In Situ Simultaneous Quantitative Analysis Multi-Elements of Archaeological Ceramics Via Laser Induced Breakdown Spectroscopy Combined with Machine Learning Strategy

[1]  P. Zhang,et al.  Composition analysis of ceramic raw materials using laser-induced breakdown spectroscopy and autoencoder neural network. , 2022, Analytical methods : advancing methods and applications.

[2]  Chunhua Yan,et al.  Hybrid variable selection strategy coupled with random forest (RF) for quantitative analysis of methanol in methanol-gasoline via Raman spectroscopy. , 2021, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[3]  Tianlong Zhang,et al.  Novel Method Based on Hollow Laser Trapping-LIBS-Machine Learning for Simultaneous Quantitative Analysis of Multiple Metal Elements in a Single Microsized Particle in Air. , 2021, Analytical chemistry.

[4]  Verónica Bolón-Canedo,et al.  Ensembles for feature selection: A review and future trends , 2019, Inf. Fusion.

[5]  Zhen-Tao Liu,et al.  Electroencephalogram Emotion Recognition Based on Empirical Mode Decomposition and Optimal Feature Selection , 2019, IEEE Transactions on Cognitive and Developmental Systems.

[6]  Chunhua Yan,et al.  Simultaneous quantitative analysis of four metal elements in oily sludge by laser induced breakdown spectroscopy coupled with wavelet transform-random forest (WT-RF) , 2019, Chemometrics and Intelligent Laboratory Systems.

[7]  M. Joubert,et al.  Determination of interactions between antibody biotherapeutics and copper by size exclusion chromatography (SEC) coupled with inductively coupled plasma mass spectrometry (ICP/MS). , 2019, Analytica chimica acta.

[8]  Michael I. Miller,et al.  A comparison of random forest variable selection methods for classification prediction modeling , 2019, Expert Syst. Appl..

[9]  Shungeng Min,et al.  A new hybrid filter/wrapper algorithm for feature selection in classification. , 2019, Analytica chimica acta.

[10]  Jianwei Huang,et al.  A hybrid model combining wavelet transform and recursive feature elimination for running state evaluation of heat-resistant steel using laser-induced breakdown spectroscopy. , 2019, The Analyst.

[11]  Yongfeng Lu,et al.  Determination of chlorine with radical emission using laser-induced breakdown spectroscopy coupled with partial least square regression. , 2019, Talanta.

[12]  Muhua Liu,et al.  Quantitative analysis of chromium in pork by PSO-SVM chemometrics based on laser induced breakdown spectroscopy , 2019, Journal of Analytical Atomic Spectrometry.

[13]  Taoreed O. Owolabi,et al.  Development of hybrid extreme learning machine based chemo-metrics for precise quantitative analysis of LIBS spectra using internal reference pre-processing method. , 2018, Analytica chimica acta.

[14]  C. Douvris,et al.  Investigation of the metal content of sediments around the historically polluted Potomac River basin in Washington D.C., United States by inductively coupled plasma-optical emission spectroscopy (ICP-OES) , 2018, Microchemical Journal.

[15]  V. Lazic,et al.  Applications of laser-induced breakdown spectroscopy for cultural heritage: A comparison with X-ray Fluorescence and Particle Induced X-ray Emission techniques , 2018, Spectrochimica Acta Part B: Atomic Spectroscopy.

[16]  M. Rizzutto,et al.  Computed Radiography, PIXE and XRF analysis of pre-colonial pottery from Maranhão, Brazil , 2018 .

[17]  Chunhua Yan,et al.  Quantitative detection of harmful elements in alloy steel by LIBS technique and sequential backward selection-random forest (SBS-RF) , 2017 .

[18]  Takeshi Inomata,et al.  High-precision radiocarbon dating of political collapse and dynastic origins at the Maya site of Ceibal, Guatemala , 2017, Proceedings of the National Academy of Sciences.

[19]  Dianguo Xu,et al.  A Permutation Importance-Based Feature Selection Method for Short-Term Electricity Load Forecasting Using Random Forest , 2016 .

[20]  J. Madariaga,et al.  Multispectroscopic and Isotopic Ratio Analysis To Characterize the Inorganic Binder Used on Pompeian Pink and Purple Lake Pigments. , 2016, Analytical chemistry.

[21]  B. Gómez-Tubío,et al.  Combining XRF and GRT for the analysis of ancient silver coins , 2016 .

[22]  Yongfeng Lu,et al.  Analytical-performance improvement of laser-induced breakdown spectroscopy for steel using multi-spectral-line calibration with an artificial neural network , 2015 .

[23]  M. Moinester,et al.  RHX Dating of Archeological Ceramics Via a New Method to Determine Effective Lifetime Temperature , 2015 .

[24]  M. Menu,et al.  Palaeolithic paint palettes used at La Garma Cave (Cantabria, Spain) investigated by means of combined in situ and synchrotron X-ray analytical methods , 2015 .

[25]  K. Faull,et al.  Analytical chemistry in archaeological research. , 2015, Analytical chemistry.

[26]  Y. Duan,et al.  A novel approach for the quantitative analysis of multiple elements in steel based on laser-induced breakdown spectroscopy (LIBS) and random forest regression (RFR) , 2014 .

[27]  Roberta Fantoni,et al.  Laser Induced Breakdown Spectroscopy in archeometry: A review of its application and future perspectives , 2014 .

[28]  Peter C. Jordan,et al.  Earliest evidence for the use of pottery , 2013, Nature.

[29]  J. Moros,et al.  Adaptive approach for variable noise suppression on laser-induced breakdown spectroscopy responses using stationary wavelet transform. , 2012, Analytica chimica acta.

[30]  Jesús S. Aguilar-Ruiz,et al.  Fast feature selection aimed at high-dimensional data via hybrid-sequential-ranked searches , 2012, Expert Syst. Appl..

[31]  Michael D. Glascock,et al.  The forest or the trees? Behavioral and methodological considerations for geochemical characterization of heavily-tempered ceramic pastes using NAA and LA-ICP-MS , 2012 .

[32]  Ishan Barman,et al.  Incorporation of support vector machines in the LIBS toolbox for sensitive and robust classification amidst unexpected sample and system variability. , 2012, Analytical chemistry.

[33]  Demetrios Anglos,et al.  The application of LIBS for the analysis of archaeological ceramic and metal artifacts , 2002 .

[34]  L. Breiman Random Forests , 2001, Encyclopedia of Machine Learning and Data Mining.

[35]  A. Brunetti,et al.  Energy dispersive X-ray fluorescence spectroscopy/Monte Carlo simulation approach for the non-destructive analysis of corrosion patina-bearing alloys in archaeological bronzes: The case of the bowl from the Fareleira 3 site (Vidigueira, South Portugal) , 2015 .

[36]  Thomas Marill,et al.  On the effectiveness of receptors in recognition systems , 1963, IEEE Trans. Inf. Theory.