Index

ACA, see Axial chromaticaberration

Acousto-optic tunable filter(AOTF)

light sources, 137–138wavelength dispersion, 144–146,

457Adaptive thresholding, image

segmentation,110–111

ANN, see Artificial neural networkAOTF, see Acousto-optic tunable

filterApple

bruise damage

causes, 295–296hyperspectral imaging

algorithms for bruisedetection, 303–305,310–311, 313–315

cameras, 301–302illumination unit, 302imaging spectrograph,

299–301preprocessing of images,

302–303, 307sample preparation and

system setup, 305–306spectral characteristics of

normal and bruisedsurfaces, 311–313

wavelength selection, 303,307–310

traditional detectionmethods, 297–299

grading, 296market, 295

Area scanning, see Staring imageArtificial neural network (ANN)

apple bruise detection, 305

back propagation neural network,462–463

hyperspectral imageclassification, 91–92

meat quality assessment, 203ASCC, see Average squared

canonical correlationAutomation, importance in quality

assessment, 4–5Average squared canonical

correlation (ASCC),461

Axial chromatic aberration (ACA),465

Back propagation neural network(BPNN), 462–463

Band Interleaved by Line (BIL),132

Band Interleaved by Pixel (BIP),132

Band number, 19Band Sequential (BSQ), 132Bandpass filter, 143Bandwidth, 19, 144Beef, see Meat quality assessmentBIL, see Band Interleaved by LineBIP, see Band Interleaved by PixelBPNN, see Back propagation

neural networkBSQ, see Band Sequential

CA, see Correlation analysisCalibration, hyperspectral imaging

instrumentation

flat-field correction, 164–165radiometric calibration,

166spatial calibration, 159–161spectral calibration, 161–164

overview, 32–36preprocessing

overview, 37, 45–46radiometric calibration

normalization, 65overview, 55–56percentage reflectance,

56–63relative reflectance

calibration, 63–64transmittance image

calibration, 64wavelength calibration

imaging system, 48–50purpose, 46technique, 50–55

reflectance calibration, 35Candling, nematode detection in

fish fillets, 215CART, see Classification and

regression treeCCD, see Charge-coupled deviceCharge-coupled device (CCD)

architectures, 153–154low light cameras, 156–158on-line poultry inspection

systems, 246–247,253–257

overview, 28, 31performance parameters,

154–156sensor materials, 153

Chemometrics, data analysis, 38Chicken

quality assessment withhyperspectral imaging

automated systemdevelopment

charge-coupled devicedetector, 246–247

471

Index472

Chicken (continued )

laboratory-based

photodiode arraydetection systems,245–246

pilot-scale system, 246spectral classification, 248

contamination detection,220–227

line-scan imaging for on-linepoultry inspection

commercial applications,266–267

hyperspectral imaginganalysis, 257–261

in-plant evaluation,262–266

multispectral inspection,261–262

spectral line-scan imagingsystem, 255–257

on-line inspection, 229–230overview, 220target-triggered imaging

system developmentdual-camera and color

imaging, 249–250multispectral imaging

systems, 252–255two-dimensional spectral

correlation and colormixing, 250–252

tumor and disease detection,227–229

United States poultry inspectionprogram, 243–245

Chromatic aberration, 465Circular variable filter (CVF), 152Citrus fruit

defects, 321–322hyperspectral imaging

automated rotten fruitdetection, 339–344

hardware, 330illumination system, 328–330integration time correction at

each wavelength,331–333

overview, 326–328spatial correction of intensity

at light source, 333–334spherical shape corrections,

334–339

market, 321multispectral identification of

blemishes, 323–325Classification and regression tree

(CART), 255,342–343

CMOS, see Complementary metaloxide semiconductor

Color, meat quality assessment,179, 205

Complementary metal oxidesemiconductor(CMOS), cameras, 31,158–159

Computer vision systemadvantages and limitations, 5–6meat quality assessment,

183–184wheat classification, 455

Convolution, see Imageenhancement

Correlation analysis (CA), citrusfruit analysis, 341

Cucumberclassification, 431–432damage, 432–433hyperspectral imaging of pickling

cucumbers

bruise detection, 433–438internal defect detection, 438prospects, 445

production, 431–432CVF, see Circular variable filter

DA, see Discriminant analysisDARF, see Directional average

ridge followerDark current, subtraction, 66DASH, see Digital array scanned

interferometerDatacube, 20Derivative filtering, image

enhancement,103–104

Digital array scanned interferometer(DASH), 152

Directional average ridge follower(DARF), fish qualityassessment, 215

Discriminant analysis (DA), 38Discriminant partial least squares

(DPLS), 219

DPLS, see Discriminant partialleast squares

ECHO, see Extraction andclassification ofhomogeneous objects

Edge-based segmentationedge detection, 112–113edge linking and boundary

finding, 114Electromagnetic spectrum, 14–15Electron-multiplying charge-

coupled device(EMCCD), 156–157,255–257

EMCCD, see Electron-multiplyingcharge-coupled device

Enhancement, see Imageenhancement

ENVI, see Environment forVisualizing Images

Environment for VisualizingImages (ENVI), imageprocessing, 119, 121

Essential wavelength, dataanalysis, 38

Extraction and classification ofhomogeneous objects(ECHO), 396–397

Factorial analysis, 465FDA, see Fisher’s discriminant

analysisFecal contamination, detection on

chicken, 220–227Filter wheel, 143–144Fish

quality assessment withhyperspectral imaging

freshnessidentification with

subjective region ofinterest, 277–282

morphometricsuperimposition fortopographical freshnesscomparison, 282–287

overview, 205–206, 273–277qualitative measurements,

210–220quantitative measurements,

206–210

Index 473

traditional quality assessment,273–274

Fish, see Meat quality assessmentFisher’s discriminant analysis

(FDA), 84–86Flat-field correction, 164–165FLIM, see Fluorescence lifetime

imaging microscopyFluorescence lifetime imaging

microscopy (FLIM), 10Focal plane scanning, see Staring

imageFourier transform

image enhancement

high-pass filtering, 106low-pass filtering, 105–106

imaging spectrometers, 148–150Full width at half maximum

(FWHM), bandwidth,19, 144, 200, 252

FWHM, see Full width at halfmaximum

GA, see Genetic algorithmGabor filter, texture

characterization,117–118, 120

Gaussian kernel, 94Gaussian Mixture Model (GMM),

hyperspectral imageclassification, 80,89–91

Gel electrophoresis, wheatclassification, 453

Genetic algorithm (GA), citrus fruitanalysis, 342

GLCM, see Graylevelco-occurrence matrix

Global thresholding, imagesegmentation, 110

GMM, see Gaussian MixtureModel

Graylevel co-occurrence matrix(GLCM)

meat quality assessment, 195,198

texture characterization, 116–117

HACCP, see Hazard analysiscritical control point

Halogen lamp, light sources,133–134

Hazard analysis critical controlpoint (HACCP), 6, 24

Hemoglobin, fish qualityassessment, 214

High-performance liquidchromatography(HPLC)

compound distributionmeasurement inripening tomatoes,379–380, 383

wheat classification, 453–454Histogram equalization, image

enhancement,100–102

HPLC, see High-performanceliquid chromatography

HSI, see Hyperspectral imagingHypercube, 20–23Hyperspec, image processing,

122–123Hyperspectral imaging (HSI)

acquisition modes, 24–28,131–132

advantages, 3, 7–8calibration, see Calibration,

hyperspectral imagingcomparison with imaging and

spectroscopy,6–7, 130

components of system, 29–32disadvantages, 9–11fruit and vegetable analysis, see

Apple; Citrus fruit;Cucumber; Melon sugardistribution; Mushroom;Tomato

image classification, see Imageclassification

image data, 20–24image processing, see Image

enhancement; Imagesegmentation; Objectmeasurement

instrumentation

detectors, 28, 152–159light sources, 133–139wavelength dispersion devices,

139–152meat, see Meat quality

assessmentsoftware, 118–123spectral data analysis, 36–39

synonyms, 6wheat kernels, see Wheat

ICCD, see Intensified charge-coupled device

ICM, see Iterated conditional modeIDA, see Independent component

analysisImage classification

artificial neural networks, 91–92Gaussian Mixture Model, 80,

89–91optimal feature and band

extraction

combination principal

component analysis andFisher’s discriminantanalysis, 85–86

feature search strategy, 82–83feature selection metric,

81–82Fisher’s discriminant analysis,

84–85independent component

analysis, 86–88principal component analysis,

83–84overview, 79–80support vector machine, 92–94

Image enhancementhistogram equalization, 100–102overview, 100spatial filtering

arithmetic operations, 109convolution, 102derivative filtering, 103–104Fourier transform

high-pass filtering, 106low-pass filtering, 105–106

median filtering, 103pseudo-coloring, 107–109smoothing linear filtering,

102–103wavelet thresholding,

105–106

Image segmentation

edge-based segmentation

edge detection, 112–113edge linking and boundary

finding, 114morphological processing,

111–112

Index474

Image segmentation (continued )overview, 109spectral image segmentation,

114–115thresholding

adaptive thresholding,110–111

global thresholding, 110

Imaging spectrograph, 32, 139–142Imaging spectroscopy, see

Hyperspectral imagingImSpector V10E imaging

spectrograph, 141,160, 162

Independent component analysis(IDA), 86–88,383–385, 465

Intensified charge-coupled device(ICCD), 156–158

Iterated conditional mode (ICM),396

Kernel visual distinguishability(KVD), 451

KVD, see Kernel visualdistinguishability

Laser, light sources, 136–137Lateral chromatic aberration

(LCA), 465LCA, see Lateral chromatic

aberrationLCTF, see Liquid crystal tunable

filterLDA, see Linear discriminant

analysisLED, see Light emitting diodeLight

characteristics, 13–14electromagnetic spectrum, 14–15interaction with samples, 16–18

Light emitting diode (LED), lightsources, 134–136, 458

Light sourceshalogen lamps, 133–134lasers, 136–137light emitting diodes, 134–136,

458tunable sources, 137–139

Line-scan imaging, see PushbroomLinear discriminant analysis (LDA)

citrus fruit analysis, 342–344

tomato maturity, 373–374,377–378

Linear variable filter (LVF), 152Liquid crystal tunable filter (LCTF),

146–148Luminosity value, see L-valueL-value, mushroom grading,

403–404, 425LVF, see Linear variable filterLycopene, see Tomato

Machine vision, see Computervision system

MATLAB, image processing,121–122, 464

Meat quality assessmentcolor, 179computer vision, 183–184destructive measurements,

179–182hyperspectral imaging

applicationsbeef, 194–202chicken, see Chickenfish, see Fishpork, 202–205

chemical imaging, 187–189data exploitation, 189–192overview, 185–186techniques, 192–193

objective technique assessment,182–183

overview, 175–177purpose, 178–179spectroscopy, 184–185standards, 178

Median filtering, imageenhancement, 103

Melon sugar distributionimaging spectroscopy

half-cut melon, 353instrumentation, 352–353intensity conversion to sugar

content, 354–355noise correction, 353–354partial image for sugar content

calibration, 353sugar absorption band

calibration, 362–363image acquisition, 362instrumentation, 360–361visualization, 364–365

sugar distributionvisualization, 355–356

melon features for study, 350near infrared spectroscopy

sample preparation, 350, 357sugar absorption band

calibration, 359–360data acquisition and sugar

content, 357–358second-derivative

spectrum, 358–359wavelength selection,

350–351overview, 349

MEMS, see Micromechanicalsystems

MI, see Mutual informationMicromechanical systems

(MEMS), 152Minimum noise fraction (MNF),

transformation,70, 304

Moisture content, mushrooms,423–424

Morphological processing, imagesegmentation,111–112

Multiplicative scatter correction(MSC), 408–410

Multispectral imagingcitrus peel blemishes, 323–325overview, 23poultry, 252–255, 261–262

Multivariate image analysis (MVI),464–465

Mushroombrowning and bruising,

403–404color vision, 404–405hyperspectral imaging

curvature and spectralvariation, 407–410

equipment, 405–407image classification

model building, 410–413regression models,

420–427supervised classification

for freezing injurydetection, 416–420

unsupervised classificationfor surface damagedetection, 413–416

Index 475

overview, 405prospects, 427–428sliced mushroom quality

attributes, 420–423whole mushroom quality

attributescolor prediction, 425–427moisture content, 423–424

L-value in grading, 403–404,425

market for Ireland whitemushrooms, 403

spectroscopy, 403–404Mutual information (MI), citrus

fruitanalysis, 341–342MVI, see Multivariate image

analysis

Near infrared spectroscopy (NIRS)cucumber bruise detection,

434–437meat quality assessment, 183,

185, 195–196, 229multispectral identification of

citrus peel blemishes,323–325

principles, 6, 12–13wheat classification, 454

Nematodes, detection in fish fillets,215–220

NIRS, see Near infraredspectroscopy

Noise reduction, see Preprocessing

Object measurementintensity-based measures,

115–116relative reflectance equation,

115texture

Gabor filter, 117–118, 120graylevel co-occurrence

matrix, 116–117

Offner imaging spectrograph,

141–142OPD, see Optical path distanceOptical path distance (OPD),

148–149

Partial least squares (PLS), 10,191, 207, 308–309,379–380, 413, 424

Partial least squares-discriminantanalysis (PLS-DA)

cucumber evaluation, 440fish freshness analysis, 279,

281–282, 285, 287mushroom evaluation, 411,

416–420PCA, see Principal component

analysisPCR, see Polymerase chain

reaction; Principalcomponent regression

pH, meat quality assessment, 205Phenol test, wheat classification,

453Pickle, see CucumberPlanck’s relation, 14PLS, see Partial least squaresPLS-DA, see PLS-DAPoint-scan imaging, see

WhiskbroomPolymerase chain reaction (PCR),

wheat classification,454

Polynomial kernel, 94Pork, see Meat quality assessmentPoultry, see ChickenPoultry Product Inspection Act

(PPIA), 243PPIA, see Poultry Product

Inspection ActPreprocessing

apple bruise detection, 302–303,307

calibration

radiometric calibration

normalization, 65overview, 55–56percentage reflectance,

56–63relative reflectance

calibration, 63–64transmittance image

calibration, 64wavelength calibration

imaging system, 48–50purpose, 46technique, 50–55

noise reduction and removaldark current subtraction, 66minimum noise fraction

transformation, 70noisy band removal, 69–70

Savitzky–Golay filtering,67–69

spectral low pass filtering, 67overview, 37, 45–46

Principal component analysis(PCA)

cucumber quality evaluation forpickling, 435, 437, 459,465

image classification, 38, 79,83–86

meat quality evaluation

beef, 197–198chicken, 228overview, 191pork, 203–204

mushroom quality evaluation,411, 414–416, 418–419

tomato ripening analysis,383–385

Principal component regression(PCR), 10, 413,421–422

Prism-grating-prism imagingspectrograph,139–141

Pseudo-coloring, imageenhancement,107–109

Pushbroom, 25, 27–28, 131–132,456

Quartz–tungsten–halogen lamp,133

Radiometric calibration, seeCalibration,hyperspectral imaging

Raster-scanning imaging, seeWhiskbroom

RDLE, see Refreshed delayed lightemission

Reflectance calibration, 35Refreshed delayed light emission

(RDLE), 433Relative prediction deviation

(RPD), 424, 426Ripening, see Melon sugar

distribution; TomatoRMSECV, see Root mean square

error of cross-validation

Index476

RMSEP, see Root mean square errorof prediction

Root mean square error of cross-validation (RMSECV),421, 427

Root mean square error ofprediction (RMSEP),380, 421, 427

RPD, see Relative predictiondeviation

Savitzky–Golay filtering, noise,67–69

SBFS, see Sequential backwardfloating selection

SBS, see Sequential backwardselection

SEE, see Standard error of estimateSegmentation, see Image

segmentationSequential backward floating

selection (SBFS),feature searchstrategy, 83

Sequential backward selection(SBS), feature searchstrategy, 82–83

Sequential forward floating selection(SFFS), feature searchstrategy, 83

Sequential forward selection (SFS),feature searchstrategy, 82–83

SFFS, see Sequential forwardfloating selection

SFS, see Sequential forwardselection

SG-FCM, see Spatially guidedfuzzy C-means

Shortwave near infrared spectralcamera, fish qualityassessment, 210

Sigmoid kernel, 94Signal-to-noise ratio (SNR), 19–20Single shot hyperspectral imagers,

150–152Slice shear force (SSF), meat quality

assessment, 181–182,194

Smoothing linear filtering, imageenhancement,102–103

SNR, see Signal-to-noise ratioSNV, see Standard normal variateSpatial filtering, see Image

enhancementSpatially guided fuzzy C-means

(SG-FCM), 396Spatial resolution, 19SpectraCube, image processing,

122–123Spectral image segmentation,

114–115Spectral low pass filtering, noise, 67Spectral range, 18Spectral resolution, 18–19Spectral signature, 20Spectrograph, see Imaging

spectrographSpectroscopy

hyperspectral imagingcomparison, 6–7, 130

principles, 11–13SSF, see Slice shear forceStandard error of estimate (SEE), 55Standard normal variate (SNV),

408–409, 424Staring image, 24–26, 131–132, 456Stepwise multivariate regression

(SW), citrus fruitanalysis, 342

Sugar distribution, see Melon sugardistribution

Support vector machine (SVM),hyperspectral imageclassification, 80,92–94, 460

SVM, see Support vector machineSW, see Stepwise multivariate

regression

Tenderness, meat qualityassessment, 179–182

Thresholding, see Imagesegmentation

Tomatocolor imaging of maturity,

371–372compound distribution

measurement inripening tomatoes,379–381

health benefits, 369hyperspectral imaging of maturity

combining spectral and spatialdata analysis

integrated spectral andspatial classifiers,396–398

overview, 390parallel spectral and spatial

classifiers, 391–395sequential spectral and

spatial classifiers, 391comparison with color

imaging, 375–376image acquisition, 373linear discriminant analysis,

373–374, 377–378normalization of images,

376–377preprocessing, 373prospects, 398–399spectral data classification,

377–379spectral data reduction,

387–390market, 369on-line unsupervised

measurement ofmaturity, 382–387

optical properties, 370–371ripening process, 370

Tumors, detection on chicken,227–228

Tunable filter scanning, see Staringimage

Ultraspectral imaging, 23–24

Variable importance in projection(VIP), 309

VHIS, see Volume holographicimaging spectrometer

VIP, see Variable importance inprojection

Volume holographic imagingspectrometer (VHIS),152

Warner–Bratzler shear force(WBSF), meat qualityassessment, 181–182,184, 194, 202

Water holding capacity (WHC),meat, 179, 205

Index 477

Wavelength calibration, seeCalibration,hyperspectral imaging

Wavelength difference, 437–438Wavelength ratio, 437–438Wavelength scanning, see Staring

imageWavelet thresholding, image

enhancement,105–106

WBSF, see Warner–Bratzler shearforce

WHC, see Water holding capacityWheat

applications, 449

classificationcomputer vision system, 455gel electrophoresis, 453high-performance liquid

chromatography,453–454

near-infrared spectroscopy,454

overview, 449–452phenol test, 453polymerase chain reaction,

454visual identification, 452

hyperspectral imaging forclassification

Canadian wheat classificationand accuracy, 462–463

challenges, 464–465detectors, 456–457hardware and software

integration, 458illumination sources, 458image classification, 459–461prospects, 465–466system types, 455–456vitreous versus non-vitreous

kernels, 461wavelength filtering devices,

457–458

Whiskbroom, 24–27, 131–132