|
|
|
|
|
|
| Shrimp Freshness Detection Method Based on Broad Learning System |
| YE Rong-ke1, KONG Qing-chen1, LI Dao-liang1, 2, CHEN Ying-yi1, 2, ZHANG Yu-quan1, LIU Chun-hong1, 2* |
1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
2. National Innovation Center for Digital Fishery, China Agricultural University, Beijing 100083, China |
|
|
|
|
Abstract To improve the accuracy of shrimp freshness discrimination, we proposed a shrimp freshness detection method based on a broad learning system in this paper. In this study, firstly, multivariate scatter correction (MSC), standard normal variate (SNV), and direct orthogonal signal correction (DOSC) were used to preprocess the raw hyperspectral data of shrimp with different days of refrigeration. And secondly, t-distributed stochastic neighbor embedding (t-SNE) was used to visualize the data after preprocessing, and the visualization results showed that the DOSC-processed data had the best clustering effect. Then, the spectral data after DOSC preprocessing were used for feature extraction using random forest (RF), principal component analysis (PCA), and two-dimensional correction spectroscopy (2D-COS). Finally, the shrimp freshness was modeled based on the characteristic wavelength, and the broad learning system (BLS) was used in shrimp freshness modeling for the first time in this paper and compared with the classical discriminant models such as partial least squares discrimination analysis (PLS-DA) and extreme learning machine (ELM). The results indicated that the RF method minimized the redundant information in the spectra, while the BLS had high accuracy and shorter discrimination time than the linear modeling method PLS-DA and the nonlinear modeling method ELM, and thus the combined RF-BLS model obtained the best freshness discrimination performance. The experimental results indicated that the hyperspectral imaging technology combined with broad learning system to identify shrimp freshness is feasible. The method proposed in this paper can provide a theoretical basis for developing an online shrimp freshness detection system.
|
|
Received: 2020-10-12
Accepted: 2021-02-04
|
|
|
|
Corresponding Authors:
LIU Chun-hong
E-mail: sophia_liu@cau.edu.cn
|
|
[1] WANG Wei(王 伟). Food Research and Development(食品研究与开发), 2017, 38(22): 176.
[2] WANG Su-hua, CHEN Ji-ming, ZHU Hai, et al(王素华, 陈积明, 朱 海, 等). Science and Technology of Food Industry(食品工业科技), 2011, 32(7): 386.
[3] LING Ping-hua, XIE Jing, ZHAO Hai-peng, et al(凌萍华, 谢 晶, 赵海鹏, 等). Jiangsu Journal of Agricultural Sciences(江苏农业学报), 2010, 26(4): 828.
[4] WANG Wei, CHAI Chun-xiang, LU Xiao-xiang(王 伟, 柴春祥, 鲁晓翔). Food & Machinery(食品与机械), 2013, 29(4): 233.
[5] Baek I, Kusumaningrum D, Kandpal L, et al. Sensors, 2019, 19(2): 271.
[6] Yu X J, Tang L, Wu X F, et al. Food Analytical Methods, 2018, 11(3): 768.
[7] Zhang C, Wu W Y, Zhou L, et al. Food Chemistry, 2020, 319: 126536.
[8] Zhou X, Sun J, Tian Y, et al. Food Chemistry, 2020, 321: 126503.
[9] Cheng J H, Dai Q, Sun D W, et al. Journal of Food Engineering, 2015, 161: 33.
[10] HUANG Chong, XU Zhao-xin, ZHANG Chen-chen, et al(黄 翀, 许照鑫, 张晨晨, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2020, 36(9): 177.
[11] Zhang L, Sun H, Rao Z H, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2020, 229: 117973.
[12] Jiang H Z, Cheng F N, Shi M H. Foods, 2020, 9(2): 154.
[13] Bai Z Z, Hu X J, Tian J P, et al. Food Chemistry, 2020, 331: 127290.
[14] Ye R K, Chen Y Y, Guo Y C, et al. Applied Sciences, 2020, 10(16): 5498.
[15] Chen C L, Liu Z L, et al. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(1): 10.
[16] Zhang L, Rao Z H, Ji H Y. Spectroscopy Letters, 2020, 53(3): 207. |
| [1] |
LI Quan-lun1, CHEN Zheng-guang1*, SUN Xian-da2. Rapid Detection of Total Organic Carbon in Oil Shale Based on Near
Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1691-1697. |
| [2] |
TAO Wan-cheng1, 2, ZHANG Ying1, 2, XIE Zi-xuan1, 2, WANG Xin-sheng1, 2, DONG Yi1, 2, ZHANG Ming-zheng 1, 2, SU Wei1, 2*, LI Jia-yu1, 2, XUAN Fu1, 2. Intelligent Recognition of Corn Residue Cover Area by Time-Series
Sentinel-2A Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1948-1955. |
| [3] |
LI Cong-cong1, LUO Qi-wu2, ZHANG Ying-ying1, 3*. Determination of Net Photosynthetic Rate of Plants Based on
Environmental Compensation Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1561-1566. |
| [4] |
LI Meng1, 2, ZHANG Xiao-bo2, LIU Shao-bo3, CHEN Xing-feng4*, HUANG Lu-qi5*, SHI Ting-ting2, YANG Rui6, LIU Shu7, ZHENG Feng-jie8. Partly Interpretable Machine Learning Method of Ginseng Geographical Origins Recognition and Analysis by Hyperspectral Measurements[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1217-1221. |
| [5] |
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. |
| [6] |
DUAN Wei-na1, 2, JING Xia1*, LIU Liang-yun2, ZHANG Teng1, ZHANG Li-hua3. Monitoring of Wheat Stripe Rust Based on Integration of SIF and Reflectance Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 859-865. |
| [7] |
CHEN Feng-xia1, YANG Tian-wei2, LI Jie-qing1, LIU Hong-gao3, FAN Mao-pan1*, WANG Yuan-zhong4*. Identification of Boletus Species Based on Discriminant Analysis of Partial Least Squares and Random Forest Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 549-554. |
| [8] |
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. |
| [9] |
LUO De-fang1, LIU Wei-yang1*, PENG Jie1, FENG Chun-hui1, JI Wen-jun2, BAI Zi-jin1. Field in Situ Spectral Inversion of Cotton Organic Matter Based on Soil Water Removal Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(01): 222-228. |
| [10] |
WU Ye-lan1, CHEN Yi-yu1, LIAN Xiao-qin1, LIAO Yu2, GAO Chao1, GUAN Hui-ning1, YU Chong-chong1. Study on the Identification Method of Citrus Leaves Based on Hyperspectral Imaging Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3837-3843. |
| [11] |
LUO De-fang1, PENG Jie1*, FENG Chun-hui1, LIU Wei-yang1, JI Wen-jun2, WANG Nan3. Inversion of Soil Organic Matter Fraction in Southern Xinjiang by Visible-Near-Infrared and Mid-Infrared Spectra[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3069-3076. |
| [12] |
FANG Yuan, HE Zhang-ping, ZHU Shi-chao, LIANG Xian-rong, JIN Gang*. In-Line Identification of Different Grades of GPPS Based on Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(09): 2759-2763. |
| [13] |
ZHANG Zi-han1, YAN Lei1,2, LIU Si-yuan1, FU Yu1, JIANG Kai-wen1, YANG Bin3, LIU Sui-hua4, ZHANG Fei-zhou1*. Leaf Nitrogen Concentration Retrieval Based on Polarization Reflectance Model and Random Forest Regression[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(09): 2911-2917. |
| [14] |
XU Lu*, WANG Hui, QIU Si-yi, LIAN Jing-wen, WANG Li-juan. Coastal Soil Salinity Estimation Based Digital Images and Color Space Conversion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(08): 2409-2414. |
| [15] |
YANG Si-jie1,2, FENG Wei-wei2,3,4*, CAI Zong-qi2,3, WANG Qing2,3. Study on Rapid Recognition of Marine Microplastics Based on Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(08): 2469-2473. |
|
|
|
|
