Quantitative Analysis of Elements in Fertilizer Using Laser-Induced Breakdown Spectroscopy Coupled with Support Vector Regression Model

Abstract

The rapid detection of the elements nitrogen (N), phosphorus (P), and potassium (K) is beneficial to the control of the compound fertilizer production process, and it is of great significance in the fertilizer industry. The aim of this work was to compare the detection ability of laser-induced breakdown spectroscopy (LIBS) coupled with support vector regression (SVR) and obtain an accurate and reliable method for the rapid detection of all three elements. A total of 58 fertilizer samples were provided by Anhui Huilong Group. The collection of samples was divided into a calibration set (43 samples) and a prediction set (15 samples) by the Kennard–Stone (KS) method. Four different parameter optimization methods were used to construct the SVR calibration models by element concentration and the intensity of characteristic line variables, namely the traditional grid search method (GSM), genetic algorithm (GA), particle swarm optimization (PSO), and least squares (LS). The training time, determination coefficient, and the root-mean-square error for all parameter optimization methods were analyzed. The results indicated that the LIBS technique coupled with the least squares–support vector regression (LS-SVR) method could be a reliable and accurate method in the quantitative determination of N, P, and K elements in complex matrix like compound fertilizers.

Accuracy improvement for multimetal analysis by laser‐induced breakdown spectroscopy with least squares support vector machine

“…The LSSVM is a machine learning method which combinates least square and support vector machine (SVM). In this paper, the LSSVM algorithm deformed the optimization formula of SVM by using the formula of least squares 22‐30 …”

Quantitative Evaluation of Food-Waste Components in Organic Fertilizer Using Visible–Near-Infrared Hyperspectral Imaging

“…Wang et al (2018) evaluated the nitrogen fertilizer levels of a tea plant using visible and near-infrared HSI techniques with multivariate classification algorithms; hence, they discriminated tea plants subjected to three different nitrogen treatments [18]. Finally, Sha et al (2019) proposed a HSI method, including multivariate techniques, to discriminate fertilized from unfertilized grasses and showed that specific wavelengths can be effectively used to assess the influence of different fertilizers on the grasses [19]. However, to the best of the authors' knowledge, no nondestructive HIS technology for FWC evaluation in OF within the visible-near-infrared (Vis/NIR) region has been developed to date.…”

Application of Scikit and Keras Libraries for the Classification of Iron Ore Data Acquired by Laser-Induced Breakdown Spectroscopy (LIBS)

“…Sha Wen et al proposed the method of combining LIBS and support vector regression (SVR) to detect three elements of nitrogen, phosphorus, and potassium in fertilizer. The results show that LIBS coupled with the least squares-support vector regression (LS-SVR) can accurately and quantitatively analyze elements in complex matrices [30]. Liwen Sheng et al applied the combination of LIBS and random forest (RF) for the identification and discrimination of iron ore.…”