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<p>Dried daylilies are valued for their significant medicinal and edible characteristics. The origins of dried daylilies are becoming increasingly diverse, yet origin-based brand recognition remains weak. Counterfeit products frequently appear in the market, hindering the international expansion of local origin-based brands. It is crucial to ensure the traceability and uniqueness of dried daylily products. While techniques like mass spectrometry are commonly used for origin tracing, new methods for detecting and analyzing dried daylilies remain urgently needed to advance traceability strategies. Therefore, this study selected commercially available dried daylilies from six different regions (18 sample groups per region, a total of 108 groups). This study achieves qualitative identification of dried daylilies from different origins by integrating porous nanomaterials with multivariable chemometric algorithms on a sensor array. Here, we initially employed headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS–SPME–GC–MS) to analyze the primary volatile components distinguishing dried daylilies across different regions. A total of 63 volatile organic compounds (VOCs) were identified, with 18 serving as key discriminating compounds. Subsequently, a colorimetric sensor array (CSA) was designed and constructed to identify these compounds, focusing on the application of colorimetric dyes and the feasibility of combining the sensor array with chemometrics for origin identification. The CSA unit produced a visible color difference after the reaction. In addition, various chemometric algorithms, including principal component analysis (PCA), k-nearest neighbors (KNN), support vector machine (SVM), linear discriminant analysis (LDA), and random forest (RF) were employed to construct classification models for predicting the geographical origin of dried daylilies. The model achieved an area under the curve (AUC) exceeding 0.96 and an average classification accuracy of 87.7%. Therefore, this approach offers a promising strategy for rapid identification of the origin of dried daylilies and demonstrates potential for classification and grading of dried foods.</p>

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Volatile Flavor Profiling and Origin Identification of Dried Daylilies Using a Nanocomposite Colorimetric Sensor Array with Chemometrics

  • 期刊论文

  • 发表日期：

    2026年5月4日

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  • Hailing Wang,
  • Xiangshuo Meng,
  • Yun Wang,
  • Sanxia Wang,
  • Xiaoli Wang,
  • Yuting Zhang,
  • Nandi Zhou

摘要

Dried daylilies are valued for their significant medicinal and edible characteristics. The origins of dried daylilies are becoming increasingly diverse, yet origin-based brand recognition remains weak. Counterfeit products frequently appear in the market, hindering the international expansion of local origin-based brands. It is crucial to ensure the traceability and uniqueness of dried daylily products. While techniques like mass spectrometry are commonly used for origin tracing, new methods for detecting and analyzing dried daylilies remain urgently needed to advance traceability strategies. Therefore, this study selected commercially available dried daylilies from six different regions (18 sample groups per region, a total of 108 groups). This study achieves qualitative identification of dried daylilies from different origins by integrating porous nanomaterials with multivariable chemometric algorithms on a sensor array. Here, we initially employed headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS–SPME–GC–MS) to analyze the primary volatile components distinguishing dried daylilies across different regions. A total of 63 volatile organic compounds (VOCs) were identified, with 18 serving as key discriminating compounds. Subsequently, a colorimetric sensor array (CSA) was designed and constructed to identify these compounds, focusing on the application of colorimetric dyes and the feasibility of combining the sensor array with chemometrics for origin identification. The CSA unit produced a visible color difference after the reaction. In addition, various chemometric algorithms, including principal component analysis (PCA), k-nearest neighbors (KNN), support vector machine (SVM), linear discriminant analysis (LDA), and random forest (RF) were employed to construct classification models for predicting the geographical origin of dried daylilies. The model achieved an area under the curve (AUC) exceeding 0.96 and an average classification accuracy of 87.7%. Therefore, this approach offers a promising strategy for rapid identification of the origin of dried daylilies and demonstrates potential for classification and grading of dried foods.