Laser-induced fluorescence (LIF) spectroscopy is widely used for the analysis and classification of olive oil. This paper proposes the classification of LIF data using a specific 1-dimensional convolutional neural network (1D-CNN) model, which does not require pre-processing steps such as normalisation or denoising and can be flexibly applied to massive data. However, by adding a dual convolution structure (Dual-conv) to the model, the features of the 1-dimensional spectra are more scattered within one convolution-pooling process; thus, the classification effects are improved. The models were validated through an olive oil classification experiment which contained a total of 72,000 sets of LIF spectra data, and the classification accuracy rate reached ∼99.69%. Additionally, a common classification approach, the support vector machine (SVM), was utilised for the comparison of the results. The results show that the neural networks perform better than the SVM. The Dual-conv model structure has a faster convergence speed and higher evaluation parameters than those of the 1D-CNN in the same period of iterations, without increasing the data dimension.

激光诱导荧光 (LIF) 光谱广泛用于橄榄油的分析和分类。本文提出使用特定的一维卷积神经网络（1D-CNN）模型对LIF数据进行分类，不需要归一化或去噪等预处理步骤，可以灵活应用于海量数据。然而，通过在模型中添加对偶卷积结构（Dual-conv），一维光谱的特征在一个卷积池化过程中更加分散；从而提高了分类效果。该模型通过橄榄油分类实验进行验证，该实验共包含 72,000 组 LIF 光谱数据，分类准确率达到 99.69%。此外，一种常见的分类方法，支持向量机 (SVM)，用于比较结果。结果表明，神经网络的性能优于 SVM。Dual-conv模型结构在相同迭代周期内比1D-CNN具有更快的收敛速度和更高的评价参数，且不增加数据维度。