| 光谱学与光谱分析 |
|
|
|
|
|
| Research on the Pre-Processing Methods of Wheat Hardness Prediction Model Based on Visible-Near Infrared Spectroscopy |
| HUI Guang-yan1, SUN Lai-jun1*, WANG Jia-nan1, WANG Le-kai2, DAI Chang-jun2 |
1. Key Laboratory of Electronics Engineering, College of Heilongjiang Province, Heilongjiang University, Harbin 150080, China
2. Cereal and Products Quality Supervisory Inspection and Test Center of Ministry of Agriculture, Harbin 150080, China |
|
|
|
|
Abstract Grain hardness is an important quality parameter of wheat which has great influence on the classification, usage and composition research of wheat. To achieve rapid and accurate detection of wheat hardness, radial basis function (RBF) neural network model was built to predict the hardness of unknown samples on the basis of analyzing the absorptive characteristics of the composition of wheat grain in infrared, besides, the effects of different spectral pretreatment methods on the predictive accuracy of models were emphatically analyzed. 111 wheat samples were collected from major wheat-producing areas in China; then, spectral data were obtained by scanning samples. Mahalanobis distance method was used to identify and eliminated abnormal spectra. The optimized method of sample set partitioning based on joint X-Y distance (SPXY) was used to divide sample set with the number of calibration set samples being 84 and prediction set samples being 24. Successive projections algorithm (SPA) was employed to extract 47 spectral features from 262. SPA, first derivatives, second derivatives, standard normal variety (SNV) and their combinations were applied to preprocess spectral data, and the interplay of different prediction methods was analyzed to find the optimal prediction combination. Radial basis function (RBF) was built with preprocessed spectral data of calibration set being as inputs and the corresponding hardness data determined via hardness index (HI) method being as outputs. Results showed that the model got the best prediction accuracy when using the combination of SNV and SPA to preprocess spectral data, with the discriminant coefficient (R2), standard error of prediction (SEP) and ratio of performance to standard deviate (RPD) being 0.844, 3.983 and 2.529, respectively, which indicated that the RBF neural network model built based on visible-near infrared spectroscopy (Vis-NIR) could accurately predict wheat hardness, having the advantages of easy, fast and nondestructive compared with the traditional method. It provides a more convenient and practical method for estimating wheat hardness.
|
|
Received: 2015-06-08
Accepted: 2015-10-11
|
|
|
|
Corresponding Authors:
SUN Lai-jun
E-mail: slaijun@126.com
|
|
[1] Sebastian Gasparis, Waclaw Orczyk, Anna Nadolska-Orczyk. BMC Plant Biology, 2013, 13: 190.
[2] Morten Lillemo, Chen Feng, Xi Xianchun, et al. Journal of Cereal Science, 2006, 44(1): 86.
[3] ZHAO Ren-yong(赵仁勇). Flour Milling(面粉通讯), 2003, 04: 26.
[4] HU Xin-zhong, WEI Yi-min, ZHANG Guo-quan, et al(胡新中，魏益民，张国权，等). Journal of Triticeae Crops(麦类作物学报), 2001, 21(4): 22.
[5] Grant M Campbell, Calum Sharp, Kevin Wall, et al. Journal of Cereal Science, 2012, 55(3):415.
[6] WU Cun-rong, TANG Huai-jian, WANG Yan-yan(吴存荣，唐怀建，王艳艳). Cereal & Feed Industry(制粉工业), 2011, (2): 12.
[7] Silvia Arazuri, Ignacio Arana J, Nerea Arias, et al. Journal of Food Engineering, 2011, 111(1): 115.
[8] Mao Xiaodong, Sun Laijun, Hui Guangyan, et al. Journal of Food and Drug Analysis, 2014, 22(2): 230.
[9] Lankapalli Ravikanth, Chandra B Singh, Digvir S Jayas, et al. Biosystems Engineering, 2015, 135: 73.
[10] Nikiforos Misailidis, Grant M Campbell. Journal of Cereal Science, 2013, 57(2): 222.
[11] Fox G P, Kelly A, N. Sweeney N, et al. Journal Institute of Brewing, 2011, 117: 582.
[12] YUAN Cui-ping, TIAN Ji-chun, WANG Yong-jun(袁翠平, 田纪春, 王永军). Journal of Triticeae Crops(麦类作物学报), 2004, 02: 106.
[13] Agu R C, Swanston J S, Walker J W, et al. Journal of the Institute of Brewing, 2009, 115(3): 183.
[14] Swanston J S, Smith P L, Agu R C, et al. Field Crops Research, 2012, 127: 146.
[15] WANG Wei-ming, DONG Da-ming, ZHENG Wen-gang(王伟明, 董大明, 郑文刚). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2013, 33(2): 359.
[16] Qu Nan, Mi Hong, Wang Bin, et al. Journal of the Taiwan Institute of Chemical Engineers, 2009, 40(2): 162.
[17] Sindhuja Sankaran, Ashish Mishra, Joe Mari Maja, et al. Computers and Electronics in Agriculture, 2011, 77(2): 127.
[18] Bart M Nicolai, Katrien Beullens, Els Bobelyn, et al. Postharvest Biology and Technology, 2007, 46(2): 99. |
| [1] |
ZHENG Pei-chao, YIN Yi-tong, WANG Jin-mei*, ZHOU Chun-yan, ZHANG Li, ZENG Jin-rui, LÜ Qiang. Study on the Method of Detecting Phosphate Ions in Water Based on
Ultraviolet Absorption Spectrum Combined With SPA-ELM Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 82-87. |
| [2] |
ZHU Wen-jing1, 2，FENG Zhan-kang1, 2，DAI Shi-yuan1, 2，ZHANG Ping-ping3，JI Wen4，WANG Ai-chen1, 2，WEI Xin-hua1, 2*. Multi-Feature Fusion Detection of Wheat Lodging Information Based on UAV Multispectral Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 197-206. |
| [3] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
| [4] |
ZHU Zhi-cheng1, WU Yong-feng2*, MA Jun-cheng2, JI Lin2, LIU Bin-hui3*, JIN Hai-liang1*. Response of Winter Wheat Canopy Spectra to Chlorophyll Changes Under Water Stress Based on Unmanned Aerial Vehicle Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3524-3534. |
| [5] |
JIA Zong-chao1, WANG Zi-jian1, LI Xue-ying1, 2*, QIU Hui-min1, HOU Guang-li1, FAN Ping-ping1*. Marine Sediment Particle Size Classification Based on the Fusion of
Principal Component Analysis and Continuous Projection Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3075-3080. |
| [6] |
FENG Hai-kuan1, 2, FAN Yi-guang1, TAO Hui-lin1, YANG Fu-qin3, YANG Gui-jun1, ZHAO Chun-jiang1, 2*. Monitoring of Nitrogen Content in Winter Wheat Based on UAV
Hyperspectral Imagery[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3239-3246. |
| [7] |
AN Bai-song1, 2, WANG Xue-mei1, 2*, HUANG Xiao-yu1, 2, KAWUQIATI Bai-shan1, 2. Hyperspectral Estimation of Soil Lead Content Based on Random Frog Band Selection Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3302-3309. |
| [8] |
ZHANG Jun-he, YU Hai-ye, DANG Jing-min*. Research on Inversion Model of Wheat Polysaccharide Under High Temperature and Ultraviolet Stress Based on Dual-Spectral Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2705-2709. |
| [9] |
ZHU Yu-chen1, 2, WANG Yan-cang3, 4, 5, LI Xiao-fang6, LIU Xing-yu3, GU Xiao-he4*, ZHAO Qi-chao3, 4, 5. Study on Quantitative Inversion of Leaf Water Content of Winter Wheat Based on Discrete Wavelet Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2902-2909. |
| [10] |
TANG Ruo-han1, 2, LI Xiu-hua1, 2*, LÜ Xue-gang1, 2, ZHANG Mu-qing2, 3, YAO Wei2, 3. Transmittance Vis-NIR Spectroscopy for Detecting Fibre Content of
Living Sugarcane[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2419-2425. |
| [11] |
SONG Cheng-yang1, GENG Hong-wei1, FEI Shuai-peng2, LI Lei2, GAN Tian2, ZENG Chao-wu3, XIAO Yong-gui2*, TAO Zhi-qiang2*. Study on Yield Estimation of Wheat Varieties Based on Multi-Source Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2210-2219. |
| [12] |
ZHANG Hai-liang1, XIE Chao-yong1, TIAN Peng1, ZHAN Bai-shao1, CHEN Zai-liang1, LUO Wei1*, LIU Xue-mei2*. Measurement of Soil Organic Matter and Total Nitrogen Based on Visible/Near Infrared Spectroscopy and Data-Driven Machine Learning Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2226-2231. |
| [13] |
ZHOU Qi1, 2, WANG Jian-jun1, 2*, HUO Zhong-yang1, 2*, LIU Chang1, 2, WANG Wei-ling1, 2, DING Lin3. UAV Multi-Spectral Remote Sensing Estimation of Wheat Canopy SPAD Value in Different Growth Periods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1912-1920. |
| [14] |
LIN Jing-tao, XIN Chen-xing, LI Yan*. Spectral Characteristics of “Trapiche-Like Sapphire” From ChangLe, Shandong Province[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1199-1204. |
| [15] |
MAO Xiao-tian1, CHEN Chang2, YIN Zuo-wei1*, WANG Zi-min1. Spectra Characterization of Cr-Grossular (Tsavorite) With “Frogspawn” Color Zoning From Canada[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 520-525. |
|
|
|
|
