Journal
FOOD ANALYTICAL METHODS
Volume 7, Issue 8, Pages 1612-1618Publisher
SPRINGER
DOI: 10.1007/s12161-014-9796-8
Keywords
Beef strip loins (M. longissimus lumborum); Freshness analysis; Electronic nose; Stochastic resonance; Signal-to-noise ratio
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Funding
- National Natural Science Foundation of China [81000645, 31071628]
- Zhejiang Province Science and Technology Research Project [2011C21051]
- Zhejiang Province Natural Science Foundation [Y1100150, Y1110074]
- International Science and Technology Cooperation Project of China [2010DFB34220]
- Higher Education Research Project of Zhejiang Gongshang University [Xgy13080]
- Student Innovation Projects of Zhejiang Gongshang University [2012-160, 2012-161, 2013-157, 2013-158]
- Student Innovation Projects of Zhejiang Province [2012R408041, 2010R408047, 2010R408015]
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A beef strip loins (Musculus longissimus lumborum) freshness determination method utilizing electronic nose (e-nose) was investigated in this paper. Fresh beef strip loins samples were stored at 4A degrees C continuously for 10 days. Total viable count (TVC) index, total volatile basic nitrogen (TVB-N) index, and e-nose responses to beef strip loins samples were measured every day. TVC and TVB-N index rose with the increase of storage time. Principal component analysis (PCA) only partially discriminated beef samples under different storage days. Stochastic resonance (SR) signal-to-noise ratio (SNR) spectrum discriminated all beef samples successfully. Beef strip loins freshness discrimination model was developed using SR SNR maximums (SNRmax) linear fitting regression. The proposed method forecasted beef freshness with high accuracy. It is holds promise in meat freshness determination applications.
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