T
Tianlong Zhang
Researcher at Northwest University (China)
Publications - 38
Citations - 987
Tianlong Zhang is an academic researcher from Northwest University (China). The author has contributed to research in topics: Laser-induced breakdown spectroscopy & Chemistry. The author has an hindex of 16, co-authored 25 publications receiving 700 citations.
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Journal ArticleDOI
Quantitative and classification analysis of slag samples by laser induced breakdown spectroscopy (LIBS) coupled with support vector machine (SVM) and partial least square (PLS) methods
TL;DR: In this paper, a support vector machine (SVM) and partial least square (PLS) methods were used to perform quantitative and classification analysis of 20 slag samples, and the performance of the SVM calibration model was investigated by 5-fold cross-validation.
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A novel approach for the quantitative analysis of multiple elements in steel based on laser-induced breakdown spectroscopy (LIBS) and random forest regression (RFR)
TL;DR: In this article, a novel method based on laser induced breakdown spectroscopy (LIBS) and random forest regression (RFR) was proposed for the quantitative analysis of multiple elements in fourteen steel samples.
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Classification of iron ores by laser-induced breakdown spectroscopy (LIBS) combined with random forest (RF)
TL;DR: The study presented here demonstrates that LIBS–RF is a useful technique for the identification and discrimination of iron ore samples, and is promising for automatic real-time, fast, reliable, and robust measurements.
Journal ArticleDOI
Chemometrics in laser‐induced breakdown spectroscopy
TL;DR: In this article, the authors reviewed the research progress of chemometrics methods in LIBS for spectral data preprocessing as well as for qualitative and quantitative analyses in the most recent 5 years (2012•2016).
Journal ArticleDOI
Machine Learning Allows Calibration Models to Predict Trace Element Concentration in Soil with Generalized LIBS Spectra
Chen Sun,Ye Tian,Liang Gao,Yi-Shuai Niu,Tianlong Zhang,Hua Li,Yuqing Zhang,Zengqi Yue,Nicole Delepine-Gilon,Jin Yu +9 more
TL;DR: In this article, a multivariate model has been developed to reduce the influence of matrix effect on the accuracy of soil-independent calibration models, where variables representing the soil type are explicitly included.