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Table of contents
INTRODUCTION
1. CONTEXT AND OBJECTIVES
2. STRUCTURE OF THE MANUSCRIPT
A. Main contributions
B. List of the published works
C. List of communications
3. THE INTEREST OF POWDER IN THE FOOD INDUSTRY
4. NEAR-INFRARED SPECTROSCOPY
5. NEAR-INFRARED HYPERSPECTRAL IMAGING
D. Physical contaminations
E. Defects
F. Microbiological contaminations
6. THE PENETRATION AND THE DETECTION DEPTH OF NIR RADIATIONS
A. The penetration depth
B. The detection depth
7. THE DETECTION OF SUBPIXEL FOOD PARTICLES
A. Classification algorithms
B. Spectral similarities
C. Quantification methods
D. Unmixing methods
E. Subspace detector
I. THE DETECTION DEPTH OF A NEAR-INFRARED HYPERSPECTRAL IMAGING SYSTEM
1. INTRODUCTION
2. MATERIAL AND METHODS
A. Samples
B. Hyperspectral imaging system
C. Data processing
D. Thickness target values
E. Reflectance profile extraction
F. Partial Least-Squares Regression
3. RESULTS AND DISCUSSIONS
A. Reflectance evolution for each wavelength
B. Physical interpretation
C. Determination of the penetration depth
D. Partial Least-Squares regression results
4. ADDITIONAL DISCUSSIONS
A. The detection depth versus the penetration depth
B. The effective detection depth
C. The consequences of the detection depth
D. The parameters influencing the detection depth
5. CONCLUSION AND PERSPECTIVES
II. THE DETECTION OF PEANUT FLOUR USING THE MATCHED SUBSPACE DETECTOR
1. INTRODUCTION
2. MATERIAL AND METHODS
A. Samples
B. Hyperspectral imaging system
C. Data processing
D. Spectral simulation using Principal Component Analysis
E. Detection using the Matched Subspace Detector
F. Software
3. RESULTS AND DISCUSSIONS
A. Evaluation of data simulation for the detector design
B. Evaluation of the Matched Subspace Detector Algorithm
4. CONCLUSIONS
III. THE DETECTION OF PEANUT FLOUR IN CHOCOLATE POWDER USING MULTIVARIATE CURVE RESOLUTION
1. INTRODUCTION
2. MATERIAL AND METHODS
A. Sample preparation
B. Hyperspectral imaging system
C. Data Processing
D. Hyperspectral cube unfolding
E. Multivariate Curve Resolution – Alternating Least Squares
F. Detection algorithm
G. Software
3. RESULTS AND DISCUSSIONS
A. Principal Component Analysis
B. MCR-ALS
C. MCR-ALS-CSEL
D. The detection results
4. ADDITIONAL DISCUSSIONS
A. The pixel unmixing strategy
B. The detection sensitivity
C. The particle detection in hyperspectral images
5. CONCLUSION
CONCLUSION AND FUTURE WORK
1. CONCLUSION
2. FUTURE WORKS
APPENDICES
APPENDIX A: THE GAUSSIAN MIXTURE MODEL
APPENDIX B: THE MAHALANOBIS DISTANCE FOR OUTLIER DETECTION
REFERENCES
