| 基于BP和Adaboost-BP神经网络的 羊肉新鲜度高光谱定性分析 |
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| 引用本文： | 范中建,朱荣光,张凡凡,姚雪东,邱园园,阎聪.基于BP和Adaboost-BP神经网络的 羊肉新鲜度高光谱定性分析[J].新疆农业科学,2018,55(1):183-188. |
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| 作者姓名： | 范中建 朱荣光 张凡凡 姚雪东 邱园园 阎聪 |
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| 作者单位： | 1.石河子大学机械电气工程学院,新疆石河子 832003;2.石河子大学动物科技学院,新疆石河子 832003 |
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| 基金项目： | 国家自然科学基金“新疆冷却羊肉储藏品质的动力学及不同光学速测技术融合研究”(31460418);石河子大学青年创新人才培育计划项目“基于多源光学信息的羊肉新鲜度快速检测机理及系统开发研究”(CXRC201707) |
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| 摘 要： | 【目的】 实现对羊肉新鲜度的快速准确鉴别。【方法】 研究通过对460~1 000 nm的羊肉高光谱图像纯肌肉部分提取光谱数据,以挥发性盐基氮(TVB-N)值对新鲜度等级进行划分,对预处理后的光谱数据分别采用连续投影算法(SPA)、主成分分析(PCA)两种压缩降维方法和反向传播(BP)神经网络、自适应提升BP(Adaboost-BP)神经网络两种建模方法开展羊肉新鲜度的分类比较。【结果】 其中采用SPA、PCA建立的BP模型校正集与预测集准确率均为100%、83.33%,建立的Adaboost-BP模型校正集与预测集准确率均为100%、94.44%,两种压缩降维方法下Adaboost-BP模型效果均优于BP模型。【结论】 利用高光谱图像技术结合Adaboost-BP方法对羊肉新鲜度等级进行分类判别是可行的。
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| 关 键 词： | 高光谱 羊肉新鲜度 特征选取 BP神经网络 Adaboost算法 |
Qualitative Analysis of Mutton Freshness by Hyperspectral Imaging Based on BP and Adaboost-BP Neural Network |
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| FAN Zhong-jian,ZHU Rong-guang,ZHANG Fan-fan,YAO Xue-dong,QIU Yuan-yuan,YAN Cong.Qualitative Analysis of Mutton Freshness by Hyperspectral Imaging Based on BP and Adaboost-BP Neural Network[J].Xinjiang Agricultural Sciences,2018,55(1):183-188. |
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| Authors: | FAN Zhong-jian ZHU Rong-guang ZHANG Fan-fan YAO Xue-dong QIU Yuan-yuan YAN Cong |
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| Institution: | 1.College of Mechanical and Electrical Engineering,Shihezi University,Shihezi Xinjiang 832003,China; 2.College of Animal Science and Technology,Shihezi University,Shihezi XingJiang 832003,China |
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| Abstract: | 【Objective】 To realize the rapid and accurate identification of mutton freshness.【Method】 The spectral data of the pure muscle was extracted from the spectral range of 460-1,000 nm of the mutton hyperspectral image, and the freshness grades were divided according to the volatile base nitrogen (TVB-N) value. After spectral pretreatment, a study on classification comparison of mutton freshness was carried out by using two dimensionality compression method of successive projections algorithm (SPA) and principal component analysis (PCA), two modeling methods of back propagation (BP) neural network and adaptive boosting BP (Adaboost-BP) neural network.【Result】 Based on SPA and PCA methods, the accuracy of calibration and prediction sets of the established BP models was 100% and 83.33%, and that of the Adaboost-BP models was 100% and 94.44%, respectively. The results of Adaboost-BP model were better than BP model under these two dimensionality compression methods.【Conclusion】 The study shows that hyperspectral image technology combined with Adaboost-BP method can achieve the accurate qualitative discrimination of freshness of mutton. |
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| Keywords: | hyperspectral imaging mutton freshness feature selection BP neural network Adaboost algorithm |
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