光谱学与光谱分析 |
|
|
|
|
|
Visualization of the Chilling Storage Time for Turbot Flesh Based on Hyperspectral Imaging Technique |
ZHU Feng-le, ZHANG Hai-liang, SHAO Yong-ni, HE Yong* |
College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China |
|
|
Abstract This study proposed a new method using visible and near infrared (Vis/NIR) hyperspectral imaging for the detection and visualization of the chilling storage time for turbot flesh rapid and nondestructively. A total of 160 fish samples with 8 different storage days were collected for hyperspectral image scanning, and mean spectra were extracted from the region of interest (ROI) inside each image. Partial least squares regression (PLSR) was applied as calibration method to correlate the spectral data and storage time for the 120 samples in calibration set. Then the PLSR model was used to predict the storage time for the 40 prediction samples, which achieved accurate results with determination coefficient (R2) of 0.966 2 and root mean square error of prediction (RMSEP) of 0.679 9 d. Finally, the storage time of each pixel in the hyperspectral images for all prediction samples was predicted and displayed in different colors for visualization based on pseudo-color images with the aid of an IDL program. The results indicated that hyperspectral imaging technique combined with chemometrics and image processing allows the determination and visualization of the chilling storage time for fish, displaying fish freshness status and distribution vividly and laying a foundation for the automatic processing of aquatic products.
|
Received: 2013-09-01
Accepted: 2013-12-26
|
|
Corresponding Authors:
HE Yong
E-mail: yhe@zju.edu.cn
|
|
[1] ZHANG Lu, HOU Hong-man, LUN Cheng-cheng(张 璐, 侯红漫, 伦成成). Food Science and Technology(食品科技), 2010, 35(7): 158. [2] Li X L, He Y, Wu C Q. Journal of Stored Products Research, 2008, 44(3): 264. [3] Bkns N, Jensen K N, Andersen C M, et al. LWT - Food Science and Technology, 2002, 35(7): 628. [4] Nilsen H, Esaiassen M, Heia K, et al. Journal of Food Science, 2002, 67(5): 1821. [5] Sivertsen A H, Kimiya T, Heia K. Journal of Food Engineering, 2011, 103(3): 317. [6] Elmasry G, Kamruzzaman M, Sun D-W, et al. Critical Reviews in Food Science and Nutrition, 2012, 52(11): 999. [7] Elmasry G, Wold J P. Journal of Agricultural and Food Chemistry, 2008, 56(17): 7672. [8] Segtnan V H, Hy M, Lundby F, et al. Journal of Near Infrared Spectroscopy, 2009, 17(5): 247. [9] Segtnan V H, Hy M, Srheim O, et al. Journal of Agricultural and Food Chemistry, 2009, 57(5): 1705. [10] He H-J, Wu D, Sun D-W. Innovative Food Science and Emerging Technologies, 2013, 18: 237. [11] Zhu F L, Zhang D R, He Y, et al. Food and Bioprocess Technology, 2013,6(10):2931. [12] Sivertsen A H, Chu C-K, Wang L-C, et al. Journal of Food Engineering, 2009, 90(3): 317. [13] Sivertsen A H, Heia K, Hindberg K, et al. Journal of Food Engineering, 2012, 111(4): 675. [14] Menesatti P, Costa C, Aguzzi J. In Sun D-W (Ed. ), Hyperspectral Imaging for Food Quality Analysis and Control. London, Burlington, San Diego: Academic Press, 2010. 273. [15] Geladi P, Kowalski B R. Analytica Chimica Acta, 1986, 185: 1. [16] lafsdóttir G, Martinsdóttir E, Oehlenschlger J, et al. Trends in Food Science and Technology, 1997, 8(8): 258. [17] LI Bian-sheng, YU Yu-ming, ZHU Zhi-wei, et al(李汴生, 俞裕明, 朱志伟, 等). Journal of South China University of Technology(华南理工大学学报), 2007, 35(12): 126. [18] Osborne B G, Fearn T. Near Infrared Spectroscopy in Food Analysis. New York: Longman Scientific & Technical, 1986. |
[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] |
CHU Bing-quan1, 2, LI Cheng-feng1, DING Li3, GUO Zheng-yan1, WANG Shi-yu1, SUN Wei-jie1, JIN Wei-yi1, HE Yong2*. Nondestructive and Rapid Determination of Carbohydrate and Protein in T. obliquus Based on Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3732-3741. |
[3] |
YUAN Wei-dong1, 2, JU Hao2, JIANG Hong-zhe1, 2, LI Xing-peng2, ZHOU Hong-ping1, 2*, SUN Meng-meng1, 2. Classification of Different Maturity Stages of Camellia Oleifera Fruit
Using Hyperspectral Imaging Technique[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3419-3426. |
[4] |
SHEN Ying, WU Pan, HUANG Feng*, GUO Cui-xia. Identification of Species and Concentration Measurement of Microalgae Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3629-3636. |
[5] |
YANG Lei1, 2, 3, ZHOU Jin-song1, 2, 3, JING Juan-juan1, 2, 3, NIE Bo-yang1, 3*. Non-Uniformity Correction Method for Splicing Hyperspectral Imager Based on Overlapping Field of View[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3582-3590. |
[6] |
DONG Jian-jiang1, TIAN Ye1, ZHANG Jian-xing2, LUAN Zhen-dong2*, DU Zeng-feng2*. Research on the Classification Method of Benthic Fauna Based on
Hyperspectral Data and Random Forest Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3015-3022. |
[7] |
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. |
[8] |
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. |
[9] |
SUN Bang-yong1, YU Meng-ying1, YAO Qi2*. Research on Spectral Reconstruction Method From RGB Imaging Based on Dual Attention Mechanism[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2687-2693. |
[10] |
WU Yong-qing1, 2, TANG Na1, HUANG Lu-yao1, CUI Yu-tong1, ZHANG Bo1, GUO Bo-li1, ZHANG Ying-quan1*. Model Construction for Detecting Water Absorption in Wheat Flour Using Vis-NIR Spectroscopy and Combined With Multivariate Statistical #br#
Analyses[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2825-2831. |
[11] |
LIU Rui-min, YIN Yong*, YU Hui-chun, YUAN Yun-xia. Extraction of 3D Fluorescence Feature Information Based on Multivariate Statistical Analysis Coupled With Wavelet Packet Energy for Monitoring Quality Change of Cucumber During Storage[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2967-2973. |
[12] |
ZHANG Hai-liang1, XIE Chao-yong1, LUO Wei1, WANG Chen2, NIE Xun1, TIAN Peng1, LIU Xue-mei3, ZHAN Bai-shao1*. Salmon Fat Visualization Based on MCR-ALS Hyperspectral
Reconstruction[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2601-2607. |
[13] |
ZHANG Hai-liang1, XIE Chao-yong1, TIAN Peng1, ZHAN Bai-shao1, CHEN Zai-liang1, LUO Wei1*, LIU Xue-mei2*. Measurement of Soil Organic Matter and Total Nitrogen Based on Visible/Near Infrared Spectroscopy and Data-Driven Machine Learning Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2226-2231. |
[14] |
MAO Yi-lin1, LI He1, WANG Yu1, FAN Kai1, SUN Li-tao2, WANG Hui3, SONG Da-peng3, SHEN Jia-zhi2*, DING Zhao-tang1, 2*. Quantitative Judgment of Freezing Injury of Tea Leaves Based on Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2266-2271. |
[15] |
LIU Gang1, LÜ Jia-ming1, NIU Wen-xing1, LI Qi-feng2, ZHANG Ying-hu2, YANG Yun-peng2, MA Xiang-yun2*. Detection of Sulfur Content in Vessel Fuel Based on Hyperspectral
Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1697-1702. |
|
|
|
|