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pls toolbox 8.7 software  (Eigenvector Research Inc)

 
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    Eigenvector Research Inc pls toolbox 8.7 software
    Interval <t> partial least square regression </t> cross-validation and prediction results for clay, silt, sand, and SOC.
    Pls Toolbox 8.7 Software, supplied by Eigenvector Research Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/the+pls+toolbox+8%2E7/pls+toolbox+8+7+software/pmc11280583-127-16-20
    Average 90 stars, based on 1 article reviews
    pls toolbox 8.7 software - by Bioz Stars, 2026-10
    90/100 stars

    Images

    1) Product Images from "Combining Laser-Induced Breakdown Spectroscopy and Visible Near-Infrared Spectroscopy for Predicting Soil Organic Carbon and Texture: A Danish National-Scale Study"

    Article Title: Combining Laser-Induced Breakdown Spectroscopy and Visible Near-Infrared Spectroscopy for Predicting Soil Organic Carbon and Texture: A Danish National-Scale Study

    Journal: Sensors (Basel, Switzerland)

    doi: 10.3390/s24144464

    Interval partial least square regression cross-validation and prediction results for clay, silt, sand, and SOC.
    Figure Legend Snippet: Interval partial least square regression cross-validation and prediction results for clay, silt, sand, and SOC.

    Techniques Used:

    Related Articles

    other:

    Article Title: Factors that Influence the Quality of Metabolomics Data in in Vitro Cell Toxicity Studies: A Systematic Survey
    Article Snippet: Data analysis was carried out in MATLAB R2019a (Mathworks Inc., Natick, MA, USA) using in-house written scripts and the PLS Toolbox 8.7 (Eigenvector Research Inc., Wenatchee, USA).



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    Image Search Results


    Interval partial least square regression cross-validation and prediction results for clay, silt, sand, and SOC.

    Journal: Sensors (Basel, Switzerland)

    Article Title: Combining Laser-Induced Breakdown Spectroscopy and Visible Near-Infrared Spectroscopy for Predicting Soil Organic Carbon and Texture: A Danish National-Scale Study

    doi: 10.3390/s24144464

    Figure Lengend Snippet: Interval partial least square regression cross-validation and prediction results for clay, silt, sand, and SOC.

    Article Snippet: All multivariate data analyses were carried out in MatLab R2021a (MathWorks, Inc., Natick, MA, USA) and PLS Toolbox 8.7 software (Eigenvector Research Inc., Manson, WA, USA).

    Techniques: