College of Horticulture and Landscape Architecture, Yangzhou University, Yangzhou 225009, China.
Jiangsu Key Laboratory of Crop Genetics and Physiology/Co-Innovation Center for Modern Production Technology of Grain Crops, College of Agriculture, Yangzhou University, Yangzhou 225009, China.
Int J Mol Sci. 2024 Sep 4;25(17):9584. doi: 10.3390/ijms25179584.
is a famous economic tree. Ginkgo leaves have been utilized as raw materials for medicines and health products due to their rich active ingredient composition, especially flavonoids. Since the routine measurement of total flavones is time-consuming and destructive, rapid, non-destructive detection of total flavones in ginkgo leaves is of significant importance to producers and consumers. Hyperspectral imaging technology is a rapid and non-destructive technique for determining the total flavonoid content. In this study, we discuss five modeling methods, and three spectral preprocessing methods are discussed. Bayesian Ridge (BR) and multiplicative scatter correction (MCS) were selected as the best model and the best pretreatment method, respectively. The spectral prediction results based on the BR + MCS treatment were very accurate (R = 0.87; RMSE = 1.03 mg/g), showing a high correlation with the analytical measurements. In addition, we also found that the more and deeper the leaf cracks, the higher the flavonoid content, which helps to evaluate leaf quality more quickly and easily. In short, hyperspectral imaging is an effective technique for rapid and accurate determination of total flavonoids in ginkgo leaves and has great potential for developing an online quality detection system for ginkgo leaves.
银杏是一种著名的经济树种。由于银杏叶含有丰富的活性成分，特别是类黄酮，因此被用作药品和保健品的原料。由于常规测量总黄酮的方法既耗时又具有破坏性，因此快速、无损地检测银杏叶中的总黄酮对生产者和消费者都具有重要意义。高光谱成像技术是一种快速无损的方法，可用于确定总黄酮的含量。在本研究中，我们讨论了五种建模方法，并讨论了三种光谱预处理方法。贝叶斯脊（BR）和乘法散射校正（MCS）分别被选为最佳模型和最佳预处理方法。基于 BR+MCS 处理的光谱预测结果非常准确（R=0.87；RMSE=1.03mg/g），与分析测量值高度相关。此外，我们还发现，叶片裂缝越多、越深，黄酮类化合物的含量越高，这有助于更快速、轻松地评估叶片质量。总之，高光谱成像技术是一种快速、准确测定银杏叶总黄酮的有效技术，在开发银杏叶在线质量检测系统方面具有很大的潜力。
