|
|
|
|
|
|
Discrimination of Oleoresins from Pinus msssoniana and Pinus elliottii by Near Infrared Spectroscopy |
YAN Jun1, HUANG Xiao-ping1, HUANG Yin-ning1, WU Ye-yu1, LIANG Zhong-yun2, LEI Fu-hou1*, TAN Xue-cai1 |
1. School of Chemistry Engineering, Guangxi University for Nationalities, Key Laboratory of Guangxi Colleges and Universities for Food Safe and Pharmaceutical Analytical Chemistry, Guangxi Key Laboratory of Chemistry and Engineering of Forest Products, Nanning 530008, China
2. Guangxi Research Institute of Forestry, Nanning 530001, China |
|
|
Abstract It is crucial to ensure the quality stability of rosin material since the different chemical constituents of various kinds of rosins will obviously influence the quality of down-stream product. Herein, a method based on near infrared spectroscopy and partial least squares discriminant analysis was proposed to discriminate oleoresins from Pinus msssoniana and Pinus elliottii, which could be helpful to identify the species of oleoresins during the purchasing process. Eighty-two oleoresin samples from six different producing areas of Guangxi, i. e. Wuming, Fangcheng, Fuchuan, Wuzhou, Baise and Leye, were collected to develop classification model. These collected samples were consisted of 51 Pinus msssoniana and 31 Pinus elliottii. Diffuse reflection modes were applied to obtain near infrared spectrum range from 900~1 700 nm. Then, several chemometrics techniques such as sub-window permutation analysis and repeated double cross validation were used to select optimal variables and the number of principal component. Finally, 300 variables were extracted from the original variable pool and the optimal number of principal component was set to 8. Results showed that the obtained model can accurately discriminate oleoresins from Pinus msssoniana and Pinus elliottii, and the classification accuracy of external test is 96.30%, which can meet the need of quality control. The proposed method is less time-consuming, easy to operate and low-cost, and it is suitable for the quality control of purchasing process.
|
Received: 2017-08-29
Accepted: 2017-12-25
|
|
Corresponding Authors:
LEI Fu-hou
E-mail: yanjun03@163.com
|
|
[1] DONG Jing-xi, GUO Hui-jun, ZHANG Zi-yi(董静曦,郭辉军,张子翼). Scientia Silvae Sinicae(林业科学),2016, 52(12):112.
[2] AN Ning, DING Gui-jie(安 宁,丁贵杰). Journal of Central South University of Forestry & Technology(中南林业科技大学学报),2012, 32(2):59.
[3] Galtier O, Abbas O, Le Dreau Y, et al. Vib. Spectrosc, 2011, 55(1): 132.
[4] Fan Wei, Li Hongdong, Shan Yang, et al. Anal. Methods, 2011, 3: 1872.
[5] Thyholt K, Isaksson T. J. Sci. Food Agric., 2015, 73(4): 525.
[6] Silvana N, Felipe Z S, Francielli R, et al. Wood Sci. Technol., 2016, 50: 71.
[7] Cao Dongsheng, Liang Yizeng, Xu Qingsong, et al. J. Chomb. Chem., 2010, 31(3): 592.
[8] Norinder U. J. Chemom., 2015, 10(2): 95.
[9] Li Hongdong, Zeng Maomao, Tan Bingbing, et al. Metabolomics, 2010, 6(3): 353. |
[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] |
BAO Hao1, 2,ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
[3] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
[4] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
[5] |
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. |
[6] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
[7] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
[8] |
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. |
[9] |
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. |
[10] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
[11] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
[12] |
KANG Ming-yue1, 3, WANG Cheng1, SUN Hong-yan3, LI Zuo-lin2, LUO Bin1*. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550. |
[13] |
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. |
[14] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
[15] |
GUO Ge1, 3, 4, ZHANG Meng-ling3, 4, GONG Zhi-jie3, 4, ZHANG Shi-zhuang3, 4, WANG Xiao-yu2, 5, 6*, ZHOU Zhong-hua1*, YANG Yu2, 5, 6, XIE Guang-hui3, 4. Construction of Biomass Ash Content Model Based on Near-Infrared
Spectroscopy and Complex Sample Set Partitioning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3143-3149. |
|
|
|
|