Use of hyperspectral imaging technologies for prediction of beef meat quality

Tong Qiao, Jinchang Ren, Jaime Zabalza, Cameron Craigie, Charlotte Maltin

Research output: Contribution to conferencePoster

Abstract

As an emerging technology, hyperspectral imaging (HSI) providesa unique non-destructive way of analysing food quality. In the current application HSI is applied to meat quality analysis, based on an image cube captured at different wavelengths, which usually covers from visible (VIS) to near infrared (NIR) bands. Many researchers have found that there is a relationship between eating quality of beef and corresponding sensory properties such as tenderness and flavour. The tenderness can be assessed by measuring the slice shear force (SSF) and the ultimate pH value is an important shelf-life and colour parameter. In this project, HSI has been employed to predict the SSF measurement and pH value of captured beef samples at 7 days and 14 days post mortem and the results are compared with the existing NIR reflectance spectroscopy. Principal component analysis (PCA) is employed for feature extraction and selection with support vector machine (SVM) used for the prediction.>600 beef M. longissimusthoracissamples at 48 hours post mortemhave been scanned in three abattoirs (200 per abattoir over two consecutive days), using both hyperspectral imaging system and NIR reflectance spectroscopy. SSF and pH measures of steaks were collected by QMS. Preliminary results show that both HSI and NIR predict pH value more successfully than SSF. For SSF prediction, HSI (visible bands only)shows great potential as it yields higher coefficient of determination R2 than NIR. For the pH value prediction, the coefficient of determination (R2 ) of HSI is also higher than that of NIR. This indicates that HSI techniques can be more favourablethan NIR reflectance spectroscopy for accurate prediction of beef SSF and ultimate pH.
LanguageEnglish
Publication statusPublished - 1 Oct 2013
EventFarm Animal IMaging (FAIM) II - Kaposvar, Hungary
Duration: 29 Oct 201330 Oct 2013

Conference

ConferenceFarm Animal IMaging (FAIM) II
CountryHungary
CityKaposvar
Period29/10/1330/10/13

Fingerprint

Beef
Meats
Meat
meat quality
beef
image analysis
Technology
shears
prediction
Infrared radiation
Near-Infrared Spectroscopy
Abattoirs
near-infrared spectroscopy
Spectroscopy
slaughterhouses
Feature extraction
Food Quality
Principal Component Analysis
beef quality
Hyperspectral imaging

Keywords

  • Hyperspectral imaging
  • near-infrared spectroscopy
  • beef quality prediction
  • support vector machine
  • principal component analysis

Cite this

Qiao, T., Ren, J., Zabalza, J., Craigie, C., & Maltin, C. (2013). Use of hyperspectral imaging technologies for prediction of beef meat quality. Poster session presented at Farm Animal IMaging (FAIM) II, Kaposvar, Hungary.
Qiao, Tong ; Ren, Jinchang ; Zabalza, Jaime ; Craigie, Cameron ; Maltin, Charlotte. / Use of hyperspectral imaging technologies for prediction of beef meat quality. Poster session presented at Farm Animal IMaging (FAIM) II, Kaposvar, Hungary.
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Qiao, T, Ren, J, Zabalza, J, Craigie, C & Maltin, C 2013, 'Use of hyperspectral imaging technologies for prediction of beef meat quality' Farm Animal IMaging (FAIM) II, Kaposvar, Hungary, 29/10/13 - 30/10/13, .

Use of hyperspectral imaging technologies for prediction of beef meat quality. / Qiao, Tong; Ren, Jinchang; Zabalza, Jaime; Craigie, Cameron; Maltin, Charlotte.

2013. Poster session presented at Farm Animal IMaging (FAIM) II, Kaposvar, Hungary.

Research output: Contribution to conferencePoster

TY - CONF

T1 - Use of hyperspectral imaging technologies for prediction of beef meat quality

AU - Qiao, Tong

AU - Ren, Jinchang

AU - Zabalza, Jaime

AU - Craigie, Cameron

AU - Maltin, Charlotte

PY - 2013/10/1

Y1 - 2013/10/1

N2 - As an emerging technology, hyperspectral imaging (HSI) providesa unique non-destructive way of analysing food quality. In the current application HSI is applied to meat quality analysis, based on an image cube captured at different wavelengths, which usually covers from visible (VIS) to near infrared (NIR) bands. Many researchers have found that there is a relationship between eating quality of beef and corresponding sensory properties such as tenderness and flavour. The tenderness can be assessed by measuring the slice shear force (SSF) and the ultimate pH value is an important shelf-life and colour parameter. In this project, HSI has been employed to predict the SSF measurement and pH value of captured beef samples at 7 days and 14 days post mortem and the results are compared with the existing NIR reflectance spectroscopy. Principal component analysis (PCA) is employed for feature extraction and selection with support vector machine (SVM) used for the prediction.>600 beef M. longissimusthoracissamples at 48 hours post mortemhave been scanned in three abattoirs (200 per abattoir over two consecutive days), using both hyperspectral imaging system and NIR reflectance spectroscopy. SSF and pH measures of steaks were collected by QMS. Preliminary results show that both HSI and NIR predict pH value more successfully than SSF. For SSF prediction, HSI (visible bands only)shows great potential as it yields higher coefficient of determination R2 than NIR. For the pH value prediction, the coefficient of determination (R2 ) of HSI is also higher than that of NIR. This indicates that HSI techniques can be more favourablethan NIR reflectance spectroscopy for accurate prediction of beef SSF and ultimate pH.

AB - As an emerging technology, hyperspectral imaging (HSI) providesa unique non-destructive way of analysing food quality. In the current application HSI is applied to meat quality analysis, based on an image cube captured at different wavelengths, which usually covers from visible (VIS) to near infrared (NIR) bands. Many researchers have found that there is a relationship between eating quality of beef and corresponding sensory properties such as tenderness and flavour. The tenderness can be assessed by measuring the slice shear force (SSF) and the ultimate pH value is an important shelf-life and colour parameter. In this project, HSI has been employed to predict the SSF measurement and pH value of captured beef samples at 7 days and 14 days post mortem and the results are compared with the existing NIR reflectance spectroscopy. Principal component analysis (PCA) is employed for feature extraction and selection with support vector machine (SVM) used for the prediction.>600 beef M. longissimusthoracissamples at 48 hours post mortemhave been scanned in three abattoirs (200 per abattoir over two consecutive days), using both hyperspectral imaging system and NIR reflectance spectroscopy. SSF and pH measures of steaks were collected by QMS. Preliminary results show that both HSI and NIR predict pH value more successfully than SSF. For SSF prediction, HSI (visible bands only)shows great potential as it yields higher coefficient of determination R2 than NIR. For the pH value prediction, the coefficient of determination (R2 ) of HSI is also higher than that of NIR. This indicates that HSI techniques can be more favourablethan NIR reflectance spectroscopy for accurate prediction of beef SSF and ultimate pH.

KW - Hyperspectral imaging

KW - near-infrared spectroscopy

KW - beef quality prediction

KW - support vector machine

KW - principal component analysis

UR - http://www.cost-faim.eu/page13.html

M3 - Poster

ER -

Qiao T, Ren J, Zabalza J, Craigie C, Maltin C. Use of hyperspectral imaging technologies for prediction of beef meat quality. 2013. Poster session presented at Farm Animal IMaging (FAIM) II, Kaposvar, Hungary.