Rapid Nondestructive Prediction of Multiple Quality Attributes for Different Commercial Meat Cut Types Using Optical System

被引:10
作者
An, Jiangying [1 ]
Li, Yanlei [1 ,2 ]
Zhang, Chunzhi [1 ]
Zhang, Dequan [2 ]
机构
[1] Beijing Polytech Coll, Mech & Elect Engn Coll, Beijing 100042, Peoples R China
[2] Chinese Acad Agr Sci, Inst Food Sci & Technol, Key Lab Agroprod Qual & Safety Control Storage &, Minist Agr & Rural Affairs, Beijing 100193, Peoples R China
关键词
rapid detection; multiple quality parameters; different types of meat cut; optical system; visible and near-infrared (Vis/NIR); PORK; SPECTROSCOPY; BEEF; FAT; TECHNOLOGIES; MOISTURE; PROTEIN; INTACT;
D O I
10.5851/kosfa.2022.e28
中图分类号
TS2 [食品工业];
学科分类号
0832 ;
摘要
There are differences of spectral characteristics between different types of meat cut, which means the model established using only one type of meat cut for meat quality prediction is not suitable for other meat cut types. A novel portable visible and near-infrared (Vis/NIR) optical system was used to simultaneously predict multiple quality indicators for different commercial meat cut types (silverside, back strap, oyster, fillet, thick flank, and tenderloin) from Small-tailed Han sheep. The correlation coefficients of the calibration set (R-c) and prediction set (R-p) of the optimal prediction models were 0.82 and 0.81 for pH, 0.88 and 0.84 for L*, 0.83 and 0.78 for a*, 0.83 and 0.82 for b*, 0.94 and 0.86 for cooking loss, 0.90 and 0.88 for shear force, 0.84 and 0.83 for protein, 0.93 and 0.83 for fat, 0.92 and 0.87 for moisture contents, respectively. This study demonstrates that Vis/NIR spectroscopy is a promising tool to achieve the predictions of multiple quality parameters for different commercial meat cut types.
引用
收藏
页码:655 / 671
页数:17
相关论文
共 35 条
  • [1] Partial least squares regression and projection on latent structure regression (PLS Regression)
    Abdi, Herve
    [J]. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS, 2010, 2 (01): : 97 - 106
  • [2] Predicting pork quality using Vis/NIR spectroscopy
    Balage, Juliana Monteiro
    da Luz e Silva, Saulo
    Gomide, Catarina Abdalla
    Bonin, Marina de Nadai
    Figueira, Ana Cristina
    [J]. MEAT SCIENCE, 2015, 108 : 37 - 43
  • [3] Non-destructive determination of chemical composition in intact and minced pork using near-infrared hyperspectral imaging
    Barbin, Douglas F.
    ElMasry, Gamal
    Sun, Da-Wen
    Allen, Paul
    [J]. FOOD CHEMISTRY, 2013, 138 (2-3) : 1162 - 1171
  • [4] Che TY, 2016, CHIN J ANIM SCI, V55, P130
  • [5] Chen TY, 2016, HEILONGJIANG ANIM SC, V13, P123
  • [6] Identification of animal meat muscles by visible and near infrared reflectance spectroscopy
    Cozzolino, D
    Murray, I
    [J]. LEBENSMITTEL-WISSENSCHAFT UND-TECHNOLOGIE-FOOD SCIENCE AND TECHNOLOGY, 2004, 37 (04): : 447 - 452
  • [7] The relevance of different near infrared technologies and sample treatments for predicting meat quality traits in commercial beef cuts
    De Marchi, M.
    Penasa, M.
    Cecchinato, A.
    Bittante, G.
    [J]. MEAT SCIENCE, 2013, 93 (02) : 329 - 335
  • [8] Feasibility of the direct application of near-infrared reflectance spectroscopy on intact chicken breasts to predict meat color and physical traits
    De Marchi, M.
    Penasa, M.
    Battagin, M.
    Zanetti, E.
    Pulici, C.
    Cassandro, M.
    [J]. POULTRY SCIENCE, 2011, 90 (07) : 1594 - 1599
  • [9] Challenges in Model Development for Meat Composition Using Multipoint NIR Spectroscopy from At-Line to In-Line Monitoring
    Dixit, Y.
    Casado-Gavalda, Maria P.
    Cama-Moncunill, R.
    Cullen, P. J.
    Sullivan, Carl
    [J]. JOURNAL OF FOOD SCIENCE, 2017, 82 (07) : 1557 - 1562
  • [10] Hyperspectral Model Optimization for Protein of Tan Mutton Based on Box-Behnken
    Fan Nai-yun
    Liu Gui-shan
    Zhang Jing-jing
    Zhang Chong
    Yuan Rui-rui
    Ban Jing-jing
    [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41 (03) : 918 - 923