The aim of this work is to solve the practical problem that there are relatively few fast, intelligent, and objective methods to distinguish dairy products and to further improve the quality control methods of them. Therefore, an approach of cheese product brand discrimination method based on Raman spectroscopy and probabilistic neural network algorithm was developed. The experimental results show that the spectrum contains abundant molecular vibration information of carbohydrates, fats, proteins, and other components, and the Raman spectral data collection time of a single sample is only 100 s. Due to the high spectral similarity between samples, it is impossible to identify them with naked eyes. Characteristic peak intensity combined with statistical process control method was employed to study the fluctuation characteristics of samples. The results show that the characteristic peak of experimental samples fluctuates within a certain control limit. However, due to the high similarity between the Raman spectra of different brand samples, they cannot be effectively identified as well. This paper further studied and established the analytical approach based on Raman spectroscopy, including wavelet denoising, normalization, principal component analysis, and probabilistic neural network discrimination. In db1 wavelet processing, [−1, 1] normalization, 74 principal components (cumulative contribution rate of 100%) can realize the effective discrimination of different brands of cheese products in 1 s, with the average recognition accuracy of 96%. The discriminant method established in this work has the advantages of simple operation, rapid analysis, and accurate results. It provides a technical reference for the fight against counterfeit products and has a broad application prospect.

中文翻译：

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基于概率神经网络和拉曼光谱的奶酪产品快速判别

这项工作的目的是解决一个实际问题，即区分乳制品的快速，智能和客观的方法相对较少，并进一步改进其质量控制方法。因此，提出了一种基于拉曼光谱和概率神经网络算法的奶酪产品品牌识别方法。实验结果表明，该光谱包含丰富的碳水化合物，脂肪，蛋白质和其他成分的分子振动信息，单个样品的拉曼光谱数据采集时间仅为100 s。由于样品之间的光谱相似性很高，因此无法用肉眼识别它们。采用特征峰强度与统计过程控制相结合的方法研究样品的波动特征。结果表明，实验样品的特征峰在一定的控制范围内波动。但是，由于不同品牌样品的拉曼光谱之间的高度相似性，因此也无法有效地识别它们。本文进一步研究并建立了基于拉曼光谱的分析方法，包括小波去噪，归一化，主成分分析和概率神经网络判别。在db1小波处理中，通过[-1,1]归一化，74个主要成分（累计贡献率100％）可以在1 s内有效区分不同品牌的奶酪产品，平均识别准确度为96％。本文建立的判别方法具有操作简单，分析迅速，结果准确的优点。