| Citation: | CHENG Yunling, YANG Shuqin. Selection of measurement variables for hyperspectra of total phenol content in grape seeds based on Monte Carlo frequency method[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(12): 2431-2437. doi: 10.13700/j.bh.1001-5965.2019.0361(in Chinese)
|
In order to solve the problems of too many variables and high model complexity, it is necessary to effectively reduce the dimension of the data according to the characteristics in establishing the prediction model of total phenol content in grape seeds by using hyperspectral data. In this paper, a Monte Carlo frequency (MCF) method was proposed to select the wavelength of hyperspectral data, and the support vector regression (SVR) prediction model of grape seed total phenols was established. The method uses Monte Carlo sampling to select wavelength subset, then establishes a large number of SVR sub-models, and selects sub-models with smaller root mean square error (RMSE) to count the frequency of each wavelength. Finally, the number of wavelengths is determined by exponential decline function, and the wavelength subset with the highest frequency is selected as the characteristic wavelength. The results show that the prediction performance of the model can be improved by using MCF method at the same time of dimensionality reduction. The number of wavelengths can be reduced from 196 to 9, the range of wavelengths is between 950 and 1400 nm, and the RMSE value can be reduced from 0.42 to 0.37. The prediction accuracy is better than other wavelength selection methods such as SPA. The results show that the proposed MCF method can effectively select characteristic wavelengths in hyperspectral data processing, which provides an effective method for the accurate establishment of prediction model.
| [1] |
张保华, 李江波, 樊书祥, 等.高光谱成像技术在果蔬品质与安全无损检测中的原理及应用[J].光谱学与光谱分析, 2014, 34(10):2743-2751. doi: 10.3964/j.issn.1000-0593(2014)10-2743-09
ZHANG B H, LI J B, FAN S X, et al.Principle and application of high spectral imaging technology in nondestructive testing of fruit and vegetable quality and safety[J].Spectroscopy and Spectral Analysis, 2014, 34(10):2743-2751(in Chinese). doi: 10.3964/j.issn.1000-0593(2014)10-2743-09
|
| [2] |
CHEN S, ZHANG F, NING J, et al.Predicting the anthocyanin content of wine grapes by NIR hyperspectral imaging[J].Food Chemistry, 2015, 172:788-793. doi: 10.1016/j.foodchem.2014.09.119
|
| [3] |
EIMASRY G, SU D W, ALLEN P, et al.Near-infrared hyperspectral imaging for predicting colour, pH and tenderness of fresh beef[J].Journal of Food Engineering, 2012, 110(1):127-140. doi: 10.1016/j.jfoodeng.2011.11.028
|
| [4] |
LEO L, ROGER J M, HERRERO-LANGREO A, et al.Comparison of multispectral indexes extracted from hyperspectral images for the assessment of fruit ripening[J].Journal of Food Engineering, 2011, 104(4):612-620. doi: 10.1016/j.jfoodeng.2011.01.028
|
| [5] |
GMES V M, FERNANDES A M, FAIA A, et al.Comparison of different approaches for the prediction of sugar content in new vintages of whole port wine grape berries using hyperspectral imaging[J].Computers and Electronics in Agriculture, 2017, 140:244-254. doi: 10.1016/j.compag.2017.06.009
|
| [6] |
LI W, PRASAD S, FOWLER J E, et al.Locality-preserving dimensionality reduction and classification for hyperspectral image analysis[J].IEEE Transactions on Geoscience & Remote Sensing, 2012, 50(4):1185-1198.
|
| [7] |
宦克为, 刘小溪, 郑峰, 等.基于蒙特卡罗特征投影法的小麦蛋白质近红外光谱测量变量选择[J].农业工程学报, 2013, 29(4):266-271.
HUAN K W, LIU X X, ZHENG F, et al.Selection of variables for wheat protein near infrared spectroscopy based on monte carlo characteristic projection[J].Journal of Agricultural Engineering, 2013, 29(4):266-271(in Chinese).
|
| [8] |
郝勇, 孙旭东, 潘圆媛, 等.蒙特卡罗无信息变量消除方法用于近红外光谱预测果品硬度和表面色泽的研究[J].光谱学与光谱分析, 2011, 31(5):1225-1229. doi: 10.3964/j.issn.1000-0593(2011)05-1225-05
HAO Y, SUN X D, PAN Y Y, et al.Monte-carlo method of elimination of uninformed variables was used to predict fruit hardness and surface color by near infrared spectroscopy[J].Spectroscopy and Spectral Analysis, 2011, 31(5):1225-1229(in Chinese). doi: 10.3964/j.issn.1000-0593(2011)05-1225-05
|
| [9] |
顾章源, 刘翔, 苏枫, 等.基于流形学习的多光谱优化波段选择算法研究[J].上海航天, 2017, 34(3):40-46.
GU Z Y, LIU X, SU F, et al.Research on multi-spectral optimal band selection algorithm based on manifold learning[J].Aerospace Shanghai, 2017, 34(3):40-46(in Chinese).
|
| [10] |
ARAUJO M C U, SALDANHA T C B, GALVAO R K H, et al.The successive projections algorithm for variable selection in spectroscopic multicomponent analysis[J].Chemometrics & Intelligent Laboratory Systems, 2001, 57(2):65-73.
|
| [11] |
CENTNER V, MASSART D L, NOORD O E D, et al.Elimination of uninformative variables for multivariate calibration[J].Analytical Chemistry, 1996, 68(21):3851-3858. doi: 10.1021/ac960321m
|
| [12] |
MOROS J, KULIGOWSKI J, QINTAS G, et al.New cut-off criterion for uninformative variable elimination in multivariate calibration of near-infrared spectra for the determination of heroin in illicit street drugs[J].Analytica Chimica Acta, 2008, 630(2):150-160. doi: 10.1016/j.aca.2008.10.024
|
| [13] |
LI H, LIANG Y, XU Q, et al.Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration[J].Analytica Chimica Acta, 2009, 648(1):77-84. doi: 10.1016/j.aca.2009.06.046
|
| [14] |
CAI W, LI Y, SHAO X.A variable selection method based on uninformative variable elimination for multivariate calibration of near-infrared spectra[J].Chemometrics & Intelligent Laboratory Systems, 2008, 90(2):188-194.
|
| [15] |
LI H, LIANG Y, XU Q, et al.Model population analysis for variable selection[J].Journal of Chemometrics, 2010, 24(7-8):418-423. doi: 10.1002/cem.1300
|
| [16] |
LI H, XU Q, ZHANG W, et al.Variable complementary network:A novel approach for identifying biomarkers and their mutual associations[J].Metabolomics, 2012, 8(6):1218-1226. doi: 10.1007/s11306-012-0410-z
|
| [17] |
HARBERTSON J F, PICCIOTTO E A, ACKERMANN K.Phenolic and anthocyanin assay for use with spectrophotometer[D].Davis, CA: University of California, 2005.
|
| [18] |
褚小立, 袁洪福, 陆婉珍.近红外分析中光谱预处理及波长选择方法进展与应用[J].化学进展, 2004, 16(4):528-542. doi: 10.3321/j.issn:1005-281X.2004.04.008
CHU X L, YUAN H F, LU W Z.Progress and application of spectral pretreatment and wavelength selection methods in nir analysis[J].Progress in Chemistry, 2004, 16(4):528-542(in Chinese). doi: 10.3321/j.issn:1005-281X.2004.04.008
|
| [19] |
GORRY P A.General least-squares smoothing and differentiation by the convolution (Savitzky-Golay) method[J].Analytical Chemistry, 1990, 62(6):570-573.
|
| [20] |
ZHANG C, GUO C, LIU F, et al.Hyperspectral imaging analysis for ripeness evaluation of strawberry with support vector machine[J].Journal of Food Engineering, 2016, 179:11-18. doi: 10.1016/j.jfoodeng.2016.01.002
|
| [21] |
NI Z, XU L, XIAODUO J, et al.Determination of total iron-reactive phenolics, anthocyanins and tannins in wine grapes of skins and seeds based on near-infrared hyperspectral imaging[J].Food Chemistry, 2017, 237:811-817. doi: 10.1016/j.foodchem.2017.06.007
|
| [22] |
CHANG C C, LIN C J.LIBSVM:A library for support vector machines[J].ACM Transactions on Intelligent Systems and Technology, 2011, 2(3):27
|
| [1] | YANG Rong-tai, SHAO Yu-bin, DU Qing-zhi, LONG Hua, QI Yu-ting, ZHANG Feng. Few-shot entity linking prediction based on Graph-Transformer network[J]. Journal of Beijing University of Aeronautics and Astronautics. doi: 10.13700/j.bh.1001-5965.2024.0023 |
| [2] | NIE Li, LI Chenliang, LIU Wangkui, SHEN Haidong, LIU Yanbin, CHEN Jinbao. Adaptive neural network based fixed-time command-filtered control for quadrotor unmanned aerial vehicles[J]. Journal of Beijing University of Aeronautics and Astronautics. doi: 10.13700/j.bh.1001-5965.2024.0403 |
| [3] | GE Jian-hao, GUO Jie, WANG Hao-ning, ZHANG Bao-chao, WAN Yang-yang, TANG Sheng-jing. Adaptive model predictive control for hypersonic morphing gliding vehicle[J]. Journal of Beijing University of Aeronautics and Astronautics. doi: 10.13700/j.bh.1001-5965.2024.0081 |
| [4] | FANG Z N，LIU C，ZHANG C Q，et al. Influencing analysis of temperature controlling accuracy of loop heat pipes and capillary limit prediction[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（12）：3788-3793 （in Chinese）. doi: 10.13700/j.bh.1001-5965.2022.0886. |
| [5] | CHEN B，LIU Y，YIN K L，et al. Runway temperature data mechanism joint prediction based on LSTM under ice and snow[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（7）：2184-2194 （in Chinese）. doi: 10.13700/j.bh.1001-5965.2022.0579. |
| [6] | WEN Bo-qun, MIAO Jing-gang, LU Ying, ZHOU Shu-yu. Short-term prediction of stratospheric wind field based on POD-LSTM network[J]. Journal of Beijing University of Aeronautics and Astronautics. doi: 10.13700/j.bh.1001-5965.2023.0608 |
| [7] | LIU H，LIN J Q. Energy consumption prediction of aircraft ground air conditioning based on IPSO-AM-LSTM[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（11）：3595-3602 （in Chinese）. doi: 10.13700/j.bh.1001-5965.2022.0872. |
| [8] | ZHAO Ru-jian, YANG Wei, WU Zhen-yu, CENG Hong-cheng, CHEN Jie, MA Lei. Ship track prediction method based on LSTM and nautical chart constraints[J]. Journal of Beijing University of Aeronautics and Astronautics. doi: 10.13700/j.bh.1001-5965.2023.0516 |
| [9] | YANG Pei-dong, HUANG Hua, WEI Wei-wei, GUO Bao-dao. Toolwear prediction based on attention mechanism and PSO-BiLSTM[J]. Journal of Beijing University of Aeronautics and Astronautics. doi: 10.13700/j.bh.1001-5965.2023.0545 |
| [10] | LIU F，WANG Z，DAI Y Y，et al. A robust adaptive filtering algorithm based on predicted residuals in integrated navigation[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（6）：1301-1310 （in Chinese）. doi: 10.13700/j.bh.1001-5965.2021.0460. |
| [11] | ZHANG Wei, CHANG Ben-qiang, YANG Xu, XIONG Xiao. Fire source localization prediction for long and short term memory networks based on Bayesian optimization algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics. doi: 10.13700/j.bh.1001-5965.2023.0482 |
| [12] | ZHU Z H，XIAO T H，XU Y N，et al. Adjoint-based adaptive Cartesian mesh refinement for sonic boom prediction[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（9）：2432-2441 （in Chinese）. doi: 10.13700/j.bh.1001-5965.2021.0689. |
| [13] | ZHAO J Y，HU J，YAO J Y，et al. EHA fault diagnosis and fault tolerant control based on adaptive neural network robust observer[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（5）：1209-1221 （in Chinese）. doi: 10.13700/j.bh.1001-5965.2021.0416. |
| [14] | BAI Chao-ping, ZHANG Shen-yi, ZHANG Xin, SUN Yue-qiang, ZHANG Shuai, WANG Zi-ting. Spaceborne particle identification platform and application based on convolutional neural network[J]. Journal of Beijing University of Aeronautics and Astronautics. doi: 10.13700/j.bh.1001-5965.2023.0171 |
| [15] | JIANG Y，CHEN M Y，YUAN Q，et al. Departure flight delay prediction based on spatio-temporal graph convolutional networks[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（5）：1044-1052 （in Chinese）. doi: 10.13700/j.bh.1001-5965.2021.0415. |
| [16] | TANG Z Y，MA F Y，PEI Z C. Improved PSO-RBF neural network adaptive sliding mode control for quadrotor systems[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（7）：1563-1572 （in Chinese）. doi: 10.13700/j.bh.1001-5965.2021.0477. |
| [17] | ZHANG P H，TANG Y，TANG J，et al. Simulation of cavity flow at high Mach number based on adaptive unstructured hybrid mesh[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（6）：1311-1318 （in Chinese）. doi: 10.13700/j.bh.1001-5965.2021.0424. |
| [18] | LI Zheyang, ZHANG Ruyi, TAN Wenming, REN Ye, LEI Ming, WU Hao. A graph convolution network based latency prediction algorithm for convolution neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(12): 2450-2459. doi: 10.13700/j.bh.1001-5965.2021.0149 |
| [19] | LONG Yuan, DENG Xiaolong, YANG Xixiang, HOU Zhongxi. Short-term rapid prediction of stratospheric wind field based on PSO-BP neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(10): 1970-1978. doi: 10.13700/j.bh.1001-5965.2021.0068 |
| [20] | LIU Qianqian, LIU Yushan, WEN Yeting, HE Jie, LI Xiao, BI Daqiang. Short-term load forecasting method based on PCC-LSTM model[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(12): 2529-2536. doi: 10.13700/j.bh.1001-5965.2021.0145 |
| 1. | 胡蝶，唐敏，朱永梁，朱耀东. 基于注意力机制的人脸情绪识别. 智能计算机与应用. 2024(08): 65-69 .  | |
| 2. | 左安全，秦伦明，王悉，边后琴，陈思林. 基于改进DeepLabv3+模型的电力线语义分割方法. 无线电工程. 2023(01): 96-104 . | |
| 3. | 陈扬，吕艳辉. 基于改进YOLOv5s的头盔佩戴检测算法. 沈阳理工大学学报. 2023(05): 11-17 . | |
| 4. | 刘宁，孙萍，冯宇平，鞠伯伦. 基于ResNet18与胶囊网络结合的人脸表情识别. 青岛科技大学学报(自然科学版). 2023(05): 109-114 . |
Figures(6) / Tables(3)
Copyright © Journal of Beijing University of Aeronautics and Astronautics
Address: Editorial Department of Journal of Beijing University of Aeronautics and Astronautics, 37 Xueyuan Road, Haidian District, Beijing Post Code: 100191 Email: jbuaa@buaa.edu.cn
Supported by:
Beijing Renhe Information Technology Co., Ltd.
Lan Yuqing, Zhao Tong. Evaluate interoperability of foundational software platform by Bayesian networks[J]. Journal of Beijing University of Aeronautics and Astronautics, 2009, 35(9): 1148-1151. (in Chinese)
DownLoad:
