Use of hyperspectral imaging technologies for prediction of beef meat quality

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

Use of hyperspectral imaging technologies for prediction of beef meat quality

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

Electronic And Electrical Engineering

Research output: Contribution to conference › Poster

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.

Language	English
Publication status	Published - 1 Oct 2013
Event	Farm Animal IMaging (FAIM) II - Kaposvar, Hungary Duration: 29 Oct 2013 → 30 Oct 2013

Conference

Conference	Farm Animal IMaging (FAIM) II
Country	Hungary
City	Kaposvar
Period	29/10/13 → 30/10/13

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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keywords = "Hyperspectral imaging, near-infrared spectroscopy, beef quality prediction, support vector machine, principal component analysis",

author = "Tong Qiao and Jinchang Ren and Jaime Zabalza and Cameron Craigie and Charlotte Maltin",

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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 conference › Poster

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.