Automated prediction of sensory scores for color and appearance in canned black beans (Phaseolus vulgaris L.) using machine vision

References

• Siddiq, M., & Uebersax, M.A. Dry Beans And Pulses Production And Consumption—An Overview. In Dry Beans and Pulses Production, Processing and Nutrition; Siddiq, M.; Uebersax, M.A.; Eds.; Blackwell Publishing: Oxford, UK, 2012; 3–22.
   Google Scholar
• USDA-ERS. (U.S. Department of Agriculture, Economic Research Service). Vegetables and Pulses Outlook No. (VGS-354): Special Article, Commodity Highlight: Dry Beans, 2014. http://www.ers.usda.gov/media/1680744/vgs-354-sa1.pdf ( accessed July 30, 2015).
   Google Scholar
• Mitchell, D.C.; Lawrence, F.R.; Hartman, T.J.; Curran, J.M. Consumption of Dry Beans, Peas, and Lentils Could Improve Diet Quality in the U.S. Population. Journal of the American Dietetic Association 2009, 109, 909–913.
   PubMed Web of Science ®Google Scholar
• White, B.L.; Howard, L.R. Canned Whole Dry Beans and Bean Products. In Dry Beans and Pulses Production, Processing, and Nutrition; Siddiq, M.; Uebersax, M.A.; Eds.; Blackwell Publishing: Oxford, UK, 2012; 23–54.
   Google Scholar
• Bushey, S.M.; Hosfield, G.L. A Test to Predict Color Loss in Black Bean During Thermal Processing. Annual Report of the Bean Improvement Cooperative 2007, 50, 41–42.
   Google Scholar
• Mendoza, F.A.; Cichy, K.; Lu, R.; Kelly, J.D. Evaluation of Canning Quality Traits in Black Beans (Phaseolus Vulgaris L.) by Visible/Near-Infrared Spectroscopy. Food and Bioprocess Technology 2014, 7, 2666–2678.
   Web of Science ®Google Scholar
• Hosfield, G.L. Genetic Control of Production and Food Quality Factors in Dry Bean. Food Technology 1991, 45, 98–103.
   Web of Science ®Google Scholar
• Cichy, K.A.; Fernandez, A.; Kilian, A.; Kelly, J.D.; Galeano, C.H.; Shaw, R.S.; Brick, M.; Hodkinson, D.; Troxtell, E. QTL Analysis of Canning Quality and Color Retention in Black Beans (Phaseolus Vulgaris L.). Molecular Breeding 2014, 33, 139–154.
   Web of Science ®Google Scholar
• Zareifard, M.R.; Savard, T.; Marcotte, M.; Lecompte, J.-Y.; Grabowski, S. Combined Effects of Mild Acidification and Thermal Treatment on Color and Texture of Green Beans. International Journal of Food Properties 2015, 18, 1847–1867.
   Google Scholar
• Mendoza, F. Characterization of surface appearance and color of some fruits and vegetables using image analysis. Ph.D. Thesis, 2005, Department of Chemical Engineering and Bioprocess. Pontificia Universidad Católica de Chile. http://www.lth.se/fileadmin/livsmedelsteknik/pershemsidor/collab/THESIS2005_FMendoza_UC-Chile.pdf ( accessed July 30, 2015).
   Google Scholar
• Mendoza, F.; Dejmek, P.; Aguilera, J.M. Calibrated Color Measurements of Agricultural Foods Using Image Analysis. Postharvest Biology and Technology 2006, 41, 285–295.
   Web of Science ®Google Scholar
• Valous, N.A.; Mendoza, F.; Sun, D.-W.; Allen, P. Colour Calibration of a Laboratory Computer Vision System for Quality Evaluation of Pre-Sliced Hams. Meat Science 2009, 81, 132–141.
   PubMed Web of Science ®Google Scholar
• Ross, E.P. Impact of Computers on Sensory Evaluation: Past, Present, and Future. Food Quality and Preference 1989, 1, 165–170.
   Google Scholar
• Risvik, E.; Rogers, R. Sensory Analysis: A View on the Use of Computers. Food Quality and Preference 1989, 1, 81–85.
   Google Scholar
• Johansen, S.M.B.; Laugesen, J.L.; Janhøj, T.; Ipsen, R.H.; Frøst, M.B. Prediction of Sensory Properties of Low-Fat Yoghurt and Cream Cheese from Surface Images. Food Quality and Preference 2008, 19, 232–246.
   Web of Science ®Google Scholar
• Wada, Y.; Arce-Lopera, C.; Masuda, T.; Kimura, A.; Dan, I.; Goto, S.; Tsuzuki, D.; Okajima, K. Influence of Luminance Distribution on the Appetizingly Fresh Appearance of Cabbage. Appetite 2013, 54, 363–368.
   Web of Science ®Google Scholar
• Arce-Lopera, C.; Masuda, T.; Kimura, A.; Wada, Y.; Okajima, K. Luminance Distribution as a Determinant for Visual Freshness Perception: Evidence from Image Analysis of a Cabbage Leaf. Food Quality and Preference 2013, 27, 202–207.
   Web of Science ®Google Scholar
• Haralick, R.M.; Shanmugam, K.; Dinstein, I. Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics 1973, 3, 610–621.
   Web of Science ®Google Scholar
• Hosfield, G.L.; Uebersax, M.A. Variability in Physico-Chemical Properties and Nutritional Components of Tropical and Domestic Dry Bean Germplasm. Journal of the American Society for Horticultural Science 1980, 105, 246–252.
   Web of Science ®Google Scholar
• Randolph, J.J. Free-Marginal Multirater Kappa: An Alternative to Fleiss’ Fixed-Marginal Multirater Kappa. In Proceedings of the Learning and Instruction Symposium 2005, Joensuu University, October 14–15, 2005, Joensuu, Finland.
   Google Scholar
• Feinstein, A.R.; Cicchetti, D.V. High Agreement But Low Kappa: I. The Problems of Two Paradoxes. Journal of Clinical Epidemiology 1990, 43, 543–549.
   PubMed Web of Science ®Google Scholar
• Westland, S.; Ripamonti, C.; Cheung, V. Computational Colour Science Using MATLAB® (2nd Ed.). John Wiley and Sons, Ltd.: Chichester, UK, 2012; Chapter 10.
   Google Scholar
• Hong, G.; Luo, R.M.; Rhodes, P.A. A Study of Digital Camera Colorimetric Characterization Based on Polynomial Modeling. Color Research and Application 2001, 26, 76–84.
   Web of Science ®Google Scholar
• ITU. Rec. ITU-R BT. 709-5. Parameter Values for the HDTV Standards for Production and International Programme Exchange. International Telecommunication Union (ITU). pp. 30, 2002. http://www.itu.int/publications/bookshop/index.html ( accessed July 30, 2015).
   Google Scholar
• CIE. Colorimetry—Official Recommendations of the International Commission on Illumination, CIE Publication No. 15.2; CIE Central Bureau: Vienna, Austria, 1986.
   Google Scholar
• Boser, B.E.; Guyon, I.M.; Vapnik, V.N. A Training Algorithm for Optimal Margin Classifiers. In COLT ‘92 Proceedings of the 5th Annual Workshop on Computational Learning Theory. Pittsburgh, PA, July 27–29, 1992; 144–152.
   Google Scholar
• Mery, D. BALU: A Toolbox for Computer Vision, Pattern Recognition, and Image Processing, 2011. http://dmery.ing.puc.cl/index.php/balu ( accessed July 30, 2015).
   Google Scholar
• Kelly, J.D.; Schabenberger, O. Analysis of the Effect of Judges’ Experience on Canning Quality of Four Major Commercial Dry Beans Classes. Michigan Dry Bean Digest 1998, 23, 18–19.
   Google Scholar