Detection of total viable count in spiced beef using hyperspectral imaging combined with wavelet transform and multiway partial least squares algorithm

The feasibility of using hyperspectral imaging technology combined with wavelet transform and multiway partial least squares (N-PLS) algorithm to predict the total viable count (TVC) of spiced beef during storage was investigated. The mean spectral data were extracted from the hyperspectral images and further decomposed in nine levels by daubechies8 (db8) wavelet function to obtain an approximation coefficient (A9) and nine detail coefficients (D1–D9). Selecting wavelet coefficients to compose different three-dimension matrixes, further integrating N-PLS algorithm to establish the predictive models for detecting TVC in spiced beef. The experimental results show that the N-PLS model with D4, D5, D6, D7 coefficient (also named D4,5,6,7-N-PLS) exhibit an excellent prediction capability for TVC of spiced beef sample with a higher determination coefficients in prediction ( Rp2) of 0.934 and lower root mean squared errors estimated by prediction of 0.755 than other N-PLS models, raw spectra PLS model, and Unfold-PLS models. Therefore, the established model using three-dimension data array and N-PLS algorithm has great potential in the TVC value detection of spiced beef and other meat productions. Practical applications Spiced beef is one of the most popular meat products in China owing to its good taste, low fat, and high protein. Unfortunately, the spiced beef during storage is just a suitable habitat for many pathogens and spoilage microbes to colonize. Thus, it is necessary to monitor the microbiological contamination to guarantee the sanitary quality of meat productions. The total viable count (TVC) of bacteria was considered as a key indicator for the freshness evaluation of meat productions, Traditional techniques for determination of TVC are laborious, time consuming, and unsuitable for modern meat industrial processing and production technologies. Therefore, this study attempted to use hyperspectral imaging technology combined with wavelet transform and multiway partial least squares (N-PLS) algorithm to predict the TVC of spiced beef during storage. The proposed method is helpful for most consumers to judge easily the freshness state of meat productions and further purchase them.

[1]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  R. Bro Multiway calibration. Multilinear PLS , 1996 .

[3]  E. Borch,et al.  Bacterial spoilage of meat and cured meat products. , 1996, International journal of food microbiology.

[4]  D. Massart,et al.  Application of wavelet transform to extract the relevant component from spectral data for multivariate calibration. , 1997, Analytical chemistry.

[5]  R. Bro PARAFAC. Tutorial and applications , 1997 .

[6]  R. Bonner,et al.  Application of wavelet transforms to experimental spectra : Smoothing, denoising, and data set compression , 1997 .

[7]  Sijmen de Jong,et al.  Multiway calibration in 3D QSAR , 1997 .

[8]  B. Bakheit,et al.  Triple test cross and six-population techniques for partitioning the components of genetic variance in faba bean (Vicia faba) , 2002, The Journal of Agricultural Science.

[9]  S. Engelsen,et al.  Screening for dioxin contamination in fish oil by PARAFAC and N‐PLSR analysis of fluorescence landscapes , 2002 .

[10]  Sinthop Kaewpijit,et al.  Automatic reduction of hyperspectral imagery using wavelet spectral analysis , 2003, IEEE Trans. Geosci. Remote. Sens..

[11]  Kim H. Esbensen,et al.  Multi-way methods in image analysis : Relationships and applications , 2003 .

[12]  Marcelo M Sena,et al.  N-way PLS applied to simultaneous spectrophotometric determination of acetylsalicylic acid, paracetamol and caffeine. , 2004, Journal of pharmaceutical and biomedical analysis.

[13]  David H. Burns,et al.  Parsimonious calibration models for near-infrared spectroscopy using wavelets and scaling functions , 2006 .

[14]  Fang Liu,et al.  Correlations between growth parameters of spoilage micro-organisms and shelf-life of pork stored under air and modified atmosphere at -2, 4 and 10 degrees C. , 2006, Food microbiology.

[15]  S. Reinikainen,et al.  Predicting the drug concentration in starch acetate matrix tablets from ATR-FTIR spectra using multi-way methods. , 2007, Analytica chimica acta.

[16]  G. Nychas,et al.  Meat spoilage during distribution. , 2008, Meat science.

[17]  R. Yu,et al.  Treating NIR data with orthogonal discrete wavelet transform: Predicting concentrations of a multi-component system through a small-scale calibration set , 2008 .

[18]  N. Z. Ballin Authentication of meat and meat products. , 2010, Meat science.

[19]  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.

[20]  Yankun Peng,et al.  Potential prediction of the microbial spoilage of beef using spatially resolved hyperspectral scattering profiles , 2011 .

[21]  Gamal Elmasry,et al.  Near-infrared hyperspectral imaging for grading and classification of pork. , 2012, Meat science.

[22]  Yankun Peng,et al.  Prediction of beef quality attributes using VIS/NIR hyperspectral scattering imaging technique , 2012 .

[23]  Gamal ElMasry,et al.  Near-infrared hyperspectral imaging for predicting colour, pH and tenderness of fresh beef , 2012 .

[24]  Renjie Yang,et al.  Detection of adulterated milk using two-dimensional correlation spectroscopy combined with multi-way partial least squares , 2013 .

[25]  Douglas Fernandes Barbin,et al.  Prediction of water and protein contents and quality classification of Spanish cooked ham using NIR hyperspectral imaging , 2013 .

[26]  Quansheng Chen,et al.  Recent advances in emerging imaging techniques for non-destructive detection of food quality and safety , 2013 .

[27]  Jiewen Zhao,et al.  Rapid detection of total viable count (TVC) in pork meat by hyperspectral imaging , 2013 .

[28]  Yao-Ze Feng,et al.  Determination of total viable count (TVC) in chicken breast fillets by near-infrared hyperspectral imaging and spectroscopic transforms. , 2013, Talanta.

[29]  Nagy I. Elkalashy,et al.  Arcing fault identification using combined Gabor Transform-neural network for transmission lines , 2014 .

[30]  Hongbin Pu,et al.  Feasibility of using hyperspectral imaging to predict moisture content of porcine meat during salting process. , 2014, Food chemistry.

[31]  J. Claus,et al.  Encapsulated phosphates reduce lipid oxidation in both ground chicken and ground beef during raw and cooked meat storage with some influence on color, pH, and cooking loss. , 2014, Meat science.

[32]  M. Tortorello TOTAL VIABLE COUNTS | Microscopy , 2014 .

[33]  Jens Michael Carstensen,et al.  Potential of multispectral imaging technology for rapid and non-destructive determination of the microbiological quality of beef filets during aerobic storage. , 2014, International journal of food microbiology.

[34]  Lu Wang,et al.  Combination of spectra and texture data of hyperspectral imaging for prediction of pH in salted meat. , 2014, Food chemistry.

[35]  Jingfeng Huang,et al.  Detection of Crude Protein, Crude Starch, and Amylose for Rice by Hyperspectral Reflectance , 2014 .

[36]  Jun-Hu Cheng,et al.  Rapid and non-invasive detection of fish microbial spoilage by visible and near infrared hyperspectral imaging and multivariate analysis , 2015 .

[37]  Zhenjie Xiong,et al.  Non-destructive prediction of thiobarbituricacid reactive substances (TBARS) value for freshness evaluation of chicken meat using hyperspectral imaging. , 2015, Food chemistry.

[38]  Jun-Hu Cheng,et al.  Integration of classifiers analysis and hyperspectral imaging for rapid discrimination of fresh from cold-stored and frozen-thawed fish fillets , 2015 .

[39]  Stephen Marshall,et al.  Quantitative Prediction of Beef Quality Using Visible and NIR Spectroscopy with Large Data Samples Under Industry Conditions , 2015 .

[40]  José Blasco,et al.  VIS/NIR hyperspectral imaging and N-way PLS-DA models for detection of decay lesions in citrus fruits , 2016 .

[41]  Jun-Hu Cheng,et al.  Pork biogenic amine index (BAI) determination based on chemometric analysis of hyperspectral imaging data , 2016 .

[42]  Jiewen Zhao,et al.  Nondestructively sensing of total viable count (TVC) in chicken using an artificial olfaction system based colorimetric sensor array , 2016 .