Evaluation of biological contaminants in foods by hyperspectral imaging: A review

References

• Feng, Y.Z.; Sun, D.W. Application of Hyperspectral Imaging in Food Safety Inspection and Control: A Review. Critical Reviews in Food Science and Nutrition 2012, 52(11), 1039–1058.
   PubMed Web of Science ®Google Scholar
• Gowen, A.A.; Feng, Y.; Gaston, E.; Valdramidis, V. Recent Applications of Hyperspectral Imaging in Microbiology. Talanta 2015, 137, 43–54.
   PubMed Web of Science ®Google Scholar
• Liu, D.; Zeng, X.A.; Sun, D.W. Recent Developments and Applications of Hyperspectral Imaging for Quality Evaluation of Agricultural Products: A Review. Critical Reviews in Food Science and Nutrition 2015, 55(12), 1744–1757.
   PubMed Web of Science ®Google Scholar
• Chen, Q.; Zhang, C.; Zhao, J.; Ouyang, Q. Recent Advances in Emerging Imaging Techniques for Non-Destructive Detection of Food Quality and Safety. Trends in Analytical Chemistry 2013, 52, 261–274.
   Web of Science ®Google Scholar
• Cheng, J.H.; Sun, D.W. Hyperspectal Imaging as an Effective Tool for Quality Analysis and Control of Fish and Other Seafoods: Current Research and Potential Applications. Trends in Food Science and Technology 2014, 37(2), 78–91.
   Web of Science ®Google Scholar
• ElMasry, G.; Barbin, D.F.; Sun, D.W.; Allen, P. Meat Quality Evaluation by Hyperspectral Imaging Technique: An Overview. Critical Reviews in Food Science and Nutrition 2012a, 52(8), 689–711.
   PubMed Web of Science ®Google Scholar
• ElMasry, G.; Kamruzzaman, M.; Sun, D.W.; Allen, P. Principles and Applications of Hyperspectral Imaging in Quality Evaluation of Agro-Food Products: A Review. Critical Reviews in Food Science and Nutrition 2012b, 52(11), 999–1023.
   PubMed Web of Science ®Google Scholar
• Gowen, A.A.; O’Donnell, C.P.; Cullen, P.J.; Downey, G.; Frias, J.M. Hyperspectral Imaging: An Emerging Process Analytical Tool for Food Quality and Safety Control. Trends in Food Science and Technology 2007, 18(12), 590–598.
   Web of Science ®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 and Food Safety 2015, 14(2), 176–188.
   PubMed Web of Science ®Google Scholar
• Qin, J.; Chao, K.; Kim, M.S.; Lu, R.; Burks, T. Hyperspectral and Multispectral Imaging for Evaluating Food Safety and Quality. Journal of Food Engineering 2013, 118(2), 157–171.
   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 I: Fundamentals. Innovative Food Science and Emerging Technologies 2013a, 19, 1–14.
   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 Technologies 2013b, 19, 15–28.
   Web of Science ®Google Scholar
• Wang, N.N.; Sun, D.W.; Yang, Y.C.; Pu, H.; Zhu, Z. Recent Advances in the Application of Hyperspectral Imaging for Evaluating Fruit Quality. Food Analytical Methods 2015a, 9(1), 178–191.
   Web of Science ®Google Scholar
• Cheng, J.H.; Sun, D.W. Recent Applications of Spectroscopic and Hyperspectral Imaging Techniques with Chemometric Analysis for Rapid Inspection of Microbial Spoilage in Muscle Foods. Comprehensive Reviews in Food Science and Food Safety 2015b, 14(4), 478–490.
   Web of Science ®Google Scholar
• He, H.J.; Sun, D.W. Hyperspectral Imaging Technology for Rapid Detection of Various Microbial Contaminants in Agricultural and Food Products. Review. Trends in Food Science and Technology 2015a, 46(1), 99–109.
   Web of Science ®Google Scholar
• Baird-Parker, T.C. The Production of Microbiologically Safe and Stable Foods. In Microbiological Safety and Quality of Food; Lund, B.M.; Baird-Parker, T.C.; Gould, G.W.; Eds.; Springer Science & Business Media: New York, 2000; 3–18.
   Google Scholar
• Yeni, F.; Acar, S.; Polat, Ö.G.; Soyer, Y.; Alpas, H. Rapid and Standardized Methods for Detection of Foodborne Pathogens and Mycotoxins on Fresh Produce. Food Control 2014, 40, 359–367.
   Web of Science ®Google Scholar
• Scallan, E.; Hoekstra, R.M.; Angulo, F.J.; Tauxe, R.V.; Widdowson, M.A.; Roy, S.L.; Jones, J.L.; Griffin, P.M. Foodborne Illness Acquired in the United States: Major Pathogens. Emerging Infectious Diseases 2011, 17(1), 7–15.
   PubMed Web of Science ®Google Scholar
• Velusamy, V.; Arshak, K.; Korostynska, O.; Oliwa, K.; Adley, C. An Overview of Foodborne Pathogen Detection: In the Perspective of Biosensors. Biotechnology Advances 2010, 28(2), 232–254.
   PubMed Web of Science ®Google Scholar
• Siripatrawan, U.; Makino, Y.; Kawagoe, Y.; Oshita, S. Rapid Detection of Escherichia coli Contamination in Packaged Fresh Spinach using Hyperspectral Imaging. Talanta 2011, 85(1), 276–281.
   PubMed Web of Science ®Google Scholar
• Takeuchi, K.; Frank, J.F. Confocal Microscopy and Microbial Viability Detection for Food Research. Journal of Food Protection 2001, 64(12), 2088–2102.
   PubMed Web of Science ®Google Scholar
• Teena, M.A.; Manickavasagan, A.; Ravikanth, L.; Jayas, D.S. Near Infrared (NIR) Hyperspectral Imaging to Classify Fungal Infected Date Fruits. Journal of Stored Products Research 2014, 59, 306–313.
   Web of Science ®Google Scholar
• Betts, R.P.; Bankes, P.; Banks, J.G. Rapid Enumeration of Viable Micro-Organisms by Staining and Direct Microscopy. Letters in Applied Microbiology 1989, 9(5), 199–202.
   Web of Science ®Google Scholar
• Koch, H.A.; Bandler, R.; Gibson, R.R. Fluorescence Microscopy Procedure for Quantitation of Yeasts in Beverages. Applied and Environmental Microbiology 1986, 52(3), 599–601.
   PubMed Web of Science ®Google Scholar
• Meng, J.; Doyle, M.P. Introduction. Microbiological Food Safety. Microbes and Infection 2002, 4(4), 395–397.
   PubMed Web of Science ®Google Scholar
• Zhang, M.; Meng, Q. Automatic Citrus Canker Detection from Leaf Images Captured in Field. Pattern Recognition Letters 2011, 32(15), 2036–2046.
   Web of Science ®Google Scholar
• Schrader, C.; Schielke, A.; Ellerbroek, L.; Johne, R. PCR Inhibitors: Occurrence, Properties and Removal. Journal of Applied Microbiology 2012, 113(5), 1014–1026.
   PubMed Web of Science ®Google Scholar
• Cheng, Y.; Liu, Y.; Huang, J.; Li, K.; Zhang, W.; Xian, Y.; Jin, L. Combining Biofunctional Magnetic Nanoparticles and ATP Bioluminescence for Rapid Detection of Escherichia coli. Talanta 2009, 77(4), 1332–1336.
   PubMed Web of Science ®Google Scholar
• Hong, S.R.; Jeong, H.D.; Hong, S. QCM DNA Biosensor for the Diagnosis of a Fish Pathogenic Virus VHSV. Talanta 2010, 82(3), 899–903.
   PubMed Web of Science ®Google Scholar
• Elvira, L.; Durán, C.M.; Urréjola, J.; Montero de Espinosa, F.R. Detection of Microbial Contamination in Fruit Juices using Non-Invasive Ultrasound. Food Control 2014, 40, 145–150.
   Web of Science ®Google Scholar
• Folch-Fortuny, A.; Prats-Montalbán, J.M.; Cubero, S.; Blasco, J.; Ferrer, A. VIS/NIR Hyperspectral Imaging and N-way PLS-DA Models for Detection of Decay Lesions in Citrus Fruits. Chemometrics and Intelligent Laboratory Systems 2016, 156, 241–248.
   Web of Science ®Google Scholar
• Shahin, M.A.; Symons, S.J. Design of a Multispectral Imaging System for Detecting Mildew Damage on Wheat Kernels. The Canadian Society for Engineering in Agricultural, Food, Environmental and Biological Systems, 2009, Paper CSBE09-701.
   Google Scholar
• Kim, M.S.; Lefcourt, A.; Chao, K.; Chen, Y.; Kim, I.; Chan, D. Multispectral Detection of Fecal Contamination on Apples based on Hyperspectral Imagery: Part I. Application of Visible and Near-Infrared Reflectance Imaging. Transactions of the ASAE 2002, 45, 2027–2037.
   Google Scholar
• He, H.J.; Sun, D.W. Microbial Evaluation of raw and Processed Food Products by Visible/Infrared, Raman and Fluorescence Spectroscopy. Review. Trends in Food Science and Technology 2015c, 46(2), 199–210.
   Web of Science ®Google Scholar
• Kim, S.; Park, J.; Chun, J.H.; Lee, S.M. Determination of Ergosterol used as a Fungal Marker of Red Pepper (Capsicum annuum L.) Powders by Near-Infrared Spectroscopy. Food Science and Biotechnology 2003, 12(3), 257–261.
   Web of Science ®Google Scholar
• Cozzolino, D. Recent Trends on the use of Infrared Spectroscopy to Trace and Authenticate Natural and Agricultural Food Products. Applied Spectroscopy Reviews 2012, 47(7), 518–530.
   Web of Science ®Google Scholar
• Muthomi, J.W.; Ndung’u, J.K.; Gathumbi, J.K.; Mutitu, E.W.; Wagacha, J.M. The Occurrence of Fusarium Species and Mycotoxins in Kenyan Wheat. Crop Protection 2008, 27(8), 1215–1219.
   Web of Science ®Google Scholar
• Notermans, S.; Dufrenne, J.; Van Schothorst, M. Enzyme-linked Immunosorbent Assay for Detection of Clostridium botulinum Toxin Type A. Japanese Journal of Medical Science and Biology 1978, 31(1), 81–85.
   PubMedGoogle Scholar
• Rammanee, K.; Hongpattarakere, T. Effects of Tropical Citrus Essential Oils on Growth, Aflatoxin Production, and Ultrastructure Alterations of Aspergillus flavus and Aspergillus parasiticus. Food and Bioprocess Technology 2011, 4(6), 1050–1059.
   Web of Science ®Google Scholar
• Saunders, G.C.; Bartlett, M.L. Double-Antibody Solid-phase Enzyme Immunoassay for the Detection of Staphylococcal Enterotoxin A. Applied and Environmental Microbiology 1977, 34(5), 518–22.
   PubMed Web of Science ®Google Scholar
• Fernández-Ibáñez, V.; Soldado, A.; Martínez-Fernández, A.; de la Roza-Delgado, B. Application of Near Infrared Spectroscopy for Rapid Detection of Aflatoxin B1 in Maize and Barley as Analytical Quality Assessment. Food Chemistry 2009, 113(2), 629–634.
   Web of Science ®Google Scholar
• Firrao, G.; Torelli, E.; Gobbi, E.; Raranciuc, S.; Bianchi, G.; Locci, R. Prediction of Milled Maize Fumonisin Contamination by Multispectral Image Analysis. Journal of Cereal Science 2010, 52(2), 327–330.
   Web of Science ®Google Scholar
• Yao, H.; Hruska, Z.; Kincaid, R.; Brown, R.; Bhatnagar, D.; Cleveland, T. Detecting Maize Inoculated with Toxigenic and Atoxigenic Fungal Strains with Fluorescence Hyperspectral Imagery. Biosystems Engineering 2013, 115(2), 125–135.
   Web of Science ®Google Scholar
• Borman, A.M.; Linton, C.J.; Miles, S.J.; Johnson, E.M. Molecular Identification of Pathogenic Fungi. Journal of Antimicrobial Chemotherapy 2008, 61(1), 7–12.
   Web of Science ®Google Scholar
• Del Fiore, A.; Reverberi, M.; Ricelli, A.; Pinzari, F.; Serranti, S.; Fabbri, A.A.; Bonifazi, G.; Fanelli, C. Early Detection of Toxigenic Fungi on Maize by Hyperspectral Imaging Analysis. International Journal of Food Microbiology 2010, 144(1), 64–71.
   PubMed Web of Science ®Google Scholar
• Christensen, D.; Allesø, M.; Rosenkrands, I.; Rantanen, J.; Foged, C.; Agger, E.M.; Andersen, P.; Nielsen, H.M. NIR Transmission Spectroscopy for Rapid Determination of Lipid and Lyoprotector Content in Liposomal Vaccine Adjuvant System CAF01. European Journal of Pharmaceutics and Biopharmaceutics 2008, 70(3), 914–920.
   PubMed Web of Science ®Google Scholar
• Kalkan, K.; Beriat, P.; Yardimci, Y.; Pearson, T.C. Detection of Contaminated Hazelnuts and Ground Red Chili Pepper Flakes by Multispectral Imaging. Computers and Electronics in Agriculture 2011, 77(1), 28–34.
   Web of Science ®Google Scholar
• Kandpal, L.M.; Lee, S.; Kim, M.S.; Bae, H.; Cho, B.K. Short Wave Infrared (SWIR) Hyperspectral Imaging Technique for Examination of Aflatoxin B₁ (AFB₁) on Corn Kernels. Food Control 2015, 51, 171–176.
   Google Scholar
• Klich, M.A. Aspergillus Flavus: The Major Producer of Aflatoxin. Molecular Plant Pathology 2007, 8(6), 713–722.
   PubMed Web of Science ®Google Scholar
• Yao, H.; Hruska, Z.; Brown, R.; Cleveland, T. Hyperspectral Bright Greenish-Yellow Fluorescence (BGYF) Imaging of Aflatoxin Contaminated Corn Kernels Proceeding of SPIE.2006, 6381, 63810B-1-63810B-8.
   Google Scholar
• Basappa, S.C.; Sreenivasamurthy, V.; Parpia, H.A.B. Aflatoxin and Kojic Acid Production by Resting Cells of Aspergillus flavus Link. Journal of General Microbiology 1970, 61(1), 81–86.
   PubMedGoogle Scholar
• Gordon, S.H.; Jones, R.W.; McClelland, J.F.; Wicklow, D.T.; Greene, R.V. Transient Infrared Spectroscopy for Detection of Toxigenic Fungi in Corn: Potential for On-Line Evaluation. Journal of Agricultural and Food Chemistry 1999, 47(12), 5267–5272.
   PubMed Web of Science ®Google Scholar
• Jin, J.; Tang, L.; Hruska, Z.; Yao, H. Classification of Toxigenic and Atoxigenic Strains of Aspergillus flavus with Hyperspectral Imaging. Computers and Electronics in Agriculture 2009, 69(2), 158–164.
   Web of Science ®Google Scholar
• Mireei, S.A.; Amini-Pozveh, S.; Nazeri, M. Selecting Optimal Wavelengths for Detection of Insect Infested Tomatoes based on SIMCA-aided CFS Algorithm. Postharvest Biology and Technology 2017, 123, 22–32.
   Web of Science ®Google Scholar
• Huang, M.; Wan, X.; Zhang, M.; Zhu, Q. Detection of Insect-Damaged Vegetable Soybeans using Hyperspectral Transmittance Image. Journal of Food Enginnering 2013, 116(1), 45–49.
   Web of Science ®Google Scholar
• Hansen, J.; Schlaman, D.W.; Haff, R.P.; Yee, W.L. Potential Postharvest Use of Radiograph to Detect Internal Pests in Deciduous Tree Fruits. Journal of Entomological Science 2005, 40(3), 255–262.
   Web of Science ®Google Scholar
• Jackson, E.S.; Haff, R.P. X-ray Detection and Sorting of Olives Damaged by Fruit Fly. ASAE, St. Joseph, MI, United Sates, 2006, Paper number 066062.
   Google Scholar
• Lu, R.; Ariana, D.P. Detection of Fruit Fly Infestation in Pickling Cucumbers using a Hyperspectral Reflectance/Transmittance Imaging System. Postharvest Biology and Technology 2013, 81, 44–50.
   Web of Science ®Google Scholar
• Xing, J.; Guyer, D. Detecting Internal Insect Infestation in Tart Cherry using Transmittance Spectroscopy. Postharvest Biology and Technology 2008, 49(3), 411–416.
   Web of Science ®Google Scholar
• Hafsteinsson, H.; Rizvi, S. A Review of the Sealworm Problem. Journal of Food Protection 1987, 50(1), 70–84.
   PubMed Web of Science ®Google Scholar
• Bublitz, C.G.; Choudhury, G.S. Effect of Light Intensity and Color on Worker Productivity and Parasite Detection Efficiency during Candling of Cod Fillets. Journal of Aquatic Food Product Technology 1992, 1(2), 75–89.
   Google Scholar
• Sivertsen, A.H.; Heia, K.; Hindberg, K.; Godtliebsen, F. Automatic Nematode Detection in Cod Fillets (Gadus morhua L.) by Hyperspectral Imaging. Journal of Food Engineering 2012, 111(4), 675–681.
   Web of Science ®Google Scholar
• Pippy, J.H.C. Use of Ultraviolet Light to Find Parasitic Nematodes in situ. Journal of the Fisheries Research Board of Canada 1970, 27(5), 963–965.
   Web of Science ®Google Scholar
• Heia, K.; Sivertsen, A.H.; Stormo, S.K.; Elvevoll, E.; Wold, J.P.; Nilsen, H. Detection of Nematodes in Cod (Gadus morhua) Fillets by Imaging Spectroscopy. Journal of Food Science 2007, 72(1), E011–E015.
   PubMed Web of Science ®Google Scholar
• Hafsteinsson, H.; Parker, K.; Chivers, R.; Rizvi, S. Application of Ultrasonic Waves to Detect Sealworms in Fish Tissue. Journal of Food Science 1989, 54(2), 244–247.
   Web of Science ®Google Scholar
• Cuttell, L.; Owen, H.; Lew-Tabor, A.E.; Traub, R.J. Bovine Cysticercosis-development of a Real-time PCR to Enhance Classification of Suspect Cysts Identified at Meat Inspection. Veterinary Parasitology 2013, 194(1), 65–69.
   PubMed Web of Science ®Google Scholar
• Fæste, C.K.; Plassen, C.; Løvberg, K.E.; Moen, A.; Egaas, E. Detection of Proteins from the Fish Parasite Anisakis simplex in Norwegian Farmed Salmon and Processed Fish Products. Food Analytical Methods 2015, 8(6), 1390–1402.
   Web of Science ®Google Scholar
• Nöckler, K.; Reckinger, S.; Broglia, A.; Mayer-Scholl, A.; Bahn, P. Evaluation of a Western Blot and ELISA for the Detection of anti-Trichinella-IgG in Pig Sera. Veterinary Parasitology 2009, 163(4), 341–347.
   PubMed Web of Science ®Google Scholar
• Ogunremi, O.; Benjamin, J. Development and Field Evaluation of a New Serological Test for Taenia saginata Cysticercosis. Veterinary Parasitology 2010, 169(1–2), 93–101.
   PubMed Web of Science ®Google Scholar
• Ramahefarisoa, R.M.; Rakotondrazaka, M.; Jambou, R.; Carod, J.F. Comparison of ELISA and PCR Assays for the Diagnosis of Porcine Cysticercosis. Veterinary Parasitology 2010, 173(3–4), 336–339.
   Web of Science ®Google Scholar
• Werner, M.T.; Fæste, C.K.; Levsen, A.; Egaas, E. A Quantitative Sandwich ELISA for the Detection of Anisakis simplex Protein in Seafood. European Food Research and Technology 2011, 232(1), 157–166.
   Web of Science ®Google Scholar
• Fæste, C.K.; Moen, A.; Schniedewind, B.; Anonsen, J.H.; Klawitter, J.; Christians, U. Development of Liquid Chromatography-Tandem Mass Spectrometry Methods for the Quantitation of Anisakis simplex Proteins in Fish. Journal of Chromatography A 2016, 1432, 58–72.
   PubMed Web of Science ®Google Scholar
• Foca, G.; Ferrari, C.; Ulrici, A.; Sciutto, G.; Prati, S.; Morandi, S.; Brasca, M.; Lavermicocca, P.; Lanteri, S.; Oliveri, P. The Potential of Spectral and Hyperspectral-Imaging Techniques for Bacterial Detection in Food: A Case Study on Lactic Acid Bacteria. Talanta 2016, 153, 111–119.
   PubMed Web of Science ®Google Scholar
• Peng, Y.K.; Zhang, J.; Wang, W.; Li, Y.Y.; Wu, J.H.; Huang, H. Potential Prediction of the Microbial Spoilage of Beef using Spatially Resolved Hyperspectral Scattering Profiles. Journal of Food Engineering 2011, 102(2), 163–169.
   Web of Science ®Google Scholar
• Tao, F.; Peng, Y.; Gomes, C.L.; Chao, K.; Qin, J. A Comparative Study for Improving Prediction of Total Viable Count in Beef based on Hyperspectral Scattering Characteristics. Journal of Food Engineering 2015, 162, 38–47.
   Web of Science ®Google Scholar
• Shaw, G.; Manolakis, D. Signal Processing for Hyperspectral Image Explotation. IEEE Signal Processing Magazine 2002, 19( 1), 12–16.
   Google Scholar
• Wold, J.P.; Westad, F.; Heia, K. Detection of Parasites in Cod Fillets by using SIMCA Classification in Multispectral Images in the Visible and NIR Region. Applied Spectroscopy 2001, 55(8), 1025–1034.
   Web of Science ®Google Scholar
• Siche, R.; Vejarano, R.; Aredo, V.; Velasquez, L.; Saldaña, E.; Quevedo, R. Evaluation of Food Quality and Safety with Hyperspectral Imaging (HSI). Food Engineering Reviews 2016, 8(3), 306–322.
   Web of Science ®Google Scholar
• Tsakanikas, P.; Pavlidis, D.; Panagou, E.; Nychas, G.J. Exploiting Multispectral Imaging for Non-Invasive Contamination Assessment and Mapping of Meat Samples. Talanta 2016, 161, 606–614.
   PubMed Web of Science ®Google Scholar
• Yeh, Y.H.; Chung, W.C.; Liao, J.Y.; Chung, C.L.; Kuo, Y.F.; Lin, T.T. Strawberry Foliar Anthracnose Assessment by Hyperspectral Imaging. Computers and Electronics in Agriculture 2016, 122, 1–9.
   Web of Science ®Google Scholar
• Ausmus, B.S.; Hilty, J.W. Reflectance Studies of Healthy, Maize Dwarf Mosaic Virus-Infected, and Helminthosporium maydis-Infected Corn Leaves. Remote Sensing of Environment 1971, 2, 77–81.
   Google Scholar
• Hamid Muhammed, H.; Larsolle, A. Feature-Vector based Analysis of Hyperspectral Crop Reflectance Data for Discrimination Quantification of Fungal Disease Severity in Wheat. Biosystems Engineering 2003, 86(2), 125–134.
   Web of Science ®Google Scholar
• Delwiche, S.R.; Kim, M.S. Hyperspectral Imaging for Detection of Scab in Wheat. In Biological Quality and Precision Agriculture II; DeShazer, J.A.; Meyer, G.E.; Eds.; Proceedings of SPIE, Boston, Massachusetts, 2000; 4203, 13–20.
   Google Scholar
• Bauriegel, E.; Giebel, A.; Geyer, M.; Schmidt, U.; Herppich, W.B. Early Detection of Fusarium Infection in Wheat using Hyper-Spectral Imaging. Computers and Electronics in Agriculture 2011, 75(2), 304–312.
   Web of Science ®Google Scholar
• Qin, J.; Burks, T.; Zhao, X.; Niphadkar, N.; Ritenour, M. Multispectral Detection of Citrus Canker using Hyperspectral Band Selection. Transactions of the ASABE 2011a, 54(6), 2331–2341.
   Web of Science ®Google Scholar
• Zhao, X.; Burks, T.F.; Qin, J.; Ritenour, M.A. Effect of Fruit Harvest Time on Citrus Canker Detection using Hyperspectral Reflectance Imaging. Sensing and Instrumentation for Food Quality and Safety 2010, 4(3), 126–135.
   Google Scholar
• Qin, J.; Burks, T.; Zhao, X.; Niphadkar, N.; Ritenour, M. Development of a Two-Band Spectral Imaging System for Real-Time Citrus Canker Detection. Journal of Food Engineering 2012, 108(1), 87–93.
   Web of Science ®Google Scholar
• Endeshaw, S.; Sabbatini, P.; Romanazzi, G.; Schilder, A.; Neri, D. Effects of Grapevine Leafroll Associated Virus 3 Infection on Growth, leaf Gas Exchange, Yield and Basic Fruit Chemistry of Vitis vinifera L. cv. Cabernet Franc. Scientia Horticulturae 2014, 170, 228–236.
   Web of Science ®Google Scholar
• Monis, J.; Bestwick, R.K. Serological Detection of Grapevine Associated Closteroviruses in Infected Grapevine Cultivars. Plant Disease 1997, 81(7), 802–808.
   PubMed Web of Science ®Google Scholar
• Rowhani, A.; Uyemoto, J.K.; Golino, D.A. A Comparison between Serological and Biological Assays in Detecting Grapevine Leafroll Associated Viruses. Plant Disease 1997, 81(7), 799–801.
   PubMed Web of Science ®Google Scholar
• Polischuk, V.P.; Shadchina, T.M.; Kompanetz, T.I.; Budzanivskaya, I.G.; Sozinov, A.A. Changes in Reflectance Spectrum Characteristic of Nicotiana debneyi Plant Under the Influence of Viral Infection. Archives of Phytopathology and Plant Protection 1997, 31(1), 115–119.
   Google Scholar
• MacDonald, S.L.; Staid, M.; Staid, M.; Cooper, M.L. Remote Hyperspectral Imaging of Grapevine Leafroll-Associated Virus 3 in Cabernet Sauvignon Vineyards. Computers and Electronics in Agriculture 2016, 130, 109–117.
   Web of Science ®Google Scholar
• Naidu, R.; Perry, E.; Pierce, F.; Mekuria, T. The Potential of Spectral Reflectance Technique for the Detection of Grapevine leafroll-associated virus-3 in Two Red-Berried Wine Grape Cultivars. Computers and Electronics in Agriculture 2009, 66(1), 38–45.
   Web of Science ®Google Scholar
• Lee, H.; Kim, M.S.; Lim, H.S.; Park, E.; Lee, W.H.; Cho, B.K. Detection of Cucumber Green Mottle Mosaic Virus-Infected Watermelon Seeds using a Near-Infrared (NIR) Hyperspectral Imaging System: Application to Seeds of the “Sambok Honey” Cultivar. Biosystems Engineering 2016, 148, 138–147.
   Web of Science ®Google Scholar
• Popoff, M.R.; Poulain, B. Bacterial Toxins and the Nervous System: Neurotoxins and Multipotential Toxins Interacting with Neuronal Cells. Toxins (Basel) 2010, 2(4), 683–737.
   PubMed Web of Science ®Google Scholar
• Zain, M.E. Impact of Mycotoxins on Humans and Animals. Journal of Saudi Chemical Society 2011, 15(2), 129–144.
   Web of Science ®Google Scholar
• Horn, B.W. Biodiversity of Aspergillus Section Flavi in the United States: A Review. Food Additives & Contaminants 2007, 24(10), 1088–1101.
   PubMed Web of Science ®Google Scholar
• Senthilkumar, T.; Jayas, D.S.; White, N.D.G.; Fields, P.G.; Gräfenhan, T. Detection of Fungal Infection and Ochratoxin A Contamination in Stored Barley using Near-Infrared Hyperspectral Imaging. Biosystems Engineering 2016, 147, 162–173.
   Web of Science ®Google Scholar
• Murillo-Arbizu, M.; González-Peñas, E.; Amézqueta, S. Comparison between Capillary Electrophoresis and High Performance Liquid Chromatography for the Study of the Occurrence of Patulin in Apple Juice Intended for Infants. Food and Chemical Toxicology 2010, 48(8–9), 2429–2434.
   Web of Science ®Google Scholar
• Williams, P.; Geladi, P.; Britz, T.; Manley, M. Investigation of Fungal Development in Maize Kernels using NIR Hyperspectral Imaging and Multivariate Data Analysis. Journal of Cereal Science 2012, 55(3), 272–278.
   Web of Science ®Google Scholar
• Feng, Y.Z.; Sun, D.W. Near-infrared Hyperspectral Imaging in Tandem with Partial Least Squares Regression and Genetic Algorithm for Non-Destructive Determination and Visualization of Pseudomonas loads in Chicken Fillets. Talanta 2013b,109, 74–83.
   Google Scholar
• Pearson, T.C.; Wicklow, D.T.; Maghirang, E.B.; Xie, F.; Dowell, F.E. Detecting Aflatoxin in Single Corn Kernels by Transmittance and Reflectance Spectroscopy. Transactions of the ASAE 2001, 44(5), 1247–1254.
   Google Scholar
• Yao, H.; Hruska, Z.; Kincaid, R.; Brown, R.; Cleveland, T.; Bhatnagar, D. Correlation and Classification of Single Kernel Fluorescence Hyperspectral Data with Aflatoxin Concentration in Corn Kernels Inoculated with Aspergillus flavus Spores. Food Additives & Contaminants: Part A 2010, 27(5), 701–709.
   Web of Science ®Google Scholar
• Wang, W.; Heitschmidt, G.W.; Ni, X.; Windham, W.R.; Hawkins, S.; Chu, X. Identification of Aflatoxin B1 on Maize Kernel Surfaces using Hyperspectral Imaging. Food Control 2014, 42, 78–86.
   Web of Science ®Google Scholar
• Wang, W.; Lawrence, K.C.; Ni, X.; Yoon, S.C.; Heitschmidt, G.W.; Feldner, P. Near-Infrared Hyperspectral Imaging for Detecting Aflatoxin B1 of Maize Kernels. Food Control 2015c, 51, 347–355.
   Web of Science ®Google Scholar
• Wang, W.; Heitschmidt, G.; Windham, W.; Feldner, P.; Ni, X.; Chu, X. Feasibility of Detecting Aflatoxin B1 on Inoculated Maize Kernels Surface using Vis/NIR Hyperspectral Imaging. Journal of Food Science 2015b, 80(1), M116–M122.
   PubMed Web of Science ®Google Scholar
• Chelladurai, V.; Jayas, D.S.; White, N.D.G. Thermal Imaging for Detecting Fungal Infection in Stored Wheat. Journal of Stored Products Research 2010, 46(3), 174–179.
   Web of Science ®Google Scholar
• Muir, W.E.; White, N.D.G. Microorganisms in Stored Grain. In Grain Preservation Biosytems; Muir, W.E.; Ed.; Department of Biosystems Engineering University of Manitoba, Winnipeg, Canada, 2001.
   Google Scholar
• Abramson, D.; Sinha, R.N.; Mills, J.T. Mycotoxin and Odor Formation in Moist Cereal Grain during Granary Storage. Cereal Chemistry 1980, 57(5), 346–351.
   Web of Science ®Google Scholar
• Di Crispino, K.; Haibo, Y.; Hruska, Z.; Kori, B.; Lewis, D.; Beach, J.; Brown, R.; Cleveland, T.E. Hyperspectral Imagery for Observing Spectral Signature Change in Aspergillus flavus. In Optical Sensors and Sensing Systems for Natural Resources and Food Safety and Quality; Chen, Y.R.; Meyer, G.E.; Tu, S.I.; Eds.; Proceedings of SPIE, Boston, Massachusetts, 2005; 5996, 599–606.
   Google Scholar
• Singh, C.B.; Jayas, D.S.; Paliwal, J.; White, N.D.G. Fungal Damage Detection in Wheat using Short-Wave Near-Infrared Hyperspectral and Digital Colour Imaging. International Journal of Food Properties 2012, 15(1), 11–24.
   Web of Science ®Google Scholar
• Zhang, H.; Paliwal, J.; Jayas, D.S.; White, N.D.G. Classification of Fungal Infected Wheat Kernels using Near-Infrared Reflectance Hyperspectral Imaging and Support Vector Machine. Transactions of the ASABE 2007, 50, 1779–1785.
   Google Scholar
• Pearson, T.C.; Wicklow, D.T. Detection of Corn Kernels Infected by Fungi. Transactions of the ASABE 2006, 49(4), 1235–1245.
   Web of Science ®Google Scholar
• Farag, R.S.; Khalil, F.A.; Mohsen, S.M.; Bosyony, A.E. Effect of Certain Fungi on the Lipids Contents of Wheat Kernels, Sesame and Soybean Seeds. Grasas y Aceites 1985, 36, 362–367.
   Web of Science ®Google Scholar
• Boyacioǧlu, D.; Hettiarachchy, N.S. Changes in Some Biochemical Components of Wheat Grain that was Infected with Fusarium graminearum. Journal of Cereal Science 1995, 21(1), 57–62.
   Web of Science ®Google Scholar
• Shahin, M.A.; Symons, S.J. Detection of Fusarium Damaged Kernels in Canada Western Red Spring Wheat using visible/Near-Infrared Hyperspectral Imaging and Principal Component Analysis. Computers and Electronics in Agriculture 2011, 75(1), 107–112.
   Web of Science ®Google Scholar
• Shahin, M.A.; Symons, S.J. Detection of Fusarium Damage in Canadian Wheat using visible/Near-Infrared Hyperspectral Imaging. Journal of Food Measurement and Characterization 2012, 6(1), 3–11.
   Google Scholar
• Siripatrawan, U.; Makino, Y. Monitoring Fungal Growth on Brown Rice Grains using Rapid and Non-Destructive Hyperspectral Imaging. International Journal of Food Microbiology 2015, 199, 93–100.
   PubMed Web of Science ®Google Scholar
• Müller-Fischer, N. Nutrient-Focused Processing of Rice. In Agricultural Sustainability; Bhullar, G.S.; Bhullar, N.K.; Eds.; Academic Press: San Diego, United States, 2013; 197–220.
   Google Scholar
• Carlos, C.; Marreto, D.A.; Poppi, R.J.; Sato, M.I.Z.; Ottoboni, L.M.M. Fourier Transform Infrared Microspectroscopy as a Bacterial Source Tracking Tool to Discriminate Fecal E. coli strains. Microchemical Journal 2011, 99(1), 15–19.
   Web of Science ®Google Scholar
• De Sousa Marques, A.; Nicácio, J.T.; Cidral, T.A.; de Melo, M.C.; de Lima, K.M. The Use of Near Infrared Spectroscopy and Multivariate Techniques to Differentiate Escherichia coli and Salmonella enteritidis Inoculated into Pulp Juice. Journal of Microbiological Methods 2013, 93(2), 90–94.
   PubMed Web of Science ®Google Scholar
• Guentzel, J.L.; Lam, K.L.; Callan, M.A.; Emmons, S.A.; Dunham, V.L. Reduction of Bacteria on Spinach, Lettuce, and Surfaces in Food Service Areas using Neutral Electrolyzed Oxidizing Water. Food Microbiology 2008, 25(1), 36–41.
   PubMed Web of Science ®Google Scholar
• Mahmoud, B.S.M.; Bachman, G.; Linton, R.H. Inactivation of Escherichia coli O157: H7,Listeria Monocytogenes, Salmonella enterica and Shigella flexneri on Spinach Leaves by X-ray. Food Microbiology 2010, 27(1), 24–28.
   PubMed Web of Science ®Google Scholar
• Xicohtencatl-Corte, J.; Sánchez Chacon, S.; Saldaña, Z.; Freer, E.; Girón, J.A. Interaction of Escherichia coli O157: H7with Leafy Green Produce. Journal of Food Protection 2009, 72(7), 1531–1537.
   PubMed Web of Science ®Google Scholar
• Kim, M.S.; Chen, Y.R.; Cho, B.K.; Chao, K.; Yang, C.C.; Lefcourt, A.M.; Chan, D. Hyperspectral Reflectance and Fluorescence Line-Scan Imaging for Online Defects and Fecal Contamination Inspection of Apples. Sensing and Instrumentation for Food Quality and Safety 2007, 1(3), 151–159.
   Google Scholar
• Yang, C.C.; Jun, W.; Kim, M.S.; Chao, K.; Kang, S.; Chan, D.E.; Lefcourt, A. Classification of Fecal Contamination on Leafy Greens by Hyperspectral Imaging. In Sensing for Agriculture and Food Quality and Safety II; Kim, L.S.; Ma, S.Y.; Chao, K.; Eds.; Proceedings of SPIE, Orlando, Florida 2010; 76760F.
   Google Scholar
• Yang, C.C.; Kim, M.S.; Kang, S.; Cho, B.K.; Chao, K.; Lefcourt, A.M.; Chan, D.E. Red to Far-Red Multispectral Fluorescence Image Fusion for Detection of Fecal Contamination on Apples. Journal of Food Engineering 2012, 108(2), 312–319.
   Web of Science ®Google Scholar
• Palou, L. Penicillium Digitatum, Penicillium Italicum (green mold, blue mold). In Postharvest Decay. Control Strategies; Bautista-Baños S.; Ed.; Academic Press, Elsevier Inc.: London, United Kingdom, 2014; 2, 45–102.
   Google Scholar
• Mehl, P.M.; Chen, Y.R.; Kim, M.S.; Chan, D.E. Development of Hyperspectral Imaging Technique for the Detection of Apple Surface Defects and Contaminations. Journal of Food Engineering 2004, 61(1), 67–81.
   Web of Science ®Google Scholar
• Li, J.; Huang, W.; Tian, X.; Wang, C.; Fan, S.; Zhao, C. Fast Detection and Visualization of Early Decay in Citrus using Vis-NIR Hyperspectral Imaging. Computers and Electronics in Agriculture 2016, 127, 582–592.
   Web of Science ®Google Scholar
• Lorente, D.; Blasco, J.; Serrano, A.J.; Soria-Olivas, E.; Aleixos, N.; Gómez-Sanchis, J. Comparison of ROC Feature Selection Method for the Detection of Decay in Citrus Fruit using Hyperspectral Images. Food and Bioprocess Technology 2013, 6(12), 3613–3619.
   Web of Science ®Google Scholar
• Gómez-Sanchis, J.; Gómez-Chova, L.; Aleixos, N.; Camps-Valls, G.; Montesinos-Herrero, C.; Moltó, E.; Blasco, J. Hyperspectral System for Early Detection of Rottenness Caused by Penicillium digitatum in Mandarins. Journal of Food Engineering 2008, 89(1), 80–86.
   Web of Science ®Google Scholar
• Hamad, S.H. The Microbial Quality of Processed Date Fruits Collected from a Factory in Al-Hofuf City, Kingdom of Saudi Arabia. Emirates Journal of Food and Agriculture 2012, 24(2), 105–112.
   Google Scholar
• Liu, G.; He, J.; Wang, S.; Luo, Y.; Wang, W.; Wu, L.; Si, Z.; He, X. Application of Near-Infrared Hyperspectral Imaging for Detection of External Insect Infestations on Jujube Fruit. International Journal of Food Properties 2016, 19(1), 41–52.
   Web of Science ®Google Scholar
• Xing, J.; Guyer, D.; Ariana, D.; Lu, R. Determining Optimal Wavebands using Genetic Algorithm for Detection of Internal Insect Infestation in Tart Cherry. Sensing and Instrumentation for Food Quality and Safety 2008, 2(3), 161–167.
   Google Scholar
• Singh, C.B.; Jayas, D.S.; Paliwal, J.; White, N.D.G. Detection of Insect-Damaged Wheat Kernels using Near-Infrared Hyperspectral Imaging. Journal of Stored Products Research 2009, 45(3), 151–158.
   Web of Science ®Google Scholar
• Millner, P.; Martin, P.; Reynolds, S.; Reynnells, R. Survival of E. coli O157:H7, Salmonella, and Listeria in Frass from Hornworms on Tomato Leaves. International Association of Food Protection Annual Meeting, 2011, 2–77.
   Google Scholar
• Yang, C.C.; Kim, M.S.; Millner, P.; Chao, K.; Cho, B.K.; Mo, C.; Lee, H.; Chan, D.E. Development of Multispectral Imaging Algorithm for Detection of Frass on Mature Red Tomatoes. Postharvest Biology and Technology 2014, 93, 1–8.
   Web of Science ®Google Scholar
• Schirmer, B.C.; Langsrud, S. Evaluation of Natural Antimicrobials on Typical Meat Spoilage Bacteria in vitro and in Vacuum-Packed Pork Meat. Journal of Food Science 2010, 75(2), 98–102.
   Web of Science ®Google Scholar
• Xiong, Z.; Sun, D.W.; Zeng, X.A.; Xie, A. Recent Developments of Hyperspectral Imaging Systems and their Applications in Detecting Quality Attributes of Red Meats: A Review. Journal of Food Engineering 2014, 132, 1–13.
   Web of Science ®Google Scholar
• Feng, Y.Z.; ElMasry, G.; Sun, D.W.; Walsh, D.; Morcy, N. Near-Infrared Hyperspectral Imaging and Partial Least Squares Regression for Rapid and Reagentless Determination of Enterobacteriaceae on Chicken Fillets. Food Chemistry 2013, 138(2–3), 1829–1836.
   PubMed Web of Science ®Google Scholar
• Tao, F.; Peng, Y.; Li, Y.; Chao, K.; Dhakal, S. Simultaneous Determination of Tenderness and Escherichia coli Contamination of Pork using Hyperspectral Scattering Technique. Meat Science 2012, 90(3), 851–857.
   PubMed Web of Science ®Google Scholar
• Feng, Y.Z.; Sun, D.W. Determination of Total Viable Count (TVC) in Chicken Breast Fillets by Near-Infrared Hyperspectral Imaging and Spectroscopic Transforms. Talanta 2013a, 105, 244–249.
   PubMed Web of Science ®Google Scholar
• Wu, D.; Sun, D.W. Potential of Time Series-Hyperspectral Imaging (TS-HSI) for Non-Invasive Determination of Microbial Spoilage of Salmon Flesh. Talanta 2013c, 111, 39–46
   PubMed Web of Science ®Google Scholar
• Peng, Y.K.; Zhang, J.; Wu, J.H.; Huang, H. Hyperspectral Scattering Profiles for Prediction of the Microbial Spoilage of Beef. Proceeding of SPIE 7315, Sensing for Agriculture and Food Quality and Safety 2009, 73150Q–1.
   Google Scholar
• Panagou, E.Z.; Papadopoulou, O.; Carstensen, J.M.; Nychas, G.J. Potential of Multispectral Imaging Technology for Rapid and Non-Destructive Determination of the Microbiological Quality of Beef Filets during Aerobic Storage. International Journal of Food Microbiology 2014, 174, 1–11.
   PubMed Web of Science ®Google Scholar
• Barlocco, N.; Vadell, A.; Ballesteros, F.; Galietta, G.; Cozzolino, D. Predicting Intramuscular Fat, Moisture and Warner-Bratzler Shear Force in Pork Muscle using Near Infrared Reflectance Spectroscopy. Animal Science 2006, 82(1), 111–116.
   Google Scholar
• Peng, Y.K.; Wang, W. Prediction of Pork Meat Total Viable Bacteria Count using Hyperspectral Imaging System and Support Vector Machines. In Food Processing Automation Conference Proceedings, Providence, Rhode Island, United States, 2008, Paper 701P0508cd.
   Google Scholar
• Wang, W.; Peng, Y.K.; Zhang, X.L. Study on Modeling Method of Total Viable Count of Fresh Pork Meat based on Hyperspectral Imaging System. Spectroscopy and Spectral Analysis 2010, 30(2), 411–415.
   Web of Science ®Google Scholar
• Wang, W.; Peng, Y.K.; Huang, H.; Wu, J. Application of Hyperspectral Imaging Technique for the Detection of Total Viable Bacteria Count in Pork. Sensor Letters 2011, 9(3), 1024–1030.
   Web of Science ®Google Scholar
• Dissing, B.S.; Papadopoulou, O.S.; Tassou, C.; Ersbøll, B.K.; Carstensen, J.M.; Panagou, E.Z.; Nychas, G.J. Using Multispectral Imaging for Spoilage Detection of Pork Meat. Food and Bioprocess Technology 2013, 6(9), 2268–2279.
   Web of Science ®Google Scholar
• Barbin, D.F.; ElMasry, G.; Sun, D.W.; Allen, P.; Morsy, N. Non-Destructive Assessment of Microbial Contamination in Porcine Meat using NIR Hyperspectral Imaging. Innovative Food Science & Emerging Technologies 2013, 17, 180–191.
   Web of Science ®Google Scholar
• Ma, F.; Yao, J.; Xie, T.; Liu, C.; Chen, W.; Chen, C.; Zheng, L. Multispectral Imaging for Rapid and Non-Destructive Determination of Aerobic Plate Count (APC) in Cooked Pork Sausages. Food Research International 2014, 62, 902–908.
   Web of Science ®Google Scholar
• Tao, F.; Wang, W.; Li, Y.; Peng, Y.; Wu, J.; Shan, J.; Zhang, L. A Rapid Nondestructive Measurement Method for Assessing the Total Plate Count on Chilled Pork Surface. Spectroscopy and Spectral Analysis 2010, 30(12), 3405–3409.
   Web of Science ®Google Scholar
• Liu, Y.; Windham, W.R.; Lawrence, K.C.; Park, B. Simple Algorithms for the Classification of Visible/NIR and Hyperspectral Imaging Spectra of Chicken Skins, Feces, and Fecal Contaminated Skins. Applied Spectroscopy 2003, 57(12), 1609–1612.
   PubMed Web of Science ®Google Scholar
• Chao, K.L.; Yang, C.C.; Kim, M.S. Spectral Line-Scan Imaging System for High-Speed Non-Destructive Wholesomeness Inspection of Broilers. Trends in Food Science & Technology 2010, 21(3), 129–137.
   Web of Science ®Google Scholar
• Yang, C.C.; Chao, K.L.; Chen, Y.R.; Early, H.L. Systemically Diseased Chicken Identification using Multispectral Images and Region of Interest Analysis. Computers and Electronics in Agriculture 2005, 49(2), 255–271.
   Web of Science ®Google Scholar
• Yoon, S.C.; Park, B.; Lawrence, K.C.; Windham, W.R.; Heitschmidt, G.W. Line-scan Hyperspectral Imaging System for Real-Time Inspection of Poultry Carcasses with Fecal Material and Ingesta. Computers and Electronics in Agriculture 2011, 79(2), 159–168.
   Web of Science ®Google Scholar
• Berrang, M.E.; Smith, D.P.; Windham, W.R.; Feldner, P.W. Effect of Intestinal Content Contamination on Broiler Carcass Campylobacter Counts. Journal of Food Protection 2004, 67(2), 235–238.
   PubMed Web of Science ®Google Scholar
• Windham, W.R.; Smith, D.P.; Park, B.; Lawrence, K.C. Algorithm development with visible/near infrared spectra for detection of poultry feces and ingesta. Transactions of the ASAE 2003, 46, 1733–1738.
   Google Scholar
• Park, B.; Lawrence, K.C.; Windham, W.R.; Smith, D.P. Performance of Hyperspectral Imaging System for Poultry Surface Fecal Contaminant Detection. Journal of Food Engineering 2006, 75(3), 340–348.
   Web of Science ®Google Scholar
• Park, B.; Yoon, S.C.; Windham, W.; Lawrence, K.; Kim, M.; Chao, K. Line-scan hyperspectral Imaging for Real-Time In-Line Poultry Fecal Detection. Sensing and Instrumentation for Food Quality and Safety 2011, 5(1), 25–32.
   Google Scholar
• Ye, X.; Iino, K.; Zhang, S. Monitoring of Bacterial Contamination on Chicken Meat Surface using a Novel Narrowband Spectral Index Derived From Hyperspectral Imagery Data. Meat Science 2016, 122, 25–31.
   PubMed Web of Science ®Google Scholar
• Alexandrakis, D.; Downey, G.; Scannell, A. Rapid Non-Destructive Detection of Spoilage of Intact Chicken Breast Muscle using Near-Infrared and Fourier Transform Mid-Infrared Spectroscopy and Multivariate Statistics. Food and Bioprocess Technology 2012, 5(1), 338–347.
   Web of Science ®Google Scholar
• Nychas, G.; Dillon, V.M.; Board, R.G. Glucose, The Key Substrate in the Microbiological Changes Occurring in Meat and Certain Meat Products. Biotechnology and Applied Biochemistry 1988, 10(3), 203–231.
   PubMed Web of Science ®Google Scholar
• Warriss, P.D. Meat Science: An Introductory Text, 2nd ed.; Cabi Series & Modular Texts Wallingford, Oxfordshire, United Kingdom, 2010; 130–150.
   Google Scholar
• Board, R.J.; Davies, A.R. The Microbiology of Meat and Poultry; Blackie Academic & Professional: London, United Kingdom, 1998, 266–282.
   Google Scholar
• Qin, J.; Chao, K.; Kim, M.S.; Kang, S.; Cho, B.K.; Jun, W. Detection of Organic Residues on Poultry Processing Equipment Surfaces by LED-Induced Fluorescence Imaging. Applied Engineering in Agriculture 2011b, 27(1), 153–161.
   Web of Science ®Google Scholar
• Jun, W.; Kim, M.S.; Cho, B.K.; Millner, P.D.; Chao, K.; Chan, D.E. Microbial Biofilm Detection on Food Contact Surfaces by Macro-Scale Fluorescence Imaging. Journal of Food Engineering 2010, 99(3), 314–322.
   Web of Science ®Google Scholar
• Sone, I.; Olsen, R.L.; Sivertsen, A.H.; Eilertsen, G.; Heia, K. Classification of Fresh Atlantic Salmon (Salmo salar L.) Fillets Stored under Different Atmospheres by Hyperspectral Imaging. Journal of Food Engineering 2012, 109(3), 482–489.
   Web of Science ®Google Scholar
• Gram, L.; Dalgaard, P. Fish Spoilage Bacteria - Problems and Solutions. Current Opinion in Biotechnology 2002, 13(3), 262–266.
   PubMed Web of Science ®Google Scholar
• He, H.J.; Sun, D.W.; Wu, D. Rapid and Real-Time Prediction of Lactic Acid Bacteria (LAB) in Farmed Salmon Flesh using Near-Infrared (NIR) Hyperspectral Imaging Combined with Chemometric Analysis. Food Research International 2014, 62, 476–483.
   Web of Science ®Google Scholar
• Limbo, S.; Sinelli, N.; Torri, L.; Riva, M. Freshness Decay and Shelf Life Predictive Modelling of European Sea Bass (Dicentrarchus labrax) Applying Chemical Methods and Electronic Nose. LWT - Food Science and Technology 2009, 42(5), 977–984.
   Web of Science ®Google Scholar
• Restuccia, D.; Spizzirri, U.G.; Bonesi, M.; Tundis, R.; Menichini, F.; Picci, N.; Loizzo, M.R. Evaluation of Fatty Acids and Biogenic Amines Profiles in Mullet and Tuna Roe during Six Months of Storage at 4 °C. Journal of Food Composition and Analysis 2015, 40, 52–60.
   Web of Science ®Google Scholar
• Lee, S.; Joo, S.T.; Alderton, A.L.; Hill, D.W.; Faustman, C. Oxymyoglobin and Lipid Oxidation in Yellow Fin Tuna (Thunnus albacares) Loins. Journal of Food Science 2003, 68(5), 1664–1668.
   Web of Science ®Google Scholar
• Maestre, R.; Pazos, M.; Medina, I. Involvement of Methemoglobin (metHb) Formation and Hemin Loss in the Pro-Oxidant Activity of Fish Hemoglobins. Journal of Agricultural and Food Chemistry 2009, 57(15), 7013–7021.
   PubMed Web of Science ®Google Scholar
• Shikama, K. The Molecular Mechanism of Autoxidation for Myoglobin and Hemoglobin: A Venerable Puzzle. Chemical Reviews 1998, 98(4), 1357–1374.
   PubMed Web of Science ®Google Scholar
• Cheng, J.H.; Sun, D.W. Rapid and Non-Invasive Detection of Fish Microbial Spoilage by Visible and Near Infrared Hyperspectral Imaging and Multivariate Analysis. LWT - Food Science and Technology 2015a, 62(2), 1060–1068.
   Web of Science ®Google Scholar
• Diaz, P.; Garrido, M.D.; Banon, S. Spoilage of Sous Vide Cooked Salmon (Salmo salar) Stored under Refrigeration. Food Science and Technology International 2011, 17(1), 31–37.
   PubMed Web of Science ®Google Scholar
• He, H.J.; Sun, D.W. Inspection of Harmful Microbial Contamination Occurred in Edible Salmon Flesh using Imaging Technology. Journal of Food Engineering 2015b, 150, 82–89.
   Web of Science ®Google Scholar
• He, H.J.; Sun, D.W. Selection of Informative Spectral Wavelength for Evaluating and Visualising Enterobacteriaceae Contamination of Salmon Flesh. Food Analytical Methods 2015d, 8(10), 2427–2436.
   Web of Science ®Google Scholar
• Cheng, J.H.; Sun, D.W. Rapid Quantification Analysis and Visualization of Escherichia coli Loads in Grass Carp Fish Flesh by Hyperspectral Imaging Method. Food and Bioprocess Technology 2015c, 8(5), 951–959.
   Web of Science ®Google Scholar
• Abdel-Nour, N.; Ngadi, M. Detection of Omega-3 Fatty Acid in Designer Eggs using Hyperspectral Imaging. International Journal of Food Sciences and Nutrition 2011, 62(4), 418–422.
   PubMed Web of Science ®Google Scholar
• Zhang, W.; Pan, L.; Tu, S.; Zhan, G.; Tu, K. Non-destructive Internal Quality Assessment of Eggs using a Synthesis of Hyperspectral Imaging and Multivariate Analysis.
   Google Scholar
• Journal of Food Engineering 2015, 157, 41–48.
   Google Scholar
• Gole, V.; Roberts, J.; Sexton, M.; May, D.; Kiermeier, A.; Chousalkar, K. Effect of Egg Washing and Correlation between Cuticle and Egg Penetration by various Salmonella Strains. International Journal of Food Microbiology 2014, 182–183, 18–25.
   PubMed Web of Science ®Google Scholar
• Lunadei, L.; Ruiz-Garcia, L.; Bodria, L.; Guidetti, R. Automatic Identification of Defects on Eggshell through a Multispectral Vision System. Food and Bioprocess Technology 2012, 5(8), 3042–3050.
   Web of Science ®Google Scholar
• Carrada, T. Cisticercosis Subcutánea: Parasitosis Letal y Emergente. Piel 2005, 20(4), 177–182.
   Google Scholar
• Molina-Fernández, D.; Malagón, D.; Gómez-Mateos, M.; Benítez, R.; Martín-Sánchez, J.; Adroher, F.J. Fishing Area and Fish Size as Risk Factors of Anisakis Infection in Sardines (Sardina pilchardus) from Iberian Waters, Southwestern Europe. International Journal of Food Microbiology 2015, 203, 27–34.
   PubMed Web of Science ®Google Scholar
• Pozio, E. Searching for Trichinella: Not All Pigs are Created Equal. Trends in Parasitology 2014, 30(1), 4–11.
   PubMed Web of Science ®Google Scholar
• Sivertsen, A.H.; Heia, K.; Stormo, S.K.; Elvevoll, E.; Nilsen, H. Automatic Nematode Detection in Cod Fillets (Gadus morhua) by Transillumination Hyperspectral Imaging. Journal of Food Science 2011, 76(1), S77–S83.
   PubMed Web of Science ®Google Scholar
• Gonzalez, M.; Jaramillo, E. The Association between Mulinia edulis (Mollusca, Bivalvia) and Edotea magellanica (Crustacea, Isopoda) in Southern Chile. Revista Chilena de Historia Natural 1991, 64, 37–51.
   Web of Science ®Google Scholar
• Coelho, P.A.; Soto, M.E.; Torres, S.N.; Sbarbaro, D.G.; Pezoa, J.E. Hyperspectral Transmittance Imaging of the Shell-Free Cooked Clam Mulinia edulis for Parasite Detection. Journal of Food Engineering 2013, 117(3), 408–416.
   Web of Science ®Google Scholar
• Gómez-De-Anda, F.; Dorantes-Álvarez, L.; Gallardo-Velázquez, T.; Osorio-Revilla, G.; Calderón-Domínguez, G.; Martínez Labat, P.; de-la-Rosa-Arana J.L., Determination of Trichinella spiralis in Pig Muscles using Mid-Fourier Transform Infrared Spectroscopy (MID-FTIR) with Attenuated Total Reflectance (ATR) and Soft Independent Modeling of Class Analogy (SIMCA). Meat Science 2012, 91(3), 240–246.
   PubMed Web of Science ®Google Scholar
• Organización Panamericana de la Salud ( OPS). Riesgos a la salud por la crianza de cerdos alimentados en sitios de disposición final de residuos sólidos en América Latina y el Caribe. Investigación bibliográfica. Lima, Perú, 2007.
   Google Scholar
• Holman, H.Y.; Miles, R.; Hao, Z.; Wozei, E.; Anderson, L.M.; Yang, H. Real-time Chemical Imaging of Bacterial Activity in Biofilms using Open-Channel Microfluidics and Synchrotron FTIR Spectromicroscopy. Analytical Chemistry 2009, 81(20), 8564–8570.
   PubMed Web of Science ®Google Scholar
• Goel, M.; Whitmire, E.; Mariakakis, A.; Saponas, T.S.; Joshi, N.; Morris, D.; Guenter, B.; Gavriliu, M.; Borriello, G.; Patel, S.N. HyperCam: Hyperspectral Imaging for Ubiquitous Computing Applications In UbiComp 2015 - Proceedings of the 2015. ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2015, 145–156.
   Google Scholar
• Malorny, B.; Paccassoni, E.; Fach, P.; Bunge, C.; Martin, A.; Helmuth, R. Diagnostic real-time PCR for Detection of Salmonella in Food. Applied and Environmental Microbiology 2004, 70(12), 7046–7052.
   PubMed Web of Science ®Google Scholar
• Golmohammadi, M.; Cubero, J.; Penalver, J.; Quesada. J.; Lopez, M.; Llop, P. Diagnosis of Xanthomonas Axonopodis pv. citri, Causal Agent of Citrus Canker, in Commercial Fruits by Isolation and PCR-based Methods. Journal of Applied Microbiology 2007, 103(6), 2309–2315.
   PubMed Web of Science ®Google Scholar
• Levin, R.E. PCR detection of Aflatoxin Producing Fungi and its Limitations. Review. International Journal of Food Microbiology 2012, 156(1), 1–6.
   PubMed Web of Science ®Google Scholar
• Kaliramesh, S.; Chelladurai, V.; Jayas, D.; Alagusundaram, K.; White, N.; Fields, P. Detection of Infestation by Callosobruchus maculatus in Mung Bean using near-Infrared Hyperspectral Imaging. Journal of Stored Products Research 2013, 52, 107–111.
   Web of Science ®Google Scholar