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[Study on Outliers Influence in NIR Quantitative Analysis Model].

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    This study introduces a novel method for identifying and managing outliers in near-infrared spectroscopy (NIRS) quantitative analysis for wheat protein. Effective outlier handling improves the reliability of NIRS models.

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    Area of Science:

    • Agricultural Science
    • Analytical Chemistry
    • Spectroscopy

    Background:

    • Reproducibility and reliability in quantitative analysis using near-infrared spectroscopy (NIRS) are heavily influenced by the modeling process.
    • Outlier detection and management are critical for robust quantitative models, especially in complex sample sets.

    Purpose of the Study:

    • To investigate the impact of outliers on near-infrared spectroscopy (NIRS) quantitative protein models for wheat.
    • To develop and validate a method for identifying and treating outliers in partial least squares regression (PLSR) modeling.

    Main Methods:

    • Utilized deviation between interpretative percentage curves in PLSR as an indicator for outliers.
    • Employed a sub-model ergodic calculation method for gradual outlier identification and selection.
    • Used the standard deviation of model prediction residuals for classifying outlier degrees (significant, relative, potential).

    Main Results:

    • Identified significant outliers at approximately 7.8% and relative outliers at 15.6% of the wheat sample set.
    • Demonstrated that outliers increase data dispersity and skew model fitting lines.
    • Showcased improved model accuracy by either removing outliers or applying sample weighting techniques.

    Conclusions:

    • The proposed outlier analysis and treatment method enhance the accuracy and reliability of NIRS quantitative models for wheat protein.
    • Effective outlier management ensures that model fitting is more representative of the majority of samples, reducing the influence of aberrant data points.