| 光谱学与光谱分析 |
|
|
|
|
|
| Pretreatment Method of Near-Infrared Diffuse Reflection Spectra Used for Sugar Content Prediction of Pears |
| WANG Wei-ming1,2, DONG Da-ming1*, ZHENG Wen-gang1, ZHAO Xian-de1, JIAO Lei-zi1, WANG Ming-fei1, 2 |
1. National Engineering Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
2. School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China |
|
|
|
|
Abstract The content of sugar is an important quality index for pears. However, the traditional sugar measurement methods are time-consuming and destructive. In the present study, the authors measured the sugar content of pears using visible and near infrared diffuse reflection spectroscopy. The pretreatment methods of multiplicative scatter correction (MSC), baseline correction, standard normal variate (SNV) transformation, and moving average algorithms were used on the original absorbance spectrum. Results indicate that the absorbance spectra after pretreatment are better than the original absorbance spectra for prediction. Partial least squares (PLS) regression was also used on the original absorbance spectrum and the absorbance spectrum after moving average and baseline correction. It follows that the forecast accuracy of the absorbance spectra after moving average is higher than that of the original absorbance spectra. The models gave good predictions of the sugar content of pears, with corresponding r values of 0.990 8, and standard errors of predictions of 0.019 0.
|
|
Received: 2012-05-21
Accepted: 2012-09-18
|
|
|
|
Corresponding Authors:
DONG Da-ming
E-mail: dongdm@nercita.org.cn
|
|
[1] Lammertyn J, Nicolai B, Ooms. Trans. K of the ASAE，1998，41(4): 1089.
[2] Peiris K H S, Dull G G. Hort Science，1999，34: 114.
[3] Lu R, Ariana D. Trans. of the ASAE, 2002, 18: 585.
[4] CHEN Shi-ming, ZHANG Wen-hong, ZHANG Guang-wen(陈世铭，张文宏，张广文). Journal of Agricultural Machinery(农业机械学报)，1998，7(3): 41.
[5] GUO Li-ying, CHEN Shi-ming, ZHANG Wen-hong(郭立颖，陈世铭，张文宏). Journal of Agricultural Machinery(农业机械学报)，1998，7(1): 75.
[6] ZHANG Hai-liang, SUN Xu-dong(章海亮，孙旭东). Journal of Northwest A&F University (Nat. Sci. Ed.)(西北农林科技大学学报·自然科学版), 2010，38: 128.
[7] LIU Yan-de, YING Yi-bin, JIANG Huan-yu(刘燕德，应义斌，蒋焕煜). Chinese Journal of Sensors and Actuators(传感技术学报)，2003，16: 328.
[8] CHEN Zhi-ping，XIAO Jie-zong(陈致平，萧介宗). Journal of Agricultural Machinery(农业机械学报)，1999，8: 49.
[9] Dull G G, Birth G S, Smittle D. Journal of Food Science, 1989，54: 393.
[10] Lu R. Transactions of the ASAE，2001，44: 393.
[11] CHU Xiao-li(褚小立). Molecular Spectroscopy Analytical Technology Combined with Chemometrics and its Applications(化学计量学方法与分子光谱分析技术). Beijing: Chemical Industry Press(北京：化学工业出版社), 2011. 59. |
| [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] |
LI Yu1, ZHANG Ke-can1, PENG Li-juan2*, ZHU Zheng-liang1, HE Liang1*. Simultaneous Detection of Glucose and Xylose in Tobacco by Using Partial Least Squares Assisted UV-Vis Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 103-110. |
| [3] |
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. |
| [4] |
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. |
| [5] |
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. |
| [6] |
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. |
| [7] |
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. |
| [8] |
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. |
| [9] |
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. |
| [10] |
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. |
| [11] |
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. |
| [12] |
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. |
| [13] |
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. |
| [14] |
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. |
| [15] |
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. |
|
|
|
|
