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Application of Near-Infrared Hyperspectral Imaging to Detect Sulfur Dioxide Residual in the Fritillaria thunbergii Bulbus Treated by Sulfur Fumigation

机译:应用近红外高光谱成像技术检测硫熏蒸处理后贝母贝母中二氧化硫残留

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Sulfur-fumigated Chinese medicine is a common issue in the process of Chinese medicines. Detection of sulfur dioxide (SO 2 ) residual content in Fritillaria thunbergii Bulbus is important to evaluate the degree of sulfur fumigation and its harms. It helps to control the use of sulfur fumigation in Fritillaria thunbergii Bulbus. Near-infrared hyperspectral imaging (NIR-HSI) was explored as a rapid, non-destructive, and accurate technique to detect SO 2 residual contents in Fritillaria thunbergii Bulbus. An HSI system covering the spectral range of 874–1734 nm was used. Partial least squares regression (PLSR) was applied to build calibration models for SO 2 residual content detection. Successive projections algorithm (SPA), weighted regression coefficients ( Bw ), random frog (RF), and competitive adaptive reweighted sampling (CARS) were used to select optimal wavelengths. PLSR models using the full spectrum and the selected optimal wavelengths obtained good performance. The Bw -PLSR model was applied on a hyperspectral image to form a prediction map, and the results were satisfactory. The overall results in this study indicated that HSI could be used as a promising technique for on-line visualization and monitoring of SO 2 residual content in Fritillaria thunbergii Bulbus. Detection and visualization of Chinese medicine quality by HSI provided a new rapid and visual method for Chinese medicine monitoring, showing great potential for real-world application.
机译:硫熏蒸中药是中药加工过程中的普遍问题。检测贝母中的二氧化硫(SO 2)残留含量对于评估硫熏蒸程度及其危害非常重要。它有助于控制贝母中的硫熏蒸。探索近红外高光谱成像(NIR-HSI)是一种快速,无损且准确的技术,可用于检测贝母中贝母中的SO 2残留含量。使用覆盖874-1734 nm光谱范围的HSI系统。应用偏最小二乘回归(PLSR)建立用于SO 2残留含量检测的校准模型。使用连续投影算法(SPA),加权回归系数(Bw),随机青蛙(RF)和竞争性自适应加权采样(CARS)来选择最佳波长。使用全光谱和选定的最佳波长的PLSR模型获得了良好的性能。将Bw -PLSR模型应用于高光谱图像上,形成预测图,结果令人满意。这项研究的总体结果表明,HSI可以作为一种有前途的技术用于在线观察和监测贝母中贝母中SO 2的残留量。 HSI对中药质量的检测和可视化为中药监测提供了一种新的快速,可视化方法,显示了在实际应用中的巨大潜力。

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