Comparing atomic spectroscopy, molecular spectroscopy and multi-source spectroscopy synergetic fusion for quantitation of total potassium in culture substrates
Bing Lu, Xufeng Wang, Can Hu, Shiping Zhu and Xiangyou Li
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引用次数: 0
Abstract
The rapid assessment of total potassium in a culture substrate is of great significance for scientific planting and reducing agricultural non-point-source pollution. In this study, laser-induced breakdown spectroscopy (LIBS) and near-infrared spectroscopy (NIRS) were explored for the rapid detection of culture substrate total potassium at atomic and molecular scales, respectively. In addition, a new method based on atomic and molecular spectral information synergetic fusion for detection was also proposed. Single-spectrum detection models and LIBS–NIRS synergetic fusion prediction models were established. The results showed that LIBS univariate calibration curves and the NIRS detection model showed poor detection performance. The detection accuracy of models constructed from LIBS full variables, UVE-filtered LIBS variables and two LIBS variables with strong spectral line characteristics was significantly improved, but there was still great room for improvement. High-precision detection would be realized through LIBS–NIRS synergetic fusion. The simplest and optimal model would be constructed by combining the strong spectral line characteristics of LIBS and the NIRS spectral characteristics of SPA screening, and this model was conducive to the development of special detection equipment based on a photomultiplier tube as the core component of signal detection. At this moment, the number of model input variables was only 9, the determination coefficient and root mean square error of the calibration set were 0.9910 and 0.8523 g kg−1, respectively, and the corresponding values of the prediction set were 0.9900 and 0.8802 g kg−1. The fusion of LIBS and NIRS was feasible, and it could improve the robustness of the prediction model.
快速测定栽培基质中全钾含量对科学种植和减少农业面源污染具有重要意义。本研究利用激光诱导击穿光谱(LIBS)和近红外光谱(NIRS)分别在原子和分子尺度上快速检测培养底物总钾。此外,还提出了一种基于原子与分子光谱信息协同融合的检测新方法。建立了单光谱检测模型和LIBS-NIRS协同融合预测模型。结果表明,LIBS单变量标定曲线和近红外光谱检测模型的检测性能较差。由LIBS全变量、uve滤波后的LIBS变量和两个谱线特征较强的LIBS变量构建的模型检测精度明显提高,但仍有很大的提升空间。通过LIBS-NIRS协同融合实现高精度检测。结合LIBS的强谱线特性和SPA筛选的近红外光谱特性,可以构建最简单、最优的模型,该模型有利于开发以光电倍增管为核心部件的专用检测设备。此时,模型输入变量只有9个,校准集的决定系数和均方根误差分别为0.9910和0.8523 g kg - 1,预测集的对应值分别为0.9900和0.8802 g kg - 1。LIBS和NIRS的融合是可行的,可以提高预测模型的鲁棒性。