Non-destructive assessment of microbial contamination in porcine meat using NIR hyperspectral imaging

被引:106
|
作者
Barbin, Douglas F. [1 ]
ElMasry, Gamal [1 ]
Sun, Da-Wen [1 ]
Allen, Paul [2 ]
Morsy, Noha [1 ]
机构
[1] Natl Univ Ireland, Univ Coll Dublin, Sch Agr Food Sci & Vet Med, Agr & Food Sci Ctr, Dublin 4, Ireland
[2] TEAGASC, Ashtown Food Res Ctr, Dublin 15, Ireland
关键词
Meat quality; Hyperspectral imaging; Contamination; Safety; Total viable count; Spoilage; Psychrothrophic; VACUUM COOLING PROCESS; FOOD QUALITY EVALUATION; TOTAL VIABLE COUNT; WAVELENGTH SELECTION; COMPUTER VISION; BACTERIAL SPOILAGE; COOKED MEAT; RED-MEAT; SPECTROSCOPY; CALIBRATION;
D O I
10.1016/j.ifset.2012.11.001
中图分类号
TS2 [食品工业];
学科分类号
0832 ;
摘要
Temperature fluctuation during cold storage of meat products usually leads to undesirable microbial growths, which affect the overall product quality. In this study, a pushbroom hyperspectral imaging system in the near-infrared (NIR) range (900-1700 nm) as a rapid and non-destructive technique was exploited for determining the total viable count (TVC) and psychrotrophic plate count (PPC) in chilled pork during storage. Fresh pork samples from the longissimus dorsi muscle were obtained directly from a commercial slaughtering plant, and stored in the refrigerated temperatures at 0 degrees C and 4 degrees C for 21 days. Every 48 h, a NIR hyperspectral image in the reflectance mode was acquired directly for each sample. The TVC and PPC were determined simultaneously by classical microbiological plating methods and multivariate statistical models for predicting contamination and spoilage conditions in the samples were then developed. Partial least squares regression (PLS) was applied to fit the spectral information extracted from the samples to the logarithmic values of TVC and PPC. The best regressions were obtained with R-2 of 0.86 and 0.89 for log (NC) and log (PPC), respectively. The most important wavelengths were then selected for regression and for spatial visualization of contamination. Results are encouraging and show the promising potential of hyperspectral technology for detecting bacterial spoilage in pork and tracking the increase of microbial growth of chilled pork during storage at different temperatures. Industrial relevance: A novel method based on hyperspectral imaging technique has been successfully developed for determining the total viable count (TVC) and psychrotrophic plate count (PPC) in chilled pork during storage non-destructively for the meat industry. (C) 2012 Elsevier Ltd. All rights reserved.
引用
收藏
页码:180 / 191
页数:12
相关论文
共 50 条
  • [1] Non-destructive assessment of instrumental and sensory tenderness of lamb meat using NIR hyperspectral imaging
    Kamruzzaman, Mohammed
    ElMasry, Gamal
    Sun, Da-Wen
    Allen, Paul
    [J]. FOOD CHEMISTRY, 2013, 141 (01) : 389 - 396
  • [2] Recent advances in rapid and non-destructive assessment of meat quality using hyperspectral imaging
    Tao, Feifei
    Ngadi, Michael
    [J]. HYPERSPECTRAL IMAGING SENSORS: INNOVATIVE APPLICATIONS AND SENSOR STANDARDS 2016, 2016, 9860
  • [3] Non-destructive prediction and visualization of chemical composition in lamb meat using NIR hyperspectral imaging and multivariate regression
    Kamruzzaman, Mohammed
    ElMasry, Gamal
    Sun, Da-Wen
    Allen, Paul
    [J]. INNOVATIVE FOOD SCIENCE & EMERGING TECHNOLOGIES, 2012, 16 : 218 - 226
  • [4] Non-destructive assessment of the internal quality of intact persimmon using colour and VIS/NIR hyperspectral imaging
    Munera, Sandra
    Besada, Cristina
    Aleixos, Nuria
    Talens, Pau
    Salvador, Alejandra
    Sun, Da-Wen
    Cubero, Sergio
    Blasco, Jose
    [J]. LWT-FOOD SCIENCE AND TECHNOLOGY, 2017, 77 : 241 - 248
  • [5] Non-destructive measurement of bitter pit in apple fruit using NIR hyperspectral imaging
    Nicolaï, BM
    Lötze, E
    Peirs, A
    Scheerlinck, N
    Theron, KI
    [J]. POSTHARVEST BIOLOGY AND TECHNOLOGY, 2006, 40 (01) : 1 - 6
  • [6] Non-destructive assessment of the myoglobin content of Tan sheep using hyperspectral imaging
    Cheng, Lijuan
    Liu, Guishan
    He, Jianguo
    Wan, Guoling
    Ma, Chao
    Ban, Jingjing
    Ma, Limin
    [J]. MEAT SCIENCE, 2020, 167
  • [7] Non-destructive quality assessment of herbal tea blends using hyperspectral imaging
    Sandasi, Maxleene
    Chen, Weiyang
    Vermaak, Ilze
    Viljoen, Alvaro
    [J]. PHYTOCHEMISTRY LETTERS, 2018, 24 : 94 - 101
  • [8] Non-destructive prediction of salt contents and water activity of porcine meat slices by hyperspectral imaging in a salting process
    Liu, Dan
    Qu, Jiahuan
    Sun, Da-Wen
    Pu, Hongbin
    Zeng, Xin-An
    [J]. INNOVATIVE FOOD SCIENCE & EMERGING TECHNOLOGIES, 2013, 20 : 316 - 323
  • [9] Rapid and Non-Destructive Detection of Tan Sheep Meat MetMb Contents Using Hyperspectral Imaging
    Cheng Li-juan
    Liu Gui-shan
    He Jian-guo
    Wan Guo-ling
    Ma Chao
    Ban Jing-jing
    Ma Li-min
    Yang Guo-hua
    Yuan Rui-rui
    [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40 (04) : 1263 - 1269
  • [10] Non-Destructive Detection of Bone Fragments Embedded in Meat Using Hyperspectral Reflectance Imaging Technique
    Lim, Jongguk
    Lee, Ahyeong
    Kang, Jungsook
    Seo, Youngwook
    Kim, Balgeum
    Kim, Giyoung
    Kim, Seong Min
    [J]. SENSORS, 2020, 20 (14) : 1 - 13