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1. Study on detections of free and bound water in sausage based on multispectral imaging technique | |||
Wang Ju,LIU Changhong,ZHENG Lei | |||
Food Science and Technology 19 May 2016 | |||
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Abstract:Free and bound water are the two physical states of moisture, which directly influence on the qualities of meat products. The goal of this work was to explore the potential of multispectral imaging in combination with multivariate analysis for the identification of free and bound water in cooked pork sausages (CPS). Spectra and textures of 144 CPS treated by six salt concentrations (0-2.5%) were analyzed using different calibration models to find the most optimal results for predicting free and bound water contents in CPS. Based on a combination of spectral and textural features, partial least squares regression models performed well for predicting free and bound water contents with the correlation coefficient (r) of 0.832 and 0.918, respectively. The prediction equation was also transferred to each pixel in the image for visualizing the spatial distribution of the two water contents in CPS. The results indicated that multispectral imaging technique has the potential as a fast and non-invasive method for identifying the physical state of moisture in meat products. | |||
TO cite this article:Wang Ju,LIU Changhong,ZHENG Lei. Study on detections of free and bound water in sausage based on multispectral imaging technique[OL].[19 May 2016] http://en.paper.edu.cn/en_releasepaper/content/4690243 |
2. A Comparison Study Of Three Nonlinear Multivariate Data Analysis Methods For Smartongue: Kernel PCA, LLE and Sammon mapping | |||
Tian Shiyi,Chen Min,Deng Shaoping | |||
Food Science and Technology 26 January 2012 | |||
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Abstract:Smartongue was a voltammetric electronic tongue developed by the laboratory, which was based on multifrequency large amplitude pulse voltammetry (MLAPV) and a non-specific sensors array of six naked metallic electrodes. Three non-linear multivariate data analysis methods, such as Kernel principal component analysis (Kernel PCA), Locally linear Embedding (LLE) and Sammon mapping were studied about the data processing ability for Smartongue. Normal principal component analysis (PCA) was used as a reference method and discrimination index (DI value) was used as a quantitative indicator for evaluation the separation ability of different plot. The result indicated that non-linear data processing methods had a more powerful ability than PCA for processing the data of Smartongue. Among these techniques, Sammon mapping successfully classified three bitter solutions, six artificial green tea products and five milk power solutions with different storage time well and exhibited the best data processing ability for extracting the useful information from the data of Smartongue. | |||
TO cite this article:Tian Shiyi,Chen Min,Deng Shaoping. A Comparison Study Of Three Nonlinear Multivariate Data Analysis Methods For Smartongue: Kernel PCA, LLE and Sammon mapping[OL].[26 January 2012] http://en.paper.edu.cn/en_releasepaper/content/4463710 |
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