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<p>A monitoring solution for the spatial distribution visualization of fruit quality is crucial for developing intelligent drying strategies. Hyperspectral imaging is one of the most representative approaches in this field; however, its high cost has limited widespread adoption. To overcome this limitation, a highly cost-effective hyperspectral imaging technology based on a compact light guide system is proposed. The technology relies on a novel optimized compact optical light guide to eliminate the spectral non-uniformity of the target surface caused by the different light fields of multiple monochromatic LEDs. During the design process, an embedded microprocessor-based control unit is developed to synchronize LED flashing with image acquisition. Based on this, a prototype system is constructed, covering the 400–1000-nm range with 28 spectral channels. In practical application, the hyperspectral optical performance of this system is tested, and it is further integrated with a PLS model to visualize the moisture content and SSC distribution in apple slices. For moisture content prediction, the training set achieved an <i>R</i><sup>2</sup> value of 0.977 and an RMSE of 3.14, while the test set achieved an <i>R</i><sup>2</sup> value of 0.972 and an RMSE of 3.62. For SSC content prediction, the training set yielded an <i>R</i><sup>2</sup> value of 0.979 and an RMSE of 1.62, while the test set produced an <i>R</i><sup>2</sup> value of 0.973 and an RMSE of 2.35. The results indicate that this simpler and more cost-effective hyperspectral imaging technology still achieves remarkable accuracy and is an important step toward dynamic quality monitoring of smart dryers.</p>

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LED Light-Pipe Hyperspectral Technology for Visualizing Apple Quality

  • 期刊论文

  • 发表日期：

    2025年2月6日

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  • Jia-Yong Song,
  • Ze-Sheng Qin,
  • Chang Ma,
  • Li-Feng Bian,
  • Chen Yang

摘要

A monitoring solution for the spatial distribution visualization of fruit quality is crucial for developing intelligent drying strategies. Hyperspectral imaging is one of the most representative approaches in this field; however, its high cost has limited widespread adoption. To overcome this limitation, a highly cost-effective hyperspectral imaging technology based on a compact light guide system is proposed. The technology relies on a novel optimized compact optical light guide to eliminate the spectral non-uniformity of the target surface caused by the different light fields of multiple monochromatic LEDs. During the design process, an embedded microprocessor-based control unit is developed to synchronize LED flashing with image acquisition. Based on this, a prototype system is constructed, covering the 400–1000-nm range with 28 spectral channels. In practical application, the hyperspectral optical performance of this system is tested, and it is further integrated with a PLS model to visualize the moisture content and SSC distribution in apple slices. For moisture content prediction, the training set achieved an R2 value of 0.977 and an RMSE of 3.14, while the test set achieved an R2 value of 0.972 and an RMSE of 3.62. For SSC content prediction, the training set yielded an R2 value of 0.979 and an RMSE of 1.62, while the test set produced an R2 value of 0.973 and an RMSE of 2.35. The results indicate that this simpler and more cost-effective hyperspectral imaging technology still achieves remarkable accuracy and is an important step toward dynamic quality monitoring of smart dryers.