| 光谱学与光谱分析 |
|
|
|
|
|
| Establishment and Application of Model for Determining Oil Content of CottonseedUsing Near Infrared Spectroscopy |
| SHANG Lian-guang1, LI Jun-hui2, WANG Yu-mei3, LI Yu-hua1, WANG Dan1, XIONG Min1, HUA Jin-ping1* |
1. Department of Plant Genetics and Breeding, College of Agronomy & Biotechnology/Key Laboratory of Crop Heterosis and Utilization of Ministry of Education/Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China
2. Department of Electrical Engineering, College of Information & Electrical Engineering, China Agricultural University, Beijing 100083, China
3. Institute of Cash Crops, Hubei Academy of Agricultural Sciences, Wuhan 430064, China |
|
|
|
|
Abstract Cotton is one of the important oil crops, and it is great significance for screeningand identification of breeding materials to establish a method of the rapid, nondestructive testing of cotton seed oil content. In this study, near-infrared diffuse reflection spectroscopy of 118 high and low oil materials were adopted to establish models for fast nondestructive determining oil content of cottonseedusing near infrared spectroscopy (NIR). One hundred and six cottonseed samples as calibration set that covered the range of seed oil content for upland cotton were used in this experiment. The spectral data of cottonseed were processed using the first derivative and multiplicative scatter correction (MSC). The correction NIR modelof oil content was built based on partial least squares (PLS) method with the spectral regions 5 446～8 848 cm-1 and main components (5). The determination coefficient (R2) of calibration model was 0.975, standard error ofcalibration (SEC) was 0.67. The authors test the model’s actual ability to predict using external validation set. The correlation coefficient (r) of predicted values and the chemistry value was 0.978, the range of prediction error was 0.1%～1.7%. The model established has good predictability. The oil content of 784breeding stocks were predicted by NIR model, statistical analysis of predictable results elucidated that the NIR model of oil content developed can be well applied to selective breeding and oil related study in cotton.
|
|
Received: 2014-02-25
Accepted: 2014-05-21
|
|
|
|
Corresponding Authors:
HUA Jin-ping
E-mail: 05077@cau.edu.cn
|
|
[1] WANG Yan-xia, LIU Zheng-jie, MA Zhi-ying, et al(王彦霞，刘正杰，马峙英, 等). Cotton Science(棉花学报), 2011, 23(2): 178.
[2] SHEN Duan-zhuang, QIAN Si-ying, LIU Gui-lin, et al(沈端庄, 钱思颖, 刘桂林, 等). Crop Genetic Resources(作物品种资源), 1987，(4): 21.
[3] GB/T 5512-2008.
[4] GAN Li, SUN Xiu-li, JIN Liang, et al(甘莉, 孙秀丽, 金 良, 等). Scientia Agricultura Sinica(中国农业科学), 2003, 36(12): 1609.
[5] Sundaram J , Kandala C V, Holser R A, et al.J. Am. Oil. Chem. Soc., 2010, 87(10): 1103.
[6] SONG Li-hua, LIU Li-feng, CHEN Huan-ying, et al(宋丽华, 刘立峰, 陈焕英, 等). Chinese Agricultural Science Bulletin(中国农学通报), 2011, 27(15): 85.
[7] Lin Chen, Chen Xue, Jian Lei, et al. Food Chemistry, 2014，162：10.
[8] Hacisalihoglu G, Larbi B, Settles A M. J. Agric. Food Chem., 2010, 58: 702.
[9] Yang Xiushi, Wang Lili, Zhou Xian-rong, et al. Food Science and Biotechnology, 2013, 22(6): 1495.
[10] Alfred Q, Zhuang R H, Jian G W, et al. Journal of the American Oil Chemists’ Society, 2012, 89(4): 567.
[11] HUANG Zhuang-rong, SHA Sha, RONG Zheng-qin, et al(黄庄荣, 沙 莎, 荣正勤, 等). Chinese J. Anal. Chem.(分析化学), 2013, 41(6): 922.
[12] Huang Zhuangrong, Sha Sha, Rong Zheng-qin, et al. Industrial Crops and Products, 2013, 43: 654.
[13] HAN Zhi-biao, GUO Xiao-ping(韩智彪, 郭小平). Chinese Agricultural Science Bulletin(中国农学通报), 2014, 30(3): 173.
[14] LI Wei, XIAO Ai-ping, LENG Juan(李 伟, 肖爱平, 冷 鹃). Chinese Agricultural Science Bulletin(中国农学通报), 2009, 25(3): 56.
|
| [1] |
WANG Xue-pei1, 2, ZHANG Lu-wei1, 2, BAI Xue-bing3, MO Xian-bin1, ZHANG Xiao-shuan1, 2*. Infrared Spectral Characterization of Ultraviolet Ozone Treatment on Substrate Surface for Flexible Electronics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1867-1873. |
| [2] |
SHI Wen-qiang1, XU Xiu-ying1*, ZHANG Wei1, ZHANG Ping2, SUN Hai-tian1, 3, HU Jun1. Prediction Model of Soil Moisture Content in Northern Cold Region Based on Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1704-1710. |
| [3] |
WANG Yue1, 3, 4, CHEN Nan1, 2, 3, 4, WANG Bo-yu1, 5, LIU Tao1, 3, 4*, XIA Yang1, 2, 3, 4*. Fourier Transform Near-Infrared Spectral System Based on Laser-Driven Plasma Light Source[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1666-1673. |
| [4] |
FENG Rui-jie1, CHEN Zheng-guang1, 2*, YI Shu-juan3. Identification of Corn Varieties Based on Bayesian Optimization SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1698-1703. |
| [5] |
YU Zhi-rong, HONG Ming-jian*. Near-Infrared Spectral Quantitative Analysis Network Based on Grouped Fully Connection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1735-1740. |
| [6] |
MENG Fan-jia1, LUO Shi1, WU Yue-feng1, SUN Hong1, LIU Fei2, LI Min-zan1*, HUANG Wei3, LI Mu3. Characteristic Extraction Method and Discriminant Model of Ear Rot of Maize Seed Base on NIR Spectra[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1716-1720. |
| [7] |
PENG Yan-fang1, WANG Jun1, WU Zhi-sheng2*, LIU Xiao-na3, QIAO Yan-jiang2*. NIR Band Assignment of Tanshinone ⅡA and Cryptotanshinone by
2D-COS Technology and Model Application Tanshinone Extract[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1781-1785. |
| [8] |
WANG Li-qi1, YAO Jing1, WANG Rui-ying1, CHEN Ying-shu1, LUO Shu-nian2, WANG Wei-ning2, ZHANG Yan-rong1*. Research on Detection of Soybean Meal Quality by NIR Based on
PLS-GRNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1433-1438. |
| [9] |
FU Yan-hua1, LIU Jing2*, MAO Ya-chun2, CAO Wang2, HUANG Jia-qi2, ZHAO Zhan-guo3. Experimental Study on Quantitative Inversion Model of Heavy Metals in Soda Saline-Alkali Soil Based on RBF Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1595-1600. |
| [10] |
LI Jia-yi1, YU Mei1, LI Mai-quan1, ZHENG Yu2*, LI Pao1, 3*. Nondestructive Identification of Different Chrysanthemum Varieties Based on Near-Infrared Spectroscopy and Pattern Recognition Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1129-1133. |
| [11] |
CHEN Chu-han1, ZHONG Yang-sheng2, WANG Xian-yan3, ZHAO Yi-kun1, DAI Fen1*. Feature Selection Algorithm for Identification of Male and Female
Cocoons Based on SVM Bootstrapping Re-Weighted Sampling[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1173-1178. |
| [12] |
LI Xue-ying1, 2, LI Zong-min3*, CHEN Guang-yuan4, QIU Hui-min2, HOU Guang-li2, FAN Ping-ping2*. Prediction of Tidal Flat Sediment Moisture Content Based on Wavelet Transform[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1156-1161. |
| [13] |
ZHANG Xiao-hong1, JIANG Xue-song1*, SHEN Fei2*, JIANG Hong-zhe1, ZHOU Hong-ping1, HE Xue-ming2, JIANG Dian-cheng1, ZHANG Yi3. Design of Portable Flour Quality Safety Detector Based on Diffuse
Transmission Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1235-1242. |
| [14] |
ZHENG Kai-yi1, ZHANG Wen1, DING Fu-yuan1, ZHOU Chen-guang1, SHI Ji-yong1, Yoshinori Marunaka2, ZOU Xiao-bo1*. Using Ensemble Refinement (ER) Method to OptimizeTransfer Set of Near-Infrared Spectra[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1323-1328. |
| [15] |
CHENG Jie-hong1, CHEN Zheng-guang1, 2*, YI Shu-juan2. Wavelength Selection Algorithm Based on Minimum Correlation Coefficient for Multivariate Calibration[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 719-725. |
|
|
|
|
