目的 为了快速、无损地检测花椰菜上的农药残留，采用高光谱成像技术分别对花椰菜上是否含有苏云金杆菌、高效氯氰菊酯和虫螨茚虫威等3种农药进行无损检测研究，并且跟踪研究检测效果最好的农药安全间隔期。方法 对含有农药和不含农药的花椰菜样本进行高光谱成像处理，提取感兴趣区域的光谱数据。剔除前后20个波段的原始光谱数据，以降低噪声的影响，针对剩余216个波段(950~1 666 nm)的数据分别采用卷积平滑(S–G)、多元散射校正(MSC)和变量标准化(SNV)等3种算法对光谱数据进行优化。为了提高判别运行速率，采用竞争性自适应重加权算法(CARS)提取3种农药光谱数据的特征波长，并建立偏最小二乘法(PLS)判别模型。结果 基于SNV优化后的PLS模型对花椰菜上3种农药的识别准确率相对最高，其中虫螨茚虫威农药样本的测试效果相对最好，识别率为100%，随后对该农药进行了连续7 d的检测，其结果符合农药的消散规律。结论 将高光谱图像技术应用于花椰菜的农药残留检测具有很好的应用前景。

The work aims to conduct non-destructive testing to the bacillus thuringiensis, beta-cypermethrin and indoxacarb on cauliflower with hyperspectral imaging technology, in order to detect pesticide residues on cauliflower quickly and non-destructively and track and study the safe interval for the best detection effects. After hyperspectral imaging was performed on cauliflower samples with and without pesticides, the spectral data of the region of interest were extracted. The original spectral data of the front and back 20 bands were eliminated to reduce the effect of noise, convolution smoothing method (S-G), multivariate scattering correction (MSC) and standard normal variate (SNV) algorithms were used to optimize the data of the remaining 216 bands (950-1 666 nm), respectively. The characteristic wavelength of spectral data of the three pesticides was extracted by the competitive adaptive reweighted algorithm (CARS) to improve the discriminant operation speed, and finally the partial least squares (PLS) discriminant model was established. The PLS model optimized based on SNV had the highest recognition accuracy of the three pesticides on cauliflower. The test results of the pesticide samples with indoxacarb were the best, with a recognition rate of 100%, and then the 7-day detection was carried out to this pesticide, of which the results were consistent with dissipation law of pesticides. The hyperspectral imaging technology has a good application prospect in the detection of pesticide residues on cauliflowers.