激光诱导击穿光谱
偏最小二乘回归
校准
材料科学
光谱学
分析化学(期刊)
吸收(声学)
定量分析(化学)
人工神经网络
生物系统
激光器
光学
化学
数学
计算机科学
色谱法
机器学习
物理
复合材料
统计
量子力学
生物
作者
Ping Yang,Xiangyou Li,Zhanglong Nie
出处
期刊:Optics Express
[Optica Publishing Group]
日期:2020-07-20
卷期号:28 (15): 23037-23037
被引量:10
摘要
Nutrient profile determination for plant materials is an important task to determine the quality and safety of the human diet. Laser-induced breakdown spectroscopy (LIBS) is an atomic emission spectrometry of the material component analytical technique. However, quantitative analysis of plant materials using LIBS usually suffers from matrix effects and nonlinear self-absorption. To overcome this problem, a hybrid quantitative analysis model of the partial least squares-artificial neural network (PLS-ANN) was used to detect the compositions of plant materials in the air. Specifically, fifty-eight plant materials were prepared to split into calibration, validation and prediction sets. Nine nutrient composition profiles of Mg, Fe, N, Al, B, Ca, K, Mn, and P were employed as the target elements for quantitative analysis. It demonstrated that the prediction ability can be significantly improved by the use of the PLS-ANN hybrid model compared to the method of standard calibration. Take Mg and K as examples, the root-mean-square errors of calibration (RMSEC) of Mg and K were decreased from 0.0295 to 0.0028 wt.% and 0.2884 to 0.0539 wt.%, and the mean percent prediction errors (MPE) were decreased from 5.82 to 4.22% and 8.82 to 4.12%, respectively. This research provides a new way to improve the accuracy of LIBS for quantitative analysis of plant materials.
科研通智能强力驱动
Strongly Powered by AbleSci AI