| 光谱学与光谱分析 |
|
|
|
|
|
| Discrimination of Varieties of Paddy Based on Vis/NIR Spectroscopy Combined with Chemometrics |
| LI Xiao-li,TANG Yue-ming,HE Yong,YING Xia-fang* |
| College of Biosystems Engineering and Food Science,Zhejiang University,Hangzhou 310029,China |
|
|
|
|
Abstract A simple,fast and non-destructive method based on visible/near infrared reflectance (Vis/NIR) spectroscopy and chemometrics was put forward for discriminating varieties of paddy. Firstly,A field spectroradiometer was used for collecting spectra in the wavelength range from 325 to 1 025 nm. The Vis/NIR spectra were acquired from 150 samples of five varieties of paddy. Secondly,original spectral data were decomposed as low-frequency wavelet coefficients and high-frequency wavelet coefficients by wavelet transform (WT) at first level. High-frequency wavelet coefficients were deleted as they contained too many noise,so the reconstructed signals from low-frequency wavelet coefficients were used as replacer of original spectral data. Thirdly,principal component analysis (PCA) compressed the above data into several new variables that were the linear combination of original spectral variables. The analysis suggested that the first four PCs (principle components) could account for 99.89% of the original spectral information,it means that the four PCs could explain most variation of original variables. In order to set up the model for discriminating varieties of paddy,the four diagnostic PCs were applied as inputs of back propagation artificial neural network (BP-ANN),and the values of varieties of different paddy were applied as the outputs of BP-ANN. The threshold of error was set as 0.2,the optimal structure of BP-ANN was three layers with nodes as 4-9-3. The whole 150 samples were randomly divided into two parts,one of which that consisted of 100 samples was used to model,and the other one containing 50 samples was used to predict. This model has been used to predict the varieties of 50 unknown samples,and the discrimination rate 96% has been obtained. It proved that the model was very reliable and practicable. In short,it is feasible to discriminate varieties of paddy based on visible/near infrared reflectance (Vis/NIR) spectroscopy and chemometrics.
|
|
Received: 2006-11-02
Accepted: 2007-01-12
|
|
|
|
Corresponding Authors:
YING Xia-fang
E-mail: yhe@zju.edu.cn
|
|
[1] Esteban-Diez I,Gonzalez-Saiz J M,Pizarro C. Analytica Chimica Acta,2004,514(1): 57.
[2] SUN Su-qin,TANG Jun-ming,YUAN Zi-min,et al(孙素琴，汤俊明，袁子民，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2003,23(2): 258.
[3] HE Yong，LI Xiao-li，SHAO Yong-ni(何 勇，李晓丽，邵咏妮). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2006,26(5): 850.
[4] Seregely Z,Deak D,Bisztray. Chemometrics and Intelligent Laboratory Systems,2004,72(2): 195.
[5] Cocchi M,Corbellini M,Foca G,et al Analytica Chimica Acta,2005,544(1-2): 100.
[6] Li Xiaoli,He Yong,Wu Changqing,et al. Journal of Food Engineering,2007,82(3): 316.
[7] YAN Yan-lu,ZHAO Long-lian,HAN Dong-hai,et al(严衍禄,赵龙莲,韩东海,等). The Foundation of Near Infrared Spectroscopy Analysis and Application(近红外光谱分析基础与应用). Beijing: China Light Industry Press(北京: 中国轻工业出版社),2005. 108.
[8] Cen Haiyan,He Yong,Huang Min. Journal of Agricultural and Food Chemistry. 2006,54(20): 7437.
[9] Shao Yongni,He Yong. Journal of Food Engineering,2007,79(3): 1015.
[10] Wand D,Ram M S,Dowell E E. Transactions of the ASAE,2002,45(6): 1943.
[11] Mouwen D J M,Capita R,Alonso-Calleja C,et al. Journal of Microbiological Methods,2006,67(1): 131.
[12] Dubey B P,Bhagwat S G,Shouche S P,et al. Biosystems Engineering,2006,95(1): 61.
[13] Burks T F,Shearer S A,Heath J R,et al. Biosystems Engineering,2005,91(3): 293.
[14] Donald D,Everinghamy,Coomans D. Chemometrics and Intelligent Laboratory Syatems,2005,77(1-2): 32.
[15] Vannucci M,Sha N J,Brown P J. Chemometrics and Intelligent Laboratory Systems,2005,77(1-2): 139.
[16] CHEN Bin,HUANG Chuan-xu,LU Dao-li(陈 斌，黄传旭，陆道礼). Journal of Jiangsu University (Natural Science Edition(江苏大学学报·自然科学版),2004,25(2): 105. |
| [1] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
| [2] |
GAO Hong-sheng1, GUO Zhi-qiang1*, ZENG Yun-liu2, DING Gang2, WANG Xiao-yao2, LI Li3. Early Classification and Detection of Kiwifruit Soft Rot Based on
Hyperspectral Image Band Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 241-249. |
| [3] |
CHU Bing-quan1, 2, LI Cheng-feng1, DING Li3, GUO Zheng-yan1, WANG Shi-yu1, SUN Wei-jie1, JIN Wei-yi1, HE Yong2*. Nondestructive and Rapid Determination of Carbohydrate and Protein in T. obliquus Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3732-3741. |
| [4] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
| [5] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
| [6] |
MU Da1, 2, WANG Qi-shu1, 2*, CUI Zong-yu1, 2, REN Jiao-jiao1, 2, ZHANG Dan-dan1, 2, LI Li-juan1, 2, XIN Yin-jie1, 2, ZHOU Tong-yu3. Study on Interference Phenomenon in Terahertz Time Domain
Spectroscopy Nondestructive Testing of Glass Fiber Composites[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3031-3040. |
| [7] |
HUANG Hua1, LIU Ya2, KUERBANGULI·Dulikun1, ZENG Fan-lin1, MAYIRAN·Maimaiti1, AWAGULI·Maimaiti1, MAIDINUERHAN·Aizezi1, GUO Jun-xian3*. Ensemble Learning Model Incorporating Fractional Differential and
PIMP-RF Algorithm to Predict Soluble Solids Content of Apples
During Maturing Period[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3059-3066. |
| [8] |
CAI Jian-rong1, 2, HUANG Chu-jun1, MA Li-xin1, ZHAI Li-xiang1, GUO Zhi-ming1, 3*. Hand-Held Visible/Near Infrared Nondestructive Detection System for Soluble Solid Content in Mandarin by 1D-CNN Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2792-2798. |
| [9] |
ZHANG Fu1, 2, WANG Xin-yue1, CUI Xia-hua1, YU Huang1, CAO Wei-hua1, ZHANG Ya-kun1, XIONG Ying3, FU San-ling4*. Identification of Maize Varieties by Hyperspectral Combined With Extreme Learning Machine[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2928-2934. |
| [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] |
LUO Zheng-fei1, GONG Zheng-li1, 2, YANG Jian1, 2*, YANG Chong-shan2, 3, DONG Chun-wang3*. Rapid Non-Destructive Detection Method for Black Tea With Exogenous Sucrose Based on Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2649-2656. |
| [12] |
ZHANG Yue1, 2, LI Yang1, 2, SONG Yue-peng1, 2*. Nondestructive Detection of Slight Mechanical Damage of Apple by Hyperspectral Spectroscopy Based on Stacking Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2272-2277. |
| [13] |
ZHANG Mei-zhi1, ZHANG Ning1, 2, QIAO Cong1, XU Huang-rong2, GAO Bo2, MENG Qing-yang2, YU Wei-xing2*. High-Efficient and Accurate Testing of Egg Freshness Based on
IPLS-XGBoost Algorithm and VIS-NIR Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1711-1718. |
| [14] |
ZHENG Shuo-zai1, LI Hao-miao1, WENG Lian-xi1, HAN Bin2*, SHI Ji-long1*. Nondestructive Analysis of “Sprinkled Gold” Materials of Gold Sprinkled Paper Samples in Qing Dynasty[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1781-1786. |
| [15] |
GAO Lu-yue1, SHEN Ling2, ZHANG Juan1*, ZHANG Hui1. Non-Destructive Analysis for Dyeing Process of Memorials From the Late Qing Dynasty[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1063-1067. |
|
|
|
|
