Application of FT-NIR spectroscopy to the prediction of Chromium contamination in soil by evolutionary chemometrics
Shaoyong Hong, Zhanhong Liang, Huazhou Chen, Jia Weng, Ken Cai, Xianchuan Wu
TL;DR
This paper introduces a new method combining machine learning and spectral analysis to quickly and accurately detect chromium contamination in soil.
Contribution
A novel BDE-PSSVM system is proposed for optimizing soil Cr prediction using FT-NIR spectroscopy.
Findings
The BDE-PSSVM model achieved a minimal root mean square error of 8.114 using only 56 variables.
The proposed system outperformed other methods in prediction accuracy and feature efficiency.
The model was validated using soil samples from a waste treatment area with preprocessed FT-NIR data.
Abstract
Fourier-transform near-Infrared (FT-NIR) technology offers a promising alternative to traditional methods for detecting soil Chromium (Cr) contamination. However, the relationship between soil Cr content and the spectra may involve complex non-linear dynamics and data redundancy. Therefore, selecting spectral feature variables and constructing parametric scaling models for rapid estimation has become a focal point in current research. In this study, the parametric scaling support vector machine (PSSVM) method is proposed for optimizing the modeling parameters, the binary modified differential evolution (BDE) algorithm is designed for selecting the feature variables. In combination, a novel combined optimization system is established by embedding the PSSVM model into the BDE iterative process. The system (BDE-PSSVM) is validated by estimating the soil Cr content based on the FT-NIR…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
Click any figure to enlarge with its caption.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure 21
Figure 22
Figure 23
Figure 24
Figure 25
Figure 26
Figure 27
Figure 28
Figure 29
Figure 30
Figure 31
Figure 32
Figure 33
Figure 34
Figure 35
Figure 36
Figure 37
Figure 38
Figure 39
Figure 40
Figure 41
Figure 42
Figure 43
Figure 44
Figure 45
Figure 46
Figure 47
Figure 48
Figure 49
Figure 50Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSoil Geostatistics and Mapping · Spectroscopy and Chemometric Analyses · Remote Sensing in Agriculture