|
|
|
|
|
|
| Quantitative Detection of Mutton Hardness Based on Twice Iterative Monte Carlo Method |
| BAI Xue-bing1, LI Xin-xing1, ZHANG Xiao-shuan2, LUO Hai-ling3, FU Ze-tian2* |
1. Beijing Laboratory of Food Quality and Safety, College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
2. College of Engineering, China Agricultural University, Beijing 100083, China
3. State Key Laboratory of Animal Nutrition, College of Animal Science and Technology, China Agricultural University, Beijing 100083, China |
|
|
|
|
Abstract Mutton, as a kind of meat with high protein content and low fat and cholesterol content, is becoming more and more popular with consumers. The demand for mutton is on the rise. According to the National Bureau of Statistics, China’s mutton production rose from 6.27% to 9.02% from 2012 to 2019. This study proposed a quantitative detection PLSR model of mutton hardness based on the twice iterative Monte Carlo (MC) method. In this study, the Image-λ-V10E-H camera of the GaiaSorter hyperspectral sorter was used to collect the hyperspectral data of mutton samples at 400~950 nm, and the Image-λ-N17E camera was used to collect the hyperspectral data of mutton samples at 900~1 650 nm. Firstly, the study compared and analyzed four spectral pretreatment methods (S-G smoothing, 2 derivations, MSC and SNV) in eliminating interference factors, such as noise and baseline drift. Then, in the first MC sampling, the samples were divided into normal samples, suspicious samples and abnormal samples according to the 2.5 and 3 times the means of the prediction error means and standard deviations of each sample. The second MC sampling was performed based on rejecting abnormal samples, retaining and labeling suspicious samples. The new abnormal samples were eliminated by 3 times of the means of the prediction error means and standard deviations of each sample. Finally, the PLSR model based on the full wavelengths and the characteristic wavelengths extracted by the regression coefficient method (RC) were established and analyzed. The experiment results show that the twice iterative Monte Carlo method proposed in the study could abnormal samples, optimize the sample set, and provide a good foundation for modeling. With MSC as the spectral preprocessing algorithm, the PLSR model based on 400~950 and 900~1 650 nm hyperspectral data was superior to the other three spectral preprocessing algorithms R2P=0.947 2 and 0.978 3，RMSEP=47.789 9 g and 30.590 1 g. And, the accuracy and stability of the PLSR model based on 900~1 650 nm were significantly better than that based on 400~950 nm. 14 characteristic wavelengths (410, 438, 450, 464, 539, 558, 612, 684, 701, 734, 778, 866, 884, 935 nm) and 10 characteristic wavelengths (915, 949, 1 085, 1 156, 1 206, 1 262, 1 318, 1 384, 1 542 and 1 580 nm) of mutton hardness were selected by RC algorithm from 900~1 650 and 400~950 nm. The PLSR model based on 900~1 650 nm was the optimal model for predicting the hardness of mutton with R2P=0.985 0 and RMSEP=24.397 0 g. In conclusion, the PLSR model based on the twice iterative MC algorithm can effectively predict the changing trend of mutton hardness during cold storage and provide a reference for related research on non-destructive detection of mutton quality.
|
|
Received: 2020-07-19
Accepted: 2020-12-06
|
|
|
|
Corresponding Authors:
FU Ze-tian
E-mail: fzt@cau.edu.cn
|
|
[1] Xiong Z, Sun D, Zeng X, et al. Journal of Food Engineering, 2014, 132: 1.
[2] Pu H, Kamruzzaman M, Sun D. Trends in Food Science & Technology, 2015, 45(1): 86.
[3] Sanchez B G, Bowker B C, Zhuang H. J. Poult Sci., 2016, 95(10): 2472.
[4] Cheng J H, Sun D W, Han Z, et al. Comprehensive Reviews in Food ence & Food Safety, 2013, 1(13): 52.
[5] Cheng J, Qu J, Sun D, et al. Food Research International, 2014, 56: 190.
[6] Cheng J H, Sun D W, Pu H, et al. Food & Bioprocess Technology, 2014, 11(7): 3109.
[7] Tao F, Peng Y, Li Y, et al. Meat Science, 2012, 90(3): 851.
[8] Kamruzzaman M, ElMasry G, Sun D, et al. Food Chemistry, 2013, 141(1): 389.
[9] LIU Gui-shan, ZHANG Chong, FAN Nai-jun, et al(刘贵珊，张 翀，樊奈昀, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2020, 40(8): 2558.
[10] Jun L, Yutong F, Weijian L, et al. IEEE Transactions on Signal Processing, 2020, 68(5): 3022.
[11] Lobanova E G, Lobanov S V. Analytica Chimica Acta, 2019, 1050: 32.
[12] Reis M M, Van Beers R, Al-Sarayreh M, et al. Meat Science, 2018, 144: 100.
[13] Xinhua J, Heru X, Lina Z, et al. Computers and Electronics in Agriculture, 2018, 155: 371.
[14] Estelles-Lopez L, Ropodi A, Pavlidis D, et al. Food Res. Int., 2017, 99(1): 206.
[15] Xiong Z, Sun D, Xie A, et al. Food Chemistry, 2015, 175: 417. |
| [1] |
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. |
| [2] |
TANG Ruo-han1, 2, LI Xiu-hua1, 2*, LÜ Xue-gang1, 2, ZHANG Mu-qing2, 3, YAO Wei2, 3. Transmittance Vis-NIR Spectroscopy for Detecting Fibre Content of
Living Sugarcane[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2419-2425. |
| [3] |
LIN Jing-tao, XIN Chen-xing, LI Yan*. Spectral Characteristics of “Trapiche-Like Sapphire” From ChangLe, Shandong Province[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1199-1204. |
| [4] |
MAO Xiao-tian1, CHEN Chang2, YIN Zuo-wei1*, WANG Zi-min1. Spectra Characterization of Cr-Grossular (Tsavorite) With “Frogspawn” Color Zoning From Canada[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 520-525. |
| [5] |
BAO Pei-jin1, CHEN Quan-li1, 2*, WU Yan-han1, LI Xuan1, ZHAO An-di1. Spectroscopy Characteristics of Emerald From Swat Valley, Pakistan[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 213-219. |
| [6] |
HU Guo-tian1, 2, 3, SHANG Hui-wei1, 2, 3, TAN Rui-hong1, XU Xiang-hu1, PAN Wei-dong1. Research on Model Transfer Method of Organic Matter Content
Estimation of Different Soils Using VNIR Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3148-3154. |
| [7] |
ZHANG Fu1, 2, 3, WANG Xin-yue2, CUI Xia-hua2, CAO Wei-hua2, ZHANG Xiao-dong1*, ZHANG Ya-kun2. Classification of Qianxi Tomatoes by Visible/Near Infrared Spectroscopy Combined With GMO-SVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3291-3297. |
| [8] |
LI Rui1, LI Bo1*, WANG Xue-wen1, LIU Tao1, LI Lian-jie1，2, FAN Shu-xiang2. A Classification Method of Coal and Gangue Based on XGBoost and
Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(09): 2947-2955. |
| [9] |
ZHONG Xiang-jun1, 2, YANG Li1, 2*, ZHANG Dong-xing1, 2, CUI Tao1, 2, HE Xian-tao1, 2, DU Zhao-hui1, 2. Effect of Different Particle Sizes on the Prediction of Soil Organic Matter Content by Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(08): 2542-2550. |
| [10] |
LI Xue-ying1, 2, LI Zong-min3*, CHEN Guang-yuan4, QIU Hui-min2, HOU Guang-li2, FAN Ping-ping2*. Prediction of Tidal Flat Sediment Moisture Content Based on Wavelet Transform[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1156-1161. |
| [11] |
YU Guo-wei1, MA Ben-xue1,2*, CHEN Jin-cheng1,3, DANG Fu-min4,5, LI Xiao-zhan1, LI Cong1, WANG Gang1. Vis-NIR Spectra Discriminant of Pesticide Residues on the Hami Melon Surface by GADF and Multi-Scale CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3701-3707. |
| [12] |
LÜ Xue-gang1, 2，LI Xiu-hua1, 2*，ZHANG Shi-min2，ZHANG Mu-qing1, JIANG Hong-tao1. A Method for Detecting Sucrose in Living Sugarcane With Visible-NIR Transmittance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3747-3752. |
| [13] |
HAO Yong1，DU Jiao-jun1, ZHANG Shu-min2, WANG Qi-ming1. Research on Construction of Visible-Near Infrared Spectroscopy Analysis Model for Soluble Solid Content in Different Colors of Jujube[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3385-3391. |
| [14] |
FAN Shu-xiang1, WANG Qing-yan1, YANG Yu-sen2, LI Jiang-bo1, ZHANG Chi1, TIAN Xi1, HUANG Wen-qian1*. Development and Experiment of a Handheld Visible/Near Infrared Device for Nondestructive Determination of Fruit Sugar Content[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3058-3063. |
| [15] |
LIU Chen-yang1,2, XU Huang-rong2,3, DUAN Feng4, WANG Tai-sheng1, LU Zhen-wu1, YU Wei-xing3*. Spectral Discrimination of Rabbit Liver VX2 Tumor and Normal Tissue Based on Genetic Algorithm-Support Vector Machine[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(10): 3123-3128. |
|
|
|
|
