Adulterated sesame oil seriously damages the interests of consumers and the health of market. In this paper, a simple, fast and real-time model for identifying adulterated sesame oil (ASO) was proposed by combining 3D fluorescence spectra with wavelet moments (WMs). First, noise and data volume of the experimental data were reduced by wavelet multiresolution decomposition (WMRSD), which improved the stability and real-time of the model. Next, WMs were used to extract the features of the 3D fluorescence spectra and proved to be effective by hierarchical clustering results. Then, the qualitative quality of WMs of the same orders, different orders and the combinations were evaluated by Dunn's validity index (DVI), and the rules were given, respectively. Finally, the target WMs for identifying ASO were determined. This model is simple and fast, and expandable to online measurement, providing a reference for identification and adulteration of vegetable oils.

er杂香油会严重损害消费者利益和市场健康。本文通过结合3D荧光光谱和小波矩（WMs），提出了一种简单，快速，实时的掺假香油（ASO）识别模型。首先，通过小波多分辨率分解（WMRSD）减少了实验数据的噪声和数据量，从而提高了模型的稳定性和实时性。接下来，使用WM提取3D荧光光谱的特征，并通过分层聚类结果证明是有效的。然后，使用邓恩的有效性指数（DVI）评估了相同订单，不同订单和组合的仓库管理人员的定性质量，并给出了规则。最后，确定了用于识别ASO的目标WM。这个模型简单快捷