Index

A AAS. See Atomic-absorption spectroscopy Acoustic impedance, 50-51 Acoustic spectrometer, low frequency,

nondestructive texture analysis of porous cereal products with use of, 26

Adiabatic compressibility, 47 Adipose tissue characterization, tentative

FT-Raman band assignments for, 149t

Adsorption of species on capillary walls, 294

Afseth, N. K., 158 Agglomeration

automated image analysis and, 24

image analysis and detection of, 193

Aguilera, 1. M., 150 Airsense (Germany), 243 Alcoholic beverages

MIR spectroscopy and, 135-136 Alcoholic beverages, alcohol in, NIR

analysis and, 115-116 Alcoholic beverages, calibration results

for, lISt Alginates, 187 AI-lowder, 0., 131 Allais, I., 23 Alpha M.O.S. FOX 3000, 248, 249

discrimination of packaging material based on level of plasticizers and, 270

Alpha MOS (France), 243

American chocolate, 179-180 particle size distributions for bars of, 180

American Oil Chemists' Society, 110-111 American Society for Testing and

Materials, 93-94 Amide I Raman band envelopes, 147 Amide III Raman band envelopes, 147 Angular frequency, 47 ANN. See Artificial neural networks ANSYS CFX, 303 Antibody-antigen interactions, 301 AOAC. See Association of Official

Analytical Chemists AOCS. See American Oil Chemists'

Society Aparicio, R., 263 Apolar molecules, detecting, Raman

spectroscopy and, 144 Appearance of food, 283 Apple juice beverages, MIR spectroscopy

and, 135-136 Apples

evaluating maturity of, 271, 273-274 with conducting polymer-based

sensing system, 273 sorting method for, based on image

analysis, 20 spin echo pulse sequence and sample

image of internal browning in, 220 Applied Sensors, 239, 243, 246 APV Homogenizer, screenshot ofInsitec

software monitoring droplet size of food emulsion produced by, 191, 192

Archibald, D. D., 152

339

340 Index

Aroma, 283 characterizing food product in terms of,

237 Aroma exposure, to sensor array, 241 AromaScan (UK), 239, 243 Aromatic amino acid side chains, Raman

spectroscopy and, 147 Artificial neural networks, 91, 251, 261,

262 Asher, A., 147 ASPECT Magnet Technologies LLC,

228 Association of Official Analytical

Chemists, 132 ASTM. See American Society for Testing

and Materials At-line analysis, 4, 16,81-82 Atomic-absorption spectroscopy, 86 Attenuated total reflectance (ATR), 125,

126-127 crystals, 120 schematic of internal reflection in

crystal,126 ZnSe crystal, 127

Authenticity, 120 mid-infrared spectroscopy and,

l30t MIR spectroscopy and, 138

Automatic sampler for butter, 105 for milk powder, 103

Automation of production, increased use of,5

B Baby, R. E., 263 Background scanning, 124 Backscatter detection, 170, 171 Backward elimination procedure, 260 Bailey, 1. E., 298 Bairi, A., 24 Baldauf, N. A., 137 Band positions, selected parameters

pertinent to, 144 BAW mode. See Bulk acoustic mode Bawsinskiene, L., 26 Baxter, L. K., 323

Bazzo, S., 249 Beamsplitter, 74, 123 Beans, vision system and classification of,

20 Beck, M., 337 Bedson, P., 8 Beef, FT-Raman spectra of, 149t Beef adipose tissue, FT-Raman spectra of,

at various irradiation doses, 155 Beelaram, A. M., 302 Beer, computer vision and determining

bubble size distributions in, 24 Beer-Lambert Law, 88 Benady, 1. E., 263 Benedito, 1., 57, 60 Benson, I. B., 16 l3-carotene dispersion, raw image and

detected particles in, 205 Beverages, NIR analysis and, 114-116 Biopolymer structure, ultrasonic

characterization of, 57-58 Bioprocess industries, electronic nose

technology and, 268 Biorecognition molecules, biosensor

technology, interaction between target molecules and, 30 I

Biosensor response, effect of geometry of detector cell on appearance of hydrogen peroxide at electrodes and effect of flow rate on, 313

Biosensors, 283-316 case studies, 304-315

electrokinetic sample injection in Micro-FIA biosensors, 304-309

optimization of a glucose flow injection analysis biosensor, 309-315

concluding remarks about, 315-316 flow-type, 286-290

flow mechanisms in microchannels, 288-290

miniaturization and microfluidics, 287-288

principles, 286-287 governing equations for modeling of,

290-302 bulk flow modeling, 290-294

modes of component transport, 294-296

reaction kinetics, 297-302 numerical approach, 303-304 overview of, 283-286 principles of, 284 successful, performance criteria in

design of, 285 Bioterrorism, food, MIR spectroscopy a

137 Birefringence, loss of, in starch granule

a function of temperature, 150 Biscuits, missing, detection of,

325-327 Black speck detection, 197,202-204,

209 Bloch equations, magnetization vector

behavior and, 216-217 Bloodhound Sensors (UK), 243 Bosset, 1. 0., 263 Box-Behnken designs, 250 Breakage, image analysis and detection

193 Brosnan, T., 20 Brown, R. 1., 132 Brownian motion, 168 Brucella sp., 137 Bulk acoustic mode, 245 Bulk flow modeling, 290-294

electroosmotic flow, 290-294 hydrodynamic flow, 290

Bulk longitudinal (L) waves, 45

Butter automatic sampler for, 105 calibration results for, 105t NIR analysis and, 104 plot ofNIR predictions versus referel

values for moisture in, 106 Bypass instruments, 3

C Caffeine, in instant coffee, NIR analysi~

and, 114-115 Cake formulation, detection oflard

adulteration in, MIR spectrosco] and, 137

Index 341

modes of component transport, 294-296

reaction kinetics, 297-302 numerical approach, 303-304 overview of, 283-286 principles of, 284 successful, performance criteria in

design of, 285 Bioterrorism, food, MIR spectroscopy and,

137 Birefringence, loss of, in starch granules as

a function of temperature, 150 Biscuits, missing, detection of,

325-327 Black speck detection, 197, 202-204,

209 Bloch equations, magnetization vector

behavior and, 216-217 Bloodhound Sensors (UK), 243 Bosset, 1. 0., 263 Box-Behnken designs, 250 Breakage, image analysis and detection of,

193 Brosnan, T., 20 Brown, R. 1., 132 Brownian motion, 168 Brucella sp., 137 Bulk acoustic mode, 245 Bulk flow modeling, 290-294

electroosmotic flow, 290-294 hydrodynamic flow, 290

Bulk longitudinal (L) waves, 45

Butter automatic sampler for, 105 calibration results for, 105! NIR analysis and, 104 plot ofNIR predictions versus reference

values for moisture in, 106 Bypass instruments, 3

C Caffeine, in instant coffee, NIR analysis

and, 114-115 Cake formulation, detection of lard

adulteration in, MIR spectroscopy and, 137

Calibration, 8-9, 10, 34 of indirect methods, influence of

reference methods on, 33-43 multivariate, theoretical basics of,

86-89,91 reference method quality and, 17

Calibration data set, number of samples in, 93-94

Calibration line, 34, 43 for mass loss determination at 1450 C of

Lactoserum Euvoserum, 41 and sample determination based on Karl

Fischer titration, 38 and sample determination based on oven

drying, 39 for water content determination of

samples predried at 1450 C of Lactoserum Euvoserum based on Karl Fischer titration, 42

Calibration models building, 91-94

cross validation, 92 external validation, 91-92 results and parameters of validation,

93 Calibration model updating, 94 Cameras, 21 Canadian Grain Commission, 68 Canonical correlation analysis, 252-253 Canonical discriminant analysis, 240, 252,

253-254 Canonical variate analysis, 153, 154

discrimination of porcine adipose tissues and, 156

Capacitance electrode configuration options, 327

Capacitance measurement circuit, basic, 324

Capacitance measurement methods, 323-325

Capacitance moisture sensors, basic, 328

Capacitance sensor electrode configuration, cross-section, 332

Capacitance sensors, 16, 323 applications of, 325

Carageenans, 187

342 Index

Carbohydrates Raman spectroscopy and, 150-151 tentative FT-Raman band assignments

for, 151t Cascade diluters, 191 Casein-dissolving solution,

homogenization pressure for standard milk emulsion and cluster free emulsion with, 185

Casein micelles, laser diffraction, milk products and, 183, 184

Case studies, electronic nose technology, 268-274

detection of retained solvent levels in printed packaging material, 269

detection of spoilage and discrimination of raw oyster quality, 274

discrimination of frying oil quality based on usage level, 271

evaluating apple maturity, 271, 273-274

Castillo, M., 24 Cattaneo, T. M. P., 134 CCA. See Canonical correlation analysis CCD. See Charged coupled device CDA. See Canonical discriminant analysis Celadon, A., 150 Ceramic sensors, 239 Cereal biscuits, measured moisture content

of, 328 Cereal products, porous, nondestructive

texture analysis of, 26 CFD. See Computational fluid dynamics CGC. See Canadian Grain Commission Chanamai, R., 53 Chandraratne, M. R., 20 Charged coupled device, 144 Cheddar cheese, principal component

scores plot of, at 6-, 9-, and 12-month ripening stage, 128

Cheese analyzing dielectric processes of, 24 hard and slicing, calibration results for,

108t mid-infrared spectroscopy and, 131-135 NIR analysis and, 105-108 processed, mid-infrared spectra of, 122

Chemical assays, FIA technology and, 286 Chemometric methods, 127-129 Chemometrics, 157 Chemosensory system types

conducting polymer sensors, 243, 244-245

metal oxide field effect transistors, 243, 246

metal-oxide sensors, 243, 244 quartz microbalance sensors, 243,

245-246 surface acoustic wave-based sensors,

246-247 Chen, M., 133 Chen, X. D., 25 Chi, Z., 147 Cho, B., 56 Chocolate

calibration results for, 114t detection of lard adulteration in, MIR

spectroscopy and, 137 early manufacture of, 176 ingredients in, 177 laser diffraction and applications with,

176-183 achieving efficient production, 176 American and United Kingdom

chocolate products, 179-180 challenges of particle size

measurement, 179 cocoa mass, cocoa powder, and cocoa

butter, 177 conching, 178 dairy and food/flavor emulsions,

182-183 dark chocolate vs. milk chocolate,

181-182 emulsion measurements, 183 luxury brands, 180-181 manufacturing process, 176-177 milk, 178 milk and chocolate crumb, 178 optimizing production of, 178-179 sugar, 177-178

NIR analysis and, 113-114 particle size distributions for standard,

luxury, and economy brands, 181

particle size distributions for UK and American chocolate bars, 180

validation with independent samples ( calibration offat in, 114

Chocolate-coated wafer bars, detection ( missing wafers in, 325-327

Chocolate crumb, milk chocolate manufacture and, 178

Chocolate liquor, 177 Cimander, c., 268 Circular equivalent (CE) diameter

image analysis, particle shape and, 193

three different shapes with same diameter, 193

Cis/trans isomers, FT-Raman spectrosco in determination of, 150

Classical magnetization vector, motion j 213

Classification analysis, 259 Cluster analysis, 251, 261 CMOS cameras. See Complementary

metal-oxide-semiconductor cameras

Coates, 1. p., 125 Cocoa bean pods, 177 Cocoa butter, 177, 178

determination offat in, 113 Cocoa powder, 177 Coffee

flavors of, and factors affecting qualit of, 189-190

green and roasted, FT-Raman spectroscopy and discriminating botanical origin of, 156

industry background, 188-189 instant, calibration results for, 115t particle size of

pre-ground espresso and pre-groun filter coffee, 190

produced by grinder at varying speeds, 189

Coffee berry borer, 189 "Cold" sensors, 243 Collier, W. A., 263 Color changes measurement, video imal

analysis and, 27

Index 343

particle size distributions for UK and American chocolate bars, 180

validation with independent samples of calibration of fat in, 114

Chocolate-coated wafer bars, detection of missing wafers in, 325-327

Chocolate crumb, milk chocolate manufacture and, 178

Chocolate liquor, 177 Cimander, c., 268 Circular equivalent (CE) diameter

image analysis, particle shape and, 193

three different shapes with same diameter, 193

Cis/trans isomers, FT-Raman spectroscopy in determination of, 150

Classical magnetization vector, motion for, 213

Classification analysis, 259 Cluster analysis, 251, 261 CMOS cameras. See Complementary

metal-oxide-semiconductor cameras

Coates, 1. P., 125 Cocoa bean pods, 177 Cocoa butter, 177, 178

determination offat in, 113 Cocoa powder, 177 Coffee

flavors of, and factors affecting quality of, 189-190

green and roasted, FT-Raman spectroscopy and discriminating botanical origin of, 156

industry background, 188-189 instant, calibration results for, 115t particle size of

pre-ground espresso and pre-ground filter coffee, 190

produced by grinder at varying speeds, 189

Coffee berry borer, 189 "Cold" sensors, 243 Collier, W. A., 263 Color changes measurement, video image

analysis and, 27

Complementary metal-oxide-semiconductor cameras, 198

Component transport modes, 294-296 convection, 295-296 diffusion, 294, 295 electrokinetic migration

(electrophoresis), 296 total mass balance, 296

Composition, direct and indirect measurements of, 227

Compositional analysis mid-infrared spectroscopy and, 130t performing, 3

Compressed potassium bromide (KBr) pellets, 125

Computational fluid dynamics, 311 Computer vision, 20-21

bubble size distributions in beer and, 24

COMSOL Multiphysics, 303 COMSOL Multiphysics 3.2, 306 Concentrated dispersions, particle sizing

of, 169-170 Conching, 178, 180

introduction of, 176 Condensed milk, FTIR-ATR spectroscopy

and analysis of, 132 Conducting polymer-based sensing system,

apple maturity evaluation with, 273 Conducting polymer sensors, 243, 244-245 Conducting polymer technology, 238 Confocal microscopes, Raman applications

and, 144 Consolidation, in food industry, 5 Consumables, ensuring supply of, 11 Contamination monitoring, mid-infrared

spectroscopy and, 130t Control volumes, 303 Convection, 294, 295-296 Com, fermented mash, calibration and

validation results for ethanol in, 116t

Com starch, classification of, MIR spectroscopy and, 136

Correctness of results, 34-35 Corredig, M., 58

.

344 Index

Cost, management support and, 13 Coulter particle counters, 197 Coupland, 1. N., 54, 55 CP sensors. See Conducting polymer

sensors Cream Iiquers, variations in particle size

and storage of, 185, 186 Crescenza cheese

metal oxide-based sensing system and shelflife of, 268

MIR spectroscopy and analysis of, 134 Cross-correlation DLS instrumentation,

169-170 Cross validation method, 92, 93, 260

Mahalanobis distance and, 258 Crystallization, automated image analysis

and, 24 C-shaped magnet, 232 Cuboids, defining size of, 166, 166 Curda, L., 19 Cuvette holders, 78 CVA. See Canonical variate analysis Cylinder, with same volume of given

sphere, 166-167, 167 Cylindrical magnets, 232 Cyrano Sciences, 239 Cyranose 320, 256

discrimination of packaging material based on level of plasticizers and, 270

raw oyster quality differentiation and, 274

D DA. See Discriminant analysis Daestain, M -F, 20 Dairy and food/flavor emulsions, particle

size of fat droplets in, 182-183 Dairy emulsions, storage of, particle size

and, 185-186 Dairy products, mid-infrared spectroscopy

and, 131-135 Dalgleish, D., 60 Dark chocolate

milk chocolate vs., 181-182,182 particle size distributions for, 183

Dark particles in a white powder, in-process measurement of size and number of, 197,202-204

De Baerdemaeker, 1., 27 Debye length, thickness of EDL described

by, 291 Defining the method, 11 Design qualification, 8 Detector array dispersive

spectrophotometers, 73-74 principle of, 74

DFA. See Discriminant factorial analysis Dialectric materials, permittivity of,

323 Dielectric constant, 292

of the material, 323 Dielectric food, examples of, 321 Dielectric imaging, 25 Diffuse reflectance, 127

methods, 127 Diffuse reflection, 79-81 Diffuse reflection measurements, 79-81

principle of, 80 Diffusion, 294, 295 Diffusion coefficient measurements, food

material structure and, 227 Diffusive wave spectroscopy, 170 Dilute dispersions, dynamic light

scattering and, 169 Diode array dispersive instruments,

advantages and disadvantages of, 76

Dionisi, E, 17 Dioxins, 283 Direct calibration transfer, 95-96 Direct measurement, 3 Direct method, secondary methods

calibrated against, 43 Direct methods, 33 Direct standardization, 94 Discriminant analysis, 153,240,252-262

variable selection procedure, 260-262 Discriminant factorial analysis, 252 Discriminant rule, development of, for

classifying observations into categories, 259

Dispensing process, optimization of, parameter values used in numeri simulation, 306t

Dissolution, automated image analysis: 24

DLS. See Dynamic light scattering DNA, hybridization and, 301-302 Double-bonded structures, detecting,

Raman spectroscopy and, 144 Double reciprocal, 299 Drying curve, of Lactoserum Euvoseru

40 Drying oven, 36 Drying oven method, 83 Drying techniques, 43

water content determination, 35, 36 Dry sieving, 168 DST. See Direct standardization Du, C-J, 21 DWS. See Diffusive wave spectroscopy Dynamic light scattering, 168-172

food applications with, 171-172 latest advances in, 169-171 measurement positions for small, we

scattered samples, and concentrated, opaque samples,

Dynamic MRI, 221-227 Dynamic NMR microscopy, 225 Dynamic NMR pulse sequences (PGSI

pulse sequence), example of, 2;

E ECT. See Electrical capacitance

tomography Edible oils

calibration results for iodine value in, lilt

iodine value for, 110-111 EDL. See Electric double layer ED-XRF. See Energy dispersive X-ray

fluorescence Eigenvector quantification methods, I:

158 Eight-electrode cylindrical ECT sensol

330 Electrical capacitance, 321-323

Index 345

Dispensing process, optimization of, parameter values used in numerical simulation, 306t

Dissolution, automated image analysis and, 24

DLS. See Dynamic light scattering DNA, hybridization and, 301-302 Double-bonded structures, detecting,

Raman spectroscopy and, 144 Double reciprocal, 299 Drying curve, of Lactoserum Euvoserum,

40 Drying oven, 36 Drying oven method, 83 Drying techniques, 43

water content determination, 35, 36 Dry sieving, 168 DST. See Direct standardization Du, C-J, 21 DWS. See Diffusive wave spectroscopy Dynamic light scattering, 168-172

food applications with, 171-172 latest advances in, 169-171 measurement positions for small, weakly

scattered samples, and concentrated, opaque samples, 171

Dynamic MRI, 221-227 Dynamic NMR microscopy, 225 Dynamic NMR pulse sequences (PGSE

pulse sequence), example of, 225

E ECT. See Electrical capacitance

tomography Edible oils

calibration results for iodine value in, Illt

iodine value for, 110-111 EDL. See Electric double layer ED-XRF. See Energy dispersive X-ray

fluorescence Eigenvector quantification methods, 157,

158 Eight-electrode cylindrical ECT sensor,

330 Electrical capacitance, 321-323

Electrical capacitance tomography, 328-329,331

sample images, 330 test bar inside 12-electrode sensor and

image, 332, 333 two-phase flow measurement and use of,

333-335,337 Electrical capacitance tomography sensor,

eight-electrode cylindrical, 330 Electrical capacitors, basic, 322 Electrical dielectrics, 321 Electrical permittivity, techniques based on

measurement of, 321-337 Electric double layer, 290

schematic diagram of, next to a negatively charged solid surface, 291

Electric valve actuator, flow injection amperometric biosensor, 310

Electrode kinetics, 299-300 Electrokinetic flow

in microchannel, 289 simple channel intersection used for, in

micro device, 305 velocity and potential profile inside

channel during loading and injection mode, 305

Electrokinetic focusing, 304 Electrokinetic migration (electrophoresis),

296 Electrokinetic sample injection in

Micro-FIA biosensors (case study) electrokinetic dispensing mechanism,

304-306 optimization of the dispensing process,

306-307,309 Electromagnets, 228 Electronic nose applications in food

industry, 237-276, 264-267t case studies, 268-274 chemosensory systems types, 243-247

conducting polymer sensors, 244-245

metal oxide semiconductors field effect transistors, 246

metal-oxide sensors, 244

346 Index

Electronic nose applications in food industry, (cant.)

quartz microbalance, 245-246 surface acoustic wave-based sensors,

246-247 electronic nose market, 242-243 electronic nose niche, 241-242 issues or drawbacks with electronic nose

technology, 247-250 overview, 237-241 statistical analysis and, 250-262

artificial neural networks, 262 discriminant analyses, 252-262 multivariate factor analyses, 251 principal components analysis,

251-252 Electronic noses

components of, 240 description of, 238

Electronic nose systems comparison of, in discriminating

packaging material based on level of plasticizers, 270

handheld, discrimination of raw oyster quality by two types of, 275

Electronic nose technology steps in usage of, 240 summary remarks about, 274, 276

Electroosmosis, 289 Electroosmotic flow, 289, 290-294 Electrophoresis, 289 Eliminated product, image analysis and, 20 Elmehdi, H. M., 53 Emmental cheese, 135

determining ripening stage in, 268 MIR spectroscopy and predicting WSN

content of, 133-134 Energy dispersive X -ray fluorescence, 21 "Ensemble" behavior, 213 Enzyme kinetics, 297-299 EOF. See Electroosmotic flow Equipment qualification process

design qualification, 8 installation qualification, 8-9 operational qualification, 9 performance qualification, 9-10 stages in, 8

Erickson, D., 302 Espresso, pre-ground, particle size of, 190 Essential oils, Raman spectroscopy and

quality of, 156 Ethanol, calibration and validation results

for, in fermented corn mash, 1l6t Euclidean distance, 254 Everard, C. D., 24 External validation (test set validation),

91-92

F Factor analysis, 89, 251 Factorization, example, of simple spectra

into corresponding loadings and scores, 90

Fagan, C. C., 134 Faraday's constant, 292 Fast spin echo techniques, magnets and

images acquired with use of, 228-229

Fats raman instrumentation and, 148-150 Raman quality measurements of, 154

FDA. See Food and Drug Administration Fecal contamination, imaging technologies

and detection of, 21 FEM. See Finite element method Fermi resonance, 144 FET. See Field-effect transistor FlA. See Flow injection analysis FIA sensor, optimization of, 313-315 Fiber optic-based liquid probes, 78 Fiber optic probes, setup of, for different

measurement modes, 78 Fibrous impurities, detection of, in particle

suspensions, 197, 205, 207-208 Fick's law of diffusion, 300, 311 Fick's second law, 295 FlO. See Free Induction Decay Field-effect transistor, 246 Figaro, 239 Filter-based instruments

dedicated wave numbers covered by, 73

principle of, 72 Filter-based photometers, 72

Filter-based process analyzers, for watel moisture determination, 17

Filter coffee, pre-ground, particle size 0: 190

Filter instruments with interference bandpass filters, advantages and disadvantages of, 75

Final products, 2, 4 Finite element method, 303 Finite volume method, 303 Fish, Raman measurements of, 157 Fish fat, Raman analysis of, 149 Flare, laser, 170 Flatten, A., 131 Flavor, 237 Flavor emulsions

laser diffraction used to detect outsiz, particles in, 188

oil. 187 use of in food industry, types of, 187

Flocculated systems, 57 Flow curve determination Flow curve determination, for food text

measurement, 59-60 Flow injection amperometric biosensor,

310 Flow injection analysis

aroma and, 241 biosensor configuration, 286

Flow injection analysis amperometric sensor, 310

Flow profiles, 60 Flow rate switch, flow injection

amperometric biosensor, 310 Flow time, 226 Flow-type biosensors, 286-290

flow mechanisms in microchannels, 288-290

miniaturization and microfluidics, 287-288

principles, 286-287 Fluent, 303 Fluorescence spectroscopy, 23 Fluorescence tools, 21 Food and Drug Administration, 109, 1; Food composition, ultrasonic measurer

of, 52-55

Index 347

Filter-based process analyzers, for water or moisture determination, 17

Filter coffee, pre-ground, particle size of, 190

Filter instruments with interference bandpass filters, advantages and disadvantages of, 75

Final products, 2, 4 Finite element method, 303 Finite volume method, 303 Fish, Raman measurements of, 157 Fish fat, Raman analysis of, 149 Flare, laser, 170 Flatten, A., 131 Flavor, 237 Flavor emulsions

laser diffraction used to detect outsize particles in, 188

oil, 187 use of in food industry, types of, 187

Flocculated systems, 57 Flow curve determination Flow curve determination, for food texture

measurement, 59--60 Flow injection amperometric biosensor,

310 Flow injection analysis

aroma and, 24 I biosensor configuration, 286

Flow injection analysis amperometric sensor, 310

Flow profiles, 60 Flow rate switch, flow injection

amperometric biosensor, 310 Flow time, 226 Flow-type biosensors, 286-290

flow mechanisms in microchannels, 288-290

miniaturization and microfluidics, 287-288

principles, 286-287 Fluent, 303 Fluorescence spectroscopy, 23 Fluorescence tools, 21 Food and Drug Administration, 109, 120 Food composition, ultrasonic measurement

of,52-55

Food emulsions, laser diffraction and characterization of, 183

Food industry challenges facing, I 19-I20 changes in, and consequences for use of

sensors, 4-5 Food quality

relationship ofNMR properties to, 227

sensory properties related to, 283 Food quality assessments

multivariate qualitative Raman spectroscopy for, 153-157

multivariate quantitative Raman spectroscopy for, 157-158

Food quality measurements, contemporary and special applications of raman spectroscopy for, 151-153

Food research, nondestructive sensors for, 25-28

Food safety consumer's attention to, 283 MIR spectroscopy and, 137

Food samples, NIR spectra of, with assignment of spectral regions, 70

Food structure, ultrasonic measurement of, 55-58

Food texture measurement, 58-61 correlation with L-wave measurements,

60-61 flow curve determination, 59-60 shear wave methods, 58-59

Foreign bodies/particles detecting, in bottled beverages, fruit

juices, and pie fillings, 57 image analysis and detection of, 193

Forward selection procedure, 260 Fourier deconvolution, protein secondary

structure determination and, 147 Fourier transformation, stationary MRI

and, 218 Fourier transform (FT) instrument, 74 Fourier transform (FT) spectrometers, 122 Fourier transform infrared (FT-IR)

instruments, advantages and disadvantages of, 76

348 Index

Fourier transform infrared (FT-IR)spectrometers, 123

detection of fats and oils and, 148 Fourier transform infrared (FT-IR)

spectroscopy, 120, 122~124,

129 cheese flora analysis and, 133

Fourier transform infrared (FT-IR) technology, 18

dairy industry and use of, 19 Fourier transform near infrared (FT-NIR)

detector, 17 Fourier transform near infrared (FT-NIR)

spectrophotometer, principle of,75

Fourier transform near infrared (FT-NIR) technology, advantages and disadvantages of, 76

Fourier transform Raman (FT-Raman) spectrometer, 145

Fourier transform Raman (FT-Raman) spectroscopy, quantifying unsaturated acyclic components in garlic oil and, 27-28

Fox, P., 53 FPIA-3000 system, circularity, image

analysis and, 194 Fraunhofer approximation, 174 Free Induction Decay, acquisition time for,

214 Free trade, quality and authenticity of food

products and, 27 Freeze drying, 35 Freezing, measurement of, 55 Fresh products, consumer demand for, 2 Fruit beverages, MIR spectroscopy and,

135-136 Fruits

L-wave ultrasound and monitoring of ripening and softening of, 61

maturity of, sensor systems and evaluation of, 263, 268

spectrosopic techniques and assessment of quality in, 27

Fry and Sons, 176 Frying oil, discrimination of, using a

chemosensory system, 272

Frying oil quality (case study), discrimination of, based on usage level, 271

19F spectroscopy, 235 FT-NIR. See Fourier transform near

infrared detector Fu, L. M., 309 Full fat milk, size distributions recorded

for, 183-184, 184 FVM. See Finite volume method

G Galerkin finite element method, 303-304 Gan, T. H., 26 Garlic, FT-Raman spectroscopy and

quantifying unsaturated acyclic components in, 27-28

Gas chromatography, 238 Gas chromatography-mass spectrometry,

238,283 Gas chromatography olfactory methods,

238 Gas sensors, real time sensing with, 268 Gate, in metal oxide semiconductors field

effect transistors, 246 Gauche-gauche-trans conformation, 147 Gaussian discriminant function, 252 GC. See Gas chromatography GC-MS. See Gas chromatography-mass

spectrometry GCO methods. See Gas chromatography

olfactory methods GDF. See Gaussian discriminant function Gelation, Raman spectroscopy and

structural changes in proteins during, 147

Geometry, nonuniform, 294 German sausages, categories of, 99 Glucose, concentration profile of, 311, 312 Glucose flow injection analysis biosensor

(case study) flow injection analysis amperometric

sensor, 310 model formulation, 310-311 model simulations, 311-312 optimization of, 309-315 optimization of the FIA sensor, 313-315

Glucose oxidase, concentration profile 0

311,312 Glycolysis, 133 Goat's milk, processed, mid-infrared

spectra of, 122 Gomez-Carracedo, M. P., 135-136 Grains

image analysis and, 20 Raman microspectroscopy and qualit:

assessment of, 152 Griffin, S. J., 56 Guided waves, 51

alignment of transducers and ultraso~

path for, 48-49 Guillard, A. S., 131 Gum Arabic, 187

H HACCP. See Hazard Analysis and Critil

Control Point Haider, M., 15 Halbach cylinder magnet, 232, 234 Handheld probes, 78 Hansen, W G., 249 Harhay, G. P., 153 Harper, W J., 249 Hatcher, D. W, 20 Hazard Analysis and Critical Control

Point, 109 Hazelnuts, irradiated, MIR spectroscop:

and, 136 HDE. See Hydrogen-deuterium exchanJ HDPE packaging. See High-density

polyethelyne packaging Helium neon (HeNe) laser, 74, 124

dynamic light scattering and, 169 Hepworth, N. J., 24 Herrmann, N., 61 Herschel, Sir William, 68 Hewlett Packard, 240 High-density polyethelyne packaging,

249 High performance liquid chromatograp

86,158 High-speed check weighing, 331-333 HKR Sensorsystems (Germany), 243 Holdout method, 260

Index 349

Glucose oxidase, concentration profile of, 311,312

Glycolysis, 133 Goat's milk, processed, mid-infrared

spectra of, 122 Gomez-Carracedo, M. P., 135~136

Grains image analysis and, 20 Raman microspectroscopy and quality

assessment of, 152 Griffin, S. 1., 56 Guided waves, 51

alignment of transducers and ultrasonic path for, 48-49

Guillard, A. S., 131 Gum Arabic, 187

H HACCP. See Hazard Analysis and Critical

Control Point Haider, M., 15 Halbach cylinder magnet, 232, 234 Handheld probes, 78 Hansen, W G., 249 Harhay, G. P., 153 Harper, W 1., 249 Hatcher, D. W, 20 Hazard Analysis and Critical Control

Point, 109 Hazelnuts, irradiated, MIR spectroscopy

and, 136 HDE. See Hydrogen-deuterium exchange HDPE packaging. See High-density

polyethelyne packaging Helium neon (HeNe) laser, 74, 124

dynamic light scattering and, 169 Hepworth, N. 1., 24 Herrmann, N., 61 Herschel, Sir William, 68 Hewlett Packard, 240 High-density polyethelyne packaging,

249 High performance liquid chromatography,

86, 158 High-speed check weighing, 331-333 HKR Sensorsystems (Germany), 243 Holdout method, 260

Homogenization automated image analysis and, 24 ofmilk, particle sizes and, 184-185 pressure for standard milk emulsion and

cluster free emulsion containing casein-dissolving solution, 185

Honzatko, R. B., 147 HoteHing, 251 HoteHing's T2 test, 257 "Hot" sensors, 243 HPLC. See High performance liquid

chromatography Hybrid chemosensory systems, 243 Hybridization, 301 HydrocoHoids, 187 Hydrodynamic flow, 290

in microchannel, 289 Hydrogenated vegetable oil, trans isomer

content of fatty acids and texture of, 150

Hydrogen-deuterium exchange, 153 Hydrogen peroxide, concentration profile

of, 311-312, 312 Hyperspectral imaging, 21

I Ice cream, particle size of fat droplets in,

182 ICP. See Inductively coupled plasma IDE See International Dairy Federation ILS. See Inverse Least Squares Image analysis, 20--21, 197. See also

Online image analysis of particulate materials

advantages with, 193-195 Imaging spectrosocpy, 21 Indirect methods (or secondary methods),

33 influence of reference methods on

calibration of, 33--43 Inductively coupled plasma, 86 Infant cereal matrices, usc of wavelength

dispersive X-ray fluorescence and minerals in, 21-22

Inflow/outflow method, dynamic MRI and, 222

Infrared drying, 35

350 Index

Infrared Engineering (United Kingdom),

17 In-line analysis, 82 In-line magnetic resonance imaging

four slices from three-dimensional fast spin echo data set on a small lime, 230

Tesla permanent magnet and, 229 In-line particle sizer, pilot plant installation

with,23 In-process measurement, of size and

number of dark particles in a white powder, 197, 202-204

Insitec L, 190 Insitec online particle size analyzer,

schematic representation of, 191 Insitec software, screenshot, showing

change in size for four different operating conditions, 191,192

Installation identifying place of, 11-12 qualification, 8-9

Instrumentation specifications, central importance of, 7-10

Insulating material, relative permittivity of,

323 Integrating sphere

principle of, 80 rotating cup on, 81

Interferograms, 124 Interferometer, schematic of, 123 Interferometry, 122 Internal reflectance element, 126 Internal reflection, in ATR crystal, 126 Internal validation, 92 International Dairy Federation, 132 Inverse Least Squares, 88 Iodine value

calibration value for, in edible oils, lIlt

for edible oils, 110-111 Ionic migration, 294 Ionic solutions, electrical fields applied to,

296 IRE. See Internal reflectance element Irudayaraj, 1., 56, 133, 158 IV. See Iodine value

J Jackknifing, 260 Jeffries, M., 56 Jha, S. N., 27 Johnson, D. E., 259, 260 Joule heating effect, 294 Juodeikiene, G., 26 Just-in-time delivery, 2

K Kahweol, 156 Karl Fischer titration, 83, 84

calibration line and sample determination based on, 38

calibration line for water content determination of samples of Lactoserum Euvoserum, based on, 42

true values by, and oven drying and predicted values for wheat semolina samples, 37, 37t

water content determination and, 36 Karoui, R., 27, 133, 134, 135 Katsumata, T., 21 KFT. See Karl Fischer titration

Kilic, K., 20 Kim, I-H, 150 Kim, M. S., 21 Kim, S., 137 Kimbaris, A. c., 28 Kinetics

electrode, 299-300 enzyme, 297-299 hybridization, 30 I of interaction between target and

bioreceptor, 301-302 Kizil, R., 148, 154 Kohman method, butter and, 104 Kueppers, S., 15 Kukackova, 0., 19

L Laboratory (or off-line analysis), 81 Laboratory values, 34 LabVIEW, 310 Lachenbruch, P. A., 260 Lachenmeier, D. w., 136

Lactic acid fermentation, ultrasonic velocity and, 26

Lactococcus lactis spp. lactis and cern 133

Lactoserum, water content determinat for, 37

Lactoserum Euvoserum

calibration for mass loss determinat at l45° C, 41

calibration line for water content

determination of samples of, b: on Karl Fischer titration, 42

drying curve of, 40 Lamb, FT-Raman spectra of, 149t Laminar flow, uniaxial, 288 Lammertyn, 1., 310, 311 Lana, M. M., 27

Laplace equations, 292, 296, 306 Larmor frequency, transverse

magnetization precessing at, 21 214

Larmor precession frequency defined, 218

stationary MRI and, 217 Laser, 173

Laser diffraction, 172-175, 197 advantages with, 175 applications with, 172-173

chocolate, and applications with, 176-183

coffee, and applications with, 188-1! flavor emulsions

and applications with, 187-188 and detection of outsize particles

with,188

milk products, and applications with, 183-186

particle size distribution calculations, 174

Laser diffraction system, typical, 174 Lateral reservoirs, potential values, leak

and, 309 LDA. See Linear discriminant analysis Least square analysis, protein secondary

structure determination and, 147 "Leave one out" method, 258 Lee, S., 55

Index 351

Lactic acid fermentation, ultrasonic velocity and, 26

Lactococcus lactis spp. lactis and cermoris, 133

Lactoserum, water content determination for, 37

Lactoserum Euvoserum calibration for mass loss determination

at 1450 C, 41 calibration line for water content

determination of samples of, based on Karl Fischer titration, 42

drying curve of, 40 Lamb, FT-Raman spectra of, 149t Laminar flow, uniaxial, 288 Lammertyn, 1., 310, 311 Lana, M. M., 27 Laplace equations, 292, 296, 306 Larmor frequency, transverse

magnetization precessing at, 213, 214

Larmor precession frequency defined, 218 stationary MRi and, 217

Laser, 173 Laser diffraction, 172-175, 197

advantages with, 175 applications with, 172-173 chocolate, and applications with,

176-183 coffee, and applications with, 188-190 flavor emulsions

and applications with, 187-188 and detection of outsize particles

with, 188 milk products, and applications with,

183-186 particle size distribution calculations,

174 Laser diffraction system, typical, 174 Lateral reservoirs, potential values, leakage

and, 309 LDA. See Linear discriminant analysis Least square analysis, protein secondary

structure determination and, 147 "Leave one out" method, 258 Lee, S., 55

Leemans, Y, 20 Lefier, D., 133 Lennartz electronic (Germany), 243 VEtivaz cheese, MIR spectrosocpy and,

135 Li, D., 309 Liao, Q., 294 Li Chan, E., 147 Light scattering patterns, for different

particles, 174 Likelihood Rule, 259 Lindt chocolates, 176 Linear discriminant analysis, 135, 153 Linear Discriminant Function Rule, 259 Lineweaver-Burk plot, 299 Lipid crystallization, 54 Lipids, ultrasound and phase transitions in,

53 Lipolysis, 133 Lipopolysaccharides, 137 Liquid chromatography, 283 Liquid milk, mid-infrared analyzer to

control composition of, online, 19

Local Weighted Regression, 91 Longitudinal diffusion, 294 Longitudinal relaxation time, magnetic

resonance and, 215 Longitudinal wave reflectance, estimation

offoam bubble size by, 51 Low-intensity ultrasound, 45 LPS. See Lipopolysaccharides Lucas, T., 25 Lucia, Y, 133 Luxury brand chocolates, 180-181 L-wave measurements, food texture

measurement and correlation with, 60-61

L-waves, 45, 46 LWR. See Local Weighted Regression

M Magnetic resonance

dynamic, 221-227 relationship ofNMR properties to food

quality and, 227 stationary, 217-221

i

i

352 Index

theory and practical implications with, 212-227

Magnetic resonance imaging, 60, 211 applications with, 25 barriers to more widespread use of, 212

Magnetic resonance imaging spectrometer, basic components of, 212

Magnets availability of, for NMRlMRI systems,

228-233 unilateral, 229

Magnet technology, continual development of, 232

Magritek Limited, NMR spectrometers through, 233

Mahalanobis distance, 251, 254-259, 261 most useful value in comparison of,

from different systems, 258 unbiased, 256, 257

Mahalanobis Distance Rule, 259 Mallikarjunan, P., 269 Management support, getting, 12-14 Mango maturity, color measurement and

nondestructive evaluation of, 27 MANOVAS. See Multivariate analysis of

variance Marangoni, A. G., 298 Marquardt, B. 1.,149,157 Marquis, F., 248 Martini, S., 54 "Master" central spectrometer, 95 Mastersizer 2000, 179

following milk powder reconstitution with use of, 187

Mathematical modeling, 285 Mayonnaise

calibration results for, II Ot NIR analysis and, 109-1 10

McClements, D. 1., 53, 61 McElhinney,J., 131 McQueen, D. H., 132 Measurement time, 12 Meat and meat products

calibration results for, lOOt computer vision and quality evaluation

of,20

cross correlation of water and fat content in, 98

measurement of, 97-99 MIR spectroscopy applications and, 129,

131 NIR reflectance spectra of, 87

Mechanics classical, NMR phenomenon discussion and, 213

Medical field, electronic nose technology applications in, 268

Meltability models, cheeses and, 134 Mendenhall, I. v., 132 Menten, Maud, 298 Mergers, electronic nose manufacturers,

239 Mesh sieves, 167 Messtechnik Schwartz GmbH (Germany),

23 Metal oxide semiconductor gas sensors,

239 Metal oxide semiconductors, 239 Metal oxide semiconductors field effect

transistors, 239, 243, 246 Metal-oxide sensors, 243, 244, 263 Method, defining, 11 Michaelis, Leonor, 298 Michaelis-Menten constant, 299 Michaelis-Menten curve, 297, 298 Michaelis-Menten equation, 299 Michelson interferometer, 74, 145 Mickey, M. R., 260 Microchannels

flow mechanisms in, 288 hydrodynamic and electrokinetic flows

in, 289 Microelectronic developments, biosensor

technology and, 285 Microelectronics, microfluidics and, 285 Microfluidic injection system, values of

applied electrokinetic potentials of injection.and loading stages in optimization study of, 307t

Microfluidics flow-type biosensors and, 287-288 microelectronics and, 285

Micromesh sieves, 167

Microorganisms, UV-Raman identifil of, 153

Microscale structure, 57-58 Microwave absorption, 9, 16 Microwave drying, 35 Microwave spectrosocpy, milk study

24

Mid-infrared absorption frequencies, selected molecular group, 121

Mid-infrared (MIR) spectroscopy, 16 application of, to food processing

systems, 119-138 applications, 129-137

dairy products, 131-135 fruit and alcoholic beverages,

135-136 meat and poultry, 129,131 other food products, 136--137

chemometric methods, 127-129 equipment, 122-127

Fourier transform infrared spectroscopy, 122-124

sample presentation methods, 124-127

selection of reported food analysis applications of, DOt

Mid-infrared spectra, of processed chel goat's milk, and olive oil, 122

Mie Light Scattering Theory, 190 Mielle, P., 248 Mie theory, 174-175 Milk. See also Milk products

calibration results for, 102t mid-infrared spectroscopy and, 131­

Milk and dairy products, NIR analysis 99-109

Milk chocolate

dark chocolate vs., 181-182,182 particle size distributions for, 183

Milk crumb, milk chocolate manufactw and, 178

MilkoScan™FT 120, 132 Milk powder

automatic sampler for, 103 calibration results for, 102t

in a lab environment, 104t

Index 353

Microorganisms, UV-Raman identification of, 153

Microscale structure, 57-58 Microwave absorption, 9, 16 Microwave drying, 35 Microwave spectrosocpy, milk study and,

24 Mid-infrared absorption frequencies,

selected molecular group, 121 t Mid-infrared (M1R) spectroscopy, 16

application of, to food processing systems, 119-138

applications, 129-137 dairy products, 131-135 fruit and alcoholic beverages,

135-136 meat and poultry, 129, 131 other food products, 136-137

chemometric methods, 127-129 equipment, 122-127

Fourier transform infrared spectroscopy, 122-124

sample presentation methods, 124-127

selection of reported food analysis applications of, 130t

Mid-infrared spectra, of processed cheese, goat's milk, and olive oil, 122

Mie Light Scattering Theory, 190 Mielle, P., 248 Mie theory, 174-175 Milk. See also Milk products

calibration results for, 102t mid-infrared spectroscopy and, 131-135

Milk and dairy products, NIR analysis and, 99-109

Milk chocolate dark chocolate vs" 181-182, 182 particle size distributions for, 183

Milk crumb, milk chocolate manufacture and, 178

Mi1koScan™FT 120, 132 Milk powder

automatic sampler for, 103 calibration results for, 102t

in a lab environment, 104t

laser diffraction and rehydration of, 186 NIR analysis and, 101-103

Milk products for chocolate production, 178 homogenization of, 184-185,185 laser diffraction and particle size of,

183-184 Milk study, microwave spectrosocpy and,

24 Minerals in food products, MiniPal 4 and,

22,22 Miniature MIR spectrometers, 138 Miniaturization

of biosensors, 285 flow-type biosensors and, 287-288

Miniaturized visible/near infrared (VISINIR) spectrometer, 27

Minimum-distance classifier, Mahalanobis metric in, 255

MiniPal4, determining minerals in food products and, 22, 22

MIR spectroscopy. See Mid-infrared (MIR) spectroscopy

MLR. See Multiple Linear Regression MM710 near infrared analyzer, 17 Modular chemosensory systems, 243 Modular sensor system, 263 Mohr titration method, butter and, 104 Moisture calibration, of sugar-containing

samples after recalibration with moving sample, 85

Moisture content, 35 Moisture measurement, capacitance

sensors and, 327-328 Molecular diffusion, 294 Morphology G2

circularity, image analysis and, 194 image analysis of tea leaves with, 195,

195 Morrizumi, T., 245, 249 Moser, A., 298 MOSES. See Modular sensor system MOSFET. See Metal oxide semiconductors

field effect transistors MOS sensors. See Metal-oxide sensors Motech GmbH (Germany), 243

354

I \

MRl. See Magnetic resonance imaging Muik, B., 149 Multiple electrode pairs applications,

328-329,331,333 electrical capacitance tomography,

328-329,331 high-speed check weighing, 331,

333 Multiple Linear Regression, 88 Multiple scattering, 169 Multivariate analyses, Mahalanobis

distance and, 255 Multivariate analysis of variance, apple

quality evaluation and, 273 Multivariate calibration

method, 157 theoretical basics of, 86-89, 91

Multivariate discriminant analyses, 251

Multivariate factor analysis, 251 Multivariate qualitative Raman

spectroscopy, food quality assessments and, 153-157

Multivariate quantitative Raman spectroscopy, food quality assessments and, 157-158

Munkevik, P., 21

N NADH. See Nicotinamide adenine

dinucleotide Nakai, S., 147 Nakamoto, T., 245, 249 Nanoscale sensors, 243 Navel oranges, Halbach magnet and

measurement of spin-spin relaxation rates of, 232, 234

Navier-Stokes equations, 293, 296, 306, 311

hydrodynamic flow and, 290 NDE technology. See Nondestructive

evaluation (NDE) technology Near infrared analyzer MM71O, 17 Near infrared calibrations, of sweets on

moisture: sugar free and sugar containing, 85

Near infrared (NIR) irradiation, 145

Index

Near infrared (NIR) spectroscopy, 9, 16, 28,34,36

advantages and disadvantages of spectrometer technologies, 75-76

advantages of, 69-71 applications of, in food industry, 96-116 beverages, 114-116

chocolate, 113-114 mayonnaise, edible oil, and olives,

109-113 meat and meat products, 97-99 milk and dairy products, 99-109

building calibration models, 91-94 calibration model updating, 94 cross validation, 92 external validation, 91-92 how many samples in a calibration

data set?, 93-94 results and parameters of validation,

93 calibration development and, 82-96

NIR calibrations and reference analysis, 82-84, 86

theoretical basics of multivariate calibration, 86-89, 91

categories for implementation ofNIR instruments, 81-82

description of, 69 direct calibration transfer, 95-96 discovery of potential for, 67 history of, 68-69 instruments, 71-76

detector array dispersive spectrophotometers, 73-74

filter-based photometers, 72 Fourier transform instruments, 74 scanning dispersive grating

spectrophotometers, 72-73 measurement modes and sampling

techniques, 76-82 diffuse reflection, 79-81 transflection, 79 transmission, 76-78

sample areas measured with, by static and moving samples, 86

spectral transfer, 94-95 use of, in the food industry, 67-116

Near infrared (NIR) technology, advantages with, 116

Near infrared process analyzer, determination of moisture in skimmed milk powder with USt

18 Near Infrared Reflectance and

Transmission (NIT) instrumen1 78

Near-line analysis, 4 Neikov, A., 302 Neotronics (USA, UK), 243 Nestle, 176 Net magnetization moment, 213 Newman, D. 1., 242 Newton, Sir Isaac, 68 Nib, cocoa bean, 177 Nicotinamide adenine dinucleotide, 23 NIR spectroscopy. See Near infrared 0

spectroscopy NMR. See Nuclear magnetic resonanci NMR magnet, unilateral, watermelon (

top of radio frequency coil and, 231

NMR MOUSE (Mobile Universal Sur1 Explorer), 229-230, 231

NMR spectrometers, portable, 233 NMR spectroscopy. See Nuclear magn

resonance (NMR) spectroscoP) Noda, T., 21 Noncontact, ultrasound applications an

26 Noncontact measurements, 50 Nondestructive evaluation (NDE)

technology, 45 Nondestructive food control, requiremc

summary related to investment 13-14

Nondestructive instrumentation, succei use of, 6-7

Nondestructive sensors for product development and food

research, 25-28 for production control, 15-25

at-line analysis, 16 off-line analytics, 15-16 online analytics, 16-25

Index 355

Near infrared (NIR) technology, advantages with, I 16

Near infrared process analyzer, determination of moisture in skimmed milk powder with use of, 18

Near Infrared Reflectance and Transmission (NIT) instruments, 78

Near-line analysis, 4 Neikov, A., 302 Neotronics (USA, UK), 243 Nestle, 176 Net magnetization moment, 213 Newman, D. 1., 242 Newton, Sir Isaac, 68 Nib, cocoa bean, 177 Nicotinamide adenine dinucleotide, 23 NIR spectroscopy. See Near infrared (NIR)

spectroscopy NMR. See Nuclear magnetic resonance NMR magnet, unilateral, watermelon on

top of radio frequency coil and, 231

NMR MOUSE (Mobile Universal Surface Explorer), 229-230, 231

NMR spectrometers, portable, 233 NMR spectroscopy. See Nuclear magnetic

resonance (NMR) spectroscopy Noda, T., 21 Noncontact, ultrasound applications and,

26 Noncontact measurements, 50 Nondestructive evaluation (NDE)

technology, 45 Nondestructive food control, requirements

summary related to investment in, 13-14

Nondestructive instrumentation, successful use of, 6-7

Nondestructive sensors for product development and food

research, 25-28 for production control, 15-25

at-line analysis, 16 off-line analytics, 15-16 online analytics, 16-25

Nondestructive testing need for, 1-5

final products, 2, 4 process control, 2, 3-4 product development, 2, 4-5 raw material, 2-3 research, 2, 5

Nondestructive testing instrumentation, cost factors and, 13

Nonparallelism, 57 Nonuniform geometry, 294 Nonuniform zeta potential, 294 Nordic Sensors, 239 Nordic Sensor Technologies (Sweden), 243 Norris, Karl, 68 N-type oxides, 244 Nuclear magnetic resonance, 16,54 Nuclear magnetic resonance (NMR)

spectroscopy advances in nondestructive testing with,

211-235 barriers to more widespread use of, 212

Nuclear magnetic resonance systems pulse sequence advances, 233-235 recent advances in, 227-235

hardware: magnets, 228-233 hardware: spectrometers, 233

Nuclear magnetization, 213 Nuclear spin angular momentum, 212 Nunes, A. C., 24

o Oblique incidence, 57 OD. See oven drying Odors, 237

advances in sensor technologies and analyses of, 238

electronic nose and monitoring of, 241 Off-line analysis, 4 Off-line analytics, 15-16 Oil emulsions, high concentration, 187 Oils

Raman quality measurements of, 154 Raman spectroscopy and, 148-150

Olfactory system, human as basis of sensory panels, 241 food consumption and, 237-238

I I

356 Index

OligoSense (Belgium), 243 Olive oil

authenticity and quality determination in, MIR spectroscopy and, 137

calibration results for, 112t classification of, 112 NIR analysis and, 111-113 processed, mid-infrared spectra of, 122 samples in 8-mrn disposable vials,

transmission measurement of, 77 Olive paste, calibration results for, 112t Olives

FT-Raman spectroscopy and analysis of, 156-157

NIR analysis and, 111-113 Ollis, D. F., 298 Olson and Price meltability models, 134 One-dimensional imaging, applications

with, 56 One-dimensional phase-encode NMR

pulse sequences, for flow measurement, 234, 234

One-way MANOVA, 253 Online analysis, 3--4, 82 Online analytics, 16-25 Online image analysis, examples of, 197 Online image analysis ofparticulate

materials, 197-209 applications with, 208-209 detection of fibrous impurities in particle

suspensions, 205, 207-208 in-process measurement of size and

number of dark particles in a white powder, 202-203

measurement principles, 198, 201-202 measuring size and number of big

particles in concentrated submicron dispersions, 204-205

trend over time, 204-205 offline test results, 203-204

in-process installation, 204 online trend display, 206 raw image with fiber in product

suspension and analyzed mage, 207

size and count of all particles, 208 size and count of fibers only, 208

Online monitoring, 138 Online near infrared analyzer Corona,

3 Online particle size analyzers, advantages

with, 191 Online particle sizing techniques,

190-193 Operational qualification, 9 Optical measuring modes

diffuse reflection, 79-81 transflection, 79 transmission, 76-78

Organoleptic quality, cheese, determination of, 133

Organoleptic testing, of olive oil, 112, 113

Osmetech, 239 Oven drying, 35

calibration line and sample determination based on, 39

true values by Karl Fischer titration, predicted values and, for wheat semolina samples, 37, 37t

Over-fitting, avoiding, 256 Oxide sensors, 239 Oyster quality, raw (case study)

detection of spoilage and discrimination of,274

discrimination of, by two types of handheld electronic nose systems, 275

p

PANalytical (Netherlands), 22 Paradkar, M., 158 Park, B., 21 Partial differential equations, 303 Partial least squares analysis, 251, 261 Partial least squares regression, 91, 121,

128,134 Particles

defined, 165 measuring properties of, 166

Particle shapes image analysis and, 193-194 three, with same circular equivalent

(CE) diameter, 193

Particle size cocoa bean, 177 coffee flavor and, 188-190 of coffee produced by grinder at Vllf)

speeds, 189 concepts related to, 165-167 of flavor emulsions, 187-188 image analysis and, 193-195 importance of, 165 laser diffraction and detennination 0

173 of milk products, assessing, 183-18~

ofpre-ground espresso and pre-grou filter coffee, 190

scatter plot of shape VS., 202 Particle size distributions

for full fat, semi-skimmed, and skim milk, 183, 184

for milk chocolate and dark chocolat 183

for UK and American chocolate bars 180

Particle size measurement, chocolate aJ

challenges with, 179 Particle sizing, 23-24

in food and beverage industry, 165-1 online techniques for, 190-193

Particle suspensions, detection of fibrOl impurities in, 197,205,207-20:

Pasta drying, NMR imaging and, 26 PAT. See Process Analytical Technolog: Pathogenic microorganisms, MIR

spectroscopy and detection and identification of, 137

PC. See Personal computer PCA. See Principal component analysi~

PCBs. See Polychlorinated biphenyls PCR. See Principal component regressi PCs. See Principal components PDE. See Partial differential equations PDS. See Piecewise Direct Standardiza Peak height, 313, 314 Performance qualification, 9-10 Perkin Elmer, 240 Permanent magnets, advances in desigr

228 Perring, L., 21

Index 357

Particle size cocoa bean, 177 coffee flavor and, 188-190 of coffee produced by grinder at varying

speeds, 189 concepts related to, 165-167 of flavor emulsions, 187-188 image analysis and, 193-195 importance of, 165 laser diffraction and determination of,

173 of milk products, assessing, 183-184 of pre-ground espresso and pre-ground

filter coffee, 190 scatter plot of shape VS., 202

Particle size distributions for full fat, semi-skimmed, and skimmed

milk, 183, 184 for milk chocolate and dark chocolate,

183 for UK and American chocolate bars,

180 Particle size measurement, chocolate and

challenges with, 179 Particle sizing, 23-24

in food and beverage industry, 165-195 online techniques for, 190-193

Particle suspensions, detection of fibrous impurities in, 197,205,207-208

Pasta drying, NMR imaging and, 26 PAT. See Process Analytical Technology Pathogenic microorganisms, MIR

spectroscopy and detection and identification of, 137

PC. See Personal computer PCA. See Principal component analysis PCBs. See Polychlorinated biphenyls PCR. See Principal component regression PCs. See Principal components PDE. See Partial differential equations PDS. See Piecewise Direct Standardization Peak height, 313, 314 Performance qualification, 9-10 Perkin Elmer, 240 Permanent magnets, advances in design of,

228 Perring, L., 21

Personal computer, 67, 124 Petri dishes, 78, 84, 98

cheese analyses in, 107 mayonnaise samples in, 110 polysterene, 109

PGSE. See Pulsed gradient spin echo PH. See Peak height Pharmaceutical industry, food industry

and, 14 Phase-encoding method, dynamic MRI

and, 222, 223-224 pH meter, 22 Photoluminescence, 21 Photon correlation spectroscopy, 168 Physical parameters, testing and

applications with, 22-23 Physiochemical transducer, 283 Piecewise Direct Standardization, 94 Piezoelectric crystal sensors, 245 Pillonel, L., 134 Piot, 0., 152 Pitt, G. D., 145 Pizza, controlling completeness of, 21 Plasticizers

detection of, in packaging material, 269 electronic nose systems comparison, in

discriminating packaging material based on level of, 270

Plexiglas detector cell, flow injection amperometric biosensor, 310

PLSR. See Partial least squares regression Poisson-Boltzmann equation, 293

potential in EDL described by, 291 Polyaniline, 245 Polychlorinated biphenyls, 283 Polyethylene cards, 126 Polypyrrole, 245 Polythiophene, 245 Porcine adipose tissues

CVA and discrimination of, 156 FT-Raman spectra of, 149t

Portable NMR spectrometers, 233 Position-displacement conditional

probability density, 227 Posterior Probability Rule, 259 Potatoes, MR1 and perceived textural

properties of, 26

358 Index

Potato starch, ED-XRF and determining phosphorous content in, 21

Potentiostat, flow injection amperometric biosensor, 310

Poultry, MIR spectroscopy applications and, 129, 131

Predicted values, 34, 35 Primary methods, 33

secondary methods calibrated against, 43 Principal calibration phase, steps related to,

158 Principal component, 89 Principal component analysis, 89, 121,

128,134,153,240,251-252,256 Principal component regression, 89, 158 Printed packaging material (case study),

detection of retained solvent levels in, 269

PROC DISCRIM, 253 Process Analytical Technology, 138

defined, 120 Process Analytical Technology (PAT)

initiative, 14-15 ProcesScan FT process analyzer, 19 Process control, 2, 3-4

sensor test results and, 22-23 Process Control Technologies,

low-resolution NMR systems manufactured by, 228

Process Interface Solution, 191 Product development, 2, 4-5

nondestructive sensors for, 25-28 Production, automation of, increased use

of, 5 Production control, nondestructive sensors

for, 15-25 Product monitoring, mid-infrared

spectroscopy and, 130t Product safety, 120 Progression, Inc., low-resolution NMR

systems manufactured by, 228 Protein analysis, raman spectrosocpy and,

146-147 Proteolysis, 133 P-type oxides, 244 Pulsed gradient spin echo, 225, 225 Pulsed ultrasonic methods, 47

Pulse-echo, alignment of transducers and ultrasonic path for, 48-49

Pulse-free syringe pump, flow injection amperometric biosensor, 310

Pulse sequence/experimental design, advances in, 233-235

Punched hole sieves, 167 Pycnometer, 22

Q QCM sensors. See Quartz crystal

microbalance sensors QDA. See Quantitative description analysis QMB sensors. See Quartz microbalance

sensors QMB6, discrimination of packaging

material based on level of plasticizers and, 270

Qualion Ltd., high-resolution NMR based sensors and, 228

Quality assurance, food, MIR spectroscopy and, 137

Quality control, in cheese production, 106 Quality improvement, as driver for

nondestructive testing, 13 Quantitative description analysis, raw

oyster quality and, 274 Quantum Magnetics, portable NMR

systems for measuring spin-spin relaxation times and, 233

Quartz crystal microbalance sensors, 239, 243,245-246

Quartz cuvettes, 77 Quartz microbalance-based sensing

system, cheese ripening process monitored with, 268

Quasi-elastic light scattering, 168

R Raman microspectroscopy, in quality

assessment of grains, 152 Raman quality measurements, of fats and

oils, 154 Raman-scattering signals, collecting, 145 "Raman shift," 143 Raman spectrometers, classification of,

144-146

Raman spectroscopy advantages with, 145 applications of, for food quality

measurement, 143-I 59 basic principles of, 143-144 carbohydrates/carbohydrate-based f(

and, 150-15 I contemporary and special applicatio

of, for food quality measuremel 151-153

fats and oils and, 148-150 future applications with, 159 protein analysis and, 146-147 selection rules applied to, 144

Rapid near-line analysis, 5 Ratioing beam sample data, 124 Raw material, 2-3 Rayleigh scattering filters, 145 Reaction kinetics, 297-302

electrode kinetics, 299-300 enzyme kinetics, 297-299 kinetics of interaction between targel

and bioreceptor, 301-302 Read gradient, 2 I 9 Recovery time (REC), defined, 3 I3 Red wines, MIR and authentication of,

136 Reference laboratory performance, 9 Reference methods

calibration of indirect methods and influence of, 33-43

calibration of sensor and, 17 Reflectance, alignment of transducers a

ultrasonic path for, 48-49 Reflectance coefficient, 5 I Reflectance measurements, 50-52 Reflectance methods, applications of,

51 Reflectance mode, for analyzing cheese

107 Reflection probe, 78 Refractometers, 22 Refractometry, 9, 16 Regression analysis, 259 Regression coefficient, 34 Reh, c., 17, 125 Reid, L. M., 135

Index 359

Raman spectroscopy advantages with, 145 applications of, for food quality

measurement, 143-159 basic principles of, 143-144 carbohydrates/carbohydrate-based foods

and, 150-151 contemporary and special applications

of, for food quality measurements, 151-153

fats and oils and, 148-150 future applications with, 159 protein analysis and, 146-147 selection rules applied to, 144

Rapid near-line analysis, 5 Ratioing beam sample data, 124 Raw material, 2-3 Rayleigh scattering filters, 145 Reaction kinetics, 297-302

electrode kinetics, 299-300 enzyme kinetics, 297-299 kinetics of interaction between target

and bioreceptor, 301-302 Read gradient, 219 Recovery time (REC), defined, 313 Red wines, MIR and authentication of,

136 Reference laboratory performance, 9 Reference methods

calibration of indirect methods and influence of, 33-43

calibration of sensor and, 17 Reflectance, alignment of transducers and

ultrasonic path for, 48-49 Reflectance coefficient, 51 Reflectance measurements, 50-52 Reflectance methods, applications of,

51 Reflectance mode, for analyzing cheeses,

107 Reflection probe, 78 Refractometers, 22 Refractometry, 9, 16 Regression analysis, 259 Regression coefficient, 34 Reh, c., 17, 125 Reid, L. M., 135

Relaxation, typical T] relaxation curve, 216

Relaxation time, magnetic resonance and, 215

Repeatability and reproducibility, poor, electronic nose sensors and, 247, 250

Requalification of instruments degree of, 10 types of changes related to, 9

RES. See Response time Resa, P., 26 Research, 2, 5 Resonance Systems, NMR spectrometers

through, 233 Resonance ultrasonic methods, 47 Resonator, alignment of transducers and

ultrasonic path for, 48-49 Response surface analysis, electronic nose

sensors and, 250 Response time, 3 13 Resubstitution method, 260 Reynolds number, flow of microfluidic

channel characterized by, 288 Rheology, 58 Ring structures, detecting, Raman

spectroscopy and, 144 Ripoche, A., 131 RMSECV. See Root Mean Square Error of

Cross Validation RMSEP. See Root Mean Square Error of

Prediction RNA, hybridization and, 301-302 Roberts, C. A., 96 Root Mean Square Error of Cross

Validation, 92, 93 Root Mean Square Error of Prediction, 92,

93 Roussel, S., 136, 248 RST Rostock (Germany), 243 R2 value, 93 RU9nitskaya, A., 135

S Sadana,A.,302 Sadeghi-Jorachi, R., 148 Safety, product, 120

360 Index

Saggin, R., 54, 55 Salami,99

NIR reflectance spectra of, 87 Salmonella enterica serotypes, FTIR

spectroscopy applied to classification of intact cells and LPS of, 137

Salts, ultrasound and phase transitions in, 53

Sample dispersion, causes of, in electrokinetically driven micro devices systems, 294

Sample overloading, 294 Sample presentation methods, 124-127

attenuated total reflectance, 126-127 diffuse reflectance, 127 transmission windows and cells,

125-126 Sample presentation system, laser

diffraction and, 173 Sample volume, 12 Sampling procedures, 9 Sampling techniques, NIR methods and,

83-84 Sausages, 99

calibration results for, lOOt cross correlation of water and fat content

in, 98 SAW mode. See Surface acoustic wave

mode SAW sensors. See Surface acoustic

wavc-based sensors Scanning dispersive grating instruments,

advantages and disadvantages of, 75

Scanning dispersive grating spectrophotometers, 72-73

principle of, 73 Scattering theory, 57 Schaak, R. E., 248 Schaller, E., 263 Sealed transmission cells, 125 Secondary (or indirect) methods, 33,

35 calibration of, against primary or direct

method, 43 Semi-conducting polymers, 239

Semipermanent transmission cells, 125 Semi-skimmed milk, size distributions

recorded for, 183-184,184 Sensor drift, 248, 250 Sensors, food industry changes and

consequences related to use of, 4-5

Sensor technologies, for odor analyses, 238. See also Electronic nose technology

Separation, by sieving, 167-168 SFC. See Solid fat content Shear!transverse (T) waves, 45 Shear wave methods, for food texture

measurement, 58-59 Shear wave reflectance, 51 Shelf-life, NMR and, 227 Sieve analysis, 197 Sieves, types of, 167 Sieving, 167-168

automated image analysis and, 24 Sigfusson, H., 55 Signal processing system, biosensor, 284 Silicon detector, cheese analysis and, 106,

107 SIMCA. See Soft Indepcndent Modeling of

Class Analogy Simon, 1. E., 263 Singh, A. P., 54 Singh, P. C., 22 Single-electrode pairs

applications using, 325-328 missing biscuit detection and, 325-327

Single-shot measurements of relaxation times or diffusion, pulse sequence!experimental design advances and, 234-235

Siragus, F. R., 153 Sivakesava, S., 136 Sizing techniques, 167-195

image analysis, 193-195 light scattering, 168-176

dynamic, 168- 172 laser diffraction, 172-176

sieving, 167-168 Skimmed milk, size distributions recorded

for, 183-184, 184

Skimmed milk powder, determination moisture in, using a near infra process analyzer, 18

"Slaves," 95 Slice selection, 219 Smell, characterizing food product in

of, 237 "Smellprint," 240 Smith Detection Systems, 239 SNF. See Solid-non-fat Sniff tests, human sensory panel-base(

269 Soft Independent Modeling of Class

Analogy, 251,261 Sokolov, S., 302 Solid fat content, 54 Solid-non-fat, effect ofhomogenizatiOI

milk on measurcment of, 10 Solvent levels (case study), retained,

dctection of, in printed packagi: material, 269

Soxhlet extraction, 84 oil content in olives determined by,

111 quantitative determination of fat coni

from mayonnaise by, 109 Spectra, simple. example of factorizatic

of, into corresponding loadings scores, 90

Spectral transfer, 94-95 Spectrometer technologies, advantages:

disadvantages of, 75-76 Spectroscopical methods, prediction of

texture by, 23 Spectroscopy, defined, 119 Sphere, with same volume ofgiven

cylinder, 166-167,167 SpinCore Technologies, Inc., general

purpose NMR spectrometers through, 233

Spin echo pulse sequence, 221 in MRl, 219 sample image of internal browning in

apple and, 220 Spin-lattice relaxation, 215 Spin-spin relaxation, magnetic resonanc

and, 215, 216

Index 361

Skimmed milk powder, determination of moisture in, using a near infrared process analyzer, 18

"Slaves," 95 Slice selection, 219 Smell, characterizing food product in terms

of, 237 "Smellprint," 240 Smith Detection Systems, 239 SNF. See Solid-non-fat Sniff tests, human sensory panel-based,

269 Soft Independent Modeling of Class

Analogy, 251, 261 Sokolov, S., 302 Solid fat content, 54 Solid-non-fat, effect of homogenization of

milk on measurement of, 10 Solvent levels (case study), retained,

detection of, in printed packaging material, 269

Soxhlet extraction, 84 oil content in olives determined by,

III quantitative determination of fat content

from mayonnaise by, 109 Spectra, simple, example of factorization

of, into corresponding loadings and scores, 90

Spectral transfer, 94-95 Spectrometer technologies, advantages and

disadvantages of, 75-76 Spectroscopical methods, prediction of

texture by, 23 Spectroscopy, defined, 119 Sphere, with same volume of given

cylinder, 166-167,167 SpinCore Technologies, Inc., general

purpose NMR spectrometers through, 233

Spin echo pulse sequence, 221 inMRI,219 sample image of internal browning in an

apple and, 220 Spin-lattice relaxation, 215 Spin-spin relaxation, magnetic resonance

and, 215, 216

Spin-spin relaxation rates, Halbach magnet for measurement of, for whole navel oranges, 232, 234

Spin-spin relaxation times, portable NMR systems for measuring, 233

Spoilage microorganisms, MIR spectroscopy and detection and identification of, 137

Stabilizers, flavor emulsions and, 187 Staff, central importance of, 7 Starch, FT-Raman spectroscopy and

gelatinization and retrogradation processes of, 150

Static headspace analysis, 241 Stationary MRI, 217-221 Statistical analysis, electronic nose

technology, 250-262 artificial neural networks, 262 discriminant analyses, 252-262

variable selection procedure, 260-262

multivariate factor analyses, 251 principal components analysis, 251-252

Stepwise selection procedure, 260 Stokes-Einstein equation, concentration

dependence of intensity particle size distribution for emulsion sample, using solvent viscosity in, 172

Strassburger, K. J., 241 Submicron dispersions, concentrated,

measurement of size and number of big particles in, 197, 204-205

Sucrose solutions, speed of sound in, as a function of temperature, 56

Sugar in chocolate production, 177-178 ultrasound and phase transitions in, 53

Sun, D. W, 20, 21 Superconducting magnets, 228 Support vector machines, 91, 263 Surface acoustic wave-based sensors,

246--247 Surface acoustic wave mode, 245 SVM. See Support vector machines Swiss Gruyere cheese, MIR spectrosocpy

and, 135

362 Index

T Taguchi,239 Tan, 1., 20 Tanaka, E, 24 Tapp, H. S., 125 Target molecules, biosensor technology,

and interaction between biorecognition molecules and, 301

Taste, 283 Tea leaves, image analysis of, 194, 195 Tea stalks, image analysis of, 194, 195 Tecmag, Inc., portable NMR spectrometer

manufactured by, 232, 233 10-port injection, flow injection

amperometric biosensor, 310 Tesla permanent magnet, for in-line

magnctic resonance imaging of structural defects in food products, 229

Tesla permanent magnet systems, applications with, 229

Test Set, 93 Texture analysis, of meat, 20 Textures, 283

spectroscopical methods and prediction of,23

Theobroma cacao tree, 177 Thermal processing, protein structure

changes and, 147 3-CCD color digital cameras, 20 Three-dimensional fast spin echo data set,

four slices from, on a small lime using permanent magnet designed for in-line magnetic resonance imaging, 230

Three-dimensional imaging with single-sided sensor, pulse sequence/experimental design advances and, 234

Through transmission, alignment of transducers and ultrasonic path for, 48-49

Thybo, A. K., 25 Time coding, 6 Time-of-flight method, dynamic MRI and,

222 Tin oxide-based sensors, 263

Tomoflow measurement principle, 334 Tomographic analysis, 60 Tomographic flow measurement, of wheat,

337 Tomographic two-phase flow measurement

system, experimental, 336 Total fat content, nondestructive

compositional analysis and, 18 Total mass balance, 296 Toxins, 283 Traceability, 120, 138

mid-infrared spectroscopy and, BOt of production, 6

Transducer, biosensor, 284 Transflection,79 Transflection measurements, 79

principle of, 79 Transflection probes, 78, 79 Transmission measurements, 76-78

principle of, 77 Transmission probe, 78 Transmission windows and cells, 125-126 Transverse relaxation, magnetic resonance

and, 215, 216 Triangular-shaped magnet systems, 232 Triple-bonded structures, detecting, Raman

spectroscopy and, 144 True values, 34

by Karl Fischer titration and oven drying and predicted values for wheat semolina samples, 37, 37t

Tryptophan, Raman bands and, 147 T2 relaxation, first order relaxation process

and, 216 T-waves, 45, 46, 58 Twin-plane guarded multielectrode

capacitance sensor, 335 Two-phase flow measurement, and use of

electrical capacitance tomography, 333-335,337

Tyrosine, Raman bands and, 147

U Ultrasonic Doppler velocimetry (UDV),

advantages with, 60 Ultrasonic measurement of food

composition, 52-55

Index

uvUltrasonic measurement of food structure,

id 55-58

macroscale structure, 55-57 qmicroscale structure, 57-58

Ultrasonic particle sizing, 57 Ultrasonic propagation, 47

. d velopment of andUltraSOniC sensors, e .' Vtwo major strengths With, 61

Vac Ultrasonic velocimetry, ~ses for, 53 Val' Ultrasonic velocity, lactic aCid van

fermentation and, 26 . . ployment of III

van Ultrasolllc waves, em ' v

ultrasonic studies, 45

Ultrasound, 45-62 advantages with, 62 applications with, 52-61

ultrasonic measurement of food composition, 52-55

47 49-52measurement met h d s,0 , Venoncontact measurements, 50 reflectance measurements, 5~52

physical changes during processmg and,

26 ultrasonic measurement of food

structure, 55-58 measurement of food texture, y,

58-61 VUltrasound applications, noncontact and,

V 26 V

Ultrasound spectroscopy, 25 Ultrosonic Doppler veloclmetry. Vmeasurement system, alignment of

transducers and ultrasonic path for,

48-49 ",Unbiased Mahalanobis distance, 256, 257 , Unilateral geomctry, 229 Unilateral magnet, 229 United Kingdom chocolate products,

179-180 . . bars particle size distnbutIons cocoac,

for, 180 . Unknown samples, Mahalanobis distance

and identification of, 255 U S Air Force, polymer development ., technology developed by, 239

UV resonance Raman spectrometers,

h I t

145

Index 363

Ultrasonic measurement of food structure, 55-58

macroscale structure, 55-57 microscale structure, 57-58

Ultrasonic particle sizing, 57 Ultrasonic propagation, 47 Ultrasonic sensors, development of, and

two major strengths with, 61 Ultrasonic velocimetry, uses for, 53 Ultrasonic velocity, lactic acid

fermentation and, 26 Ultrasonic waves, employment of, in

ultrasonic studies, 45 Ultrasound, 45--62

advantages with, 62 applications with, 52--61

ultrasonic measurement of food composition, 52-55

measurement methods, 47, 49-52 noncontact measurements, 50 reflectance measurements, 50-52

physical changes during processing and, 26

ultrasonic measurement of food structure, 55-58

measurement of food texture, 58-61

Ultrasound applications, noncontact and, 26

Ultrasound spectroscopy, 25 Ultrosonic Doppler velocimetry

measurement system, alignment of transducers and ultrasonic path for, 48-49

Unbiased Mahalanobis distance, 256, 257 Unilateral geometry, 229 Unilateral magnet, 229 United Kingdom chocolate products,

179-180 chocolate bars, particle size distributions

for, 180 Unknown samples, Mahalanobis distance

and identification of, 255 U.S. Air Force, polymer development

technology developed by, 239 UV resonance Raman spectrometers,

145

UV resonance Raman spectroscopy identification ofmicroorganisms in pure

cultures and, 152-153 quaternary structure transition due to

aromatic amino acid residues detected by, 147

V Vacuum drying, 35 Validation, results and parameters of, 93 van Deventer, D., 269 van de Voort, F. R., 129 van Dijk, c., 27 Variable focusing potential, sample

transport as function of, showing sample concentration during loading and after injection, 308

Variable selection procedures, types of, 260

Vegetables image analysis and, 20 spectrosopic techniques and assessing

quality of, 27-28 Velocity profiles, 227

pulse sequence/experimental design advances and, 234

Verification, oflaser diffraction, 175 Vial holders, 78 Vials, 77 Vibrational frequencies, selected

parameters pertinent to, 144 Video image analysis, color changes

measurement and, 27 Virginia Tech, 269 Viscometers, 22-23 Visible excitation Raman spectroscopy, 144 Vision systems, 20-21 Volatile compounds, smell of food and,

237

W Wafer, missing, prototype detector system,

326 Warwick University (England), 239 Water, intensity size distributions for

various concentrations of emulsion sample using viscosity of, 172

364

Water content, 16 determination

example, 35-37,40 Karl Fischer titration and, 36 for lactoserum, 37

Water distribution in product, MRI and measurement of, 25

Watermelon, on top of a unilateral NMR magnet and radio frequency coil, 231

Wavelength dispersive X-ray fluorescence, 21-22

Wave propagation, description of, 47 Weighing parameters, response surfaces of

objective function for two different settings of, 314

Wet sieving, 168 Wheat

G/C against moisture content for, 329 tomographic flow measurement of, 337

Whey powder, calibration results for, 1021 White powder, in-process measurement of

size and number of dark particles in, 197, 202-204

Wiedemann, s. c., 249 Wilks' Lambda value, calculation of, 257 Williams, Phil, 68 Williams, R. w., 147 Wilson, R. H., 125 Wold, 1. P., 149, 157 Woven wire sieves, 167

X Xanthan gum, 187 Xing, H., 26 XPT-C systems, 198

concentrated submicron dispersions and, 204

detection of fibrous impurities in particle suspensions with, 205

schematic of, 200 XPT Particle Analysis instruments, 198 XPT-P probe, in-process installation,

204 XPT-P system, 198

schematic of, 199

y Yang, R. 1., 309 Yarrowia lipolylica, 133 Yogurt

calibration results for, 1091 NIR analysis and, 108-109 online monitoring of fermentation of,

268 Young, H., 268 Yu, C. X., 137

Z Zeroth moment, 224 Zhao, T. S., 294

zNose™, 239, 247 Zude, M., 27