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IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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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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A Dual-Technology Approach: Handheld NIR Spectrometer and CNN for Fritillaria spp. Quality Control.

Fengling Li1, Wen Lei1, Juan Li1

  • 1College of Life Science and Technology & School of Pharmaceutical Sciences and Institute of Materia Medica, Xinjiang University, Urumqi 830017, China.

Foods (Basel, Switzerland)
|June 13, 2025
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Summary

This study developed a rapid quality assessment method for Fritillaria spp. using handheld near-infrared spectroscopy and convolutional neural networks (CNNs). The approach accurately identifies geographical origins and quantifies adulteration, ensuring the integrity of medicinal materials.

Keywords:
CNNFritillaria spp.NIRgeographical originvisualization

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Area of Science:

  • Analytical Chemistry
  • Chemometrics
  • Pharmacognosy

Background:

  • Fritillaria spp. possess significant edible and medicinal value, with varying efficacy depending on the plant part.
  • Accurate identification of Fritillaria spp. origin and adulteration is crucial for quality control.
  • Traditional methods may lack the speed and accuracy required for real-time assessment.

Purpose of the Study:

  • To establish an efficient and convenient quality assessment method for Fritillaria spp. using near-infrared (NIR) spectroscopy.
  • To develop a convolutional neural network (CNN) model for accurate identification of Fritillaria spp. geographical origins.
  • To quantitatively determine the adulteration levels in Fritillaria spp. using CNN-based analysis.

Main Methods:

  • Integration of a handheld near-infrared spectrometer with a convolutional neural network (CNN) for spectral data acquisition and analysis.
  • Application of CNN for precise authentication of Fritillaria spp. geographical origins, achieving 100% accuracy.
  • Utilized gradient-weighted class activation mapping (Grad-CAM) for feature visualization and PCA-CNN for enhanced interpretability in adulteration detection.

Main Results:

  • The CNN model achieved perfect accuracy (100 ± 0%) in identifying the geographical origin of Fritillaria spp.
  • CNN demonstrated superior performance in detecting adulteration compared to PLSR, with a test set R² of 0.9897.
  • PCA-CNN model also showed high accuracy (R² = 0.9876), with extracted features focusing on the 1400-1500 nm range, validated by Grad-CAM.

Conclusions:

  • The combined handheld NIR spectroscopy and CNN approach provides a reliable method for authenticating Fritillaria spp. geographical origins.
  • This framework enables quantitative determination of adulteration levels, ensuring the quality and safety of Fritillaria spp. materials.
  • The study establishes a novel analytical framework for rapid and accurate quality evaluation of valuable medicinal plants.