naa a22 4500 510727328 CHVBK 20180411082940.0 cr unu---uuuuu 180411e20131101xx s 000 0 eng 10.1007/s11947-012-0982-7 doi (NATIONALLICENCE)springer-10.1007/s11947-012-0982-7 Development of a Quantitative Visualization Technique for Gluten in Dough Using Fluorescence Fingerprint Imaging [Elektronische Daten] [Mito Kokawa, Junichi Sugiyama, Mizuki Tsuta, Masatoshi Yoshimura, Kaori Fujita, Mario Shibata, Tetsuya Araki, Hiroshi Nabetani] The distribution of constituents in food affects its end qualities such as texture, and there is a growing demand to develop a method for studying this distribution easily, accurately, and nondestructively. The objective of this study was to develop an imaging method that visualizes the precise quantity of constituents, using the fluorescence fingerprint (FF). The FF is a set of fluorescence spectra acquired at consecutive excitation wavelengths, and its pattern contains abundant information on the constituents of the sample measured. In this study, the target for visualization was the distribution of gluten in dough samples. Dough samples were prepared with different ratios of gluten, starch, and water, and fluorescence images at multiple combinations of excitation and emission wavelengths were acquired. The fluorescence intensities of a pixel at these different wavelengths constructed its FF, reflecting the constituents of the corresponding point in the sample. A partial least squares regression (PLSR) model was built using the average FFs of the samples and the corresponding gluten ratios as the explanatory and objective variables, respectively. The importance of each wavelength in the PLSR model was assessed using the selectivity ratio, and optimum wavelengths for the accurate prediction of gluten ratio were selected. Finally, the gluten ratio of each pixel was predicted with the PLSR model using the selected wavelengths, and each pixel was colored according to the predicted gluten ratio. The imaging method developed enables the distribution of constituents to be visualized with colors corresponding to their actual quantities or ratios. Springer Science+Business Media New York, 2012 Excitation-emission matrix nationallicence Partial least squares (PLS) nationallicence Prediction model nationallicence Pseudo-color image nationallicence Kokawa Mito Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, 113-8657, Bunkyo ward, Tokyo, Japan aut Sugiyama Junichi Food Engineering Division, National Food Research Institute, National Agriculture and Food Research Organization, 2-1-12 Kan-nondai, 305-8642, Tsukuba, Ibaraki, Japan aut Tsuta Mizuki Food Engineering Division, National Food Research Institute, National Agriculture and Food Research Organization, 2-1-12 Kan-nondai, 305-8642, Tsukuba, Ibaraki, Japan aut Yoshimura Masatoshi Food Engineering Division, National Food Research Institute, National Agriculture and Food Research Organization, 2-1-12 Kan-nondai, 305-8642, Tsukuba, Ibaraki, Japan aut Fujita Kaori Food Engineering Division, National Food Research Institute, National Agriculture and Food Research Organization, 2-1-12 Kan-nondai, 305-8642, Tsukuba, Ibaraki, Japan aut Shibata Mario Food Engineering Division, National Food Research Institute, National Agriculture and Food Research Organization, 2-1-12 Kan-nondai, 305-8642, Tsukuba, Ibaraki, Japan aut Araki Tetsuya Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, 113-8657, Bunkyo ward, Tokyo, Japan aut Nabetani Hiroshi Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, 113-8657, Bunkyo ward, Tokyo, Japan aut Food and Bioprocess Technology Springer US; http://www.springer-ny.com 6/11(2013-11-01), 3113-3123 1935-5130 6:11<3113 2013 6 11947 https://doi.org/10.1007/s11947-012-0982-7 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11947-012-0982-7 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Kokawa Mito Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, 113-8657, Bunkyo ward, Tokyo, Japan aut NATIONALLICENCE 700 1- Sugiyama Junichi Food Engineering Division, National Food Research Institute, National Agriculture and Food Research Organization, 2-1-12 Kan-nondai, 305-8642, Tsukuba, Ibaraki, Japan aut NATIONALLICENCE 700 1- Tsuta Mizuki Food Engineering Division, National Food Research Institute, National Agriculture and Food Research Organization, 2-1-12 Kan-nondai, 305-8642, Tsukuba, Ibaraki, Japan aut NATIONALLICENCE 700 1- Yoshimura Masatoshi Food Engineering Division, National Food Research Institute, National Agriculture and Food Research Organization, 2-1-12 Kan-nondai, 305-8642, Tsukuba, Ibaraki, Japan aut NATIONALLICENCE 700 1- Fujita Kaori Food Engineering Division, National Food Research Institute, National Agriculture and Food Research Organization, 2-1-12 Kan-nondai, 305-8642, Tsukuba, Ibaraki, Japan aut NATIONALLICENCE 700 1- Shibata Mario Food Engineering Division, National Food Research Institute, National Agriculture and Food Research Organization, 2-1-12 Kan-nondai, 305-8642, Tsukuba, Ibaraki, Japan aut NATIONALLICENCE 700 1- Araki Tetsuya Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, 113-8657, Bunkyo ward, Tokyo, Japan aut NATIONALLICENCE 700 1- Nabetani Hiroshi Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, 113-8657, Bunkyo ward, Tokyo, Japan aut NATIONALLICENCE 773 0- Food and Bioprocess Technology Springer US; http://www.springer-ny.com 6/11(2013-11-01), 3113-3123 1935-5130 6:11<3113 2013 6 11947 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer