As a fast information acquisition technique, Raman spectroscopy can be used to control the quality of dairy products. Feature extraction is a necessary processing step to improve the efficiency of Raman spectral data. Principal component analysis is a traditional method that can effectively extract the features and reduce the dimension of spectral data. However, it is difficult to analyze the chemical information of the extracted feature, thus limiting its practical application. In this work, Raman spectral chemical feature extraction was carried out. The quality control of Dingxin dairy products (a famous dairy brand in China; purchased from Heilongjiang Zhaodong Tianlong Dairy Co. Ltd., Heilongjiang, China) was used as an example. Raman peak intensity, peak area, and peak ratio were extracted as chemical features and further investigated using Euclidean distance and the quality fluctuation control chart. The potential quality discrimination ability of the Raman feature extraction methods was demonstrated. The results showed that the Puzhen dairy products (purchased from Inner Mongolia Yinuo Halal Food Co. Ltd., Inner Mongolia, China) and Xueyuan dairy products (used as a control; purchased from Inner Mongolia Wulanchabu City Jining Xueyuan Dairy Co. Ltd., Inner Mongolia, China) could be distinguished from Dingxin dairy products when the Raman chemical features special peak intensity, peak area, and peak ratio were used, and their discriminatory ability increased in sequence. Hence, it was shown that Raman chemical feature extraction can achieve quality control and discriminant analysis of dairy products and that the spectral information is clear.
Raman spectroscopy combined with pattern recognition can identify rice varieties excellently. In this study, a method was established to select the key feature for identification model. Seventy‐two Raman spectra of three varieties of rice were analyzed. Seventy‐one independent variables and characteristic bands (420–560, 820–980, 1,000–1,200, and 1,300–1,500 cm−1) were obtained by principal component analysis (PCA). Window analysis further narrowed the range of characteristic bands (451–550, 951–1,000, and 1,351–1,450 cm−1). Hierarchical cluster analysis (HCA) obtained 30 wavenumbers with small correlation. The prediction accuracy was 91.71%, whereas the time was reduced by 10 times when these 30 wavenumbers were used to establish the identification model. The method combined PCA, window analysis, and HCA with support vector machine can be used as an effective feature extraction method to improve the efficiency for identification of rice varieties. Under the circumstances of large sample size or relatively complex data, the screening of Raman spectrum information is an important means to simplify the model and improve the prediction efficiency.
In this paper, laser perturbation two‐dimensional correlation Raman spectroscopy was explored as a rapid and reliable determination method for the assessment of bovine colostrum products and their analogue, namely, milk powder. Compared with the traditional instrumental analytical methods, such as chromatography, Raman spectroscopy measurements can be realized easily without the need of sample preparation. Two‐dimensional correlation spectroscopy can enhance the spectral resolution and provide more useful information of samples. Laser perturbation technique is a new way, which has been developed in this work; additionally, only few minutes (<5 min) are in demand of the whole experiment process for one sample. Correlation coefficient method was employed to quantitatively analyze the similarities between bovine colostrum products and the other brands of milk powder that include bovine colostrum products containing some milk powder based on the developed two‐dimensional correlation Raman spectroscopy. The results showed that the correlation coefficients between each bovine colostrum product with their mean value could reach 0.991 ± 0.003, which indicated that there were high similarities between these products. Under the same experimental conditions, the correlation coefficients between the bovine colostrum product (mean) and the other brands of milk powder that include bovine colostrum products containing some milk powder were located in the range of 0.596–0.761, far below the 0.991 ± 0.003. Hence, these results clearly demonstrated the utility of the laser perturbation two‐dimensional correlation Raman spectroscopy combined with correlation coefficient as a rapid method for quality control of bovine colostrum products. Copyright © 2017 John Wiley & Sons, Ltd.
The rapid quantitative analysis of food systems may offer improved control of food quality. In this work, the quantitative analysis of Chinese liquor was investigated using ultraviolet (UV) spectroscopy and a similarity algorithm. The potential application of this approach was evaluated using Chinese liquors, including the same and different batches of Gu‐Jing‐Gong spirits. The effectiveness of the proposed quality control quantitative model has been demonstrated, and the method could be applied to control the quality of Gu‐Jing‐Gong spirits and to identify counterfeit products. This analytical method is simple, rapid and efficient, and has potential practical application. Copyright © 2017 The Institute of Brewing & Distilling
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