Novel Multivariate Analysis for Soil Carbon Measurements Using Laser-Induced Breakdown Spectroscopy

2010 ◽  
Vol 74 (1) ◽  
pp. 87-93 ◽  
Author(s):  
Madhavi Z. Martin ◽  
Nicole Labbé ◽  
Nicolas André ◽  
Stan D. Wullschleger ◽  
Ronny D. Harris ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Soil Carbon ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

Multivariate Analysis of Laser-Induced Breakdown Spectroscopy Spectra of Soil Samples

Soil Science ◽  
2010 ◽  
Vol 175 (9) ◽  
pp. 447-452 ◽  
Author(s):  
Ningfang Yang ◽  
Neal S. Eash ◽  
Jaehoon Lee ◽  
Madhavi Z. Martin ◽  
Yong-Seon Zhang ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Soil Samples ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown

scholarly journals Physical and Chemical Matrix Effects in Soil Carbon Quantification Using Laser-Induced Breakdown Spectroscopy

2014 ◽  
Vol 05 (11) ◽  
pp. 722-729 ◽  
Author(s):  
Aline Segnini ◽  
Alfredo Augusto Pereira Xavier ◽  
Pedro Luís Otaviani-Junior ◽  
Edilene Cristina Ferreira ◽  
Alex Marcel Watanabe ◽  
...  
Keyword(s):  
Soil Carbon ◽  
Matrix Effects ◽  
Laser Induced Breakdown Spectroscopy ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Physical And Chemical

Fast Determination of Copper Content in Tobacco (Nicotina tabacum L.) Leaves Using Laser-Induced Breakdown Spectroscopy with Univariate and Multivariate Analysis

2018 ◽  
Vol 61 (3) ◽  
pp. 821-829 ◽  
Author(s):  
Jiyu Peng ◽  
Lanhan Ye ◽  
Tingting Shen ◽  
Fei Liu ◽  
Kunlin Song ◽  
...  
Keyword(s):  
Response Surface Methodology ◽  
Multivariate Analysis ◽  
Copper Content ◽  
Laser Induced Breakdown Spectroscopy ◽  
Fast Determination ◽  
Tobacco Leaves ◽  
Breakdown Spectroscopy ◽  
Laser Induced Breakdown ◽  
Determination Of Copper

Abstract. Fast and effective measures to determine heavy metals play an important role in ensuring food quality and safety. In this experiment, laser-induced breakdown spectroscopy (LIBS) was used to detect copper content (Cu) in tobacco ( L.) leaves. The experimental parameters for detection, including laser energy, delay time, and camera gate width, were optimized by response surface methodology (RSM). Univariate analysis and multivariate analysis, including partial least squares regression (PLSR) and extreme learning machine (ELM), were used to establish calibration models. In addition, different preprocessing methods were used to eliminate the signal variations and further improve the calibration performance, including baseline correction, background normalization, area normalization, and standard normal variate (SNV) normalization. The results showed that LIBS combined with both univariate and multivariate methods could be used to detect copper content in tobacco leaves. SNV and area normalization were efficient in dealing with signal variations and improving the calibration performance. The ELM model with SNV normalized variables in the spectral region of 324.02 to 325.98 nm achieved the best performance (R2 = 0.9552 and RMSE = 4.8416 mg kg-1 in the testing set). The results provide the first proof-of-principle data for fast determination of copper content in tobacco leaves. Keywords: Copper content, Laser-induced breakdown spectroscopy, Multivariate calibration, Response surface methodology, Tobacco leaves, Univariate calibration.

Sign in / Sign up

Export Citation Format

Share Document