Open AccessQuantitative multiple-element simultaneous analysis of seaweed fertilizer by laser-induced breakdown spectroscopy
Author(s) -
Qingyu Lin,
Pengkun Yin,
Ying Wang,
Lin Zhang,
Xiaohui Wang
Publication year - 2020
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.387858
Subject(s) - laser induced breakdown spectroscopy , partial least squares regression , analytical chemistry (journal) , fertilizer , spectroscopy , multivariate statistics , materials science , chemistry , mathematics , chromatography , statistics , physics , organic chemistry , quantum mechanics
Laser-induced breakdown spectroscopy, coupled with advanced chemometric methods, was used to quantitate multiple elements in a seaweed-based fertilizer. The influence of important parameters was determined using partial least squares regression (PLSR), support vector regression (SVR) and random forest (RF) optimizations. Optimal results for Mg, K and P were obtained using PLSR, whereas RF yielded the best results for Mn, Cu, Sr and Ca. The best predictions for Ba levels were obtained with SVR. The lowest root mean square errors in the prediction sets for Mn, Cu, Sr, Ba, Mg, K, P and Ca were 48.27 µg/g, 36.90 µg/g, 0.37 mg/g, 40.32 µg/g, 1.99 mg/g, 2.03 mg/g, 4.81 mg/g and 14.08 mg/g, respectively, with average relative standard deviations of 13.65%, 2.68%, 19.80%, 5.17%, 3.32%, 2.98%, 1.82% and 5.81%. The results showed that the optimal multivariate model depended on the specific element being analyzed. The proposed method provides a rapid means of determining multielement concentrations in seaweed-based fertilizers.