Recent Advances in De‐Noising Methods and Their Applications in Hyperspectral Image Processing for the Food Industry

There is great interest in developing hyperspectral imaging (HSI) techniques for rapid and nondestructive inspection of food quality, safety, and authenticity. In recent years, image quality has been constantly improved through advances in instrumentation, particularly in more powerful detectors. Nevertheless, pretreatment of data by de-noising is a necessary step to insure clean HSI datasets for further analysis. This review first introduces the typical and commonly used de-noising methods in HSI that correct for undesirable variations and remove noisy variables. Their advantages, disadvantages, and implementation are also discussed by giving examples of recent applications in the food industry. Finally, some advice is given for selecting the de-noising methods that are best suited for a particular application. This review offers an overview of the most frequently applied methods and the latest progress made in HSI de-noising in food applications. It provides systematic insight into future trends for generating high-accuracy predictions regarding food safety and quality.

[1]  Colm P. O'Donnell,et al.  Identification of mushrooms subjected to freeze damage using hyperspectral imaging. , 2009 .

[2]  H. Abdi,et al.  Principal component analysis , 2010 .

[3]  Gamal ElMasry,et al.  Quality classification of cooked, sliced turkey hams using NIR hyperspectral imaging system , 2011 .

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

[5]  Moon S. Kim,et al.  Detection of fecal contamination on leafy greens by hyperspectral imaging , 2011 .

[6]  Da-Wen Sun,et al.  Advances in Feature Selection Methods for Hyperspectral Image Processing in Food Industry Applications: A Review , 2015, Critical reviews in food science and nutrition.

[7]  Yankun Peng,et al.  A method for nondestructive prediction of pork meat quality and safety attributes by hyperspectral imaging technique , 2014 .

[8]  Man Bao,et al.  Research on marine and freshwater fish identification model based on hyper-spectral imaging technology , 2013, Other Conferences.

[9]  Gamal Elmasry,et al.  Near-infrared hyperspectral imaging and partial least squares regression for rapid and reagentless determination of Enterobacteriaceae on chicken fillets. , 2013, Food chemistry.

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

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

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

[13]  Di Wu,et al.  Novel non-invasive distribution measurement of texture profile analysis (TPA) in salmon fillet by using visible and near infrared hyperspectral imaging. , 2014, Food chemistry.

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

[15]  Gamal ElMasry,et al.  Non-destructive assessment of instrumental and sensory tenderness of lamb meat using NIR hyperspectral imaging. , 2013, Food chemistry.

[16]  Wouter Saeys,et al.  Comparison of Visible–Near Infrared and Short Wave Infrared hyperspectral imaging for the evaluation of rainbow trout freshness , 2014 .

[17]  J. Blasco,et al.  Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment , 2012, Food and Bioprocess Technology.

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

[19]  Gamal ElMasry,et al.  Non-destructive determination of water-holding capacity in fresh beef by using NIR hyperspectral imaging , 2011 .

[20]  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.

[21]  V. Chelladurai,et al.  Detection of infestation by Callosobruchus maculatus in mung bean using near-infrared hyperspectral imaging , 2013 .

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

[23]  Qibing Zhu,et al.  Model fusion for prediction of apple firmness using hyperspectral scattering image , 2012 .

[24]  D. Jayas,et al.  Identification of wheat classes using wavelet features from near infrared hyperspectral images of bulk samples. , 2009 .

[25]  Min Zhang,et al.  Prediction of color and moisture content for vegetable soybean during drying using hyperspectral imaging technology , 2014 .

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

[27]  José Manuel Amigo,et al.  Practical issues of hyperspectral imaging analysis of solid dosage forms , 2010, Analytical and bioanalytical chemistry.

[28]  D. Massart,et al.  Elimination of uninformative variables for multivariate calibration. , 1996, Analytical chemistry.

[29]  Di Wu,et al.  Application of visible and near infrared hyperspectral imaging for non-invasively measuring distribution of water-holding capacity in salmon flesh. , 2013, Talanta.

[30]  Santiago Velasco-Forero,et al.  Improving Hyperspectral Image Classification Using Spatial Preprocessing , 2009, IEEE Geoscience and Remote Sensing Letters.

[31]  Ashok Samal,et al.  Visible/near-infrared hyperspectral imaging for beef tenderness prediction , 2008 .

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

[33]  V. Vemuri,et al.  Artificial neural networks: an introduction , 1988 .

[34]  Pedro Larrañaga,et al.  A review of feature selection techniques in bioinformatics , 2007, Bioinform..

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

[36]  Da-Wen Sun,et al.  Non-destructive and rapid determination of TVB-N content for freshness evaluation of grass carp (Ctenopharyngodon idella) by hyperspectral imaging , 2014 .

[37]  Shiv O. Prasher,et al.  Categorization of pork quality using Gabor filter-based hyperspectral imaging technology , 2010 .

[38]  Douglas Fernandes Barbin,et al.  Grape seed characterization by NIR hyperspectral imaging , 2013 .

[39]  Lalit Mohan Kandpal,et al.  Hyperspectral Reflectance Imaging Technique for Visualization of Moisture Distribution in Cooked Chicken Breast , 2013, Sensors.

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

[41]  Karsten Heia,et al.  Classification of fresh Atlantic salmon (Salmo salar L.) fillets stored under different atmospheres by hyperspectral imaging , 2012 .

[42]  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.

[43]  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 .

[44]  S. Oshita,et al.  Rapid detection of Escherichia coli contamination in packaged fresh spinach using hyperspectral imaging. , 2011, Talanta.

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

[46]  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 .

[47]  Steven D. Brown,et al.  Transfer of multivariate calibration models: a review , 2002 .

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

[49]  P. Baranowski,et al.  Supervised classification of bruised apples with respect to the time after bruising on the basis of hyperspectral imaging data , 2013 .

[50]  Ashok Samal,et al.  Partial least squares analysis of near-infrared hyperspectral images for beef tenderness prediction , 2008 .

[51]  Di Wu,et al.  Potential of hyperspectral imaging combined with chemometric analysis for assessing and visualising tenderness distribution in raw farmed salmon fillets , 2014 .

[52]  Gamal ElMasry,et al.  Non-destructive determination of chemical composition in intact and minced pork using near-infrared hyperspectral imaging. , 2013, Food chemistry.

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

[54]  Chu Zhang,et al.  Fast detection of peroxidase (POD) activity in tomato leaves which infected with Botrytis cinerea using hyperspectral imaging. , 2014, Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy.

[55]  Michael Ngadi,et al.  Mapping of Fat and Moisture Distribution in Atlantic Salmon Using Near-Infrared Hyperspectral Imaging , 2013, Food and Bioprocess Technology.

[56]  Qianqian Li,et al.  Identification of additive components in powdered milk by NIR imaging methods. , 2014, Food chemistry.

[57]  Thomas A. Blake,et al.  Application of extended inverse scatter correction to mid‐infrared reflectance spectra of soil , 2005 .

[58]  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.

[59]  Pengcheng Nie,et al.  Application of Time Series Hyperspectral Imaging (TS-HSI) for Determining Water Distribution Within Beef and Spectral Kinetic Analysis During Dehydration , 2013, Food and Bioprocess Technology.

[60]  Colm P. O'Donnell,et al.  The potential of visible-near infrared hyperspectral imaging to discriminate between casing soil, enzymatic browning and undamaged tissue on mushroom (Agaricus bisporus) surfaces , 2011 .

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

[62]  Moon S. Kim,et al.  Detection of melamine in milk powders based on NIR hyperspectral imaging and spectral similarity analyses , 2014 .

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

[64]  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.

[65]  Michael Ngadi,et al.  Detecting Fertility and Early Embryo Development of Chicken Eggs Using Near-Infrared Hyperspectral Imaging , 2013, Food and Bioprocess Technology.

[66]  Cheng-Jin Du,et al.  Prediction of beef eating quality from colour, marbling and wavelet texture features. , 2008, Meat science.

[67]  Paul Geladi,et al.  Hyperspectral NIR image regression part I: calibration and correction , 2005 .

[68]  J. A. Fernández Pierna,et al.  Validation and transferability study of a method based on near-infrared hyperspectral imaging for the detection and quantification of ergot bodies in cereals , 2013, Analytical and Bioanalytical Chemistry.

[69]  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.

[70]  Kurt C. Lawrence,et al.  Development of classification models to detect Salmonella Enteritidis and Salmonella Typhimurium found in poultry carcass rinses by visible-near infrared hyperspectral imaging , 2013, Defense, Security, and Sensing.

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

[72]  R. Leardi,et al.  Genetic algorithms applied to feature selection in PLS regression: how and when to use them , 1998 .

[73]  Ashok Samal,et al.  Optical scattering in beef steak to predict tenderness using hyperspectral imaging in the VIS-NIR region , 2008 .

[74]  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.

[75]  Yong He,et al.  Potential of hyperspectral imaging and multivariate analysis for rapid and non-invasive detection of gelatin adulteration in prawn , 2013 .

[76]  Margarita Ruiz-Altisent,et al.  Examination of the quality of spinach leaves using hyperspectral imaging , 2013 .

[77]  Wen‐Jun Zhang,et al.  Comparison of different methods for variable selection , 2001 .

[78]  Ning Wang,et al.  Detecting chilling injury in Red Delicious apple using hyperspectral imaging and neural networks , 2009 .

[79]  Da-Wen Sun,et al.  Pizza quality evaluation using computer vision: Part 1. Pizza base and sauce spread , 2003 .

[80]  Hongdong Li,et al.  Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration. , 2009, Analytica chimica acta.

[81]  José Manuel Amigo,et al.  Pre-processing of hyperspectral images. Essential steps before image analysis , 2012 .

[82]  Kurt C. Lawrence,et al.  Differentiation of big-six non-O157 Shiga-toxin producing Escherichia coli (STEC) on spread plates of mixed cultures using hyperspectral imaging , 2013, Journal of Food Measurement and Characterization.

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

[84]  Gamal ElMasry,et al.  Fast detection and visualization of minced lamb meat adulteration using NIR hyperspectral imaging and multivariate image analysis. , 2013, Talanta.

[85]  Moon S. Kim,et al.  Development of simple algorithms for the detection of fecal contaminants on apples from visible/near infrared hyperspectral reflectance imaging , 2007 .

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

[87]  Jun-Hu Cheng,et al.  Potential of hyperspectral imaging for non-invasive determination of mechanical properties of prawn (Metapenaeus ensis) , 2014 .

[88]  Kurt C. Lawrence,et al.  Performance of hyperspectral imaging system for poultry surface fecal contaminant detection. , 2006 .

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

[90]  Colm P. O'Donnell,et al.  Preliminary study on the use of near infrared hyperspectral imaging for quantitation and localisation of total glucosinolates in freeze-dried broccoli , 2014 .

[91]  Gamal ElMasry,et al.  Application of NIR hyperspectral imaging for discrimination of lamb muscles , 2011 .

[92]  Paul Allen,et al.  Prediction of moisture, color and pH in cooked, pre-sliced turkey hams by NIR hyperspectral imaging system , 2013 .

[93]  Jun-Hu Cheng,et al.  Visible/near-infrared hyperspectral imaging prediction of textural firmness of grass carp (Ctenopharyngodon idella) as affected by frozen storage , 2014 .

[94]  J P Wold,et al.  Feasibility of NIR interactance hyperspectral imaging for on-line measurement of crude composition in vacuum packed dry-cured ham slices. , 2013, Meat science.

[95]  Cheng-Jin Du,et al.  Comparison of three methods for classification of pizza topping using different colour space transformations , 2005 .

[96]  R. Barnes,et al.  Standard Normal Variate Transformation and De-Trending of Near-Infrared Diffuse Reflectance Spectra , 1989 .

[97]  Nuria Aleixos,et al.  Selection of Optimal Wavelength Features for Decay Detection in Citrus Fruit Using the ROC Curve and Neural Networks , 2013, Food and Bioprocess Technology.

[98]  Josse De Baerdemaeker,et al.  Bruise detection on ‘Jonagold’ apples using hyperspectral imaging , 2005 .

[99]  Josse De Baerdemaeker,et al.  Detecting Bruises on ‘Golden Delicious’ Apples using Hyperspectral Imaging with Multiple Wavebands , 2005 .

[100]  Yankun Peng,et al.  Simultaneous determination of tenderness and Escherichia coli contamination of pork using hyperspectral scattering technique. , 2012, Meat science.