为了快速、准确地识别无醇啤酒和普通啤酒, 采用荧光光谱结合概率神经网络的方法, 建立了识别无醇啤酒的模型。实验中发现无醇啤酒和普通啤酒在紫外-可见光激发下, 都能产生较强荧光, 测得无醇啤酒荧光峰在420nm~620nm之间,荧光峰值波长为490nm左右。将小波变换处理荧光光谱得到的低频系数作为网络数据, 训练、建立了概率神经网络, 并对60个啤酒样本进行了识别, 识别率达到了98.33%。该研究结果为无醇啤酒和普通啤酒识别提供了一种新方法。

In order to identify alcohol-free beer and ordinary beer quickly and accurately, a recognition model of the alcohol-free beer was established, which was based on fluorescent spectroscopy and probabilistic neural network. It was found experimentally that both alcohol-free beer and ordinary beer excited by ultraviolet-visible light could generate strong fluorescence. The fluorescent spectrum for alcohol-free beer is within a range from 420nm to 620nm, its peak wavelength of the fluorescence is about 490nm. The approximate coefficients, obtained by wavelet transform, were used as the network data, and a probabilistic neural network was trained and constructed. The trained probabilistic neural network was employed to recognize sixty beer samples, and the recognition rate was up to 98.33%. The whole research outcomes will provide a new method for recognizing alcohol-free beer.