All uses, reproduction and distribution, including without limitation commercial reprints, selling or licensing copies or access, or posting on open internet sites, your personal or institution’s website or repository, are prohibited. For exceptions, permission may be sought for such use through Elsevier’s
permissions site at:
https://www.elsevier.com/about/our-business/policies/copyright/permissions
P.Y. Sacre, C. De Bleye, P.F. Chavez, L. Netchacovitch, P. Hubert and E. Ziemons (2014) Data Processing of Vibrational Chemical Imaging for Pharmaceutical Applications. J. Pharm. Biomed. Anal. 101 123-140
J.M. Amigo (2010) Practical Issues of Hyperspectral Imaging Analysis of Solid Dosage Forms. Anal. Bioanal. Chem. 398 93-109
D. Caballero, M. Bevilacqua and J.M. Amigo (2019) Application of Hyperspectral Imaging and Chemometrics for Classifying Plastics With Brominated Flame Retardants. J. Spectral Imaging 8 1-16
M. Vidal, A. Gowen and J.M. Amigo (2012) NIR Hyperspectral Imaging for Plastics Classification. NIR News 23(1), 1-13
R. Levenson and J.R. Mansfield (2006) Multispectral Imaging in Biology and Medicine: Slices of Life. Cytometry A 69(8), 748-758
M. Pisani, M. Zucco, V. Caricato and A. Egidi (2013) Hyperspectral Imaging: A Tool for Biological Measurements. Proceedings of the 16th International Congress of Metrology 14007
G. Lu and B. Fei (2014) Medical Hyperspectral Imaging: A Review. J. Biomed. Opt. 19(1), 10.1117/1.JBO.19.1.010901
G.J. Edelman, E. Gaston, T.G. van Leeuwen, P.J. Cullen and M.C. Aalders (2012) Hyperspectral Imaging for Non-Contact Analysis of Forensic Traces. Forensic Sci. Int. 223(1–3), 28-39
J. Linderholm, J.A. Fernández Pierna and V. Baeten (2013) NIR Hyperspectral Image Analysis in Archaeology—Analysing Bone Matter in Soils and Sediments. J. Near Infrared Spectrosc. 21 459-466
D. Vincke, R. Miller, E. Stassart, M. Otte, P. Dardenne, M. Collins, K. Wilkinson, J. Stewart, V. Baeten and J.A. Fernández Pierna (2014) Analysis of Collagen Preservation in Bones Recovered in Archaeological Contexts Using NIR Hyperspectral Imaging. Talanta 125 181-188
V. Baeten and P. Dardenne (2005) Applications of Near-Infrared Imaging for Monitoring Agricultural Food and Feed Products. R. Bhargava, I.W. Levin (Eds) Spectrochemical Analysis Using Infrared Multichannel Detectors Oxford, UK: Blackwell Publishing Ltd
V. Baeten, J.A. Fernández Pierna and P. Dardenne (2005) Hyperspectral Imaging Techniques: An Attractive Solution for the Analysis of Biological and Agricultural Materials. H.F. Grahn, P. Geladi (Eds) Techniques and Applications of Hyperspectral Image Analysis Chichester, UK: John Wiley & Sons Ltd
J. Mendez, L. Mendoza, J.P. Cruz-Tirado, R. Quevedo and R. Siche (2019) Trends in Application of NIR and Hyperspectral Imaging for Food Authentication. Sci. Agropecu. 10(1), 143-161
J.C. Russ (1999) The Image Processing Handbook. 3rd ed. Boca Raton, Florida: CRC Press LLC 771 p
J.M. Amigo, H. Babamoradi and S. Elcoroaristizabal (2015) Hyperspectral Image Analysis. A tutorial. Anal. Chim. Acta 896 34-51
D. Liu, D.W. Sun and X.A. Zeng (2013) Recent Advances in Wavelength Selection Techniques for Hyperspectral Image Processing in the Food Industry. Food Bioproc. Tech. 7(2), 307-323
B. Boldrini, W. Kessler, K. Rebner and R. Kessler (2012) Hyperspectral Imaging: A Review of Best Practice, Performance and Pitfalls for In-Line and On-Line Applications. J. Near Infrared Spectrosc. 20(5), 483-508
R.C. Lyon, D.S. Lester, E.N. Lewis, E. Lee, L.X. Yu, E.H. Jefferson and A.S. Hussain (2002) Near-Infrared Spectral Imaging for Quality Assurance of Pharmaceutical Products: Analysis of Tablets to Assess Powder Blend Homogeneity. AAPS PharmSciTech 3(3), article 17
G. Reich (2005) Near-Infrared Spectroscopy and Imaging: Basic Principles and Pharmaceutical Applications. Adv. Drug Deliv. Rev. 57 1109-1143
R. Lu (2003) Detection of Bruises on Apples Using Near-Infrared Hyperspectral Imaging. Trans. ASAE 46(2), 523-530
P.M. Mehl, K. Chao, M. Kim and Y.R. Chen (2002) Detection of Defects on Selected Apple Cultivars Using Hyperspectral and Multispectral Image Analysis. Appl. Eng. Agric. 18(2), 219-226
J.A. Fernández Pierna, V. Baeten, A. Michotte Renier, R.P. Cogdill and P. Dardenne (2004) Combination of Support Vector Machines (SVM) and Near Infrared (NIR) Imaging Spectroscopy for the Detection of Meat and Bone Meat (MBM) in Compound Feeds. J. Chemometr. 18 341-349
J.A. Fernández Pierna, V. Baeten and P. Dardenne (2006) Screening of Compound Feeds Using NIR Hyperspectral Data. Chemom. Intel. Lab. Syst. 84 114-118
M.A. Veronin and B.C. Youan (2004) Magic Bullet Gone Astray: Medications and the Internet. Science 305 481. 23
J. Dubois, J.C. Wolff, J.K. Warrack, J. Schoppelrei and E.N. Lewis (2007) NIR Chemical Imaging for Counterfeit Pharmaceutical Products Analysis. Spectroscopy 22(2), 40-50
E. Lee, W.X. Huang, P. Chen, E.N. Lewis and R.V. Vivilecchia (2005) High-Throughput Analysis of Pharmaceutical Tablet Content Uniformity by Near-Infrared Chemical Imaging. Spectroscopy 21(11), 25-32
E.N. Lewis, J. Schoppelrei and E. Lee (2004) Near-Infrared Chemical Imaging and the PAT Initiative. Spectroscopy 19(4), 22-31
D.L.M. Massart, B.G.M. Vandeginste, L.M.C. Buydens, S. De Jong, J.P. Lewi and J. Smeyers-Verbeke (1988) Chemometrics: A Textbook. Vol. 2 Amsterdam, The Netherlands: Elsevier
B.G.M. Vandeginste, D.L. Massart, L.M.C. Buydens, S. De Jong, P.J. Lewi and J. Smeyers-Verbeke (1998) B.G.M. Vandeginste, S.C. Rutan (Eds) Handbook of Chemometrics and Qualimetrics: Part B Amsterdam, The Netherlands: Elsevier 87-160. Chapter 31
J. Burger and P. Geladi (2005) Hyperspectral NIR Image Regression Part I: Calibration and Correction. J. Chemometr. 19 355-363
X. Cheng, Y.R. Chen, Y. Tao, C.Y. Wang, M.S. Kim and A.M. Lefcourt (2004) A Novel Integrated PCA and FLD Method on Hyperspectral Image Feature Extraction for Cucumber Chilling Damage Inspection. ASAE Trans. 47(4), 1313-1320
D.A. Forchetti and R.J. Poppi (2019) Detection and Quantification of Adulterants in Roasted and Ground Coffee by NIR Hyperspectral Imaging and Multivariate Curve Resolution. Food Anal. Methods 1-6. 10.1007/s12161-019-01502-x
X. Zhang, A. de Juan and R. Tauler (2015) Multivariate Curve Resolution Applied to Hyperspectral Imaging Analysis of Chocolate Samples. Appl. Spectrosc. 69(8), 993-1003
H. Martens and T. Naes (1989) Multivariate Calibration. 2nd ed. Vol. 1 Chichester, UK: Wiley
F. Despagne and D.L. Massart (1998) Neural Networks in Multivariate Calibration. Analyst 123 157R-178R
T. Naes, T. Isaksson, T. Fearn and T. Davies (2002) A User Friendly Guide to Multivariate Calibration and Classification. Chichester, UK: NIR Publications
R.P. Codgill and P. Dardenne (2004) Least-Squares Support Vector Machines for Chemometrics: An Introduction and Evaluation. J. Near Infrared Spectrosc. 12(1), 93-100
P. Dardenne and J.A. Fernández Pierna (2008) Soil Parameter Quantification by NIRS as a Chemometric Challenge at ‘Chimiométrie 2006’. Chemom. Intel. Lab. Syst. 91 94-98
M.J. De la Haba, J.A. Fernández Pierna, O. Fumière, A. Garrido-Varo, J.E. Guerrero, D.C. Pérez-Marín, P. Dardenne and V. Baeten (2006) Discrimination of the Class Origin of Bones Present in the Sediment Fraction of Animal By-Products Using Near Infrared Microscopy (NIRM). G.R. Burling-Claridge, S.E. Holroyd, R.M.W. Sumner (Eds) Proceedings of the NIR2005; NIR Publications: New Zealand, 2005. Near Infrared Spectroscopy: Proceedings of the 12th International Conference New Zealand: Near Infrared Spectroscopy Society Incorporated
Sá Otero, P. Improved Classification of Pollen Texture Images Using SVM and MLP. In 3rd IASTED Int. Conf. on Visualization, Imaging and Image Processing (VIIP2003); Fernández Delgado, M., Carrion Pardo, P., Cernadas García, E., Gálvez Gálvez, J. F., Hamza, M. H., Eds.; Benidorm, Spain, 2003; Vol. 2, pp 686–691
S. Zhong, C. Chang and Y. Zhang (2018) Iterative Support Vector Machine for Hyperspectral Image Classification. 25th IEEE International Conference on Image Processing (ICIP), Athens
T.A. Moughal (2013) Hyperspectral Image Classification Using Support Vector Machine. J. Phys.: Conf. Ser. 439 1-9
M.C.A. Marcelo, F.L.F. Soares, J.A. Ardila, J.C. Dias, R. Pedó, S. Kaiser, O.F.S. Pontes, C.E. Pulcinellia and G.P. Sabina (2019) Fast Inline Tobacco Classification by near-Infrared Hyperspectral Imaging and Support Vector Machine-Discriminant Analysis. Anal. Methods 11 1966-1975
Z. Jiang and S. Shekhar (2017) Spatial Big Dat Science—Classification Techniques for Earth Observation Imagery. Cham, Switzerland: Springer International Publishing
M. Ester, H.P. Kriegel, J. Sander and X. Xu (1996) A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases With Noise. KDD-96 Proceedings 226-231
M. Daszykowski, B. Walczak and D.L. Massart (2001) Looking for Natural Patterns in Data: Part 1. Density-Based Approach. Chemom. Intell. Lab. Syst. 56 83-92
R. Salzer and H.W. Siesler (2015) Infrared and Raman Spectroscopic Imaging. Anal. Bioanal. Chem. 407(19), 5551-5552
F. Tsai, E.K. Lin and K. Yoshino (2007) Spectrally Segmented Principal Component Analysis of Hyperspectral Imagery for Mapping Invasive Plant Species. Int. J. Remote Sens. 28(5), 1023-1039
J. Burger (2009) Bad Pixel Detection in Hyperspectral Staring Camera Systems. NIR News 20 9-12
P. Kubelka and F. Munk (1931) Ein Beitrag zur Optik der Far-banstriche. Z. Tech. Phys. 12 593-604
A. Savitzky and M. Golay (1964) Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Anal. Chem. 36 1627-1639
D. Hopkins (2001) Derivatives in Spectroscopy. Near Infrared Anal. 2 1-13
F. Giesbrecht, W. McClure and A. Hamid (1981) The Use of Trigonometric Polynomials to Approximate Visible and Near Infrared Spectra of Agricultural Products. Appl. Spectrosc. 35 210-214
K. Norris and P. Williams (1984) Optimization of Mathematical Treatments of Raw Near-Infrared Signal in the Measurement of Protein in Hard Red Spring Wheat. Influence of Particle Size. Cereal Chem. 61 158-165
P. Geladi (1983) Multivariate Linearity Transformations for Near-Infrared Spectrometry. H. Martens, S. Jensen, O. Christie (Eds) Proceedings of the Nordic Symposium on Applied Statistics Stavanger: Stokkand Forlag 205-233
P. Geladi, D. McDougall and H. Martens (1985) Linearization and Scatter-Correction for Near-Infrared Reflectance Spectra of Meat. Appl. Spectrosc. 39 491-500
T. Isaksson and B. Kowalski (1993) Piece-Wise Multiplicative Scatter Correction Applied to Near-Infrared Diffuse Transmittance Data From Meat Products. Appl. Spectrosc. 47 702-709
R. Barnes, M. Dhanoa and S. Lister (1989) Standard Normal Variate Transformation and Detrending of Near Infrared Diffuse Reflectance. Appl. Spectrosc. 43 772-777
M. Dhanoa, S. Lister, R. Sanderson and R. Barnes (1994) The Link Between Multiplicative Scatter Correction (MSC) and Standard Normal Variate (SNV) Transformations of NIR Spectra. J. Near Infrared Spectrosc. 2 43-47
A. Rinnan, F. van den Berg and S. Balling Engelsen (2009) Review of the Most Common Pre-Processing Techniques for Near-Infrared Spectra. TrAC Trends Anal. Chem. 28(10), 1201-1222
J.A. Fernández Pierna, P. Vermeulen, O. Amand, A. Tossens, P. Dardenne and V. Baeten (2012) NIR Hyperspectral Imaging Spectroscopy and Chemometrics for the Detection of Undesirable Substances in Food and Feed. Chemom. Intel. Lab. Syst. 117 233-239
V.W. Smail, A.K. Fritz and D.L. Wetzel (2006) Chemical Imaging of Intact Seeds With NIR Focal Plane Array Assists Plant Breeding. Vib. Spectrosc. 42 215-221
B.A. Weinstock, J. Janni, L. Hagen and S. Wright (2006) Prediction of Oil and Oleic Acid Concentrations in Individual Corn (Zea mays L.) Kernels Using Near-Infrared Reflectance Hyperspectral Imaging and Multivariate Analysis. Appl. Spectrosc. 60(1), 9-16
A. Dijon (2017) Évaluation de différents instruments de spectroscopie proche infrarouge pour la prédiction de la teneur en protéines de céréales analysées en graine à graine. Huy, Belgium: Haute Ecole Charlemagne—ISIa 108p PhD Thesis
P. Vermeulen, P. Flemal, O. Pigeon, P. Dardenne, J.A. Fernández Pierna and V. Baeten (2017) Assessment of Pesticide Coating on Cereal Seeds by Near Infrared Hyperspectral Imaging. J. Spectral Imaging 6(a1), 1-7
R.P. Cogdill, C.R. Hurburgh and G.R. Rippke (2004) Single Kernel Maize Analysis by Near-Infrared Hyperspectral Imaging. Trans. ASABE 47(1), 311-320
S.W. Stevermer, B.L. Steward, R.P. Codgill and C.R. Hurburgh (2003) Automated Sorting and Single Kernel Analysis by Near-Infrared Hyperspectral Imaging. Presented at the 2003 ASAE International Meeting, Paper Number: 036159, Las Vegas, Nevada, USA, July 27–30 St. Joseph, MI, USA: American Society of Agricultural and Biological Engineers
N. Gorretta, J.M. Roger, M. Aubert, V. Bellon-Maurel, F. Campan and P. Roumet (2006) Determining Vitreousness of Durum Wheat Kernels Using Near Infrared Hyperspectral Imaging. J. Near Infrared Spectrosc. 14 231-239
P. Vermeulen, M. Suman, J.A. Fernández Pierna and V. Baeten (2018) Discrimination Between Durum and Common Wheat Kernels Using Near Infrared Hyperspectral Imaging. J. Cereal Sci. 84 74-82
J.A. Fernández Pierna, P. Vermeulen, D. Stilmant, B. Dupuis, P. Dardenne and V. Baeten (2010) Characterisation of fonio millet by near infrared hyperspectral imaging. S. Saranwong (Eds) Proceedings of the 14th International Conference on NIR Spectroscopy, Bangkok, Thailand Chichester, UK
P. Vermeulen, J.A. Fernández Pierna, H.P. van Egmond, P. Dardenne and V. Baeten (2012) Online Detection and Quantification of Ergot Bodies in Cereals Using near Infrared Hyperspectral Imaging. Food Addit. Contam. 29(2), 232-240
P. Vermeulen, M.B. Ebene, B. Orlando, J.A. Fernández Pierna and V. Baeten (2017) Online Detection and Quantification of Particles of Ergot Bodies in Cereal Flour Using Near-Infrared Hyperspectral Imaging. Food Addit. Contam., Part A 34(8), 232-240
P.J. Williams, P. Geladi, T.J. Britz and M. Manley (2012) Near-Infrared (NIR) Hyperspectral Imaging and Multivariate Image Analysis to Study Growth Characteristics and Differences Between Species and Strains of Members of the Genus Fusarium. Anal. Bioanal. Chem. 404(6–7), 1759-1769
A. Moghimi, C. Yang, M.E. Miller, S.F. Kianian and P.M. Marchetto (2018) A Novel Approach to Assess Salt Stress Tolerance in Wheat Using Hyperspectral Imaging. Front. Plant Sci. 9 1182-1193
P. Williams, P. Geladi, G. Fox and M. Manley (2009) Maize Kernel Hardness Classification by Near Infrared (NIR) Hyperspectral Imaging and Multivariate Data Analysis. Anal. Chim. Acta 653 121-130
K. Sendin, M. Manley, V. Baeten, J.A. Fernández Pierna and P.J. Williams (2019) Near Infrared Hyperspectral Imaging for White Maize Classification According to Grading Regulations. Food Anal. Methods 12(7), 1612-1624
A. Morales Sillero, J.A. Fernández Pierna, G. Sinnaeve, P. Dardenne and V. Baeten (2018) Quantification of Protein in Wheat Using NIR Hyperspectral Imaging: Performance Comparison With Conventional NIR Spectroscopy. J. Near Infrared Spectrosc. 26(3), 186-195
A. Walter, F. Liebisch and A. Hund (2015) Plant Phenotyping: From Bean Weighing to Image Analysis. Plant Methods 11 14
L.M. Dale, A. Thewis, C. Boudry, I. Rotar, F.S. Păcurar, O. Abbas, P. Dardenne, V. Baeten, J. Pfister and J.A. Fernández Pierna (2013) Discrimination of Grassland Species and their Classification in Botanical Families by Laboratory Scale NIR Hyperspectral Imaging: Preliminary Results. Talanta 116 149-154
J. Behmann, D. Bohnenkamp, S. Paulus and A.K. Mahlein (2018) Spatial Referencing of Hyperspectral Images for Tracing of Plant Disease Symptoms. J. Imaging 4 143
L.M. Dale, J.A. Fernández Pierna, P. Vermeulen, B. Lecler, A.D. Bogdan, F.S. Păcurar, I. Rotar, A. Thewis and V. Baeten (2012) Research on Crude Protein and Digestibility of Arnica montana L. Using Conventional NIR Spectrometry and Hyperspectral Imaging NIR. J. Food Agric. Environ. 10(1), 391-396
E. Eylenbosch, B. Bodson, V. Baeten and J.A. Fernández Pierna (2017) NIR Hyperspectral Imaging Spectroscopy and Chemometrics for the Discrimination of Roots and Crop Residues Extracted From Soil Samples: Discrimination of Roots and Crop Residues on Hyperspectral Images. J. Chemometr. 32(1), 10.1002/cem.2982 e2982
D. Eylenbosch, B. Dumont, V. Baeten, B. Bodson, P. Delaplace and J.A. Fernández Pierna (2018) Quantification of Leghaemoglobin Content in Pea Nodules Based on Near Infrared Hyperspectral Imaging Spectroscopy and Chemometrics. J. Spectral Imaging 7 a9
Y.Y. Pu, Y.Z. Feng and D.W. Sun (2015) Recent Progress of Hyperspectral Imaging on Quality and Safety Inspection of Fruits and Vegetables: A Review. Compr. Rev. Food Sci. Food Saf. 14(2), 176-188
M. Manley (2014) Near-Infrared Spectroscopy and Hyperspectral Imaging: Non-Destructive Analysis of Biological Materials. Chem. Soc. Rev. 43 8200-8214
S. Mahesh, D.S. Jayas, J. Paliwal and N.D.G. White (2015) Hyperspectral Imaging to Classify and Monitor Quality of Agricultural Materials. J. Stored Prod. Res. 61 17-26
L.M. Dale, A. Thewis, C. Boudry, I. Rotar, P. Dardenne, V. Baeten and J.A. Fernández Pierna (2013) Hyperspectral Imaging Applications in Agriculture and Agro-Food Product Quality and Safety Control: A Review. Appl. Spectrosc. Rev. 48 142-159
V. Baeten, J.A. Fernández Pierna, P. Vermeulen and P. Dardenne (2010) NIR Hyperspectral Imaging Methods for Quality and Safety Control of Food and Feed Products: Contributions to Four European Projects. NIR News 21(6), 10-13
H. Huang, L. Liu and M. Ngadi (2014) Recent Developments in Hyperspectral Imaging for Assessment of Food Quality and Safety. Sensors 14(4), 7248-7276
D. Wu and D.W. Sun (2013) Advanced Applications of Hyperspectral Imaging Technology for Food Quality and Safety Analysis and Assessment: A Review—Part I: Fundamentals. Innov. Food Sci. Emerg. Technol. 19 1-14
G. Polder, G.W.A.M. van der Heijden and I.T. Young (2000) Hyperspectral Image Analysis for Measuring Ripeness of Tomatoes. Presented at the 2000 ASAE International Meeting, Paper number 003089, Milwaukee, WI, USA
G. Polder, G.W.A.M. van der Heijden and I.T. Young (2002) Spectral Image Analysis for Measuring Ripeness of Tomatoes. Trans. ASAE 45(4), 1155-1161
G. Polder, G.W.A.M. van der Heijden and I.T. Young (2003) Tomato Sorting Using Independent Component Analysis on Spectral Images. Real-Time Imaging 9 253-259
A. Peirs, N. Scheerlinck, J. De Baerdemaeker and B.M. Nicolaï (2003) Starch Index Determination of Apple Fruit by Means of a Hyperspectral Near Infrared Reflectance Imaging System. J. Near Infrared Spectrosc. 11 379-389
P. Menesatti, A. Zanella, S. D’Andrea and C. Costa (2009) Supervised Multivariate Analysis of Hyper-Spectral NIR Images to Evaluate the Starch Index of Apples. Food Bioproc. Tech. 2 308-314
P.M. Mehl, Y.R. Chen, M.S. Kim and D.E. Chan (2004) Development of Hyperspectral Imaging Technique for the Detection of Apple Surface Defects and Contaminations. J. Food Eng. 61(1), 67-81
K.C. Lawrence, W.R. Windham, B. Park and R.J. Buhr (2003) A Hyperspectral Imaging System for Identification of Faecal and Ingesta Contamination on Poultry Carcasses. J. Near Infrared Spectrosc. 11 269-281
K. Chao, P.M. Mehl and Y.R. Chen (2002) Use of Hyper- and Multi-Spectral Imaging for Detection of Chicken Skin Tumors. Appl. Eng. Agric. 18(1), 113-119
G.K. Naganathan, L.M. Grimes, J. Subbiah, C.R. Calkins, A. Samal and G.E. Meyer (2008) Partial Least Squares Analysis of Near-Infrared Hyperspectral Images for Beef Tenderness Prediction. Sens. & Instrumen. Food Qual. 2(3), 178-188
G. ElMasry, D.-W. Sun and P. Allen (2012) Near-Infrared Hyperspectral Imaging for Predicting Colour, pH and Tenderness of Fresh Beef. J. Food Eng. 110(1), 127-140
M. Kamruzzaman, G. ElMasry, D.W. Sun and P. Allen (2012) Non-Destructive Prediction and Visualization of Chemical Composition in Lamb Meat Using NIR Hyperspectral Imaging and Multivariate Regression. Innov. Food Sci. Emerg. Technol. 16 218-226
M. Kamruzzaman, D. Barbin, G. ElMasry, D.W. Sun and P. Allen (2012) Potential of Hyperspectral Imaging and Pattern Recognition for Categorization and Authentication of Red Meat. Innovative Food Sci. Emerg. Technol. 16 316-325
H.-J. He, D. Wu and D.W. Sun (2014) Potential of Hyperspectral Imaging Combined With Chemometric Analysis for Assessing and Visualising Tenderness Distribution in Raw Farmed Salmon Fillets. J. Food Eng. 126 156-164
D. Vincke, V. Baeten, G. Sinnaeve, P. Dardenne and J.A. Fernández Pierna (2014) Determination of Outer Skin in Dry Onions by Hyperspectral Imaging Spectroscopy and Chemometrics. NIR News 25(2), 9-12
J.A. Fernández Pierna, D. Vincke, P. Dardenne, Z. Yang, L. Han and V. Baeten (2014) Line Scan Hyperspectral Imaging Spectroscopy for the Early Detection of Melamine and Cyanuric Acid in Feed. Journal of NIRS 22(2), 103-112
J. Lim, G. Kim, C. Mo, M.S. Kim, K. Chao, J. Qin, X. Fu, I. Baek and B.K. Cho (2016) Detection of Melamine in Milk Powders Using Near-Infrared Hyperspectral Imaging Combined With Regression Coefficient of Partial Least Square Regression Model. Talanta 151 183-191
S. Kiani, S.M. van Ruth, L.W.D. van Raamsdonk and S. Minaei (2019) Hyperspectral Imaging as a Novel System for the Authentication of Spices: A Nutmeg Case Study. LWT- Food Sci. Technol. 104 61-69
H. Rogez, J.A. Fernández Pierna, J. Souza and V. Baeten (2018) Application of NIR Hyperspectral Spectroscopy for the Analysis of Cocoa Beans. 17th International Conference on Near Infrared Spectroscopy 2015, Campinas, Brazil
C. Mo, J. Lim, S.W. Kwon, D.K. Lim, M.S. Kim, G. Kim, J. Kang, K.D. Kwon and B.K. Cho (2017) Hyperspectral Imaging and Partial Least Square Discriminant Analysis for Geographical Origin Discrimination of White Rice. J. Biosyst. Eng. 42(4), 293-300
