期刊
FOOD CHEMISTRY
卷 170, 期 -, 页码 484-491出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.foodchem.2014.08.009
关键词
Winter jujube quality; Electronic nose; Double-layered cascaded series stochastic resonance; Signal-to-noise ratio spectrum
资金
- National Natural Science Foundation of China [81000645]
- National Spark Technology Project of China [2013GA700187]
- Funds of Zhejiang Provincial Key lab. Of Industrial Textile Materials & Manufacturing Tech, 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 [2013-157, 2013-158, 2014-166, 2014-193]
Winter jujube (Zizyphus jujuba Mill.) quality forecasting method utilising electronic nose (EN) and double-layered cascaded series stochastic resonance (DCSSR) was investigated. EN responses to jujubes stored at room temperature were continuously measured for 8 days. Jujubes' physical/chemical indexes, such as firmness, colour, total soluble solids (TSS), and ascorbic acid (AA), were synchronously examined. Examination results indicated that jujubes were getting ripe during storage. EN measurement data was processed by stochastic resonance (SR) and DCSSR. SR and DCSSR output signal-to-noise ratio (SNR) maximums (SNR-MAX) discriminated jujubes under different storage time successfully. Multiple variable regression (MVR) results between physical/chemical indexes and SR/DCSSR eigen values demonstrated that DCSSR eigen values were more suitable for jujube quality determination. Quality forecasting model was developed using non-linear fitting regression of DCSSR eigen values. Validating experiments demonstrated that forecasting accuracy of this model is 97.35%. This method also presented other advantages including fast response, non-destructive, etc. (C) 2014 Elsevier Ltd. All rights reserved.
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