Non-destructive internal quality assessment of eggs using a synthesis of hyperspectral imaging and multivariate analysis

Abstract The study develops a nondestructive test based on hyperspectral imaging using a combination of existing analytical techniques to determine the internal quality of eggs, including freshness, bubble formation or scattered yolk. Successive projections algorithm (SPA) combined with support vector regression established a freshness detection model, which achieved a determination coefficient of 0.87, a root mean squared error of 4.01%, and the ratio of prediction to deviation of 2.80 in the validation set. In addition, eggs with internal bubbles and scattered yolk could be discriminated by support vector classification (SVC) model with identification accuracy of 90.0% and 96.3% respectively. Our findings suggest that hyperspectral imaging can be useful to non-destructively and rapidly assess egg internal quality.

[1]  Gamal ElMasry,et al.  Prediction of some quality attributes of lamb meat using near-infrared hyperspectral imaging and multivariate analysis. , 2012, Analytica chimica acta.

[2]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[3]  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 .

[4]  Gamal ElMasry,et al.  Non-destructive prediction and visualization of chemical composition in lamb meat using NIR hyperspectral imaging and multivariate regression , 2012 .

[5]  Peng Liu,et al.  Prediction of TVB-N content in eggs based on electronic nose , 2012 .

[6]  M. C. U. Araújo,et al.  The successive projections algorithm for variable selection in spectroscopic multicomponent analysis , 2001 .

[7]  O. J. Cotterill,et al.  Egg Science and Technology , 1986 .

[8]  Jiewen Zhao,et al.  Identification of egg’s freshness using NIR and support vector data description , 2010 .

[9]  Gamal ElMasry,et al.  Predicting quality and sensory attributes of pork using near-infrared hyperspectral imaging. , 2012, Analytica chimica acta.

[10]  Yankun Peng,et al.  A machine vision system for identification of micro-crack in egg shell , 2012 .

[11]  B De Ketelaere,et al.  Dirt detection on brown eggs by means of color computer vision. , 2005, Poultry science.

[12]  Xiuqin Rao,et al.  Detection of common defects on oranges using hyperspectral reflectance imaging , 2011 .

[13]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[14]  Maria Fernanda Pimentel,et al.  Aspects of the successive projections algorithm for variable selection in multivariate calibration applied to plasma emission spectrometry , 2001 .

[15]  K. Lawrence,et al.  Imaging system with modified-pressure chamber for crack detection in shell eggs , 2008 .

[16]  A. C. Kennedy,et al.  Using NIRS to predict fiber and nutrient content of dryland cereal cultivars. , 2010, Journal of agricultural and food chemistry.

[17]  M. Ngadi,et al.  Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry , 2007 .

[18]  Rasmus Bro,et al.  Variable selection in regression—a tutorial , 2010 .

[19]  Renfu Lu,et al.  Detection of fruit fly infestation in pickling cucumbers using a hyperspectral reflectance/transmittance imaging system , 2013 .

[20]  Roberto Kawakami Harrop Galvão,et al.  A variable elimination method to improve the parsimony of MLR models using the successive projections algorithm , 2008 .

[21]  Irwin R. Donis-González,et al.  The potential use of visible/near infrared spectroscopy and hyperspectral imaging to predict processing-related constituents of potatoes , 2014 .

[22]  Yidan Bao,et al.  Rapid prediction of moisture content of dehydrated prawns using online hyperspectral imaging system. , 2012, Analytica chimica acta.

[23]  Jiewen Zhao,et al.  Freshness measurement of eggs using near infrared (NIR) spectroscopy and multivariate data analysis , 2011 .

[24]  Mahmoud Omid,et al.  Grading and Quality Inspection of Defected Eggs Using Machine Vision , 2010 .

[25]  Roman M. Balabin,et al.  Near-infrared (NIR) spectroscopy for motor oil classification: From discriminant analysis to support vector machines , 2011 .

[26]  Gamal ElMasry,et al.  Chemical-free assessment and mapping of major constituents in beef using hyperspectral imaging , 2013 .

[27]  P. Williams,et al.  Near-Infrared Technology in the Agricultural and Food Industries , 1987 .

[28]  D. Andueza,et al.  Prediction of lamb meat fatty acid composition using near-infrared reflectance spectroscopy (NIRS). , 2011, Food chemistry.

[29]  Kristof Mertens,et al.  Visible transmission spectroscopy for the assessment of egg freshness , 2006 .

[30]  Vis-NIR Spectroscopy for Non-destructive Classification of Juicy Peach , 2006, 2006 International Conference on Mechatronics and Automation.

[31]  Hongbin Pu,et al.  Non-destructive prediction of salt contents and water activity of porcine meat slices by hyperspectral imaging in a salting process , 2013 .

[32]  Roberto Kawakami Harrop Galvão,et al.  The successive projections algorithm for spectral variable selection in classification problems , 2005 .

[33]  Angelo Fabbri,et al.  Non-destructive freshness assessment of shell eggs using FT-NIR spectroscopy , 2008 .

[34]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .