Rapid quantitative analysis of Chinese Gu-Jing-Gong spirit for its quality control

2020

RSC Adv.

Self Cite

Section: Characterization Of Dairy Products Using Raman Spectroscopymentioning

confidence: 79%

The statistical fusion identification of dairy products based on extracted Raman spectroscopy

2020

RSC Adv.

Self Cite

“…erefore, the statistical control chart method can be employed to analyze the fats (1760 cm −1 ), carbohydrates (1458 cm −1 ), and proteins (1019 cm −1 ), respectively. e statistical control chart can be realized using the following individual and moving range chart formulae [29,30]. For the individual (a) control chart, the formula is as follows:…”

Section: Statistical Analysis Of Raman Spectral Peak Intensity Ofmentioning

confidence: 99%

Rapid Discrimination of Cheese Products Based on Probabilistic Neural Network and Raman Spectroscopy

Journal of Spectroscopy

2020

Self Cite

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.

“…The average spectrum of all the collected Raman spectra of DX dairy products was taken as the theoretical true value of the sample, and then the Euclidean distances between each experimental sample and the theoretical true value were calculated. Next, an individual and moving range quality fluctuation control chart for DX dairy products was constructed using the calculated Euclidean distances, which can evaluate the quality stability of the samples (Zhang et al, 2017b), as shown in Supplemental Figure S1 Figure S1 reflected a high stability among DX dairy products when the full spectra were applied as the feature inputs. The Euclidean distances (average value) among the PZ, XY (the other famous dairy brands in China; used as control), and DX (experimental group) dairy products were calculated, as shown in Supplemental Figure S2 (https: / / doi .org/ 10 .3168/ jds .2018 -14569).…”

Section: Feature Extraction and Analysis Of Dairy Products Based On Rmentioning

confidence: 99%

Raman chemical feature extraction for quality control of dairy products

Journal of Dairy Science

Gui

Sha

et al. 2019

Self Cite

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.