| 光谱学与光谱分析 |
|
|
|
|
|
| Study on Identification the Crack Feature of Fresh Jujube Using Hyperspectral Imaging |
| YU Ke-qiang1, ZHAO Yan-ru1, LI Xiao-li1, ZHANG Shu-juan2, HE Yong1* |
1. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
2. College of Engineering, Shanxi Agricultural University, Taigu 030801, China |
|
|
|
|
Abstract Crack is one of the most important indicators to evaluate the quality of fresh jujube. Crack not only accelerates the decay of fresh jujube, but also diminishes the shelf life and reduces the economic value severely. In this study, the potential of hyperspectral imaging covered the range of 380～1 030 nm was evaluated for discrimination crack feature (location and area) of fresh jujube. Regression coefficients of partial least squares regression (PLSR), successive projection analysis (SPA) and principal component analysis (PCA) based full-bands image were adopted to extract sensitive bands of crack of fresh jujube. Then least-squares support vector machine (LS-SVM) discriminant models using the selected sensitive bands for calibration set (132 samples) were established for identification the prediction set (44 samples). ROC curve was used to judge the discriminant models of PLSR-LS-SVM, SPA-LS-SVM and PCA-LS-SVM which are established by sensitive bands of crack of fresh jujube. The results demonstrated that PLSR-LS-SVM model had an optimal effect (area=1, std=0) to discriminate crack feature of fresh jujube. Next, images corresponding to five sensitive bands (467, 544, 639, 673 and 682 nm) selected by PLSR were executed to PCA. Finally, the image of PC4 was employed to identify the location and area of crack feature through imaging processing. The results revealed that hyperspectral imaging technique combined with image processing could achieve the qualitative discrimination and quantitative identification of crack feature of fresh jujube, which provided a theoretical reference and basis for develop instrument of discrimination of crack of jujube in further work.
|
|
Received: 2013-05-06
Accepted: 2013-07-18
|
|
|
|
Corresponding Authors:
HE Yong
E-mail: yhe@zju.edu.cn
|
|
[1] LIU Guang-sheng，LIU Yuan-yuan，MA Shu-ping, et al(刘光生，刘源源，马树平，等). Journal of Shanxi Agriculture Sciences(山西农业科学), 2012, 40(2): 150.
[2] Rajkumar P, Wang N, Elmasry G, et al. Journal of Food Engineering, 2012, 108: 194.
[3] Gabriel A, Leiva V, Lu R F, et al. Journal of Food Engineering, 2013, 115: 91.
[4] Mendoza F, Lu R F, Afriana D W, et al. Postharvest Biology and Technology, 2011, 62: 149.
[5] Wang J, Nakano K, Ohashi S, et al. Biosystems Engineering, 2011, 108: 345.
[6] Cho B K , Kim M S, et al. Postharvest Biology and Technology, 2013, 76: 40.
[7] Wu D, Shi H, Wang S J, et al. Analytic Chimica Acta, 2012, 726: 57.
[8] LI Jiang-bo, RAO Xiu-qin, YING Yin-bin, et al(李江波，饶秀勤，应义斌，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2010, 26(8): 222.
[9] Wu D, Sun D W. Talanta, 2013, 111: 39.
[10] ZOU Hong-xia, QIN Feng, CHENG Ze-kai, et al(邹洪侠，秦 锋，程泽凯，等). Computer Technology and Development(计算机技术与发展), 2009, 19(6): 109.
[11] Li X L, Nie P C, Qiu Z J, et al. Expert Systems with Applications, 2011, 38: 11149.
[12] HUANG Wen-qian, CHEN Li-ping, LI Jiang-bo, et al(黄文倩，陈立平，李江波，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2013, 29(1): 272.
[13] Abbott J A, Lu R, Upchurch B L, et al. Horticultural Review, 1997，(20): 1.
[14] ElMasry G, Wang N, Vigneault C, et al. LWT-Food Science and Technology, 2008, 41: 337.
[15] He H J, Wu D, Sun D W. Innovative Food Science and Emerging Technologies, 2013, http://dx.doi.org/10.1016/j.ifset.2013.02.009.
|
| [1] |
ZHANG Qi-yan1, YANG Jie2, 3, LI Jian-guo1*, SHI Wei-xin1, GAO Peng-xin1. Research on Quantitative Identification of Rock Color Using
Spectral Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1905-1911. |
| [2] |
ZHANG Fan1, WANG Wen-xiu1, ZHANG Yu-fan1, HU Ze-xuan1, ZHAO Dan-yang1, MA Qian-yun1, SHI Hai-yan2, SUN Jian-feng1*. Hyperspectral and Ensemble Learning Method for Rapid Identification of Black Spot in Yali Pear at Gley Stage[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1541-1549. |
| [3] |
JIA Meng-meng, YIN Yong*, YU Hui-chun, YUAN Yun-xia, WANG Zhi-hao. Hyperspectral Imaging Combined With Feature Wavelength Screening for Monitoring the Quality Change of Tomato During Storage[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 969-975. |
| [4] |
HAO Jie, DONG Fu-jia, WANG Song-lei*, LI Ya-lei, CUI Jia-rui, LIU Si-jia, LÜ Yu. Rapid Detection of Pesticide Residues on Navel Oranges by Fluorescence Hyperspectral Imaging Technology Combined With Characteristic Wavelength Selection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3789-3796. |
| [5] |
JIN Cheng-qian1, 2, GUO Zhen1, ZHANG Jing1, MA Cheng-ye1, TANG Xiao-han1, ZHAO Nan1, YIN Xiang1. Non-Destructive Detection and Visualization of Soybean Moisture Content Using Hyperspectral Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3052-3057. |
| [6] |
GAO Rong-hua1, 2, FENG Lu1, 2*, ZHANG Yue3, YUAN Ji-dong3, WU Hua-rui1, 2, GU Jing-qiu1, 2. Early Detection of Tomato Gray Mold Disease With Multi-Dimensional Random Forest Based on Hyperspectral Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3226-3234. |
| [7] |
YANG Qiao-ling1, 2, CHEN Qin2, NIU Bing2, DENG Xiao-jun3*, MA Jin-ge3, GU Shu-qing3, YU Yong-ai4, GUO De-hua3, ZHANG Feng5. Visualization of Thiourea in Bulk Milk Powder Based on Portable Raman Hyperspectral Imaging Technology On-Site Rapid Detection Method
Research[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(07): 2156-2162. |
| [8] |
CAO Yao-yao1, 2, 4, LI Xia1, BAI Jun-peng2, 4, XU Wei2, 4, NI Ying3*, DONG Chuang2, 4, ZHONG Hong-li5, LI Bin2, 4*. Study on Qualitative and Quantitative Detection of Pefloxacin and
Fleroxacin Veterinary Drugs Based on THz-TDS Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1798-1803. |
| [9] |
SHI Ji-yong, LIU Chuan-peng, LI Zhi-hua, HUANG Xiao-wei, ZHAI Xiao-dong, HU Xue-tao, ZHANG Xin-ai, ZHANG Di, ZOU Xiao-bo*. Detection of Low Chromaticity Difference Foreign Matters in Soy Protein Meat Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1299-1305. |
| [10] |
QIAO Lu, WANG Song-lei*, GUO Jian-hong, HE Xiao-guang. Combination of Spectral and Textural Informations of Hyperspectral Imaging for Predictions of Soluble Protein and GSH Contents in Mutton[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 176-183. |
| [11] |
YE Rong-ke1, KONG Qing-chen1, LI Dao-liang1, 2, CHEN Ying-yi1, 2, ZHANG Yu-quan1, LIU Chun-hong1, 2*. Shrimp Freshness Detection Method Based on Broad Learning System[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 164-169. |
| [12] |
GAO Bing, XU Shuai, HAN Lu-jia, LIU Xian*. A Sequential Classification Strategy Applied to the Detection of Terrestrial Animal Lipid in Fish Oil by MID-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(08): 2427-2431. |
| [13] |
SUN Zong-bao, LI Jun-kui, LIANG Li-ming, ZOU Xiao-bo*, LIU Xiao-yu, NIU Zeng, GAO Yun-long. Prediction and Distribution Visualization of Salmon Quality Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(08): 2591-2597. |
| [14] |
WANG Cheng1, YU Hang1, YAO Wei-rong1, CHENG Yu-liang1, GUO Ya-hui1, QIAN He1, TAN Zhi-qiang2, XIE Yun-fei1*. A Study on the Screening of Anti-Inflammatory Drug Diclofenac Sodium in Dietary Supplements by Near Infrared Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(02): 592-598. |
| [15] |
SUN Zong-bao, LIANG Li-ming, LI Jun-kui, ZOU Xiao-bo*, LIU Xiao-yu, WANG Tian-zhen. Identification of Chilled and Frozen-Thawed Salmon Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(11): 3530-3536. |
|
|
|
|
