光谱学与光谱分析 |
|
|
|
|
|
Preliminary Study on the Detection of Pork Tenderness by Three-Dimensional Diffuse Reflectance Spectroscopy |
ZHANG Zhi-yong1, ZUO Yue-ming1*, CHEN Jin-ming1, LI Gang2, CHEN Chen1, YANG Wei1 |
1. College of Engineering, Shanxi Agricultural University, Taigu 030801, China 2. State Key Laboratory of Precision Measurement Technology and Instrument, Tianjin University, Tianjin 300072, China |
|
|
Abstract Tenderness is an important index to evaluate the pork’s quality, in this paper a method called three-dimensional diffuse reflectance spectroscopy was proposed to detect pork tenderness. Because pork has a strong scattering impact on light, this method introduced more scattering information of pork samples into spectral analysis of tenderness. Using the special data acquisition system, three-dimensional diffuse reflectance spectra of 64 pork samples were constructed by collecting the emergent light signals of different distances away from the light incident point. And n-way partial least squares (NPLS) regression was applied to establish the calibration model between the pork tenderness and three-dimensional diffuse reflectance spectra which were denoised by wavelet transform. The determination coefficient of model for the calibration set (R2Cal) is 0.883 1, and the root mean squared error of calibration (RMSEC) is 3.685 0N. The determination coefficient of model for the prediction set (R2Pred) is 0.874 7, and the root mean squared error of prediction (RMSEP) is 3.975 6N. The result indicates that the NPLS model of pork tenderness built by three-dimensional diffuse reflectance spectra has higher calibration accuracy and prediction stability than the traditional diffuse reflectance spectra. Three-dimensional diffuse reflectance spectroscopy can be expected to be a new method to quickly detect the tenderness and the other qualities of pork.
|
Received: 2014-04-10
Accepted: 2014-08-12
|
|
Corresponding Authors:
ZUO Yue-ming
E-mail: zyueming88@aliyun.com
|
|
[1] Van Oeckel M J, Warnants N, Boucqué C V. Meat Sci.,1999, 53(4): 259. [2] Park B, Chen Y R, Hruschka W R, et al. Journal of Animal Science, 1998, 76: 2115. [3] Cai Jianrong, Chen Quansheng, et al. Food Chemistry,2011, 126: 1354. [4] Xia J J, Berg E P, Lee J W, et al. Meat Sci., 2007, 75: 78. [5] Bro R. Journal of Chemometrics, 1996, 10: 47. [6] FANG Yong-hua, KONG Chao, LAN Tian-ge, et al(方勇华, 孔 超, 兰天鸽,等). Optics and Precision Engineering(光学精密工程),2006, 14(6): 1089. [7] ZHANG Guang-jun, LI Li-na, LI Qing-bo(张广军, 李丽娜, 李庆波). J. Infrared Millim. Waves(红外与毫米波学报), 2009, 28(2): 108. [8] LI Tian-hua, SHI Guo-ying, WEI Min, et al(李天华,施国英,魏 珉,等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2013, 44(10): 200.
|
[1] |
ZHENG Hong-quan, DAI Jing-min*. Research Development of the Application of Photoacoustic Spectroscopy in Measurement of Trace Gas Concentration[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 1-14. |
[2] |
YANG Guang1, JIN Chun-bai1, REN Chun-ying2*, LIU Wen-jing1, CHEN Qiang1. Research on Band Selection of Visual Attention Mechanism for Object
Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 266-274. |
[3] |
GAO Hong-sheng1, GUO Zhi-qiang1*, ZENG Yun-liu2, DING Gang2, WANG Xiao-yao2, LI Li3. Early Classification and Detection of Kiwifruit Soft Rot Based on
Hyperspectral Image Band Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 241-249. |
[4] |
WU Hu-lin1, DENG Xian-ming1*, ZHANG Tian-cai1, LI Zhong-sheng1, CEN Yi2, WANG Jia-hui1, XIONG Jie1, CHEN Zhi-hua1, LIN Mu-chun1. A Revised Target Detection Algorithm Based on Feature Separation Model of Target and Background for Hyperspectral Imagery[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 283-291. |
[5] |
WANG Zhi-qiang1, CHENG Yan-xin1, ZHANG Rui-ting1, MA Lin1, GAO Peng1, LIN Ke1, 2*. Rapid Detection and Analysis of Chinese Liquor Quality by Raman
Spectroscopy Combined With Fluorescence Background[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3770-3774. |
[6] |
YI Min-na1, 2, 3, CAO Hui-min1, 2, 3*, LI Shuang-na-si1, 2, 3, ZHANG Zhu-shan-ying1, 2, 3, ZHU Chun-nan1, 2, 3. A Novel Dual Emission Carbon Point Ratio Fluorescent Probe for Rapid Detection of Lead Ions[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3788-3793. |
[7] |
LU Wen-jing, FANG Ya-ping, LIN Tai-feng, WANG Hui-qin, ZHENG Da-wei, ZHANG Ping*. Rapid Identification of the Raman Phenotypes of Breast Cancer Cell
Derived Exosomes and the Relationship With Maternal Cells[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3840-3846. |
[8] |
LIU Bo-yang1, GAO An-ping1*, YANG Jian1, GAO Yong-liang1, BAI Peng1, Teri-gele1, MA Li-jun1, ZHAO San-jun1, LI Xue-jing1, ZHANG Hui-ping1, KANG Jun-wei1, LI Hui1, WANG Hui1, YANG Si2, LI Chen-xi2, LIU Rong2. Research on Non-Targeted Abnormal Milk Identification Method Based on Mid-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3009-3014. |
[9] |
MU Da1, 2, WANG Qi-shu1, 2*, CUI Zong-yu1, 2, REN Jiao-jiao1, 2, ZHANG Dan-dan1, 2, LI Li-juan1, 2, XIN Yin-jie1, 2, ZHOU Tong-yu3. Study on Interference Phenomenon in Terahertz Time Domain
Spectroscopy Nondestructive Testing of Glass Fiber Composites[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3031-3040. |
[10] |
TAO Bei-bei, WU Ning-ning, WANG Hai-bo*. Highly Sensitive Determination of Rutin Based on Fluorescent Glutathione Stabilized Copper Nanoclusters[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3158-3162. |
[11] |
XUE Fang-jia, YU Jie*, YIN Hang, XIA Qi-yu, SHI Jie-gen, HOU Di-bo, HUANG Ping-jie, ZHANG Guang-xin. A Time Series Double Threshold Method for Pollution Events Detection in Drinking Water Using Three-Dimensional Fluorescence Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3081-3088. |
[12] |
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. |
[13] |
ZHAO Ling-yi1, 2, YANG Xi3, WEI Yi4, YANG Rui-qin1, 2*, ZHAO Qian4, ZHANG Hong-wen4, CAI Wei-ping4. SERS Detection and Efficient Identification of Heroin and Its Metabolites Based on Au/SiO2 Composite Nanosphere Array[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3150-3157. |
[14] |
HUANG Bao-kun1*, ZHAO Qian-nan2, LIU Ye-fan2, ZHU Lin1, ZHANG Hong2, ZHANG Yun-hong3*, LIU Yan4*. In Situ Detection of Fuel Engine Exhaust Components by Raman
Integrating Sphere[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3310-3313. |
[15] |
ZHANG Peng1, 3, YANG Yi-fan1, WANG Hui1, TU Zong-cai1, 2, SHA Xiao-mei2, HU Yue-ming1*. A Review of Structural Characterization and Detection Methods of Glycated Proteins in Food Systems[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2667-2673. |
|
|
|
|