1038/s41598-019-47751-y⟩ Scientific Reports, Vol 9, Iss 1, Pp 1-18 (2019) |
| ISSN: | 2045-2322 |
| DOI: | 10.1038/s41598-019-47751-y⟩ |
| Popis: | 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%. Comment: 34 pages |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|