Study of grass carp (Ctenopharyngodon idellus) quality predictive model based on electronic nose
Study of grass carp (Ctenopharyngodon idellus) quality predictive model based on electronic nose
复制标题
基于电子鼻的草鱼品质预测模型研究
DOI：
10.1016/j.snb.2012.02.066
复制
发表时间：
2012-05-20
影响因子：
8.4
通讯作者：
Zhang Lingxia
中科院分区：
文献类型：
--
作者：
Hui Guohua;Wang Lvye;Zhang Lingxia
本文提出了一种基于电子鼻的 277 K 保存草鱼（Ctenopharyngodon idellus）质量预测模型。传感器阵列对样品响应的变化是由微生物繁殖释放的新产生的气体种类引起的。主成分分析方法区分了新鲜草鱼样品、中等样品和陈化样品。随机共振信噪比最大值成功区分了新鲜、中等和老化草鱼样品。质量预测模型是基于信噪比最大值非线性拟合回归开发的。验证实验表明该模型的预测准确率为87.5%。该方法具有操作简便、响应速度快、准确度高、重复性好等优点，在水产食品质量评价中具有广阔的应用前景。 (C) 2012 Elsevier B.V. 保留所有权利。
An electronic nose based quality predictive model of grass carp (Ctenopharyngodon idellus) stored at 277 K temperature was proposed in this paper. The changes of sensor array response to samples were caused by the new-generated gas species released by microbial propagations. Principal component analysis method discriminated fresh grass carp samples from medium samples and aged samples. Stochastic resonance signal-to-noise ratio maximums distinguished fresh, medium, and aged grass carp samples successfully. The quality predicting model was developed based on signal-to- noise ratio maximums non-linear fitting regression. Validating experiments demonstrated that the predicting accuracy of this model was 87.5%. This method presented some advantages including easy operation, quick response, high accuracy, good repeatability, etc. This method is promising in aquatic food products quality evaluating applications. (C) 2012 Elsevier B.V. All rights reserved.