Issue 03
Turn off MathJax
Article Contents
LIU Yu- Jia, HE Li-Ping, ZHANG Yong, LV Xue-Juan, CAO Yong, GAO Xin-Kai. Model optimization of near- infrared spectroscopy and back propagation artificial neural network for identifying the geographical origin of Tremella fuciformis[J]. Science and Technology of Food Industry, 2016, (03): 303-306. doi: 10.13386/j.issn1002-0306.2016.03.055
Citation: LIU Yu- Jia, HE Li-Ping, ZHANG Yong, LV Xue-Juan, CAO Yong, GAO Xin-Kai. Model optimization of near- infrared spectroscopy and back propagation artificial neural network for identifying the geographical origin of Tremella fuciformis[J]. Science and Technology of Food Industry, 2016, (03): 303-306. doi: 10.13386/j.issn1002-0306.2016.03.055

Model optimization of near- infrared spectroscopy and back propagation artificial neural network for identifying the geographical origin of Tremella fuciformis

doi: 10.13386/j.issn1002-0306.2016.03.055
  • Received Date: 2015-04-29
  • Near- infrared spectroscopy in combination with artificial neural network was used to identify the geographical origin of tremella fuciformis. A total of 120 samples from Sichuan province and Fujian province were studied.After being pre- treated with average deviation and first derivative,the dimension of near- infrared absorption spectroscopy data were reduced and applied to develop classification models by principal components analysis and back propagation artificial neural network.The results showed that the cumulative contribution of first three principal components was 100%,but identification accuracy was 88.3% by principal components analysis.Thus the artificial neural network was further used to optimize the structure of classification model. Under 2 output layers and 11 hidden layers,the identification accuracy reached 100%.The study demonstrated that near- infrared absorption spectroscopy based on artificial neural network can be used as an accurate and rapid technique for identification of geographical origin of tremella fuciformis. Models builded by this study can help building geographical indications and monitoring quality for raw materials of food.
  • loading
  • [1]
    Du X,Zhang J,Lv Z,et al.Chemical modification of an acidic polysaccharide(TAPA1)from Tremella aurantialba and potential biological activities[J].Food Chemistry,2014,143(1):336-340.
    [2]
    Zhang Z,Wang X,Zhao M,et al.Free-radical degradation by Fe2+/Vc/H2O2and antioxidant activity of polysaccharide from Tremella fuciformis[J].Carbohydrate Polymers,2014,112(11):578-582.
    [3]
    Shi Z,Liu Y,Xu Y,et al.Tremella Polysaccharides attenuated sepsis through inhibiting abnormal CD4+CD25highregulatory T cells in mice[J].Cell Immunol,2014,288(2):60-65.
    [4]
    颜军,郭晓强,邬晓勇,等.银耳多糖的提取及其清除自由基作用[J].成都大学学报:自然科学版,2006,25(1):35-38.
    [5]
    黄建立,黄艳,郑宝东,等.不同干燥方式对银耳品质的影响[J].中国食品学报,2010,10(2):167-173.
    [6]
    Xiccato G,Trocino A,Tulli F,et al.Prediction of chemical composition and origin identification of european sea bass(Dicentrarchus labrax L.)by near infrared reflectance spectroscopy(NIRS)[J].Food Chemistry,2004,86(2):275-281.
    [7]
    管骁,古方青,杨永健.近红外光谱技术在食品产地溯源中的应用进展[J].生物加工过程,2014,12(2):77-82.
    [8]
    Marini F,Bucci R,Magri AL,et al.Artificial neural networks in chemometrics:History,examples and perspectives[J].Microchemical Journal,2008,88(2):178-185.
    [9]
    Marini F.Artificial neural networks in food stuff analyses:Trends and perspectives a review[J].Analytica Chimica Acta,2009,635(2):121-131.
    [10]
    王晓谦,钟赛义,秦小明,等.基于神经网络平台的牡蛎肉超高压杀菌工艺条件优化[J].食品工业科技,2014,36(6):257-261.
    [11]
    Aursand M,Standal IB,Axelson DE.High-resolution 13C nuclear magnetic resonance spectroscopy pattern recognition of fish oil capsules[J].Journal of Agricultural and Food Chemistry,2007,55(1):38-47.
    [12]
    庞涛涛,姚建斌,杜黎明.人工神经网络分类鉴别苦丁茶红外光谱[J].光谱学与光谱分析,2007,27(7):1336-1339.
    [13]
    王凤花,朱海龙,杨菊,等.基于近红外光谱荞麦淀粉、蛋白质和总黄酮含量测定方法研究[J].食品工业科技,2014,35(5):281-284.
    [14]
    洪雪珍,韦真博,海铮,等.基于电子鼻和神经网络的牛肉新鲜度的检测[J].现代食品科技,2014,30(4):279-285.
    [15]
    包刚,覃志豪,周义,等.基于高光谱数据和RBF神经网络方法的草地叶面积指数反演[J].国土资源遥感,2012,93(2):7-11.
    [16]
    欧文娟,孟耀勇,张小燕,等.紫外可见吸收光谱结合主成分-反向传播人工神经网络鉴别真假蜂蜜[J].分析化学,2011,39(7):1104-1108.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (116) PDF downloads(267) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return