光谱学与光谱分析 |
|
|
|
|
|
Discrimination of Pork Storage Time Using Near Infrared Spectroscopy and Adaboost+OLDA |
WU Xiao-hong1, 2, TANG Kai1, SUN Jun1 |
1. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China 2. School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China |
|
|
Abstract Pork storage time is closely related to its freshness. With the help of near infrared diffuse reflectance spectroscopy, pork sample data were collected. The orthogonal linear discriminant analysis (OLDA) algorithm was used to extract features. Furthermore, by introducing Adaboost algorithm to OLDA, a new algorithm, named Adaboost+OLDA, was proposed based on OLDA and Adaboost. To investigate the classification rate and the computational time of Adaboost+OLDA algorithm, the classical feature extraction methods (PCA+LDA and OLDA) were compared with Adaboost+OLDA in the experiments. Experimental results showed that Adaboost+OLDA could be computed efficiently and in improved the generalization ability of OLDA. The average classification rate of Adaboost+OLDA is more than 95%.
|
Received: 2012-05-20
Accepted: 2012-09-10
|
|
Corresponding Authors:
WU Xiao-hong
E-mail: wxh_www@163.com
|
|
[1] Chen Q S,Cai J R,Wan X M,et al. LWT-Food Science and Technology,2011,44:2053. [2] LIN Ya-qing,FANG Zi-shu(林亚青,房子舒). Meat Research(肉类研究), 2011, 25(5): 62. [3] Vinci G, Antonelli M L. Food Control, 2002, 13(8): 519. [4] ElMasry G, Sun D W, Allen P. Journal of Food Engineering, 2012, 110(1): 127. [5] Wold J P, O’Farrell M, Hoy M, et al. Meat Science, 2011, 89(3): 317. [6] Liao Y T, Fan Y X, Cheng F. Journal of Food Engineering, 2012, 109(4): 668. [7] Kamruzzaman M, ElMasry G, Sun D W, et al. Analytica Chimica Acta, 2012, 714: 57. [8] De Marchi M,Riovanto R,Penasa M, et al. Meat Science, 2012, 90(3): 653. [9] Chen Q S, Guo Z M, Zhao J W, et al. Journal of Pharmaceutical and Biomedical Analysis,2012,60:92. [10] De L M,Terouzi W,Kzaiber F,et al. International Journal of Food Science and Technology,2012,47(6):1286. [11] Chen L, Liao H, Ko M, et al. Pattern Recognition, 2000, 33(10): 1713. [12] Ye J P,Janardan R,Park C H,et al. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004b,26(8):982. [13] Ye J P. Journal of Machine Learning Research, 2005, 6:483. [14] Freund Y, Schapire R E. Journal of Japanese Society for Artificial Intelligence, 1999, 14(5): 771. [15] Meir R, Rtsch G. An introduction to boosting and leveraging. Advanced Lectures on Machine Learning. Berlin: Springer, 2003. [16] Mathanker S K, Weckler P R, Bowser T J, et al. Computers and Electronics in Agriculture, 2011, 77: 60. |
[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] |
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. |
[3] |
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. |
[4] |
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. |
[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] |
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. |
[7] |
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. |
[8] |
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. |
[9] |
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. |
[10] |
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. |
[11] |
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. |
[12] |
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. |
[13] |
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. |
[14] |
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. |
[15] |
GUO Ge1, 3, 4, ZHANG Meng-ling3, 4, GONG Zhi-jie3, 4, ZHANG Shi-zhuang3, 4, WANG Xiao-yu2, 5, 6*, ZHOU Zhong-hua1*, YANG Yu2, 5, 6, XIE Guang-hui3, 4. Construction of Biomass Ash Content Model Based on Near-Infrared
Spectroscopy and Complex Sample Set Partitioning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3143-3149. |
|
|
|
|