Chrysanthemum morifolium (Hangbaiju) is a kind of favored flower tea due to its health benefits. Rapid and accurate determination of chemical components is important for evaluating the quality of Hangbaiju. In this study, near-infrared hyperspectral imaging was used to explore the feasibility of determining the content of buddleoside, luteolin, apigenin, quercetin, and diosmetin in fresh and dry Hangbaiju. Partial least squares regression (PLSR), support vector regression (SVR) and convolutional neural network (CNN) were used to build regression models. Competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA) and a CNN based feature selection method (CNNFS) were used to select the optimal wavelengths. The prediction performances of luteolin and quercetin in fresh and dry Hangbaiju were good using both full spectra and optimal wavelengths, illustrating the feasibility of using near-infrared hyperspectral imaging to determine luteolin and quercetin in Hangbaiju. The relatively worse results of the other three components indicated that more efforts should be made to improve the prediction performances. CNN based regression and wavelength selection showed close results to the conventional regression and wavelength selection methods, indicating that CNN based regression and wavelength selection is promising to determine chemical components of Hangbaiju and other materials.

菊花（杭白菊）是一种受人喜爱的花茶，因为它对健康有益。化学成分的快速准确测定对于评价杭白菊品质具有重要意义。本研究采用近红外高光谱成像技术,探讨了测定鲜干杭白菊中布丁甙、木犀草素、芹菜素、槲皮素和地奥司美丁含量的可行性。使用偏最小二乘回归 (PLSR)、支持向量回归 (SVR) 和卷积神经网络 (CNN) 构建回归模型。竞争性自适应重加权采样 (CARS)、连续投影算法 (SPA) 和基于 CNN 的特征选择方法 (CNNFS) 用于选择最佳波长。木犀草素和槲皮素在新鲜和干燥的杭白菊中的全光谱和最佳波长预测性能良好，说明了使用近红外高光谱成像测定杭白菊中木犀草素和槲皮素的可行性。其他三个分量的相对较差的结果表明应该付出更多的努力来提高预测性能。基于CNN的回归和波长选择显示出与传统回归和波长选择方法接近的结果，表明基于CNN的回归和波长选择有望确定杭白菊等材料的化学成分。