Index [lib3.dss.go.th]lib3.dss.go.th/fulltext/index/663-665/664.117non.pdfIndex. A AAS. See...
Transcript of Index [lib3.dss.go.th]lib3.dss.go.th/fulltext/index/663-665/664.117non.pdfIndex. A AAS. See...
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