Model optimization of near- infrared spectroscopy and back propagation artificial neural network for identifying the geographical origin of Tremella fuciformis
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摘要: 研究通过近红外光谱技术（NIRS）结合人工神经网络技术（ANN）识别银耳的不同产地。实验以四川省与福建省两个产地共120组银耳样品为研究对象,对其进行近红外光谱测定,计算光谱吸收值的平均偏差与一阶导数进而选取有效数据,结合主成分分析方法将原始数据降维并采用反向人工神经网络技术构建近红外分析模型。结果显示,通过对有效数据主成分分析,前3个主成分的累计方差贡献率达到100%,判断准确率为88.3%;进一步采用人工神经网络优化模型,在输出层为2隐藏层为11时,判断准确率达100%;此时校正集与预测集的均方根误差分别为3.05×10-2与2.90×10-2,模型具有良好的泛化能力。因此,结合人工神经网络的近红外光谱检测技术,优化检测模型,能够准确、快速地识别银耳产地,为食品原材料的质量控制及地理标志的建立提供科学依据。Abstract: 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.
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