Development of a multispectral imaging system for online quality assessment of pomegranate fruit

References

• Kader, A.A. Fruit, Maturity, Ripening and Quality Relationships. Acta Horticulturae 1999, 485, 203–208.
   Google Scholar
• El Masry, G.; Wang, N.; ElSayed, A.; Ngadi, M. Hyperspectral Imaging for Nondestructive Determination of Some Quality Attributes for Strawberry. Journal of Food Engineering 2007, 81, 98–107.
   Web of Science ®Google Scholar
• Al-Said, F.A.; Opara, L.U.; Al-Yahyai, R.A. Physico-Chemical and Textural Quality Attributes of Pomegranate Cultivars (Punica Granatum L.) Grown in the Sultanate of Oman. Journal of Food Engineering 2009, 90, 129–134.
   Web of Science ®Google Scholar
• Fawole, O.A.; Opara, U.L. Changes in Physical Properties, Chemical and Elemental Composition and Antioxidant Capacity of Pomegranate (cv. “Ruby”) Fruit at five Maturity Stages. Scientia Horticulturae 2013, 150, 37–46.
   Web of Science ®Google Scholar
• Fawole, O.A.; Opara, U.L. Developmental Changes in Maturity Indices of Pomegranate Fruit: A Descriptive Review. Scientia Horticulturae 2013, 150, 152–161.
   Web of Science ®Google Scholar
• Radunić, M.; Jukić Špika, M.; Goreta Ban, S.; Gadže, J.; Díaz-Pérez, J.C.; D. MacLean, Physical and Chemical Properties of Pomegranate Fruit Accessions from Croatia. Food Chemistry 2015, 177, 53–60.
   PubMed Web of Science ®Google Scholar
• Melgarejo, P.; Salazar, D.M.; Tratado de Fruticultura para Zonas Aridas 2002, 2, Ediciones Mundi-Prensa Madrid.
   Google Scholar
• Salah, A.A.; Dilshad, A. Changes in Physical and Chemical Properties During Pomegranate (Punica Granatum L.) Fruit Maturation. Food Chemistry 2002, 76, 437–441.
   Web of Science ®Google Scholar
• Zarei, M.; Azizi, M.; Bashir-Sadr, Z. Evaluation of Physicochemical Characteristics of Pomegranate (Punica Granatum L.) Fruit During Ripening. Fruits 2011, 66, 121–129.
   Google Scholar
• Fawole, O.A.; Opara, U.L. Fruit Growth Dynamics, Respiration Rate, and Physico-Textural Properties During Pomegranate Development and Ripening. Scientia Horticulturae 2013c, 157, 90–98.
   Web of Science ®Google Scholar
• Costa, G.; Noferini, M.; Andreotti, C. Non-Destructive Determination of Internal Quality in Intact Pears by Near Infrared Spectroscopy. Acta Horticulturae 2002, 596, 821–825.
   Google Scholar
• Liu, Y.D.; Ying, Y.B.; Fu, X.P.; Lu, H.S. Experiments on Predicting Sugar Content in Apples by FT-NIR Technique. Journal of Food Engineering 2007, 80, 986–989.
   Web of Science ®Google Scholar
• Liu, Y.D.; Chen, X.M.; Sun, X.D.; Ying, Y.B. Non-Destructive Measurement of Pear Internal Quality Indices by Visible and Near-Infrared Spectrometric Techniques. New Zealand Journal of Agricultural Research 2007, 50, 1051–1057.
   Web of Science ®Google Scholar
• Nicolai, B.M.; Beullens, K.; Bobelyn, E.; Peirs, A.; Saeys, W.; Theron, K.I.; Lammertyn, J. Nondestructive Measurement of Fruit and Vegetable Quality by Means of NIR Spectroscopy: A Review. Postharvest Biology and Technology 2007, 46, 99–118.
   Web of Science ®Google Scholar
• Shao, Y.H.; He, Y.; Bao, Y.D.; Mao, J.Y. Near-Infrared Spectroscopy for Classification of Oranges and Prediction of the Sugar Content. International Journal of Food Properties 2009, 12, 644–658.
   Web of Science ®Google Scholar
• Paz, P.; Sánchez, M.T.; Pérez-Marín, D.; Guerrero, J.E.; A. Garrido-Varo, Instantaneous Quantitative and Qualitative Assessment of Pear Quality Using Near Infrared Spectroscopy. Computer and Electronic in Agriculture 2009, 69, 24–32.
   Web of Science ®Google Scholar
• Louw, E.D.; Theron, K.I. Robust Prediction Models for Quality Parameters in Japanese Plums (Prunussalicina L.) Using NIR Spectroscopy. Postharvest Biology and Technology 2010, 58, 176–184.
   Web of Science ®Google Scholar
• Morales-Sillero, A.; Fernandez-Cabanas, V.M.; Casanova, L.; Jimenez, M.R.; Suarez, M.P.; Rallo, P. Feasibility of NIR Spectroscopy for Non-Destructive Characterization of Table Olive Traits. Journal of Food Engineering 2011, 107, 99–106.
   Web of Science ®Google Scholar
• Li, J.; Huang, W.; Zhao, C.; Zhang, B. A Comparative Study for the Quantitative Determination of Soluble Solids Content, p, and Firmness of Pears by Vis/NIR Spectroscopy. Journal of Food Engineering 2013, 116, 324–332.
   Web of Science ®Google Scholar
• Gowen, A.A.; Taghizadeh, M.C.P.; O’Donnell, C.P. Identification of Mushrooms Subjected to Freeze Damage Using Hyperspectral Imaging. Journal of Food Engineering 2009, 93, 7–12.
   Google Scholar
• Cubero, S.; Aleixos, N.; Moltó, E.; Gómez-Sanchis, J.; Blasco, J. Advances in Machine Vision Applications for Automatic Inspection and Quality Evaluation of Fruits and Vegetables. Food Bioprocess and Technology 2011, 4, 487–504.
   Web of Science ®Google Scholar
• Lorente, D.; Aleixos, N.; Gomez-Sanchis, J.; Cubero, S.; Garcia-Navarrete, O.L.; J. Blasco, Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment. Food Bioprocess and Technology 2012, 5, 1121–1142.
   Web of Science ®Google Scholar
• Wu, D.; Sun, D.W. Advanced Applications of Hyperspectral Imaging Technology for Food Quality and Safety Analysis and Assessment: A Review—Part II: Applications. Innovative Food Science and Emerging Technology 2013, 19, 15–28.
   Web of Science ®Google Scholar
• Qin, J.; Chao, K.; Kim, M.S.; Lu, R.; Burks, T.F. Hyperspectral and Multispectral Imaging for Evaluating Food Safety and Quality. Journal of Food Engineering 2013, 118, 157–171.
   Web of Science ®Google Scholar
• Huang, H.; Liu, L.; Ngadi, M. Recent Developments in Hyperspectral Imaging for Assessment of Food Quality and Safety. Sensors 2014, 14, 7248.
   PubMed Web of Science ®Google Scholar
• Khodabakhshian, R.; Emadi, B.; Khojastehpour, M.; Golzarian, M.R. Combination of Conventional Imaging and Spectroscopy Methods for Food Quality Evaluation. 4th International Workshop on Computer Science and Engineering, United Arab Emirates, Dubai, August 22–23, 2014.
   Google Scholar
• Pu, Y.Y.; Feng, Y.Z.; Sun, D.W. Recent Progress of Hyperspectral Imaging on Quality and Safety Inspection of Fruits and Vegetables: A Review. Comprehensive Reviews in Food Science. Food Safety 2015, 4, 176–188.
   Web of Science ®Google Scholar
• Kim, M.S.; Lefcourt, A.M.; Chao, K.; Chen, Y.R.; Kim, I.; Chan, D.E. Multispectral Detection of Fecal Contamination on Apples Based on Hyperspectral Imagery: Part I. Application of Visible and Near-Infrared Reflectance Imaging. Transaction of the ASAE 2002, 45, 2027–2037.
   Google Scholar
• Lleo, L.; Roger, J.M.; Herrero-Langreo, A.; Diezma-Iglesias, B.; Barreiro, P. Comparison of Multispectral Indexes Extracted from Hyperspectral Images for the Assessment of Fruit Ripening. Journal of Food Engineering 2011, 104, 612–620.
   Web of Science ®Google Scholar
• Liu, C.; Liu, W.; Chen, W.; Yang, J.; Zheng, L. Feasibility in Multispectral Imaging for Predicting the Content of Bioactive Compounds in Intact Tomato Fruit. Food Chemistry 2015, 173, 482–488.
   PubMed Web of Science ®Google Scholar
• Huang, W.; Li, J.; Wang, Q.; Chen, L. Development of a Multispectral Imaging System for Online Detection of Bruises on Apples. Journal of Food Engineering 2015, 146, 62–71.
   Web of Science ®Google Scholar
• McGlone, V.A.; Jordan, R.B.; Seelye, R.; Martinsen, P.J. Comparing Density and NIR Methods for Measurement of Kiwifruit Dry Matter and Soluble Solids Content. Postharvest Biology and Technology 2002, 26, 191–198.
   Web of Science ®Google Scholar
• Gomez, H.A.; He, Y.; Pereira, A.G. Non-Destructive Measurement of Acidity, Soluble Solids, and Firmness of Satsuma Mandarin Using Vis/NIR Spectroscopy Techniques. Journal of Food Engineering 2006, 77, 313–319.
   Web of Science ®Google Scholar
• Moghimi, A.; Aghkhani, M.H.; Sazgarnia, A.; Sarmad, M. Vis/NIR Spectroscopy and Chemometrics for the Prediction of Soluble Solids Content and Acidity (pH) of Kiwifruit. Biosystem Engineering 2010, 106, 295–302.
   Web of Science ®Google Scholar
• Cen, H.; He, Y. Theory and Application of Near Infrared Reflectance Spectroscopy in Determination of Food Quality. Trends in Food Science & Technology 2007, 18, 72–83.
   Web of Science ®Google Scholar
• Naes, T.; Isaksson, T.; Fearn, T.; Davies, T. A User-Friendly Guide to Multivariate Calibration and Classification. NIR Publications: Charlton, Chichester, UK, 2004.
   Google Scholar
• Vigni, M.L.; Durante, C.; Cocchi, M. Exploratory Data Analysis. In Chemometrics in Food Chemistry; Marini, F.; Ed.; Elsevier: Amsterdam, Netherlands 2013; 55–126.
   Google Scholar
• Viscarra Rossel, R.A. ParLeS: Software for Chemometric Analysis of Spectroscopic Data. Chemometrics and Intelligent Laboratory Systems 2008, 90, 72–83.
   Web of Science ®Google Scholar
• Liu, F.; Wang, L.; He, Y. Application of Effective Wavelengths for Variety Identification of Instant Milk Teas. Journal of Zhejiang University 2010, 44, 619–623.
   Google Scholar
• Westad, F.; Bevilacqua, M.; Marini, F. Regression. In Chemometrics in Food Chemistry; Marini, F.; Ed.; Elsevier, Amsterdam, Netherlands, 2013; 127–169.
   Google Scholar
• Qin, J.; Burks, T.F.; Zhao, X.; Niphadkar, N.; Ritenour, M.A. Multispectral Detection of Citrus Canker Using Hyperspectral Band Selection. Transaction of the ASABE 2011, 54, 2331–2341.
   Web of Science ®Google Scholar
• Gomez-Sanchis, J.; Blasco, J.; Soria-Olivas, E.; Lorente, D.; Escandell-Montero, P.; Martinez-Martinez, J.M. Hyperspectral LCTF-Based System for Classification of Decay in Mandarins Caused by Penicillium Digitatum and Penicillium Italicum Using the Most Relevant Bands and Non-Linear Classifiers. Postharvest Biology and Technology 2013, 82, 76–86.
   Web of Science ®Google Scholar
• Rajkumar, P.; Wang, N.; Eimasry, G.; Raghavan, G.S.V.; Gariepy, Y. Studies on Banana Fruit Quality and Maturity Stages Using Hyperspectral Imaging. Journal of Food Engineering 2012, 108, 194–200.
   Web of Science ®Google Scholar
• Chong, I.G.; Jun, C.H. Performance of Some Variable Selection Methods When Multi Collinearity Is Present, Chemometrics. Intelligent Laboratory Systems 2005, 78, 103–112.
   Web of Science ®Google Scholar
• Kawamura, K.; Watanabe, N.; Sakanoue, S.; Lee, H.J.; Inoue, Y.; Odagawa, S. Testing Genetic Algorithm As a Tool to Select Relevant Wavebands from Field Hyperspectral Data for Estimating Pasture Mass and Quality in a Mixed Sown Pasture Using Partial Least Squares Regression. Grassland Science 2010, 56, 205–216.
   Web of Science ®Google Scholar
• Serpico, S.B.; Bruzzone, L. A New Search Algorithm for Feature Selection in Hyperspectral Remote Sensing Images. IEEE Transaction Geoscience Remote Sensing 2001, 39, 1360–1136.
   Web of Science ®Google Scholar
• Peng, H.; Long, F.; Ding, C. Feature Selection Based on Mutualinformation: Criteria of Max-Dependency, Max-Relevance, and Min Redundancy. IEEE Transaction on Pattern Analysis and Machine Intelligence 2005, 27, 1226–1238.
   PubMed Web of Science ®Google Scholar
• Lorente, D.; Aleixos, N.; Gomez-Sanchis, J.; Cubero, S.; Blasco, J. Selection of Optimal Wavelength Features for Decay Detection in Citrus Fruit Using the ROC Curve and Neural Networks. Journal of Food Engineering 2013, 6, 530–541.
   Google Scholar
• Abbott, J.A.; Lu, R.; Upchurch, B.L.; Stroshine, R.L. Technologies for Non-Destructive Quality Evaluation of Fruits and Vegetables. In Horticultural Reviews-Technologies for Nondestructive Quality Evaluation of Fruits and Vegetables; John Wiley & Sons, Inc.: New York, 1997; 1–120.
   Google Scholar
• Seeram, N.P.; Lee, R.; Scheuller, H.S.; Heber, D. Identification of Phenolic Compounds in Strawberries by Liquid Chromatography Electrospray Ionization Mass Spectroscopy. Food Chemistry 2006, 97, 1–11.
   Web of Science ®Google Scholar
• Lu, R. Predicting Firmness and Sugar Content of Sweet Cherries Using Near-Infrared Diffuse Reflectance Spectroscopy. Transaction of the ASAE 2001, 44, 1265–1271.
   Google Scholar
• Shao, Y.; He, Y.; Gomez, A.H.; Pereir, A.G.; Qiu, Z.; Zhag, Y. Visible/Near Infrared Spectrometric Technique for Nondestructive Assessment of Tomato “Heatwave” (Lycopersicumesculentum) Quality Characteristics. Journal of Food Engineering 2007, 81, 672–678.
   Web of Science ®Google Scholar
• Jamshidi, B.; Minaei, S.; Mohajerani, E.; Ghassemian, H. Reflectance Vis/NIR Spectroscopy for Nondestructive Taste Characterization of Valencia Oranges. Computer and Electronic in Agriculture 2012, 85, 64–69.
   Web of Science ®Google Scholar