Scholarlink

<p><i>Chrysanthemum morifolium</i> Ramat cv. “Chuju” (Chuju) requires precise quality grading to ensure effective quality control. This study developed a method for rapid and precise classification tof Chuju’s quality grades by combining excitation-emission matrix (EEM) fluorescence spectroscopy with chemometric analysis. First, EEM fluorescence spectra of Chuju samples were characterized analyzed using parallel factor analysis (PARAFAC) to extract key fluorescence features (such as flavonoids and amino acids). Next, we applied several classification algorithms to construct discriminant models, including <i>k</i>-nearest neighbors (kNN), N-way partial least squares discriminant analysis (N-PLS-DA), and unfolded partial least squares discriminant analysis (U-PLS-DA). Among these, U-PLS-DA demonstrated the most robust performance, achieving a 100% correct classification rate (CCR) for both training and test datasets. Additionally, the classification metrics, including accuracy (ACC), sensitivity (SEN), specificity (SPE), and precision (PRE), all reached 100%. These findings indicate that the proposed method effectively distinguishes between different quality grades of Chuju, providing a reliable and reproducible tool for quality evaluation.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Rapid Identification of Chrysanthemum morifolium cv. Chuju Grades by Excitation-Emission Matrix Fluorescence Spectroscopy Combined with Chemometric Methods

  • 期刊论文

  • 发表日期：

    2025年7月26日

查看全文
  • Leijie Hu,
  • Qian Zhou,
  • Haiyang Gu

摘要

Chrysanthemum morifolium Ramat cv. “Chuju” (Chuju) requires precise quality grading to ensure effective quality control. This study developed a method for rapid and precise classification tof Chuju’s quality grades by combining excitation-emission matrix (EEM) fluorescence spectroscopy with chemometric analysis. First, EEM fluorescence spectra of Chuju samples were characterized analyzed using parallel factor analysis (PARAFAC) to extract key fluorescence features (such as flavonoids and amino acids). Next, we applied several classification algorithms to construct discriminant models, including k-nearest neighbors (kNN), N-way partial least squares discriminant analysis (N-PLS-DA), and unfolded partial least squares discriminant analysis (U-PLS-DA). Among these, U-PLS-DA demonstrated the most robust performance, achieving a 100% correct classification rate (CCR) for both training and test datasets. Additionally, the classification metrics, including accuracy (ACC), sensitivity (SEN), specificity (SPE), and precision (PRE), all reached 100%. These findings indicate that the proposed method effectively distinguishes between different quality grades of Chuju, providing a reliable and reproducible tool for quality evaluation.