| 光谱学与光谱分析 |
|
|
|
|
|
| Nondestructive Measurement of Sugar Content of Hami Melon Based on Diffuse Reflectance Hyperspectral Imaging Technique |
| MA Ben-xue1,2, XIAO Wen-dong1,2, QI Xiang-xiang1,2, HE Qing-hai1,2, LI Feng-xia1,2 |
1. College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
2. Agricultural Machinery Key Laboratory of Xinjiang BINGTUAN, Shihezi 832003, China |
|
|
|
|
Abstract The research on nondestructive test for detecting the sugar content of Hami melon by the technology of hyperspectral imaging was put forward. The research used the hyperspectral imaging system to get the diffuse reflective spectrum information (400～1 000 nm) of anilox class Hami melon sugar content, chose effective whole wavelength(500～820 nm)to do the modeling regression analysis the sugar content of Hami melon. The research compared the correction method of MSC and SNV, and also compared the influence of accuracy of modeling in terms of the spectrum pretreatment methods of original spectrum, first order differential, second order differential; Using the methods of PLS, SMLR and PCR, the comparative analysis of sugar content detection model effect with skin Hami melon and peel Hami melon was conducted. The results showed that after the original spectrum being processed by MSC and first order differential spectrum, modeling effect could be very good using the method of PLS and SMLR. Synthesizing correction set correlation coefficient and forecast modeling effect, it’s feasible to detect the sugar content of skin Hami melon by the PLS method, with a correction sample correlation coefficient (Rc) of 0.861 and the lower root mean square errors of correction (RMSEC) of 0.627, and a prediction sample correlation coefficient (Rp) of 0.706 and root mean square errors of prediction (RMSEP) of 0.873. The best effect to detecti the sugar content of peel Hami melon was obtained by the SMLR method with a correction sample correlation coefficient (Rc) of 0.928 and the lower root mean square errors of correction (RMSEC) of 0.458, with a Prediction sample correlation coefficient (Rp) of 0.818 and root mean square errors of prediction (RMSEP) of 0.727. The results of this study indicate that the technology of hyperspectral imaging can be used to predict the sugar content of Hami melon.
|
|
Received: 2012-04-18
Accepted: 2012-08-20
|
|
|
|
Corresponding Authors:
MA Ben-xue
E-mail: mbx_shz@163.com
|
|
[1] Dull G G, Birth G S, Smittle D A, et al. Journal of Food Science, 1989, 54(2): 393.
[2] Dull G G, Leffler R G, Birth G S. HortScience, 1990, 25: 1132.
[3] Dull G G, Leffler R G, Birth G S, et al. Transactions of the ASAE, 1992, 35(2): 735.
[4] Tsuta M, Sugiyama J, Sagara Y. Journal of Agricultural and Food Chemistry, 2002, 50: 48.
[5] Maruo T, Ito T. Acta Horticulture, 2002, 588: 373.
[6] TIAN Hai-qing, YING Yi-bin, XU Hui-rong, et al(田海清, 应义斌, 徐惠荣, 等). Transaction of the Chinese Society for Agricultural Machinery(农业机械学报), 2007, 38(5): 111.
[7] TIAN Hai-qing, WANG Chun-guang, WU Gui-fang(田海清, 王春光, 吴桂芳). Transaction of the Chinese Society for Agricultural Machinery(农业机械学报), 2010, 41(12): 130.
[8] Zhao Jiewen, Vittayapadung Saritporn, Chen Quansheng, et al. Maejo International Journal of Science and Technologr, 2009, 3(01): 130.
[9] MIAO Fu-sheng, MA Yi, WANG Xi-yuan, et al(苗福生, 马 毅, 汪西原, 等). Journal of Ningxia University·Natural Science Edition(宁夏大学学报·自然科学版), 2011, 32(2): 130.
[10] LIU Yan-de, CHEN Xing-miao, OUYANG Ai-guo(刘燕德, 陈兴苗, 欧阳爱国). Acta Optica Sinica(光学学报), 2008, 28(3): 478.
[11] WANG Jia-hua, LI Peng-fei, CAO Nan-ning, et al(王加华, 李鹏飞, 曹楠宁, 等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2009, 28(5): 386.
[12] PENG Yan-kun, HUANG Hui, WANG Wei, et al(彭彦昆, 黄 慧, 王 伟, 等). Journal of Jiangsu University·Natural Science Edition(江苏大学学报·自然科学版), 2011, 32(2): 1671.
[13] XU Hui-rong, CHEN Xiao-wei, YING Yi-bin(徐惠荣, 陈晓伟, 应义斌). Transaction of the Chinese Society for Agricultural Machinery(农业机械学报), 2010, 41(12): 126.
[14] YUE Rong, GUO Wen-chuan, LIU Hui(岳 绒, 郭文川, 刘 卉). Food Science(食品科学), 2011, 32(10): 141.
|
| [1] |
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. |
| [2] |
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. |
| [3] |
YUAN Wei-dong1, 2, JU Hao2, JIANG Hong-zhe1, 2, LI Xing-peng2, ZHOU Hong-ping1, 2*, SUN Meng-meng1, 2. Classification of Different Maturity Stages of Camellia Oleifera Fruit
Using Hyperspectral Imaging Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3419-3426. |
| [4] |
SHEN Ying, WU Pan, HUANG Feng*, GUO Cui-xia. Identification of Species and Concentration Measurement of Microalgae Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3629-3636. |
| [5] |
YANG Lei1, 2, 3, ZHOU Jin-song1, 2, 3, JING Juan-juan1, 2, 3, NIE Bo-yang1, 3*. Non-Uniformity Correction Method for Splicing Hyperspectral Imager Based on Overlapping Field of View[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3582-3590. |
| [6] |
DONG Jian-jiang1, TIAN Ye1, ZHANG Jian-xing2, LUAN Zhen-dong2*, DU Zeng-feng2*. Research on the Classification Method of Benthic Fauna Based on
Hyperspectral Data and Random Forest Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3015-3022. |
| [7] |
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. |
| [8] |
WEI Zi-kai, WANG Jie, ZHANG Ruo-yu, ZHANG Meng-yun*. Classification of Foreign Matter in Cotton Using Line Scan Hyperspectral Transmittance Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3230-3238. |
| [9] |
SUN Bang-yong1, YU Meng-ying1, YAO Qi2*. Research on Spectral Reconstruction Method From RGB Imaging Based on Dual Attention Mechanism[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2687-2693. |
| [10] |
LIU Wen-bo, LIU Jin, HAN Tong-shuai*, GE Qing, LIU Rong. Simulation of the Effect of Dermal Thickness on Non-Invasive Blood Glucose Measurement by Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2699-2704. |
| [11] |
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. |
| [12] |
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. |
| [13] |
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. |
| [14] |
MAO Yi-lin1, LI He1, WANG Yu1, FAN Kai1, SUN Li-tao2, WANG Hui3, SONG Da-peng3, SHEN Jia-zhi2*, DING Zhao-tang1, 2*. Quantitative Judgment of Freezing Injury of Tea Leaves Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2266-2271. |
| [15] |
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. |
|
|
|
|
