skip to main content
10.1145/3594315.3594662acmotherconferencesArticle/Chapter ViewAbstractPublication PagesiccaiConference Proceedingsconference-collections
research-article

Improving the Accuracy of Complex Solution Detection in Wedge Sample Pool Based on Machine Learning

Published: 02 August 2023 Publication History

Abstract

In order to effectively increase the information content of multi-dimensional spectral images of complex solutions and improve the accuracy of complex solution component analysis, this article proposes a method of using a wedge-shaped model (DILA-WSM) at different incident angles to improve the detection accuracy of complex solutions. In this study, Monte Carlo method was used to simulate photon propagation in wedge-shaped models at different incident angles, and multi-dimensional spectral images were obtained under different optical parameters. A regression model of 26 multi-dimensional spectral images collected under DILA-WSM and different optical parameters was established using partial least squares regression (PLSR). Compared with the traditional flat model, the introduction of DILA-WSM increased the average correlation coefficient () of the prediction set by 0.5862 and reduced the root mean square error of the prediction set (RMSEP) by 0.0392. The results show that the introduction of DILA-WSM can effectively increase the information content of multi-dimensional spectral images of complex solutions, thereby improving the accuracy of complex solution component analysis.

References

[1]
Garini Y., Young I. T., and McNamara G. (2006). Spectral imaging: principles and applications. Cytometry part a: the journal of the international society for analytical cytology, 69(8), 735-747.
[2]
Baiano A. (2017). Applications of hyperspectral imaging for quality assessment of liquid based and semi-liquid food products: A review. Journal of Food Engineering, 214, 10-15.
[3]
Książek K., Romaszewski M., Głomb P., Grabowski B. and Cholewa, M. (2020). Blood stain classification with hyperspectral imaging and deep neural networks. Sensors, 20(22), 6666.
[4]
Ma X., Pang J., Dong R., Tang C., Shu Y. and Li Y. (2020). Rapid prediction of multiple wine quality parameters using infrared spectroscopy coupling with chemometric methods. Journal of Food Composition and Analysis, 91, 103509.
[5]
Andoh S. S., Nyave K., Asamoah B., Kanyathare B., Nuutinen T., Mingle C. and Roussey, M. (2020). Optical screening for presence of banned Sudan III and Sudan IV dyes in edible palm oils. Food Additives & Contaminants: Part A, 37(7), 1049-1060.
[6]
Mayerhöfer T. G., Pahlow S. and Popp, J. (2020). The bouguer‐beer‐Lambert law: Shining light on the obscure. ChemPhysChem, 21(18), 2029-2046.
[7]
Mei S Z, Yan R G, Xu T, Wei M L. (2023). Research on multi-wavelength infrared urine glucose detection technology. Computer measurement and control:1-10.
[8]
Zhang S., Li G., Wang J., Wang D., Han Y., Cao H., and Lin L. (2018). Nondestructive measurement of hemoglobin in blood bags based on multi-pathlength VIS-NIR spectroscopy. Scientific reports, 8(1), 1-9.
[9]
Wan X., Li G., Li T., Yan W., He G. and Lin L. (2020). A review on M+N theory and its strategies to improve the accuracy of spectrochemical composition analysis of complex liquids. Applied Spectroscopy Reviews, 55(2), 87-104.
[10]
Zhao Z., Yin H., Yan W., Wang H. and Wang H. (2019). Investigation on near-infrared quantitative detection based on heteromorphic sample pool. Infrared Physics & Technology, 97, 444-447.
[11]
Zhao Z., Yue C., Fan W., Wang Y., Zhao W., Han G. and Wang H. (2022). Hyperspectral image feature region of solution composition analysis method based on multidimensional spectra. Infrared Physics & Technology, 123, 104196.
[12]
Tarasov A. P., Persheyev S., and Rogatkin D. A. (2021). Analysis of the applicability of the classical probabilistic parameters of the Monte Carlo algorithm for problems of light transport in turbid biological media with continuous absorption and discrete scattering. Quantum Electronics, 51(5), 408.
[13]
Fang Q., and Boas D. A. (2009). Monte Carlo simulation of photon migration in 3D turbid media accelerated by graphics processing units. Optics express, 17(22), 20178-20190.
[14]
Cheng J. H., and Sun D. W. (2017). Partial least squares regression (PLSR) applied to NIR and HSI spectral data modeling to predict chemical properties of fish muscle. Food engineering reviews, 9, 36-49.
[15]
Shetty N., and Gislum R. (2011). Quantification of fructan concentration in grasses using NIR spectroscopy and PLSR. Field Crops Research, 120(1), 31-37.
[16]
Wold S., Sjöström M., and Eriksson L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and intelligent laboratory systems, 58(2), 109-130.

Index Terms

  1. Improving the Accuracy of Complex Solution Detection in Wedge Sample Pool Based on Machine Learning

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    ICCAI '23: Proceedings of the 2023 9th International Conference on Computing and Artificial Intelligence
    March 2023
    824 pages
    ISBN:9781450399029
    DOI:10.1145/3594315
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 02 August 2023

    Permissions

    Request permissions for this article.

    Check for updates

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Funding Sources

    Conference

    ICCAI 2023

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 17
      Total Downloads
    • Downloads (Last 12 months)4
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 17 Jan 2025

    Other Metrics

    Citations

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format.

    HTML Format

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media