针对传统检测方式的食用油损耗大、操作烦琐、耗时长等缺陷，提出了一种食用油种类快速无损检测的新思路。实验选用包括混合油样在内的5种待测油样本，利用搭建出的激光诱导荧光系统采集数据500组，随机选取其中400组光谱数据作为训练集，余下的100组作为测试集。选用性能更为优异的堆栈自动编码器算法对获取的荧光光谱数据进行特征提取，通过极限学习机进行分类识别，最后利用不同时间测出的食用油样本验证模型的普适性。实验结果表明，在所构建的识别模型下，样本测试网络时间仅为0.2 ms，分类准确率可达到100%，用于验证的新油样同样可取得极好的分类效果，分类速度快，准确率高。所得结果证明所建立的模型是可靠的，能够在确保精准识别的同时，实现食用油类别的快速无损检测。

In view of the defects of traditional test methods, such as large consumption of edible oil, cumbersome operation, and long time consumption, a new idea of fast nondestructive test of edible oil was put forward. In the experiment, five kinds of oil samples including mixed oil samples were selected. The laser induced fluorescence system built in the experiment was used to collect 500 groups of data, 400 groups of spectral data were randomly selected as the training set, and the remaining 100 groups of spectral data were used as the test set. After comparison, the stack autoencoder algorithm with better performance was selected to extract the features of the obtained fluorescence spectral data, and then the extreme learning machine was used for classification and recognition. Finally, the edible oil samples measured at different time were used to verify the generalization of model. The experimental results show that, under the recognition model constructed in this paper, the sample test network time is only 0.2 ms, and the classification accuracy can reach 100%. The sample test network used to validate the new sample can also achieve good classification effect, the classification is fast, and the accuracy is high. That is to say, the model established in this paper is reliable, and it can also realize fast nondestructive test of edible oil types while ensuring accurate identification.