Application of linear/non-linear classification algorithms in discrimination of pork storage time using Fourier transform near infrared (FT-NIR) spectroscopy

Abstract To address the rapid and nondestructive determination of pork storage time associated with its freshness, Fourier transform near infrared (FT-NIR) spectroscopy technique, with the help of classification algorithm, was attempted in this work. To investigate the effects of different linear and non-linear classification algorithms on the discrimination results, linear discriminant analysis (LDA), K-nearest neighbors (KNN), and back propagation artificial neural network (BP-ANN) were used to develop the discrimination models, respectively. The number of principal components (PCs) and other parameters were optimized by cross-validation in developing discrimination models. Experimental results showed that the performance of BP-ANN model was superior to others, and the optimal BP-ANN model was achieved when 5 PCs were included. The discrimination rates of the BP-ANN model were 99.26% and 96.21% in the training and prediction sets, respectively. The overall results sufficiently demonstrate that the FT-NIR spectroscopy technique combined with BP-ANN classification algorithm has the potential to determine pork storage time associated with its freshness.

[1]  I. Geornaras,et al.  Bacterial populations associated with the dirty area of a South African poultry abattoir. , 1998, Journal of food protection.

[2]  Fang Cheng,et al.  On-line prediction of fresh pork quality using visible/near-infrared reflectance spectroscopy. , 2010, Meat science.

[3]  Giuliana Vinci,et al.  Biogenic amines: quality index of freshness in red and white meat , 2002 .

[4]  Jiewen Zhao,et al.  Determination of total volatile basic nitrogen (TVB-N) content and Warner–Bratzler shear force (WBSF) in pork using Fourier transform near infrared (FT-NIR) spectroscopy , 2011 .

[5]  Danilo Ercolini,et al.  Different molecular types of Pseudomonas fragi have the same overall behaviour as meat spoilers. , 2010, International journal of food microbiology.

[6]  N. Moltschaniwskyj,et al.  Predicting glycogen concentration in the foot muscle of abalone using near infrared reflectance spectroscopy (NIRS). , 2011, Food chemistry.

[7]  Ning Wang,et al.  Pork meat quality classification using Visible/Near-Infrared spectroscopic data. , 2010 .

[8]  P. Mallikarjunan,et al.  Analysis of crab meat volatiles as possible spoilage indicators for blue crab (Callinectes sapidus) meat by gas chromatography-mass spectrometry , 2010 .

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

[10]  P. Harrington,et al.  Direct detection of trimethylamine in meat food products using ion mobility spectrometry. , 2006, Talanta.

[11]  Jiewen Zhao,et al.  Identification of the green tea grade level using electronic tongue and pattern recognition , 2008 .

[12]  Fidel Toldrá,et al.  Hypoxanthine-based enzymatic sensor for determination of pork meat freshness , 2010 .

[13]  Solveig Langsrud,et al.  Evaluation of natural antimicrobials on typical meat spoilage bacteria in vitro and in vacuum-packed pork meat. , 2010, Journal of food science.

[14]  F. Toldrá,et al.  Nucleotides and their degradation products during processing of dry-cured ham, measured by HPLC and an enzyme sensor. , 2011, Meat science.

[15]  T. Fearn,et al.  A methodology based on NIR-microscopy for the detection of animal protein by-products. , 2009, Talanta.

[16]  Yong He,et al.  Pattern recognition of visible and near-infrared spectroscopy from bayberry juice by use of partial least squares and a backpropagation neural network. , 2006, Applied optics.

[17]  Mahdi Ghasemi-Varnamkhasti,et al.  Meat Quality Assessment by Electronic Nose (Machine Olfaction Technology) , 2009, Sensors.

[18]  D. Mouwen,et al.  Artificial neural network based identification of Campylobacter species by Fourier transform infrared spectroscopy. , 2006, Journal of microbiological methods.

[19]  K. Kawai,et al.  Improvement of fish freshness determination method by the application of amorphous freeze-dried enzymes. , 2010, Journal of agricultural and food chemistry.

[20]  Desire L. Massart,et al.  Comparison of regularized discriminant analysis linear discriminant analysis and quadratic discriminant analysis applied to NIR data , 1996 .

[21]  Martin Škrlep,et al.  Accuracy of near infrared spectroscopy for prediction of chemical composition, salt content and free amino acids in dry-cured ham. , 2011, Meat science.

[22]  E. Llobet,et al.  Monitoring of physical–chemical and microbiological changes in fresh pork meat under cold storage by means of a potentiometric electronic tongue , 2011 .

[23]  Adolfo Cobo,et al.  Quality control of industrial processes by combining a hyperspectral sensor and Fisher's linear discriminant analysis , 2008 .

[24]  Quansheng Chen,et al.  Identification of Tea Varieties Using Computer Vision , 2008 .

[25]  Kenji Yokoyama,et al.  Direct evaluation of meat spoilage and the progress of aging using biosensors , 1996 .

[26]  P. Yáñez‐Sedeño,et al.  Amperometric biosensor for hypoxanthine based on immobilized xanthine oxidase on nanocrystal gold–carbon paste electrodes , 2006 .

[27]  Dejan Škorjanc,et al.  Predicting pork water-holding capacity with NIR spectroscopy in relation to different reference methods , 2010 .

[28]  Martin Sommer,et al.  Assessment of meat freshness with metal oxide sensor microarray electronic nose: A practical approach , 2010 .

[29]  Yunfei Li,et al.  Interactions of microorganisms during natural spoilage of pork at 5 °C , 2006 .

[30]  N. Sinelli,et al.  Evaluation of freshness decay of minced beef stored in high-oxygen modified atmosphere packaged at different temperatures using NIR and MIR spectroscopy. , 2010, Meat science.

[31]  B. Kong,et al.  Antimicrobial activities of spice extracts against pathogenic and spoilage bacteria in modified atmosphere packaged fresh pork and vacuum packaged ham slices stored at 4°C. , 2009, Meat science.

[32]  J. A. Ordóñez,et al.  Growth/survival of natural flora and Aeromonas hydrophila on refrigerated uncooked pork and turkey packaged in modified atmospheres , 2000 .

[33]  Royston Goodacre,et al.  VOC-based metabolic profiling for food spoilage detection with the application to detecting Salmonella typhimurium-contaminated pork , 2010, Analytical and bioanalytical chemistry.

[34]  M. M. Reis,et al.  Near infrared spectroscopy as an on-line method to quantitatively determine glycogen and predict ultimate pH in pre rigor bovine M. longissimus dorsi. , 2010, Meat science.