Computer Vision Technology for Food Quality Evaluation

 
 
Academic Press
  • 2. Auflage
  • |
  • erschienen am 7. April 2016
  • |
  • 658 Seiten
 
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
978-0-12-802599-4 (ISBN)
 
Computer Vision Technology for Food Quality Evaluation, Second Edition continues to be a valuable resource to engineers, researchers, and technologists in research and development, as well as a complete reference to students interested in this rapidly expanding field. This new edition highlights the most recent developments in imaging processing and analysis techniques and methodology, captures cutting-edge developments in computer vision technology, and pinpoints future trends in research and development for food quality and safety evaluation and control. It is a unique reference that provides a deep understanding of the issues of data acquisition and image analysis and offers techniques to solve problems and further develop efficient methods for food quality assessment.
  • Thoroughly explains what computer vision technology is, what it can do, and how to apply it for food quality evaluation
  • Includes a wide variety of computer vision techniques and applications to evaluate a wide variety of foods
  • Describes the pros and cons of different techniques for quality evaluation
  • Englisch
  • San Diego
  • |
  • USA
Elsevier Science
  • 20,23 MB
978-0-12-802599-4 (9780128025994)
0128025999 (0128025999)
weitere Ausgaben werden ermittelt
  • Front Cover
  • Computer Vision Technology for Food Quality Evaluation
  • Computer Vision Technology for Food Quality Evaluation
  • Copyright
  • Contents
  • Contributors
  • About the Editor
  • Preface to the 2nd Edition
  • 01 - Fundamentals of Computer
  • 1 - Image Acquisition Systems
  • 1.1 Introduction
  • 1.2 The Electromagnetic Spectrum
  • 1.3 Image Acquisition Systems
  • 1.3.1 Computer Vision
  • 1.3.1.1 Illumination
  • 1.3.1.2 Electronics
  • 1.3.1.3 Scanning Technologies
  • 1.3.1.4 Three-Dimensional Imaging
  • 1.3.2 Ultrasound
  • 1.3.3 Infrared
  • 1.3.3.1 Cooled Infrared Detectors
  • 1.3.3.2 Uncooled Infrared Detectors
  • 1.3.4 Tomographic Imaging
  • 1.3.4.1 Nuclear Tomography
  • 1.3.4.1.1 Computed Tomography
  • 1.3.4.1.2 Magnetic Resonance Imaging
  • 1.3.4.2 Electrical Tomography
  • 1.4 Future Trends
  • Nomenclature
  • References
  • 2 - Image Segmentation Techniques
  • 2.1 Introduction
  • 2.2 Preprocessing Techniques
  • 2.2.1 Noise Removal
  • 2.2.1.1 Linear Filter
  • 2.2.1.1.1 Gaussian Filter
  • 2.2.1.1.2 High/Low Pass Filter
  • 2.2.1.1.3 Wiener Filter
  • 2.2.1.2 Median Filter
  • 2.2.1.2.1 Standard Median Filter
  • 2.2.1.2.2 Hybrid Median Filter
  • 2.2.1.2.3 Progressive Switching Median Filter
  • 2.2.1.2.4 Adaptive Median Filter
  • 2.2.2 Contrast Enhancing
  • 2.2.2.1 Histogram Scaling
  • 2.2.2.2 Histogram Equalization
  • 2.3 Segmentation Techniques
  • 2.3.1 Thresholding-Based Segmentation
  • 2.3.1.1 Fundamentals of Thresholding-Based Segmentation
  • 2.3.1.2 Threshold Selection
  • 2.3.1.2.1 Manual Selection
  • 2.3.1.2.2 Isodata Algorithm
  • 2.3.1.2.3 Objective Function
  • 2.3.1.2.4 Histogram Clustering
  • 2.3.1.2.5 Other Techniques
  • 2.3.1.3 Image Closing and Opening
  • 2.3.2 Edge-Based Segmentation
  • 2.3.3 Region-Based Segmentation
  • 2.3.4 Gradient-Based Segmentation
  • 2.3.4.1 Fundamentals of Gradient-Based Segmentation
  • 2.3.4.2 Gradient Operator
  • 2.3.4.3 Laplace Operator
  • 2.3.4.4 Other Techniques
  • 2.3.5 Classification-Based Segmentation
  • 2.3.5.1 Fundamentals of Classification-Based Segmentation
  • 2.3.5.2 Features Extraction
  • 2.3.5.3 Classification Methods
  • 2.3.5.3.1 Dimension Reduction
  • 2.3.5.3.2 Classification
  • 2.3.6 Other Segmentation Techniques
  • 2.3.6.1 Watershed
  • 2.3.6.2 Hybrid-Based Segmentation
  • Nomenclature
  • Greek Symbols
  • List of Abbreviations
  • References
  • 3 - Object Measurement Methods
  • 3.1 Introduction
  • 3.2 Size
  • 3.3 Shape
  • 3.3.1 Size-Dependent Measurement
  • 3.3.2 Size-Independent Measurement
  • 3.3.2.1 Region-Based
  • 3.3.2.2 Boundary-Based
  • 3.3.2.2.1 Boundary Representation
  • 3.3.2.2.2 Boundary Analysis and Classification
  • 3.3.2.2.2.1 Fourier Transform
  • 3.3.2.2.2.2 Autoregressive Models
  • 3.4 Color
  • 3.4.1 Hardware-Orientated
  • 3.4.2 Human-Orientated
  • 3.4.3 Instrumental
  • 3.5 Texture
  • 3.5.1 Statistical Methods
  • 3.5.1.1 Co-occurrence matrix
  • 3.5.1.2 Run-length matrix
  • 3.5.1.3 Neighboring dependence matrix
  • 3.5.2 Structural Methods
  • 3.5.3 Transform-Based Methods
  • 3.5.3.1 Convolution mask
  • 3.5.3.2 Fourier transform
  • 3.5.3.3 Wavelet transform
  • 3.5.4 Model-Based Methods
  • 3.5.4.1 Fractal model
  • 3.5.4.2 Autoregressive model
  • 3.6 Combined Measurements
  • Nomenclatures
  • Greek
  • Subscripts
  • List of Abbreviation
  • Appendix
  • Statistical Measurements of Co-occurrence Matrix
  • Statistical Measurements of Run-Length Matrix
  • Statistical Measurements of Neighboring Dependence Matrix
  • References
  • 4 - Object Classification Methods
  • 4.1 Introduction
  • 4.2 Artificial Neural Network
  • 4.2.1 Structure of Neural Network
  • 4.2.2 Learning Process
  • 4.3 Statistical Classification (SC)
  • 4.3.1 Bayesian Classification
  • 4.3.2 Discriminant Analysis
  • 4.3.3 Nearest Neighbor
  • 4.4 Fuzzy Logic
  • 4.4.1 Creating Fuzzy Sets and Membership Functions
  • 4.4.1.1 Fuzzy Set
  • 4.4.1.2 Membership Function
  • 4.4.2 Constructing Fuzzy Rule Base
  • 4.4.3 Producing Fuzzy Outputs and Defuzzification
  • 4.5 Decision Tree
  • 4.6 Support Vector Machine
  • 4.6.1 Binary Classification
  • 4.6.2 Multiclassification
  • Nomenclature
  • References
  • 5 - Introduction to Hyperspectral Imaging Technology
  • 5.1 Introduction
  • 5.2 Fundamentals of Hyperspectral Imaging Technology
  • 5.3 Multivariate Data Analysis
  • 5.3.1 Spectral Preprocessing
  • 5.3.2 Development of Multivariate Calibration
  • 5.3.2.1 Quantitative models
  • 5.3.2.2 Qualitative models
  • 5.3.3 Model Validation and Evaluation
  • 5.3.4 Selection of Important Wavelengths
  • 5.3.5 Multivariate Image Analysis
  • 5.4 Hyperspectral Image Analysis Software
  • 5.5 Application of HSI for Muscle Foods
  • 5.5.1 Beef
  • 5.5.2 Pork
  • 5.5.3 Lamb
  • 5.5.4 Chicken
  • 5.5.5 Turkey
  • 5.5.6 Fish
  • References
  • 6 - Introduction to Raman Chemical Imaging Technology
  • 6.1 Introduction
  • 6.2 Principles of Raman Scattering
  • 6.3 Raman Spectroscopy Techniques
  • 6.3.1 Backscattering Raman Spectroscopy
  • 6.3.2 Transmission Raman Spectroscopy
  • 6.3.3 Spatially Offset Raman Spectroscopy
  • 6.3.4 Surface-Enhanced Raman Spectroscopy
  • 6.3.5 Other Raman Techniques
  • 6.4 Raman Chemical Imaging and Acquisition Methods
  • 6.5 Raman Imaging Instruments
  • 6.5.1 Major Components of Raman Imaging Systems
  • 6.5.1.1 Excitation sources
  • 6.5.1.2 Wavelength separation devices
  • 6.5.1.3 Detectors
  • 6.5.2 Raman Imaging Systems and Calibrations
  • 6.6 Raman Image Analysis Techniques
  • 6.6.1 Image Preprocessing
  • 6.6.2 Target Identification
  • 6.6.3 Mapping and Quantitative Analysis
  • 6.7 Applications for Food and Agricultural Products
  • References
  • 02 - Quality Evaluation of Meat, Poultry and Seafood
  • 7 - Quality Evaluation of Meat Cuts
  • 7.1 Introduction
  • 7.2 Quality Evaluation Using Computer Vision
  • 7.2.1 Beef Quality, Yield Grade, Composition, and Tenderness
  • 7.2.2 Pork Color, Marbling, Grade, and Composition
  • 7.2.3 Poultry Inspection, Contaminant Detection, and Composition
  • 7.2.4 Lamb Yield Grade and Tenderness
  • 7.3 Quality Evaluation Using Hyperspectral Imaging
  • 7.3.1 Beef Tenderness, Microbial Spoilage, and Composition
  • 7.3.2 Pork Grading, Composition, and Microbial Spoilage
  • 7.3.3 Poultry Classification, Contaminant Detection, and Composition
  • 7.3.4 Lamb Classification, Composition, and Tenderness
  • 7.4 Future Work
  • References
  • 8 - Quality Measurement of Cooked Meats
  • 8.1 Introduction
  • 8.2 Shrinkage
  • 8.2.1 Size and Shape Measurements
  • 8.2.1.1 Measurements of Average Diameter, Short Axis, Long Axis, and Perimeter
  • 8.2.1.2 Measurements of Surface Area and Volume
  • 8.2.2 Shrinkage Determination and Its Relations With Yield, Water Content, and Texture
  • 8.2.2.1 Shrinkage Determination
  • 8.2.2.2 Correlations With Yield, Water Content, and Texture
  • 8.3 Pores and Porosity
  • 8.3.1 Measurement of Pores and Porosity
  • 8.3.2 Correlations With Water Content, Processing Time, and Texture
  • 8.4 Color
  • 8.4.1 Color Measurement
  • 8.4.2 Correlation With Water Content
  • 8.5 Image Texture
  • 8.5.1 Extraction of Image Texture Features
  • 8.5.2 Correlations With Tenderness
  • Nomenclature
  • References
  • 9 - Quality Evaluation of Poultry Carcass
  • 9.1 Introduction
  • 9.2 Poultry Quality Inspection
  • 9.3 Color Imaging for Quality Inspection
  • 9.3.1 Detection of Splenomegaly
  • 9.3.2 Viscera Inspection
  • 9.3.3 Wholesomeness Inspection
  • 9.4 Spectral Imaging
  • 9.4.1 Quality Characterization
  • 9.4.1.1 Spectral Characterization of Poultry Carcasses
  • 9.4.2 Skin Tumor Detection
  • 9.4.3 Systemic Disease Detection
  • 9.4.4 Heart Disease Detection
  • 9.4.5 Systemic Disease Identification
  • 9.4.6 Quality Inspection by Dual-Band Spectral Imaging
  • 9.5 Poultry Image Classifications
  • 9.5.1 Air Sacs Classification by Learning Vector Quantization
  • 9.5.2 Quality Classification by Texture Analysis
  • 9.5.2.1 Spectral Poultry Image Classification in Frequency Domain
  • 9.5.2.2 Fast Power Spectra of Spectral Images
  • 9.5.2.3 Fractal Analysis
  • 9.5.2.4 Neural Network Models
  • 9.5.2.4.1 Spectral Poultry Image Data for Neural Network Models
  • 9.5.2.4.2 Neural Network Pattern Classification
  • 9.5.3 Supervised Algorithms for Hyperspectral Image Classification
  • 9.5.3.1 Hyperspectral Imaging System
  • 9.5.3.2 Classification Methods
  • 9.5.3.3 Hyperspectral Image Characteristics for Classification
  • 9.5.3.4 Comparison of Classification Methods
  • 9.5.3.5 Accuracy of Classifiers for Contaminant Identification
  • 9.5.3.6 Technology Trends for Food Quality and Safety Evaluation
  • 9.5.3.6.1 Real-Time Hyperspectral Imaging System
  • 9.5.3.6.2 Transportable Hyperspectral Imaging System
  • 9.5.3.6.3 Hand-Held Multispectral Imaging Instrument
  • 9.5.3.6.4 Hyperspectral Microscope Imaging System
  • References
  • 10 - Quality Evaluation of Seafoods
  • 10.1 Introduction
  • 10.1.1 Developments in New Hardware and Technologies
  • 10.1.2 Central Processing Unit (CPU): Computing Power
  • 10.1.3 Graphical Processing Unit: Accelerated Computing
  • 10.1.4 New Camera Technology
  • 10.1.4.1 High-Speed 3-D Line-Scan Cameras
  • 10.1.4.2 RGB-D Cameras
  • 10.1.4.3 Deep Learning
  • 10.2 New Methods
  • 10.2.1 Hyperspectral Imaging
  • 10.2.2 Sorting of Cod Roe, Liver, and Milt
  • 10.2.3 X-Rays
  • 10.2.4 Two Image Method
  • 10.3 Color
  • 10.3.1 Raw Seafood
  • 10.3.2 Processed Seafood
  • 10.3.3 Color of Gills and Eyes
  • 10.3.4 Quality, Gaping, Defects
  • 10.3.5 Area, Volume, and Shape
  • 10.3.6 View Area Versus Weight
  • 10.4 Automation
  • 10.4.1 Robot-Based Posttrimming of Salmon Fillets
  • 10.4.2 Automated Sorting of Pelagic Fish Based on 3-D Machine Vision
  • 10.5 Conclusion and Outlook
  • 10.5.1 Fusion of Sensor Data
  • 10.5.2 Dense 3-D Point Cloud Image Maps
  • 10.5.3 Dense 3-D Visual Servoing of Robots
  • 10.5.4 Robot-Based Automation
  • 10.5.5 Big Data, Cloud Computing, and Cloud Robotics
  • 10.5.6 Early Differentiation and Sorting
  • 10.5.7 Flexible and Raw Material-Adapted Handling and Processing
  • References
  • Further Reading
  • 03 - Quality Evaluation of Fruit
  • 11 - Quality Evaluation of Apples
  • 11.1 Introduction
  • 11.1.1 Apple Production
  • 11.1.2 Necessity for Quality Evaluation
  • 11.1.3 Computer Vision Technologies for Quality Evaluation
  • 11.2 Detection of Surface Defects
  • 11.3 Detection of Internal Defects
  • 11.3.1 Watercore
  • 11.3.2 Internal Browning
  • 11.4 Evaluation of Texture and Flavor
  • 11.4.1 Firmness
  • 11.4.2 Mealiness
  • 11.4.3 Soluble Solids Content
  • 11.5 Quality Evaluation Based on Optical Properties of Apples
  • 11.6 In-Orchard Sorting and Grading of Apples
  • 11.7 Future Trends
  • References
  • 12 - Quality Evaluation of Citrus Fruits
  • 12.1 Introduction
  • 12.1.1 Economic Importance of Citrus Production
  • 12.1.2 Physiological and Physicochemical Characteristics of Citrus Fruits that Affect Their Inspection
  • 12.1.3 Quality Features to be Inspected in Citrus Fruits
  • 12.1.4 Major Defects and Diseases Found in Citrus Fruits
  • 12.1.5 The Citrus Inspection Line
  • 12.2 Analysis of Visible Images for Automatic Citrus Fruit Inspection
  • 12.2.1 Preparation of the Scene
  • 12.2.2 Defect Detection
  • 12.2.3 Identification of Defects
  • 12.2.4 Automated Inspection of Citrus in Packing Lines
  • 12.2.5 Mobile Platforms
  • 12.3 Quality Inspection Using Nonstandard Computer Vision
  • 12.3.1 Detection of Rottenness
  • 12.3.2 Detection of Citrus Canker
  • 12.3.3 Detection of Other Skin Defects
  • 12.4 Internal Quality Inspection
  • References
  • 13 - Quality Evaluation of Strawberry
  • 13.1 Introduction
  • 13.1.1 Overview of Strawberries
  • 13.1.2 Necessity of Quality Measurement
  • 13.1.3 Computer Vision Technologies for Quality Measurement
  • 13.2 Grading of Size, Shape, and Ripeness
  • 13.2.1 Standards for Quality Grades
  • 13.2.2 Preliminary Study for Size and Shape Judgment
  • 13.2.3 Advance Techniques for Size and Shape Judgment
  • 13.2.4 Grading of Ripeness
  • 13.3 Detection of Bruises and Fecal Contamination
  • 13.3.1 Importance of Detecting Bruises
  • 13.3.2 Color Imaging for Bruise Detection
  • 13.3.3 NIR Imaging for Bruise Detection
  • 13.3.4 Hyperspectral Imaging for Bruise Detection
  • 13.3.4.1 Hyperspectral Imaging Setup and Data Analysis
  • 13.3.4.2 Detection of Bruises
  • 13.3.4.3 Detection of Fecal Contamination
  • 13.4 Estimation of Firmness and Soluble Solids Content
  • 13.4.1 Importance of Measurement of Internal Quality
  • 13.4.2 Measurement of Firmness
  • 13.4.3 Measurement of Soluble Solids Content
  • 13.4.4 Estimation of Anthocyanin Distribution
  • 13.5 Further Challenges
  • References
  • 14 - Classification and Quality Evaluation of Table Olives
  • 14.1 Introduction
  • 14.2 Table Olive Classification
  • 14.2.1 Production Process
  • 14.2.2 Classification by Quality
  • 14.2.2.1 External Quality
  • 14.2.2.2 Internal Quality
  • 14.2.3 Industrial Needs of Table Olive Producers
  • 14.3 Application of Computer Vision
  • 14.3.1 Conventional Machine Vision
  • 14.3.2 Near Infrared Vision
  • 14.3.3 X-Ray to Detect Internal Defects and Stone Fruits
  • 14.4 Industrial Applications
  • References
  • 15 - Grading of Potatoes
  • 15.1 Introduction
  • 15.2 Surface Potato Defects
  • 15.3 Potato Classification
  • 15.4 Applications
  • 15.4.1 Automated Defect Detection
  • 15.4.1.1 Online sorting
  • 15.4.1.2 Bruise and green spot detection
  • 15.4.2 Machine Vision System
  • 15.4.3 Characterization of Potato Defects
  • 15.4.4 Algorithm Design
  • Acknowledgments
  • References
  • Further Reading
  • 04 - Quality Evaluation of Grains
  • 16 - Wheat Quality Evaluation
  • 16.1 Introduction
  • 16.2 Machine Vision
  • 16.2.1 Context for Wheat Quality Monitoring
  • 16.2.2 Area-Scan Imaging
  • 16.2.3 Line-Scan Imaging
  • 16.2.4 Sample Presentation Devices
  • 16.2.5 Development of Separation Algorithms
  • 16.2.6 Morphological, Color, and Textural Algorithms
  • 16.2.6.1 Morphological Features
  • 16.2.6.2 Gray Scale and Color Features
  • 16.2.6.3 Textural Features
  • 16.2.6.4 Testing and Optimization
  • 16.3 Soft X-Ray Imaging
  • 16.3.1 Soft X-Rays for Insect Infestation Detection in Grain
  • 16.4 Near Infrared Spectroscopy (NIRS) and Hyperspectral Imaging
  • 16.4.1 Measurement of Near Infrared Radiation
  • 16.4.2 Near Infrared Spectroscopy Instrumentation
  • 16.4.3 Near Infrared Hyperspectral Imaging
  • 16.4.4 Application of Near Infrared Spectroscopy and Hyperspectral Imaging Systems
  • 16.5 Thermal Imaging
  • 16.5.1 Application of Thermal Imaging
  • 16.6 Potential Practical Applications
  • 16.6.1 Automation of Railcar Unloading
  • 16.6.2 Optimization of Grain Cleaning
  • 16.6.3 Quality Monitoring of Export Grains
  • 16.6.4 Detection of Low-Level Infestation
  • References
  • 17 - Quality Evaluation of Rice
  • 17.1 Introduction
  • 17.2 Quality of Rice
  • 17.3 Quality Evaluation of Raw Rice
  • 17.3.1 Morphological Features
  • 17.3.1.1 Geological Classification
  • 17.3.1.2 Segmentation of Touching Kernels
  • 17.3.1.3 Automatic Inspection
  • 17.3.1.4 Algorithm for Kernel Segmentation
  • 17.3.2 Surface and Structural Traits
  • 17.3.2.1 Polished Surface
  • 17.3.2.2 Broken Kernel
  • 17.3.2.3 Fissure and Crack
  • 17.3.2.4 Chalkiness
  • 17.3.2.5 Surface Color
  • 17.3.3 Moisture and Compound Distributions
  • 17.3.3.1 Moisture Distribution
  • 17.3.3.2 Compound Distribution
  • 17.3.4 Defect Detection
  • 17.4 Quality Evaluation of Cooked Rice
  • 17.4.1 Changes in Water Distribution During Soaking
  • 17.4.1.1 Visible Approaches
  • 17.4.1.2 Magnetic Resonance Imaging Approaches
  • 17.4.2 Water Migration and Structural Changes During Boiling
  • 17.4.2.1 Magnetic Resonance Imaging
  • 17.4.2.2 Other Visualizations
  • 17.4.3 Grain Structure of Cooked Rice
  • 17.5 Quality Evaluation of Rice-Related Products
  • References
  • 18 - Quality Evaluation of Corn/Maize
  • 18.1 Introduction
  • 18.1.1 Whole Seed Analysis for Type
  • 18.1.2 Internal Seed Characteristics
  • 18.1.3 Relating Seed Morphometry to Quality
  • 18.1.4 Assessing Seed Quality Indirectly
  • 18.1.5 Adding Color into the Analysis
  • 18.1.6 The Analysis Is Only as Good as the Sample
  • 18.1.7 Integration and Automation of Analysis
  • 18.2 Corn
  • 18.2.1 Use of Corn
  • 18.2.2 Corn Grading
  • 18.3 Machine Vision Determination of Corn Quality
  • 18.3.1 Color
  • 18.3.2 Size and Shape
  • 18.3.3 Breakage
  • 18.3.4 Stress Cracks
  • 18.3.5 Heat Damage
  • 18.3.6 Mold and Fungal Contamination
  • 18.3.7 Hardness or Vitreousness
  • 18.3.8 Seed Viability
  • 18.3.9 Other Applications
  • 18.3.10 Changing Directions
  • References
  • Further Reading
  • 05 - Quality Evaluation of Other Foods
  • 19 - Quality Evaluation of Pizzas
  • 19.1 Introduction
  • 19.2 Pizza Base Production
  • 19.2.1 Feature Extraction
  • 19.2.1.1 Size
  • 19.2.1.2 Shape
  • 19.2.1.3 Color
  • 19.2.2 Classification
  • 19.3 Pizza Sauce Spread
  • 19.3.1 Color Feature Extraction
  • 19.3.2 Classification
  • 19.3.2.1 Fuzzy Logic
  • 19.3.2.2 Support Vector Machine
  • 19.4 Pizza Toppings Applied
  • 19.4.1 Evaluating Color
  • 19.4.1.1 Color Feature Extraction
  • 19.4.1.2 Classification
  • 19.4.2 Evaluating Topping Percentage and Distribution
  • 19.4.2.1 Pizza Topping Segmentation
  • 19.4.2.2 Topping Distribution Determination
  • 19.4.2.3 Evaluating Cheese Quality As Pizza Topping
  • Nomenclature
  • References
  • 20 - Cheese Quality Evaluation
  • 20.1 Introduction
  • 20.2 Cheese Quality Attributes
  • 20.2.1 Physical Attributes
  • 20.2.1.1 Appearance
  • 20.2.1.2 Inclusions
  • 20.2.1.3 Pizza Quality
  • 20.2.2 End-Use Qualities
  • 20.2.2.1 Meltability
  • 20.2.2.2 Browning
  • 20.2.2.3 Blister Formation
  • 20.2.2.4 Oiling-Off
  • 20.2.3 Cheese Shred Morphology and Integrity
  • 20.2.3.1 Image Thinning and Skeletonization Algorithm
  • 20.2.3.2 X-Y Sweep Algorithm
  • 20.2.4 Cheese Defects
  • 20.2.4.1 Calcium Lactate Crystals
  • 20.2.4.2 Mechanical Openings
  • 20.2.5 Microstructure Evaluation
  • 20.2.5.1 Analysis of SEM Micrographs
  • 20.2.5.2 3-D Cheese Microstructure Evaluation Using CLSM
  • 20.2.5.3 Dynamic 4-D Microstructure Evaluation
  • References
  • 21 - Quality Evaluation of Bakery Products
  • 21.1 Introduction
  • 21.2 Quality Characteristics of Bakery Products
  • 21.2.1 Color
  • 21.2.2 Rheological and Textural Properties
  • 21.3 Computer Vision Inspection of Bakery Products
  • 21.3.1 Color Inspection
  • 21.3.2 Shape and Size Inspection
  • 21.3.3 Crack Inspection
  • 21.3.4 Texture Inspection
  • Nomenclature
  • References
  • Further Reading
  • 22 - Quality Evaluation and Control of Potato Chips
  • 22.1 Introduction
  • 22.2 Computer Vision
  • 22.3 Image Features
  • 22.4 Applications
  • 22.5 Fried Potato Sorting
  • 22.5.1 Browning Sorting Using Artificial Neural Networks (ANN) by CARAH (Centre pour l'agronomie et l'agro-industrie de la Provinc ...
  • 22.5.2 Browning Sorting Without ANN (Walloon Agricultural Research Center, Belgium)
  • 22.5.3 Browning Sorting and Acrylamide Estimation Using ANN by CARAH
  • Acknowledgments
  • References
  • Further Reading
  • Index
  • A
  • B
  • C
  • D
  • E
  • F
  • G
  • H
  • I
  • J
  • K
  • L
  • M
  • N
  • O
  • P
  • Q
  • R
  • S
  • T
  • U
  • V
  • W
  • X
  • Y
  • Z
  • Back Cover

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