中红外光谱法结合支持向量机快速鉴别蜂蜜品种 下载: 1469次
徐天扬, 杨娟, 孙晓荣, 刘翠玲, 李熠, 周金慧, 陈兰珍. 中红外光谱法结合支持向量机快速鉴别蜂蜜品种[J]. 激光与光电子学进展, 2018, 55(6): 063003.
Tianyang Xu, Juan Yang, Xiaorong Sun, Cuiling Liu, Yi Li, Jinhui Zhou, Lanzhen Chen. Mid-Infrared Spectroscopy Analysis Combined with Support Vector Machine for Rapid Discrimination of Botanical Origin of Honey[J]. Laser & Optoelectronics Progress, 2018, 55(6): 063003.
[1] 陈兰珍. 蜂蜜品质近红外光谱评价技术研究[D]. 北京: 中国农业科学院农业质量标准与检测技术研究所, 2010.
Chen LZ. Study on quality evaluation for honey by near infrared spectroscopy[D]. Beijing: Institute of Quality Standards and Testing Technology for AGRO-Products ofCAAS, 2010.
[2] 刘博静. 蜂蜜产地特征检测方法的研究[D]. 保定: 河北大学, 2010.
Liu BJ. The research of detection method about characteristic of honey producing area[D]. Baoding: Hebei University, 2010.
[3] 钟艳萍, 钟振声, 陈兰珍, 等. 近红外光谱技术定性鉴别蜂蜜品种及真伪的研究[J]. 现代食品科技, 2010, 26(11): 1280-1282.
Zhong Y P, Zhong Z S, Chen L Z, et al. Qualitative identification of floral origin and adulteration of honey by near-infrared spectroscopy[J]. Modern Food Science and Technology, 2010, 26(11): 1280-1282.
[7] 张文娟, 陈兰珍, 吴黎明, 等. 中红外光谱法快速鉴别不同蜜源蜂蜜[ C]. 全国蜂产业高峰论坛, 2013.
Zhang WJ, Chen LZ, Wu LM, et al. Study of mid-infrared spectroscopy analysis for rapid discrimination of botanical origin of honey[ C]. Summit Forum of National Bee Industry, 2013.
[8] 胡乐乾, 尹春玲, 马渭奎, 等. 红外光谱法对蜂蜜掺伪的模式识别[J]. 应用化学, 2011, 28(s1): 144-145.
Hu Y Q, Yin C L, Ma W K, et al. Identification of adulterated honey based on infrared spectroscopy and pattern recognition technology[J]. Chinese Journal of Applied Chemistry, 2011, 28(s1): 144-145.
[9] 孙燕, 张海华, 王铮. 中红外光谱技术应用于饶河蜂蜜产地溯源的表征[J]. 化学分析计量, 2015, 24(3): 41-44.
Sun Y, Zhang H H, Wang Z. Application of infrared spectrum technology in Raohe honey characterization of traceability[J]. Chemical Analysis and Meterage, 2015, 24(3): 41-44.
[10] 段锋华, 王先华, 叶函函, 等. 基于统计与光程分布的二氧化碳反演方法[J]. 光学学报, 2017, 37(5): 0501003.
[11] 程力勇, 米高阳, 黎硕, 等. 基于主成分分析-支持向量机模型的激光钎焊接头质量诊断[J]. 中国激光, 2017, 44(3): 0302004.
[12] 廖建尚, 王立国. 两类空间信息融合的高光谱图像分类方法[J]. 激光与光电子学进展, 2017, 54(8): 081002.
[13] 陈兰珍, 孙谦, 叶志华, 等. 基于神经网络的近红外光谱鉴别蜂蜜品种研究[J]. 食品科技, 2009, 34(8): 287-289.
Chen L Z, Sun Q, Ye Z H, et al. Determination of floral origin of honey by near infrared spectroscopy based on artificial neural network[J]. Food Science of Technology, 2009, 34(8): 287-289.
[14] 张妍楠, 陈兰珍, 薛晓锋, 等. 基于近红外光谱检测技术鉴别洋槐蜜中掺入大米糖浆的可行性研究[J]. 光谱学与光谱分析, 2015, 35(9): 2536-2539.
[15] 陈冰梅, 樊晓平, 周志明, 等. 支持向量机原理及展望[J]. 制造业自动化, 2010, 32(12): 136-138.
Chen B M, Fan X P, Zhou Z M, et al. The principle and prospect of support vector machine[J]. Manufacturing Automation, 2010, 32(12): 136-138.
[16] 陈万海. 基于支持向量机的超谱图像分类技术研究[D]. 哈尔滨: 哈尔滨工程大学, 2008.
Chen WH. Research on classification of hyperspectral images based on support vector machine[D]. Harbin: Harbin Engineering University, 2008.
[17] 陈华舟, 陈福, 许丽莉, 等. 基于网格搜索的参数优化方法用于鱼粉灰分的近红外LSSVM定量分析[J]. 分析科学学报, 2016, 32(2): 198-202.
Chen H Z, Chen F, Xu L L, et al. Grid search parameter optimization applied to near infrared LSSVM modeling quantitative analysis of fishmeal ash[J]. Journal of Analytical Science, 2016, 32(2): 198-202.
[18] Vapnik VN. The nature of statistical learning theory[M]. New York: Springer-Verlag, 1998: 1- 17.
[19] Duan KB, Rajapakse JC, Nguyen MN. One-Versus-One and One-Versus-Allmulticlass SVM-REF for gene selection in cancer classification[C]∥Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics. Valencia: [s.n.], 2007: 47- 56.
[20] Chang CC, Lin C J. LIBSVM: a library for support vector machines[EB/OL]. ( 2010-03-01) [2017-11-20]. http:∥www.csie.ntu.edu.tw/~cjlin/libsvm.
[21] 唐小彪. 基于支持向量机的地震储层预测方法研究[D]. 成都: 成都理工大学, 2009.
Tang XB. Seismic reservoir discrimination based on support vector machines[D]. Chengdu: Chengdu University of Technology, 2009.
徐天扬, 杨娟, 孙晓荣, 刘翠玲, 李熠, 周金慧, 陈兰珍. 中红外光谱法结合支持向量机快速鉴别蜂蜜品种[J]. 激光与光电子学进展, 2018, 55(6): 063003. Tianyang Xu, Juan Yang, Xiaorong Sun, Cuiling Liu, Yi Li, Jinhui Zhou, Lanzhen Chen. Mid-Infrared Spectroscopy Analysis Combined with Support Vector Machine for Rapid Discrimination of Botanical Origin of Honey[J]. Laser & Optoelectronics Progress, 2018, 55(6): 063003.