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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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Updated: Apr 28, 2026

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain
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Identification of wheat quality using THz spectrum.

Hongyi Ge, Yuying Jiang, Zhaohui Xu

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    |June 13, 2014
    PubMed
    Summary

    Terahertz (THz) time domain spectroscopy combined with principal component analysis-support vector machine (PCA-SVM) effectively identifies wheat quality. This advanced method achieved nearly 95% accuracy in distinguishing normal, worm-eaten, moldy, and sprouting grains.

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    Area of Science:

    • Agricultural Science
    • Spectroscopy
    • Machine Learning

    Background:

    • Wheat quality assessment is crucial for food security and trade.
    • Traditional methods for detecting grain deterioration are often time-consuming and subjective.
    • Developing rapid, objective, and accurate methods for wheat quality evaluation is essential.

    Purpose of the Study:

    • To investigate the feasibility of terahertz (THz) time domain spectroscopy for differentiating wheat grains based on quality.
    • To develop and evaluate a machine learning model for accurate classification of wheat grain conditions.
    • To compare the performance of the proposed THz-PCA-SVM method with other chemometric techniques.

    Main Methods:

    • Terahertz time domain spectroscopy (THz-TDS) was used to collect spectral data from wheat grains (0.2-1.6 THz).
    • Principal component analysis (PCA) was applied for feature extraction, selecting the top four principal components.
    • A support vector machine (SVM) classifier with linear, polynomial, and radial basis function kernels was trained for grain identification.

    Main Results:

    • The PCA-SVM model achieved a classification accuracy of nearly 95% for identifying four types of wheat grains.
    • The proposed THz-TDS combined with PCA-SVM demonstrated superior performance compared to principal component regression, partial least squares regression, and back-propagation neural networks.
    • The study successfully identified normal, worm-eaten, moldy, and sprouting wheat grains using the developed spectroscopic and machine learning approach.

    Conclusions:

    • Terahertz time domain spectroscopy coupled with PCA-SVM is an efficient and accurate method for assessing wheat grain quality.
    • This non-destructive technique offers a promising solution for rapid and objective evaluation of agricultural products.
    • The developed methodology provides a strong foundation for future applications in food quality control and agricultural monitoring.