Spectral absorption index in hyperspectral image analysis for predicting moisture contents in pork longissimus dorsi muscles.

Spectral absorption index was proposed to extract the morphological features of the spectral curves in pork meat samples (longissimus dorsi) under the conditions including fresh, frozen-thawed, heated-dehydrated and brined-dehydrated. Savitzky-Golay (SG) smoothing and multiplicative scatter correction (MSC) were used for calibrating both the spectral reflectance and absorbance values. The absorption values were better than the reflectance values and the calibrated spectra by MSC were better than the raw and SG smoothing corrected spectra in building moisture content predictive models. The optimized partial least square regression (PLSR) model attained good results with the MSC calibrated spectral absorption values based on the spectral absorption index features (R(2)P=0.952, RMSEP=1.396) and the optimal wavelengths selected by regression coefficients (R(2)P=0.966, RMSEP=0.855), respectively. The models proved spectral absorption index was promising in spectral analysis to predict moisture content in pork samples using HSI techniques for the first time.

[1]  Da-Wen Sun,et al.  Meat Quality Evaluation by Hyperspectral Imaging Technique: An Overview , 2012, Critical reviews in food science and nutrition.

[2]  Fei Liu,et al.  Application of Visible and Near Infrared Hyperspectral Imaging to Differentiate Between Fresh and Frozen–Thawed Fish Fillets , 2013, Food and Bioprocess Technology.

[3]  Jun-Hu Cheng,et al.  Classification of fresh and frozen-thawed pork muscles using visible and near infrared hyperspectral imaging and textural analysis. , 2015, Meat science.

[4]  Da-Wen Sun,et al.  Computer vision––an objective, rapid and non-contact quality evaluation tool for the food industry , 2004 .

[5]  Da-Wen Sun,et al.  Modelling vacuum cooling process of cooked meat: part 1: analysis of vacuum cooling system , 2002 .

[6]  Da-Wen Sun,et al.  Melting characteristics of cheese: analysis of effect of cheese dimensions using computer vision techniques , 2002 .

[7]  Jun-Hu Cheng,et al.  Discrimination of shelled shrimp (Metapenaeus ensis) among fresh, frozen-thawed and cold-stored by hyperspectral imaging technique , 2015 .

[8]  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 .

[9]  Yoshio Makino,et al.  Assessment of Visible Near-Infrared Hyperspectral Imaging as a Tool for Detection of Horsemeat Adulteration in Minced Beef , 2015, Food and Bioprocess Technology.

[10]  Jens Michael Carstensen,et al.  Using Multispectral Imaging for Spoilage Detection of Pork Meat , 2013, Food and Bioprocess Technology.

[11]  Jun-Hu Cheng,et al.  Using Wavelet Textural Features of Visible and Near Infrared Hyperspectral Image to Differentiate Between Fresh and Frozen–Thawed Pork , 2014, Food and Bioprocess Technology.

[12]  Douglas Fernandes Barbin,et al.  Non-destructive assessment of microbial contamination in porcine meat using NIR hyperspectral imaging , 2013 .

[13]  Da-Wen Sun,et al.  Prediction of beef eating qualities from colour, marbling and wavelet surface texture features using homogenous carcass treatment , 2009, Pattern Recognit..

[14]  Di Wu,et al.  Application of long-wave near infrared hyperspectral imaging for measurement of color distribution in salmon fillet , 2012 .

[15]  Da-Wen Sun,et al.  Heat transfer characteristics of cooked meats using different cooling methods , 2000 .

[16]  A. Youssef El-Sayed.  Simultaneous determination of aluminium and iron in glasses, phosphate rocks and cement using first- and second-derivative spectrophotometry , 1996, Analytical and bioanalytical chemistry.

[17]  Frans van den Berg,et al.  Review of the most common pre-processing techniques for near-infrared spectra , 2009 .

[18]  Yao-Ze Feng,et al.  Near-infrared hyperspectral imaging in tandem with partial least squares regression and genetic algorithm for non-destructive determination and visualization of Pseudomonas loads in chicken fillets. , 2013, Talanta.

[19]  Da-Wen Sun,et al.  Shape Analysis of Agricultural Products: A Review of Recent Research Advances and Potential Application to Computer Vision , 2011 .

[20]  J. De Baerdemaeker,et al.  Multiplicative Scatter Correction during On-line Measurement with Near Infrared Spectroscopy , 2007 .

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

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

[23]  Huazhou Chen,et al.  Waveband selection for NIR spectroscopy analysis of soil organic matter based on SG smoothing and MWPLS methods , 2011 .

[24]  Da-Wen Sun,et al.  Effect of operating conditions of a vacuum cooler on cooling performance for large cooked meat joints , 2004 .

[25]  Da-Wen Sun,et al.  Vacuum cooling for the food industry—a review of recent research advances , 2004 .

[26]  C. Jun,et al.  Performance of some variable selection methods when multicollinearity is present , 2005 .

[27]  Cui Zheng-wei.  PREPARATION OF DRY HONEY BY MICROWAVE-VACUUM DRYING , 2007 .

[28]  Yankun Peng,et al.  A Nondestructive Method for Prediction of Total Viable Count in Pork Meat by Hyperspectral Scattering Imaging , 2014, Food and Bioprocess Technology.

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

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

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

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

[33]  Da-Wen Sun,et al.  Principles and Applications of Hyperspectral Imaging in Quality Evaluation of Agro-Food Products: A Review , 2012, Critical reviews in food science and nutrition.

[34]  T. Kenny,et al.  Effect of rapid and conventional cooling methods on the quality of cooked ham joints. , 2000, Meat science.

[35]  Jun-Hu Cheng,et al.  Comparison of Visible and Long-wave Near-Infrared Hyperspectral Imaging for Colour Measurement of Grass Carp (Ctenopharyngodon idella) , 2014, Food and Bioprocess Technology.

[36]  Kun Tan,et al.  Feature extraction for target identification and image classification of OMIS hyperspectral image , 2009 .

[37]  Da-Wen Sun,et al.  Water crystallization and its importance to freezing of foods: A review , 2011 .

[38]  Da-Wen Sun,et al.  Modelling vacuum cooling process of cooked meat—part 2: mass and heat transfer of cooked meat under vacuum pressure , 2002 .

[39]  Jun-Hu Cheng,et al.  Rapid Quantification Analysis and Visualization of Escherichia coli Loads in Grass Carp Fish Flesh by Hyperspectral Imaging Method , 2015, Food and Bioprocess Technology.

[40]  Di Wu,et al.  Potential of time series-hyperspectral imaging (TS-HSI) for non-invasive determination of microbial spoilage of salmon flesh. , 2013, Talanta.

[41]  Hongbin Pu,et al.  Application of Vis–NIR hyperspectral imaging in classification between fresh and frozen-thawed pork Longissimus Dorsi muscles , 2015 .

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

[43]  Da-Wen Sun,et al.  Desorption isotherms and glass transition temperature for chicken meat , 2002 .

[44]  Da-Wen Sun,et al.  NIR hyperspectral imaging as non-destructive evaluation tool for the recognition of fresh and frozen–thawed porcine longissimus dorsi muscles , 2013 .

[45]  Hongbin Pu,et al.  Prediction of Color and pH of Salted Porcine Meats Using Visible and Near-Infrared Hyperspectral Imaging , 2014, Food and Bioprocess Technology.

[46]  Da-Wen Sun,et al.  Recent Advances in Wavelength Selection Techniques for Hyperspectral Image Processing in the Food Industry , 2014, Food and Bioprocess Technology.

[47]  Da-Wen Sun,et al.  Application of Hyperspectral Imaging in Food Safety Inspection and Control: A Review , 2012, Critical reviews in food science and nutrition.