Fuji Apple Storage Time Predictive Method Using Electronic Nose
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Fuji Apple Storage Time Predictive Method Using Electronic Nose Hui Guohua & Wu Yuling & Ye Dandan & Ding Wenwen
Received: 21 December 2011 / Accepted: 3 April 2012 / Published online: 27 April 2012 # Springer Science+Business Media, LLC 2012
Abstract An electronic nose-based Fuji apple storage time prediction method is investigated in this paper. A home-made electronic nose with eight metal oxide semiconductors gas sensor array was used to measure the apples stored at room temperature. Principal component analysis cannot discriminate all samples. Stochastic resonance signal-to-noise ratio spectrum distinguishes fresh, medium, and aged apples successfully. The prediction model is developed based on signalto-noise ratio maximums. In validating experiments, results show that the predicting accuracy of this model is 84.62 %. This method takes some advantages including fast detection, easy operation, high accuracy, and good repeatability.
Keywords Electronic nose . Apple storage time analysis . Principal component analysis . Stochastic resonance . Signalto-noise ratio
Introduction The fruit quality determination plays an important role in agro-industries because it influences the choice of consumers to a great deal. Currently, the common methods for fruit quality evaluation include sensory and instrumental studies. For sensory analysis, taste and aroma aspects of fruit samples are assessed by specially trained people. The evaluation result provides unique information about the acceptance degree of fruit samples. This method is widely accepted in overall fruit quality assessment. The most important problems of this H. Guohua (*) : W. Yuling : Y. Dandan : D. Wenwen College of Food Science and Biotechnology, Food Safety Key Laboratory of Zhejiang Province, Zhejiang Gongshang University, Zhejiang Province, Hangzhou 310035, China e-mail: [email protected]
method are measurement standardization, stability, and reproducibility. Sometimes, the high costs for people training and use of sensory panels also limit the applications of this technique. Instrumental analysis methods, including gas chromatography (GC), gas chromatography–mass spectrometry (GCMS), and high-performance liquid chromatography (HPLC), are usually utilized for fruit quality evaluation in lab. However, these methods are high-cost and time-consuming. Moreover, the skilled operators are required to perform the instrumental analytical experiments. A popular nondestructive method is the employment of electronic nose (E-nose), an instrumentation which consists of an array of some electronic chemical sensors with partial specificity. Appropriate patterns or fingerprints from known odors are employed to construct a database and train a pattern recognition system so that later unknown odors can subsequently be classified and identified (Hammond et al. 2002; Gardner and Bartlett 1999; Peris and Escuder-Gilabert 2009). The E-nose technology has been successfully employed in diverse fields such as agriculture (Zhang and Wang 2007), pharmaceutics (Zhu et al. 2004), env
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