| 光谱学与光谱分析 |
|
|
|
|
|
| Study on Multi-Bands Recognition for Varieties of Mutton by Using Hyperspectral Technologies |
| WANG Song-lei1，2, WU Long-guo1, MA Tian-lan2, CHEN Ya-bin2, HE Jian-guo1，2*, HE Xiao-guang2, KANG Ning-bo1 |
1. School of Construction and Hydraulic Engineering, Ningxia University, Yinchuan 750021, China
2. School of Agriculture, Ningxia University, Yinchuan 750021, China |
|
|
|
|
Abstract This paper focused on the research on identifying and classifying for mutton varieties of Tan-han hybrid sheep,Yanchi Tan-sheep and small-tailed sheep in Ningxia by using visible/ near-infrared (400~1 000 nm). Near infrared (900~1 700 nm) hyperspectral technologies, baseline and SG convolution smoothing spectra pretreatment methods were applied respectively according to the characteristics of different spectrum bands; the characteristic wavelengths were extracted by using successive projection algorithm (SPA);then combined with linear discriminant analysis (LDA) and radial basis kernel function of support vector machine (RBFSVM) model were applied to identify the different mutton varieties under characteristic wavelengths and full-wave bands. Results showed that there were good effects for mutton varieties identification in different hyperspectral bands, among which Baseline-Fullwave-RBFSVM and the same models under 12 characteristic wavelengths obtained accuracy of 100% and 98.75% in 400~1 000 nm respectively, and Baseline-Fullwave-RBFSVM and the same models under 7 characteristic wavelengths obtained accuracy of 96.25% and 87.80% in 900~1 700 nm respectively.The identification accuracy of RBFSVM nonlinear classification was higher than the LDA linear discriminant result, meanwhile the identification accuracy in 400~1 000 nm bands was better than in 1 000~1 700 nm bands, which explained that the differences of color and texture were more significant than the component contents among the 3 varieties mutton. Combined hyperspectral technologies with RBFSVM models can obtain a better recognition effect of mutton varieties.
|
|
Received: 2015-09-29
Accepted: 2015-12-21
|
|
|
|
Corresponding Authors:
HE Jian-guo
E-mail: hejg@nxu.edu.cn
|
|
[1] WANG Jia-yun, WANG Song-lei, HE Jian-guo(王家云, 王松磊, 何建国). Modern Food Science and Technology(现代食品科技)，2014, 30(6): 257.
[2] ZHU Feng-le, HE Yong, SHAO Yong-ni(朱逢乐, 何 勇, 邵永妮). Spectrosopy and Spectral Analysis(光谱学与光谱分析)，2015, 35(1)：113.
[3] Qiao J, Ngadi M O, Wang N, et al. Journal of Food Engineering, 2007, 83(1): 10.
[4] Qiao J, Wang N, Ngadi M O, et al. Meat Science, 2007, 76( 1): 1.
[5] Barbin D, Elmasry G, Sun D W, et al. Analytica Chimica Acta, 2012, 719: 30.
[6] Barbin D, Elmasry G, Sun D W, et al. Meat Science, 2012, 90(1): 259.
[7] Wu Jianhu, Peng Yankun, Li Yongyu. Journal of Food Engineering, 2012，109: 267.
[8] ZHANG Lei-lei, PENG Yan-kun, TAO Fei-fei(张雷蕾, 彭彦昆, 陶斐斐). Journal of Food Safety and Quality(食品安全质量检测学报), 2012, 3(6): 545.
[9] Wu Jianhu, Peng Yankun, Li Yongyu. Journal of Food Engineering, 2012，109: 267.
[10] Tao Feifei, Peng Yankun, Li Yongyu, et al. Meat Science, 2012，90: 851.
[11] Mohammed K, Gamal E, Sun Dawen. Innovative Food Science and Emerging Technologies, 2012, 7: 3.
[12] Douglas F B, Gamal E, Sun Dawen. Analytica Chimica Acta, 2012，719: 30.
[13] Mohammed K, Gamal E, Sun Dawen. Analytica Chimica Acta, 2012，714: 57.
[14] Mohammed K, Gamal E, Sun Dawen. Journal of Food Engineering, 2011，(104)：332.
[15] Mohammed K, Douglas B, Gamal E, et al. Innovative Food Science and Emerging Technologies, 2012, 8: 28.
[16] ZOU Xiao-bo, LI Zhi-hua, SHI Ji-yong(邹小波, 李志华, 石吉勇). Food Science(食品科学), 2014, 35(8): 89.
[17] MA Shi-bang, XU Yang, PENG Yan-kun(马世榜, 徐 杨, 彭彦昆). Journal of Food Safety and Quality(食品安全质量检测学报), 2012, 3(6): 603.
[18] WEN Zhen-cai, SUN Tong, LIU Mu-hua(温珍才, 孙 通, 刘木华). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2013，33(9): 2354.
[19] DU Min, GONG Ying, HUA Guo-dong(杜 敏, 巩 颖, 华国栋). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2013，33(5): 1211. |
| [1] |
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. |
| [2] |
CHENG Hui-zhu1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, MA Qian1, 2, ZHAO Yan-chun1, 2. Genetic Algorithm Optimized BP Neural Network for Quantitative
Analysis of Soil Heavy Metals in XRF[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3742-3746. |
| [3] |
LI Wei1, TAN Feng2*, ZHANG Wei1, GAO Lu-si3, LI Jin-shan4. Application of Improved Random Frog Algorithm in Fast Identification of Soybean Varieties[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3763-3769. |
| [4] |
SHEN Si-cong, ZHANG Jing-xue, CHEN Ming-hui, LI Zhi-wei, SUN Sheng-nan, YAN Xue-bing*. Estimation of Above-Ground Biomass and Chlorophyll Content of
Different Alfalfa Varieties Based on UAV Multi-Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3847-3852. |
| [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] |
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. |
| [7] |
LI Wen-wen1, 2, LONG Chang-jiang1, 2, 4*, LI Shan-jun1, 2, 3, 4, CHEN Hong1, 2, 4. Detection of Mixed Pesticide Residues of Prochloraz and Imazalil in
Citrus Epidermis by Surface Enhanced Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3052-3058. |
| [8] |
YANG Chun-mei1, ZHU Zan-bin1, 2*, LI Yu-cheng1, MA Yan1, SONG Hai-yang3. Bark Content Determination of Ultra-Thin Fibreboard by
Hyperspectral Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3266-3271. |
| [9] |
LIU Fei1, TAN Jia-jin1*, XIE Gu-ai2, SU Jun3, YE Jian-ren1. Early Diagnosis of Pine Wilt Disease Based on Hyperspectral Data and Needle Resistivity[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3280-3285. |
| [10] |
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. |
| [11] |
MA Qian1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, CHENG Hui-zhu1, 2, ZHAO Yan-chun1, 2. Research on Classification of Heavy Metal Pb in Honeysuckle Based on XRF and Transfer Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2729-2733. |
| [12] |
KONG De-ming1, LIU Ya-ru1, DU Ya-xin2, CUI Yao-yao2. Oil Film Thickness Detection Based on IRF-IVSO Wavelength Optimization Combined With LIF Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2811-2817. |
| [13] |
LÜ Shi-lei1, 2, 3, WANG Hong-wei1, LI Zhen1, 2, 3*, ZHOU Xu1, ZHAO Jing1. Hyperspectral Identification Model of Cantonese Tangerine Peel Based on BWO-SVM Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2894-2901. |
| [14] |
WANG Jun-jie1, YUAN Xi-ping2, 3, GAN Shu1, 2*, HU Lin1, ZHAO Hai-long1. Hyperspectral Identification Method of Typical Sedimentary Rocks in Lufeng Dinosaur Valley[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2855-2861. |
| [15] |
YAO Kun-shan1, SUN Jun1*, CHEN Chen2, XU Min1, CHENG Jie-hong1, ZHOU Xin1. Non-Destructive Identification for Panax Notoginseng Powder of Different Parts Based on Hyperspectral Imaging Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2027-2031. |
|
|
|
|
