Smartphone determination of fat in cured meat products

Abstract A method has been developed to determine the fat content in different cold meat products by image processing using the camera of a mobile phone. Salchichon , chorizo , salami and cured ham pictures were taken with a Meizu M2 Mini mobile phone camera under fixed lighting conditions of the light emitting diode flash of the mobile phone. Images were treated with Matlab to obtain the mean pixels of average red, green and blue camera values colours (RGB) of the pixels and different data pretreatments were taken into account to correlate colour parameters with fat content values determined in a series of commercially available samples by the Soxhlet method. RGB values were used as input variable and its correlation with fat content of samples was studied using Partial Least Squares (PLS) and Support Vector Machine (SVM). The best correlation between fat content and RGB colour descriptors was found in salchichon and salami samples using SVM, with relative errors of calibration, cross-validation and prediction of 18%, 20% and 16%, respectively. So, the use of a Smartphone camera provides an easy, low-cost, eco-friendly and rapid method for fat content determination in cold meat products.

[1]  Sulaiman Al-Zuhair,et al.  Extracted fat from lamb meat by supercritical CO2 as feedstock for biodiesel production. , 2011 .

[2]  Mahmoud Omid,et al.  Freshness assessment of gilthead sea bream (Sparus aurata) by machine vision based on gill and eye color changes , 2013 .

[3]  M. D. Luque de Castro,et al.  Ultrasound-assisted extraction and derivatization of sterols and fatty alcohols from olive leaves and drupes prior to determination by gas chromatography-tandem mass spectrometry. , 2010, Journal of chromatography. A.

[4]  J. Pérez-Álvarez,et al.  Physicochemical characteristics of Spanish-type dry-cured sausage , 1999 .

[5]  Sakir Tasdemir,et al.  Original papers: Determination of body measurements on the Holstein cows using digital image analysis and estimation of live weight with regression analysis , 2011 .

[6]  M D Luque de Castro,et al.  Focused microwave-assisted Soxhlet extraction: a convincing alternative for total fat isolation from bakery products. , 2004, Talanta.

[7]  Piotr Zapotoczny,et al.  Evaluation of the quality of cold meats by computer-assisted image analysis , 2016 .

[8]  J D Tatum,et al.  Online evaluation of a commercial video image analysis system (Computer Vision System) to predict beef carcass red meat yield and for augmenting the assignment of USDA yield grades. United States Department of Agriculture. , 2002, Journal of animal science.

[9]  Erik Jørgensen,et al.  Determination of live weight of pigs from dimensions measured using image analysis , 1996 .

[10]  Iciar Astiasarán,et al.  Characterization of chorizo de Pamplona: instrumental measurements of colour and texture , 2000 .

[11]  E. Björklund,et al.  Comprehensive comparison of classic Soxhlet extraction with Soxtec extraction, ultrasonication extraction, supercritical fluid extraction, microwave assisted extraction and accelerated solvent extraction for the determination of polychlorinated biphenyls in soil. , 2005, Journal of chromatography. A.

[12]  R. Martínez‐Máñez,et al.  A chromogenic sensor array for boiled marinated turkey freshness monitoring , 2014 .

[13]  C. Rosselló,et al.  Composition assessment of raw meat mixtures using ultrasonics. , 2001, Meat science.

[14]  M. B. R. Mollah,et al.  Digital image analysis to estimate the live weight of broiler , 2010 .

[15]  W. M. Robertson,et al.  A novel approach to grading pork carcasses: computer vision and ultrasound. , 2003, Meat science.

[16]  B. Uttaro,et al.  Prediction of pork belly fatness from the intact primal cut , 2010 .

[17]  J. Lu,et al.  Evaluation of pork color by using computer vision. , 2000, Meat science.

[18]  M. de la Guardia,et al.  Prediction of banana quality indices from color features using support vector regression. , 2016, Talanta.

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

[20]  M. Chmiel,et al.  Application of computer vision systems for estimation of fat content in poultry meat , 2011 .

[21]  Frank Lundby,et al.  Determination of total fat and moisture content in meat using low field NMR. , 2004, Meat science.

[22]  M. Povey,et al.  Ultrasonic analysis of edible fats and oils. , 1992, Ultrasonics.