Application of NIR hyperspectral imaging for discrimination of lamb muscles

The potential of near-infrared (NIR) hyperspectral imaging system coupled with multivariate analysis was evaluated for discriminating three types of lamb muscles. Samples from semitendinosus (ST), Longissimus dorsi (LD) and Psoas Major (PM) of Charollais breed were imaged by a pushbroom hyperspectral imaging system with a spectral range of 900–1700 nm. Principal component analysis (PCA) was used for dimensionality reduction, wavelength selection and visualizing hyperspectral data. Six optimal wavelengths (934, 974, 1074, 1141, 1211 and 1308 nm) were selected from the eigenvector plot of PCA and then used for discrimination purpose. The results showed that it was possible to discriminate lamb muscles with overall accuracy of 100% using NIR hyperspectral reflectance spectra. An image processing algorithm was also developed for visualizing classification results in a pixel-wise scale with a high overall accuracy.

[1]  Jens Petter Wold,et al.  Atlantic Salmon Average Fat Content Estimated by Near‐Infrared Transmittance Spectroscopy , 1996 .

[2]  S. Prasher,et al.  Prediction of drip-loss, pH, and color for pork using a hyperspectral imaging technique. , 2007, Meat science.

[3]  N. Prieto,et al.  Potential use of near infrared reflectance spectroscopy (NIRS) for the estimation of chemical composition of oxen meat samples. , 2006, Meat science.

[4]  Rakesh K. Singh,et al.  Application of Artificial Neural Networks to Predict the Oxidation of Menhaden Fish Oil Obtained from Fourier Transform Infrared Spectroscopy Method , 2011 .

[5]  Da-Wen Sun,et al.  Colour calibration of a laboratory computer vision system for quality evaluation of pre-sliced hams. , 2009, Meat science.

[6]  Michael Ngadi,et al.  Pork Quality Classification Using a Hyperspectral Imaging System and Neural Network , 2007 .

[7]  Ashok Samal,et al.  Visible/near-infrared hyperspectral imaging for beef tenderness prediction , 2008 .

[8]  Yong He,et al.  Theory and application of near infrared reflectance spectroscopy in determination of food quality , 2007 .

[9]  Daniel E. Guyer,et al.  Near-infrared hyperspectral reflectance imaging for detection of bruises on pickling cucumbers , 2006, Computers and Electronics in Agriculture.

[10]  S. Prasher,et al.  Pork quality and marbling level assessment using a hyperspectral imaging system , 2007 .

[11]  Daniel Cozzolino,et al.  The use of visible and near-infrared reflectance spectroscopy to predict colour on both intact and homogenised pork muscle , 2003 .

[12]  Filiberto Pla,et al.  SmartSpectra: Applying multispectral imaging to industrial environments , 2005, Real Time Imaging.

[13]  J. Aguilera,et al.  Computer Vision and Stereoscopy for Estimating Firmness in the Salmon (Salmon salar) Fillets , 2010 .

[14]  Fred Godtliebsen,et al.  Ridge detection with application to automatic fish fillet inspection , 2009 .

[15]  Y. R. Chen,et al.  Detection of Defects on Selected Apple Cultivars Using Hyperspectral and Multispectral Image Analysis , 2002 .

[16]  J. F. Tejeda,et al.  Effect of live weight and sex on physico-chemical and sensorial characteristics of Merino lamb meat. , 2008, Meat science.

[17]  Franco Pedreschi,et al.  Color of Salmon Fillets By Computer Vision and Sensory Panel , 2010 .

[18]  Gamal ElMasry,et al.  High-speed assessment of fat and water content distribution in fish fillets using online imaging spectroscopy. , 2008, Journal of agricultural and food chemistry.

[19]  Jens T. Thielemann,et al.  Non-Contact Transflectance near Infrared Imaging for Representative on-Line Sampling of Dried Salted Coalfish (Bacalao) , 2006 .

[20]  Josse De Baerdemaeker,et al.  Bruise detection on ‘Jonagold’ apples using hyperspectral imaging , 2005 .

[21]  A. el-aal,et al.  Carcass traits and meat quality of lamb fed on ration containing different levels of leucaena hay (Leucaena leucocephala L.) , 2008 .

[22]  Ning Wang,et al.  Early detection of apple bruises on different background colors using hyperspectral imaging , 2008 .

[23]  Angelo Zanella,et al.  Supervised Multivariate Analysis of Hyper-spectral NIR Images to Evaluate the Starch Index of Apples , 2009 .

[24]  Gauri S. Mittal,et al.  Rapid Detection of Microorganisms Using Image Processing Parameters and Neural Network , 2010 .

[25]  Moon S. Kim,et al.  Development of hyperspectral imaging technique for the detection of apple surface defects and contaminations , 2004 .

[26]  Ashok Samal,et al.  Partial least squares analysis of near-infrared hyperspectral images for beef tenderness prediction , 2008 .

[27]  Daniel Cozzolino,et al.  Predicting intramuscular fat, moisture and Warner-Bratzler shear force in pork muscle using near infrared reflectance spectroscopy , 2006 .

[28]  Federico Pallottino,et al.  Image Analysis Techniques for Automated Hazelnut Peeling Determination , 2010 .

[29]  Y. R. Chen,et al.  Principal component regression of near-infrared reflectance spectra for beef tenderness prediction , 2001 .

[30]  Yuval Garini,et al.  Spectral imaging: Principles and applications , 2006, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[31]  Anna Grazia Mignani,et al.  Spectral nephelometry for the geographic classification of Italian extra virgin olive oils , 2005 .

[32]  Daniel Cozzolino,et al.  Identification of animal meat muscles by visible and near infrared reflectance spectroscopy , 2004 .

[33]  David Casasent,et al.  Hyperspectral waveband selection for contaminant detection on poultry carcasses , 2008 .

[34]  D. Alomar,et al.  Chemical and discriminant analysis of bovine meat by near infrared reflectance spectroscopy (NIRS). , 2003, Meat science.

[35]  Kurt C. Lawrence,et al.  Comparison between visible/NIR spectroscopy and hyperspectral imaging for detecting surface contaminants on poultry carcasses , 2004, SPIE Optics East.

[36]  N. Prieto,et al.  Ability of near infrared reflectance spectroscopy (NIRS) to estimate physical parameters of adult steers (oxen) and young cattle meat samples. , 2008, Meat science.

[37]  Moon S. Kim,et al.  Development of a Simple Algorithm for the Detection of Chilling Injury in Cucumbers from Visible/Near-Infrared Hyperspectral Imaging , 2005, Applied spectroscopy.

[38]  R. Bro,et al.  Near-infrared chemical imaging (NIR-CI) on pharmaceutical solid dosage forms-comparing common calibration approaches. , 2008, Journal of pharmaceutical and biomedical analysis.

[39]  Jasper G. Tallada,et al.  Bruise Detection using NIR Hyperspectral Imaging for Strawberry (Fragaria * ananassa Duch.) , 2006 .

[40]  Ning Wang,et al.  Detecting chilling injury in Red Delicious apple using hyperspectral imaging and neural networks , 2009 .

[41]  Achim Kohler,et al.  Noncontact salt and fat distributional analysis in salted and smoked salmon fillets using X-ray computed tomography and NIR interactance imaging. , 2009, Journal of agricultural and food chemistry.

[42]  Moon S. Kim,et al.  Development of simple algorithms for the detection of fecal contaminants on apples from visible/near infrared hyperspectral reflectance imaging , 2007 .

[43]  Moon S. Kim,et al.  Analysis of hyperspectral fluorescence images for poultry skin tumor inspection. , 2004, Applied optics.

[44]  Aiguo Ouyang,et al.  Improvement of Near-Infrared Spectral Calibration Models for Brix Prediction in ‘Gannan’ Navel Oranges by a Portable Near-Infrared Device , 2010, Food and Bioprocess Technology.

[45]  I. Murray,et al.  The use of visible and near infrared reflectance spectroscopy to predict beef M. longissimus thoracis et lumborum quality attributes. , 2008, Meat science.

[46]  W. R. Windham,et al.  Contaminant classification of poultry hyperspectral imagery using a spectral angle mapper algorithm , 2007 .