基于紫外光谱的水产养殖水质总氮含量快速检测研究

doi:10.3964/j.issn.1000-0593(2020)01-0195-07

Abstract
References
Related Citation (15)

Download: PDF (2149 KB)
Export: BibTeX | EndNote (RIS)

Abstract The paper is intended to achieve rapid determination of total nitrogen (TN) concentration by using Ultraviolet (UV) spectroscopy technology, which was one of the most important indicators to measure the pollution degree in aquaculture water. The original dataset used in the paper contains 88 samples data with actual concentration value and spectral absorbance value. It is helpful to select the optimal model through the five stages that include sample set division algorithms, data preprocessing algorithms, feature band extraction algorithms, model selection algorithms and latent values (LVs) selection method. In the first four stages, the comparison results of different methods show that each stage is necessary, and only by comparing the advantages and disadvantages of modeling results with various algorithms can we find the most suitable modeling process and method. First of all, the original sample set is processed by the concentration gradient (CG) method, then three models are built which respectively are principal component regression (PCR), stepwise regression (SR) and partial least squares regression(PLSR), and it proves that the PLSR is the best prediction model. The number of LVs can greatly influence the accuracy of model, and usually when the value of the model root mean square error of prediction (RMSEP) is the minimum, the LV number is optimal. Secondly, it is testified that the SPXY algorithm is the best by comparing the effect of random sampling (RS) algorithm, concentration gradient (CG) algorithm, kennard stone (KS) algorithm and SPXY algorithm. Thirdly, based on SPXY algorithm, the paper uses five preprocessing algorithms which are wavelet transform (WT) method, first derivative (Der1st), and second derivative (Der2nd) three single preprocessing algorithms, WT-Der1st and WT-Der2nd. Fourthly, according to the results of data processing, using successive projections algorithm (SPA) and stepwise regression (SR) for feature band extraction algorithms, the results show that the extraction efficiency of SPA not only can greatly reduce the complexity of model, but also improve the prediction accuracy. The feature band extracted based on WT-Der1st-SPA is 218 nm, which is consistent with the characteristics of total nitrogen band range, indicating the method was relatively scientific. Finally, considering the prediction accuracy and complexity of model, the PLSR based on WT-Der1st-SPA with the best results with the determination coefficient (r²) and RMSEP being 0.996 and 0.042 mg·L^-1 for the prediction set in 10 models. In short, the prediction model established could be applied to the rapid and accurate determination of total nitrogen concentration. Moreover, this study laid the foundation for further implementation of online analysis of aquaculture water and rapid determination of other water quality parameters.

Key words：Ultraviolet spectroscopy; Total nitrogen; Wavelet transform; Successive projections algorithm; Latent values (LVs); Partial least squares regression (PLSR)

Received: 2018-11-01 Accepted: 2019-03-07

ZTFLH:

O433.4

Corresponding Authors: ZHANG Xiao-shuan E-mail: zhxshuan@cau.edu.cn

Cite this article:

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2020)01-0195-07 OR https://www.gpxygpfx.com/EN/Y2020/V40/I01/195