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Raman spectroscopy can characterize the rich molecular vibration information of soybean milk powder samples, but difficulties arise in its direct use for sample classification and identification. Therefore, it is urgent to develop an intelligent identification technology based on Raman spectroscopy. For brand identification of soybean milk powder, this work investigates and discusses a variety of spectral processing technologies including wavelet denoising, normalization, principal component analysis, and the results show that appropriate spectral processing can improve the recognition accuracy of the random forest algorithm. Under the optimal conditions (db2 wavelet, normalization, principal component analysis, 30 decision trees), the best recognition effect of soybean milk brand identification can be achieved. The Raman spectral signal acquisition time of each sample is 40 s, and the spectra pretreatment and identification operation time only takes a few minutes. The analytical approach established in this paper has the advantages of convenient and fast Raman spectra acquisition, fast and accurate model identification., Author(s): Zheng-Yong Zhang [sup.1] , Xiao-Jing Shi [sup.1] , Ya-Ju Zhao [sup.2] , Yin-Sheng Zhang [sup.2] , Hai-Yan Wang [sup.2] Author Affiliations: (1) grid.440844.8, 0000 0000 8848 7239, School of [...]

The effect of integration time on Raman spectral data collected from fresh milk products was investigated and analyzed. The collected spectral data were denoised by wavelet transform to remove signal interference. Two-dimensional correlation Raman spectra of the fresh milk products were then constructed using two-dimensional correlation analysis and laser integrating time as an external perturbation. Finally, feature extraction was carried out, and the Euclidean distance was calculated. Thus, the similarities between fresh milk products of the same brand and different brands were quantitatively analyzed and calculated from Raman spectroscopy, two-dimensional correlation Raman spectroscopy, and feature extraction. The results showed that the quality of fresh milk products of the same brand fluctuated but maintained a high consistency. The differences between different brands of fresh milk products under the same detection conditions were much greater than within the same brand samples. [ABSTRACT FROM AUTHOR]

The authenticity and adulteration of dairy products are attracting broad attention in recent years. There is a need to develop rapid, simple and accurate analytical methods for the detection of authenticity and adulteration of dairy products. To discriminate between milk powder samples, Raman spectra of FIRMUS, Nestlé and Being Mate milk powder were collected. The nearest neighbor algorithm (NN)combined with the characteristic peaks were employed for the design of a model. On the basis of 10 cross validation, the average recognition rate was 99.56%. In order to achieve the analysis of the adulteration of milk powder, FIRMUS milk powder was mixed with Nestlé milk powder according to the mass ratio 0 :1, 1 : 3, 1 : 1, 3 : 1 and 1 : 0 to get five kinds of the adulterated milk powder samples. Then, fat was extracted from the adulterated milk powder samples. Raman spectra of the fat were collected, then two methods were employed for the design of models. One was the nearest neighbor algorithm combined with the characteristic peaks, another was the kernel principal component analysis (KPCA) combined with NN. On the basis of 10 cross validation, the average recognition rate reached 93.33% and 98.89%, the average operation time was 0.085 and 0.104 s. The results of this work showed that the nearest neighbor algorithm combined with the characteristic peaks can be applied for the determination of the authenticity of milk powder while Raman-KPCA-NN model can provide a simple, accurate and rapid method to investigate the adulteration of milk power.

It is an important and difficult research point to recognize the age of Chinese liquor rapidly and exactly in the field of liquor analyzing, which is also of great significance to the healthy development of the liquor industry and protection of the legitimate rights and interests of consumers. Spectroscopy together with the pattern recognition technology is a preferred method of achieving rapid identification of wine quality, in which the Raman Spectroscopy is promising because of its little affection of water and little or free of sample pretreatment. So, in this paper, Raman spectra and support vector regression (SVR) are used to recognize different ages and different storing time of the liquor of the same age. The innovation of this paper is mainly reflected in the following three aspects. First, the application of Raman in the area of liquor analysis is rarely reported till now. Second, the concentration of studying the recognition of wine age, while most studies focus on studying specific components of liquor and studies together with the pattern recognition method focus more on the identification of brands or different types of base wine. The third one is the application of regression analysis framework, which cannot be only used to identify different years of liquor, but also can be used to analyze different storing time, which has theoretical and practical significance to the research and quality control of liquor. Three kinds of experiments are conducted in this paper. Firstly, SVR is used to recognize different ages of 5, 8, 16 and 26 years of the Gujing Liquor; secondly, SVR is also used to classify the storing time of the 8-years liquor; thirdly, certain group of train data is deleted form the train set and put into the test set to simulate the actual situation of liquor age recognition. Results show that the SVR model has good train and predict performance in these experiments, and it has better performance than other non-liner regression method such as the Partial Least Squares Regression (PLS) method, and can also be applied in the practice of liquor analysis.