naa a22 4500 510724736 CHVBK 20180411082931.0 cr unu---uuuuu 180411e20130901xx s 000 0 eng 10.1007/s11947-012-0936-0 doi (NATIONALLICENCE)springer-10.1007/s11947-012-0936-0 Determination of Amino Acid Nitrogen in Soy Sauce Using Near Infrared Spectroscopy Combined with Characteristic Variables Selection and Extreme Learning Machine [Elektronische Daten] [Qin Ouyang, Quansheng Chen, Jiewen Zhao, Hao Lin] Amino acid nitrogen (AAN) is one of the most important indicators to assess the quality grade of soy sauce in China. Near infrared (NIR) spectroscopy technique combined with characteristic variable selection and extreme learning machine (ELM) was applied to detect AAN content in soy sauce in this work. First, the optimal spectral intervals were selected by synergy interval partial least square. Then, ELM model based on the optimal spectral intervals was established, called synergy interval extreme learning machine (Si-ELM) model. Support vector machine model based on the optimal intervals was established comparatively. These models were optimized by cross validation, and the performance of each final model was evaluated according to correlation coefficient ( $$ R_{\text{p}}^2 $$ ) and root mean square error of prediction (RMSEP) in prediction set. Si-ELM showed excellent performance. The best Si-ELM model was achieved with $$ R_{\text{p}}^2 = 0.9657 $$ and RMSEP = 0.0371 in the prediction set. It was concluded that NIR spectroscopy combined with Si-ELM was an appropriate method to detect AAN content in soy sauce. Springer Science+Business Media, LLC, 2012 Soy sauce nationallicence Amino acid nitrogen nationallicence NIR spectroscopy nationallicence Extreme learning machine nationallicence Nonlinearity nationallicence Ouyang Qin School of Food and Biological Engineering, Jiangsu University, 212013, Zhenjiang, China aut Chen Quansheng School of Food and Biological Engineering, Jiangsu University, 212013, Zhenjiang, China aut Zhao Jiewen School of Food and Biological Engineering, Jiangsu University, 212013, Zhenjiang, China aut Lin Hao School of Food and Biological Engineering, Jiangsu University, 212013, Zhenjiang, China aut Food and Bioprocess Technology Springer US; http://www.springer-ny.com 6/9(2013-09-01), 2486-2493 1935-5130 6:9<2486 2013 6 11947 https://doi.org/10.1007/s11947-012-0936-0 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11947-012-0936-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Ouyang Qin School of Food and Biological Engineering, Jiangsu University, 212013, Zhenjiang, China aut NATIONALLICENCE 700 1- Chen Quansheng School of Food and Biological Engineering, Jiangsu University, 212013, Zhenjiang, China aut NATIONALLICENCE 700 1- Zhao Jiewen School of Food and Biological Engineering, Jiangsu University, 212013, Zhenjiang, China aut NATIONALLICENCE 700 1- Lin Hao School of Food and Biological Engineering, Jiangsu University, 212013, Zhenjiang, China aut NATIONALLICENCE 773 0- Food and Bioprocess Technology Springer US; http://www.springer-ny.com 6/9(2013-09-01), 2486-2493 1935-5130 6:9<2486 2013 6 11947 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer