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Rapid detection of Atlantic salmon multi-quality based on impedance properties.
Sun, Zongbao; Liang, Liming; Li, Junkui; Liu, Xiaoyu; Sun, Jian; Zou, Xiaobo; Zuo, Min; Guo, Zhiming.
Afiliação
  • Sun Z; School of Food and Biological Engineering Jiangsu University Zhenjiang China.
  • Liang L; School of Food and Biological Engineering Jiangsu University Zhenjiang China.
  • Li J; School of Food and Biological Engineering Jiangsu University Zhenjiang China.
  • Liu X; School of Food and Biological Engineering Jiangsu University Zhenjiang China.
  • Sun J; School of Electrical and Information Engineering Jiangsu University Zhenjiang China.
  • Zou X; School of Food and Biological Engineering Jiangsu University Zhenjiang China.
  • Zuo M; National Engineering Laboratory for Agri-product Quality Traceability Beijing Technology and Business University Beijing China.
  • Guo Z; School of Food and Biological Engineering Jiangsu University Zhenjiang China.
Food Sci Nutr ; 8(2): 862-869, 2020 Feb.
Article em En | MEDLINE | ID: mdl-32148795
ABSTRACT
To establish a rapid, convenient, and low-cost method to assess the quality of Atlantic salmon, we analyzed the impedance between 10-1 and 105 Hz for Atlantic salmon/rainbow trout, chilled/frozen-thawed salmon, and fresh/stale salmon. We combined chemometrics with impedance properties to create a multi-quality index for Atlantic salmon. The accuracy of all three models established can reach 100% in distinguishing Atlantic salmon from rainbow trout and distinguishing chilled salmon from frozen-thawed salmon. We applied a partial least squares method to create a quantitative prediction model of bioimpedance spectroscopy and the value of total volatile basic nitrogen. The correlation coefficients of the training and test sets were 0.9447 and 0.9387. Our results showed that the combination of impedance properties and chemometrics was a simple and effective application to evaluate Atlantic salmon quality.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2020 Tipo de documento: Article