|
|
|
|
|
|
| Nondestructive Determinations of Texture and Quality of Preserved Egg Gel by Hyperspectral Method |
| CHEN Yuan-zhe1, WANG Qiao-hua1, 2*, TIAN Wen-qiang1, XU Bu-yun1, HU Jian-chao1 |
1. College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
2. Key Laboratory of Agricultural Equipment in Mid-Lower Yangtze River，Ministry of Agriculture and Rural Affairs, Wuhan 430070, China
|
|
|
|
|
Abstract As an important quality parameter, texture can significantly affect the gelatin quality of preserved eggs. There is no effectively rapid detection method at present. In this study, hyperspectral imaging technology was used to predict preserved eggs’ textural characteristics and classify different qualities. Hyperspectral data of high-quality eggs, qualified eggs and unqualified eggs were collected. The original spectra were transformed by single and combined transformation to analyze the correction between one-dimensional spectral and textural hardness and springiness. It was found that the spectral reflectance after CR-FD transformation was most correlated with the hardness and springiness of the gel texture, and the maximum values were 0.882, 0.86 5 at 683, 715 nm, respectively; The hardness and springiness were taken as disturbance factors to explore the optimal research area of the hardness and springiness, the results showed that: When hardness was used as disturbance factor, autocorrelation peaks existed at 476, 539, 647, 672, 728 and 851 nm, Spectral signals at 483, 572, 657, 739 and 826 nm were more sensitive to springiness value. Therefore, two sensitive bands, 476~851 and 483~826 nm, were finally selected as the study regions for gel hardness and springiness, respectively. Comparing five different variable selection methods(SPA, CARS, GA, PSO and UVE), it was found that PSO-PLSR model had the highest detection accuracy: theR2p and RMSEP for predicted hardness were 0.826 and 0.874 with an RPD of 2. TheR2p and RMSEP for predicted springiness were 0.886 and 0.402 with an RPD of 1.9. Three different classifiers were used to predict preserved eggs, and the classification accuracy of high-quality eggs, qualified eggs and unqualified eggs reached 97%, 92% and 100%, respectively. The accuracy and generalization ability of the PLS-DA model were better than the BP and RF model based on the confusion matrix and ROC curves of prediction results. In conclusion, the hyperspectral technique can be used to predict preserved eggs’ textural characteristics and classify different quality of preserved eggs.
|
|
Received: 2021-05-08
Accepted: 2021-07-26
|
|
|
|
Corresponding Authors:
WANG Qiao-hua
E-mail: wqh@mail.hzau.edu.cn
|
|
[1] YU Pei, WANG Xiu-jun, XU Wen, et al(于 沛, 王修俊, 徐 雯, 等). Food Science(食品科学), 2021，42(19)：65.
[2] XUAN Xi-long, WANG Zhi-wei, HUANG Ai-lan, et al(玄夕龙, 王志威, 黄爱兰, 等). Journal of Anhui Science and Technology University(安徽科技学院学报), 2017, 31(3): 38.
[3] Ai Minmin, Zhou Quan, Guo Shangguang, et al. Food Hydrocolloids, 2019, 94: 11.
[4] ZHAO Yan, XU Ming-sheng, YAO Yao, et al(赵 燕, 徐明生, 姚 瑶, 等). Journal of Chinese Institute of Food Science and Technology(中国食品学报), 2019, 19(6): 36.
[5] LIU Gui-shan, ZHANG Chong, FAN Nai-yun, 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.
[6] TIAN You-wen, WU Wei, LU Shi-qian, et al(田有文, 吴 伟, 卢时铅, 等). Food Science(食品科学), 2021, 42(19): 260.
[7] LIU Long, FU Mei-zhang, WANG Shu-cai(刘 龙, 付美章, 王树才). Journal of Huazhong Agricultural University(华中农业大学学报), 2012, 31(4): 524.
[8] Li C H, Hsieh C H, Hung C H, et al. Foods, 2021, 10(2): 394.
[9] WANG Qiao-hua, MEI Lu, MA Mei-hu, et al(王巧华, 梅 璐, 马美湖, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2019, 35(24): 314.
[10] ZHANG Xiao-min, ZHANG Yan-ning, JIANG Hai-yi, et al(张小敏, 张延宁, 姜海益, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2021, 52(6): 232.
|
| [1] |
YANG Ke-li1, 2, PENG Jiao-yu1, 2, DONG Ya-ping1, 2*, LIU Xin1, 2, LI Wu1, 3, LIU Hai-ning1, 3. Spectroscopic Characterization of Dissolved Organic Matter Isolated From Solar Pond[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3775-3780. |
| [2] |
ZHANG Yue1, 3, ZHOU Jun-hui1, WANG Si-man1, WANG You-you1, ZHANG Yun-hao2, ZHAO Shuai2, LIU Shu-yang2*, YANG Jian1*. Identification of Xinhui Citri Reticulatae Pericarpium of Different Aging Years Based on Visible-Near Infrared Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3286-3292. |
| [3] |
ZHANG Zi-hao1, GUO Fei3, 4, WU Kun-ze1, YANG Xin-yu2, XU Zhen1*. Performance Evaluation of the Deep Forest 2021 (DF21) Model in
Retrieving Soil Cadmium Concentration Using Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2638-2643. |
| [4] |
LI Bin, SU Cheng-tao, YIN Hai, LIU Yan-de*. Hyperspectral Imaging Technology Combined With Machine Learning for Detection of Moldy Rice[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2391-2396. |
| [5] |
LI Chen-xi1, SUN Ze-yu1, 2, ZHAO Yu2*, YIN Li-hui2, CHEN Wen-liang1, 3, LIU Rong1, 3, XU Ke-xin1, 3. The Research Progress of Two-Dimensional Correlation Spectroscopy and Its Application in Protein Substances Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 1993-2001. |
| [6] |
HU Bin1, 2, WANG Pei-fang1, 2*, ZHANG Nan-nan3, SHI Yue4, BAO Tian-li1, 2, JIN Qiu-tong1, 2. Effect of pH on Interaction Between Dissolved Organic Matter and Copper: Based on Spectral Features[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1628-1635. |
| [7] |
LIU Rong1, 2, WANG Miao-miao1, 2 , SUN Ze-yu1, 2, CHEN Wen-liang1, 2, LI Chen-xi2*, XU Ke-xin1, 2. Research on Temperature Disturbance of Glucose Solution With
Two-Trace Two-Dimensional Correlation Spectrum Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1051-1055. |
| [8] |
ZHOU Ao1, 2, YUE Zheng-bo1, 2, LIU A-zuan1, 2, GAO Yi-jun3, WANG Shao-ping3, CHUAI Xin3, DENG Rui1, WANG Jin1, 2*. Spectral Analysis of Extracellular Polymers During Iron Dissimilar
Reduction by Salt-Tolerant Shewanella Aquimarina[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1320-1328. |
| [9] |
AN Huan-jiong1, ZHAI Chen2, MA Qian-yun1, ZHANG Fan1, WANG Shu-ya2, SUN Jian-feng1, WANG Wen-xiu1*. Quantitative Characterization of Wheat Starch Retrogradation by
Combining 2D-COS and Spectral Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 162-168. |
| [10] |
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. |
| [11] |
LI Yue1, LIU Gui-shan1*, FAN Nai-yun1*, HE Jian-guo1, LI Yan1, SUN You-rui1, PU Fang-ning2. A Combination of Hyperspectral Imaging With Two-Dimensional Correlation Spectroscopy for Monitoring the Hemicellulose Content in Lingwu Long Jujube[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3935-3940. |
| [12] |
PAN Hong-wei, TONG Wen-bin, LEI Hong-jun*, YANG Guang, SHI Li-li. Spectral Analysis of the Effect of Organic Fertilizer Application on the
Evolution of Organic Matter and Nitrogen in Farmaland[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3116-3123. |
| [13] |
PIAO Zhao-jia, YU Hai-ye, ZHANG Jun-he, ZHOU Hai-gen, LIU Shuang, KONG Li-juan, DANG Jing-min*. Hyperspectral Inversion Model of Pectin Content in Wheat Under Salt and Physical Damage Stresses[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(09): 2935-2940. |
| [14] |
WANG Yan1, 2, 3, WANG Bao-rui1, 2, 3*, WANG Yue1, 2, 3. Study on Radical Characteristics of Methane Laminar Premixed Flame Based on Hyperspectral Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(08): 2403-2410. |
| [15] |
BAI Xue-bing, MA Dian-kun, ZHANG Meng-jie, MA Rui-qin*. Hyperspectral Non-Destructive Analysis of Red Meat Quality: A Review[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(07): 1993-1998. |
|
|
|
|
