光谱学与光谱分析 |
|
|
|
|
|
Nondestructive Discrimination of Waxed Apples Based on Hyperspectral Imaging Technology |
GAO Jun-feng1, ZHANG Hai-liang1, KONG Wen-wen1, HE Yong1, 2* |
1. College of Biosystems Engineering and Food Science, Zhejiang University,Hangzhou 310058,China 2. Cyrus Tang Center for Sensor Materials and Applications, Zhejiang University,Hangzhou 310058,China |
|
|
Abstract The potential of hyperspectral imaging technology was evaluated for discriminating three types of waxed apples. Three types of apples smeared with fruit wax, with industrial wax, and not waxed respectively were imaged by a hyperspectral imaging system with a spectral range of 308~1 024 nm. ENVI software processing platform was used for extracting hyperspectral image object of diffuse reflection spectral response characteristics. Eighty four of 126 apple samples were selected randomly as calibration set and the rest were prediction set. After different preprocess, the related mathematical models were established by using the partial least squares (PLS), the least squares support vector machine (LS-SVM) and BP neural network methods and so on. The results showed that the model of MSC-SPA-LSSVM was the best to discriminate three kinds of waxed apples with 100%, 100% and 92.86% correct prediction respectively.
|
Received: 2012-11-21
Accepted: 2013-03-26
|
|
Corresponding Authors:
HE Yong
E-mail: yhe@zju.edu.cn
|
|
[1] Els A Veraverbeke, Jeroen Lammertyn, Stijn Saevels, et al. Postharvest Biology and Technology, 2001, 23(3): 197. [2] Stephen J Kenney, Larry R Beuchat. International Journal of Food Microbiology, 2002, 77(3): 257. [3] Glenn G M, Rom C R, Rasmussen H P. Scientia Horticulturae, 1990, 42(4): 289. [4] Gabriel A, Leiva-Valenzuela, Lu Renfu,et al. Journal of Food Engineering, 2013, 115(1): 91. [5] Ariana D, Lu R, Guyer D E. Computers and Electronics in Agriculture, 2006, 53(1): 60. [6] Chao K, Yang C C, Kim M S, et al. Applied Engineering in Agriculture, 2008, 24(4): 475. [7] Roggo Y, Edmond A, Chalus P. Analytica Chimica Acta, 2005, 535(1-2): 79. [8] Douglas Barbin, Gamal Elmasry, Sun Dawen, et al. Meat Science,2012, (90): 259. [9] Masoud Taghizadeh, Aoife A. Gowen, Colm P O’Donnell. Computers and Electronics in Agriculture, 2011, 77(1): 74. [10] SHI Ji-yong, ZOU Xiao-bo,ZHAO Jie-wen, et al(石吉勇, 邹小波, 赵杰文, 等). Journal of Jiangsu University: Natural Science Edition(江苏大学学报·自然科学版),2011, 32(2): 134. [11] Suykens J A K, Vandewalle J. Neural Processing Letters, 1999, 9(3): 293. [12] Li Xiaoli, Nie Pengcheng, He Yong, et al. Expert System with Applications, 2011, 38(9): 11149. [13] CAO Fang, WU Di, ZHENG Jin-tu, et al(曹 芳,吴 迪,郑金土, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2011, 31(4):920. [14] Wu Di, He Yong, Feng Shuijuan, et al. Journal of Food Engineering, 2008, 84(1): 124. [15] HE Yong, LI Xiao-li (何 勇, 李晓丽). Journal of Infrared and Millimeter Waves(红外与毫米波学报),2006, 25(3): 192. [16] WU Di, WU Hong-xi, CAI Jing-bo, et al(吴 迪, 吴洪喜, 蔡景波, 等). Journal of Infrared and Millimeter Waves(红外与毫米波学报),2009, 28(6): 423. [17] HUANG Ling-xia, WU Di, JIN Hang-feng, et al(黄凌霞,吴 迪,金航峰, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报),2010,26(2): 231. [18] ZHANG Xiao-chao,WU Jing-zhu, XU Yun(张小超,吴静珠,徐 云). Near Infrared Speactroscopy Analysis Technology and Its Application in Modern Agriculture(近红外光谱分析技术及其在现代农业中的应用). Beijing: Publishing House of Electronics Industy(北京:电子工业出版社),2012.
|
[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 Hu1, ZHONG Yun1, 2, FENG Ya-ting1, LIN Zhen1, ZHU Shi-jiang1, 2*. Multi-Vegetation Index Soil Moisture Inversion Model Based on UAV
Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 207-214. |
[3] |
WANG Cai-ling1,ZHANG Jing1,WANG Hong-wei2*, SONG Xiao-nan1, JI Tong3. A Hyperspectral Image Classification Model Based on Band Clustering and Multi-Scale Structure Feature Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 258-265. |
[4] |
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. |
[5] |
WU Hu-lin1, DENG Xian-ming1*, ZHANG Tian-cai1, LI Zhong-sheng1, CEN Yi2, WANG Jia-hui1, XIONG Jie1, CHEN Zhi-hua1, LIN Mu-chun1. A Revised Target Detection Algorithm Based on Feature Separation Model of Target and Background for Hyperspectral Imagery[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 283-291. |
[6] |
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. |
[7] |
HUANG You-ju1, TIAN Yi-chao2, 3*, ZHANG Qiang2, TAO Jin2, ZHANG Ya-li2, YANG Yong-wei2, LIN Jun-liang2. Estimation of Aboveground Biomass of Mangroves in Maowei Sea of Beibu Gulf Based on ZY-1-02D Satellite Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3906-3915. |
[8] |
ZHOU Bei-bei1, LI Heng-kai1*, LONG Bei-ping2. Variation Analysis of Spectral Characteristics of Reclaimed Vegetation in an Ionic Rare Earth Mining Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3946-3954. |
[9] |
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. |
[10] |
FU Gen-shen1, LÜ Hai-yan1, YAN Li-peng1, HUANG Qing-feng1, CHENG Hai-feng2, WANG Xin-wen3, QIAN Wen-qi1, GAO Xiang4, TANG Xue-hai1*. A C/N Ratio Estimation Model of Camellia Oleifera Leaves Based on
Canopy Hyperspectral Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3404-3411. |
[11] |
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. |
[12] |
XIE Peng, WANG Zheng-hai*, XIAO Bei, CAO Hai-ling, HUANG Yi, SU Wen-lin. Hyperspectral Quantitative Inversion of Soil Selenium Content Based on sCARS-PSO-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3599-3606. |
[13] |
QIAN Rui1, XU Wei-heng2, 3 , 4*, HUANG Shao-dong2, WANG Lei-guang2, 3, 4, LU Ning2, OU Guang-long1. Tea Plantations Extraction Based on GF-5 Hyperspectral Remote Sensing
Imagery in the Mountainous Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3591-3598. |
[14] |
ZHU Zhi-cheng1, WU Yong-feng2*, MA Jun-cheng2, JI Lin2, LIU Bin-hui3*, JIN Hai-liang1*. Response of Winter Wheat Canopy Spectra to Chlorophyll Changes Under Water Stress Based on Unmanned Aerial Vehicle Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3524-3534. |
[15] |
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. |
|
|
|
|