Seeing red: A review of the use of near-infrared spectroscopy (NIRS) in entomology

References

• Lazzari, S.; Ceruti, F.; Rodriguez-Fernandez, J.; Opit, G.; Lazzari, F. Intra and Interspecific Variation Assessment in Psocoptera Using near Spectoscopy. Julius-Kühn-Arch. 2010, 425, 139.
   Google Scholar
• Aw, W. C.; Ballard, J. W. O. The Effects of Temperature and Diet on Age Grading and Population Age Structure Determination in Drosophila. J. Insect Physiol. 2013, 59, 994–1000.
   PubMed Web of Science ®Google Scholar
• Pasquini, C. Near Infrared Spectroscopy: Fundamentals, Practical Aspects and Analytical Applications. J. Braz. Chem. Soc. 2003, 14, 198–219.
   Web of Science ®Google Scholar
• Dowell, F. E.; Throne, J. E.; Baker, J. E. Automated Nondestructive Detection of Internal Insect Infestation of Wheat Kernels by Using Near-Infrared Reflectance Spectroscopy. J. Econ. Entomol. 1998, 91, 899–904.
   Web of Science ®Google Scholar
• Cozzolino, D. An Overview of the Use of Infrared Spectroscopy and Chemometrics in Authenticity and Traceability of Cereals. Food Res. Int. 2014, 60, 262–265.
   Web of Science ®Google Scholar
• Johnson, J.; Collins, T.; Skylas, D.; Naiker, M. ATR-MIR: A Valuable Tool for the Rapid Assessment of Biochemically Active Compounds in Grains. In 69th Australasian Grain Science Conference, Carlton, Melbourne, Australia, August 27–29, Walker, C., Panozzo, J., Eds., 2019; Australian Grain Science Association, Carlton, Melbourne, Australia, 2019, p 73–79.
   Google Scholar
• Vance, C. K.; Tolleson, D. R.; Kinoshita, K.; Rodriguez, J.; Foley, W. J. Near Infrared Spectroscopy in Wildlife and Biodiversity. J. Near Infrared Spectrosc. 2016, 24, 1–25.
   Web of Science ®Google Scholar
• Nansen, C.; Ribeiro, L. P.; Dadour, I.; Roberts, J. D. Detection of Temporal Changes in Insect Body Reflectance in Response to Killing Agents. PloS One 2015, 10, e0124866.
   PubMed Web of Science ®Google Scholar
• Rodríguez-Fernández, J. I.; de Carvalho, C. J.; Pasquini, C.; de Lima, M. G.; Moura, M. O.; Arízaga, G. G. C. Barcoding Without DNA? Species Identification Using Near Infrared Spectroscopy. Zootaxa 2011, 2933, 46–54.
   Google Scholar
• Throne, J. E.; Dowell, F. E.; Perez-Mendoza, J.; Baker, J. E. Entomological Applications of Near-Infrared Spectroscopy. In Proceedings of the 8th International Working Conference on Stored Product Protection, York, UK, July 22–26, 2003; Credland, P.F., Armitage, D.M., Bell, C.H., Cogan, P.M., Highley, E., Eds., CAB International, Wallingford, United Kingdom, 2003; p 131–134.
   Google Scholar
• Chapman, R. F.; Espelie, K. E.; Sword, G. A. Use of Cuticular Lipids in Grasshopper Taxonomy: A Study of Variation in Schistocerca shoshone (Thomas). Biochem. Syst. Ecol. 1995, 23, 383–398.
   Web of Science ®Google Scholar
• van Wilgenburg, E.; Ryan, D.; Morrison, P.; Marriott, P. J.; Elgar, M. Nest-and Colony-Mate Recognition in Polydomous Colonies of Meat Ants (Iridomyrmex purpureus). Naturwissenschaften 2006, 93, 309–314.
   PubMedGoogle Scholar
• Thomas, M. L.; Parry, L. J.; Allan, R. A.; Elgar, M. A. Geographic Affinity, Cuticular Hydrocarbons and Colony Recognition in the Australian Meat Ant Iridomyrmex purpureus. Naturwissenschaften 1999, 86, 87–92.
   Google Scholar
• Wagner, D.; Tissot, M.; Cuevas, W.; Gordon, D. M. Harvester Ants Utilize Cuticular Hydrocarbons in Nestmate Recognition. J. Chem. Ecol. 2000, 26, 2245–2257.
   Web of Science ®Google Scholar
• Blomquist, G. J.; Bagnéres, A. Eds. Structure and Analysis of Insect Hydrocarbons. In Insect Hydrocarbons: Biology, Biochemistry, and Chemical Ecology; Cambridge University Press: New York, 2010; pp 19–34.
   Google Scholar
• Newey, P.; Robson, S.; Crozier, R. Near-Infrared Spectroscopy Identifies the Colony and Nest of Origin of Weaver Ants, Oecophylla Smaragdina. Insect. Soc. 2008, 55, 171–175.
   Web of Science ®Google Scholar
• Junior, W.; Súarez, Y.; Izida, T.; Andrade, L.; Lima, S. Intra-and Interspecific Variation of Cuticular Hydrocarbon Composition in Two Ectatomma Species (Hymenoptera: Formicidae) Based on Fourier Transform Infrared Photoacoustic Spectroscopy. Genet. Mol. Res. 2008, 7, 559–566.
   PubMed Web of Science ®Google Scholar
• Junior, W. A.; Lima, S.; Andrade, L.; Súarez, Y. Comparative Study of the Cuticular Hydrocarbon in Queens, Workers and Males of Ectatomma vizottoi (Hymenoptera, Formicidae) by Fourier Transform-Infrared Photoacoustic Spectroscopy. Genet. Mol. Res. 2007, 6, 492–499.
   PubMed Web of Science ®Google Scholar
• Newey, P. S.; Robson, S. K.; Crozier, R. H. Near-Infrared Spectroscopy as a Tool in Behavioural Ecology: A Case Study of the Weaver Ant, Oecophylla smaragdina. Anim. Behav. 2008, 76, 1727–1733.
   Web of Science ®Google Scholar
• Grace, T.; Wisely, S. M.; Brown, S. J.; Dowell, F. E.; Joern, A. Divergent Host Plant Adaptation Drives the Evolution of Sexual Isolation in the Grasshopper Hesperotettix viridis (Orthoptera: Acrididae) in the Absence of Reinforcement. Biol. J. Linn. Soc. 2010, 100, 866–878.
   Web of Science ®Google Scholar
• Jouquet, P.; Capowiez, Y.; Bottinelli, N.; Traoré, S. Potential of near Infrared Reflectance Spectroscopy (NIRS) for Identifying Termite Species. Eur. J. Soil Biol. 2014, 60, 49–52.
   Web of Science ®Google Scholar
• Aldrich, B. T.; Maghirang, E. B.; Dowell, F. E.; Kambhampati, S. Identification of Termite Species and Subspecies of the Genus Zootermopsis Using Near-Infrared Reflectance Spectroscopy. J. Insect Sci. 2007, 7, 1.
   Web of Science ®Google Scholar
• Mayagaya, V. S.; Michel, K.; Benedict, M. Q.; Killeen, G. F.; Wirtz, R. A.; Ferguson, H. M.; Dowell, F. E. Non-Destructive Determination of Age and Species of Anopheles gambiae s.l. Using Near-Infrared Spectroscopy. Am. J. Trop. Med. Hyg. 2009, 81, 622–630.
   PubMed Web of Science ®Google Scholar
• Fischnaller, S.; Dowell, F. E.; Lusser, A.; Schlick-Steiner, B. C.; Steiner, F. M. Non-Destructive Species Identification of Drosophila obscura and D. subobscura (Diptera) Using Near-Infrared Spectroscopy. Fly 2012, 6, 284–289.
   PubMed Web of Science ®Google Scholar
• Jia, F.; Maghirang, E.; Dowell, F.; Abel, C.; Ramaswamy, S. Differentiating Tobacco Budworm and Corn Earworm Using Near-Infrared Spectroscopy. J. Econ. Entomol. 2007, 100, 759–764.
   PubMed Web of Science ®Google Scholar
• Johnson, J.; Atkin, D.; Lee, K.; Sell, M.; Chandra, S. Determining Meat Freshness Using Electrochemistry: Are we Ready for the Fast and Furious?. Meat Sci. 2018, 150, 40–46.
   PubMed Web of Science ®Google Scholar
• Caporaso, N.; Whitworth, M. B.; Fisk, I. D. Near-Infrared Spectroscopy and Hyperspectral Imaging for Non-Destructive Quality Assessment of Cereal Grains. Appl. Spectrosc. Rev. 2018, 53, 667–687.
   Web of Science ®Google Scholar
• Storey, C.; Sauer, D.; Ecker, O.; Fulk, D. Insect Infestations in Wheat and Corn Exported from the United States. J. Econ. Entomol. 1982, 75, 827–832.
   Web of Science ®Google Scholar
• Banga, K. S.; Kotwaliwale, N.; Mohapatra, D.; Giri, S. K. Techniques for Insect Detection in Stored Food Grains: An Overview. Food Control 2018, 94, 167–176.
   Web of Science ®Google Scholar
• Association of Official Analytical Chemists, 16.5. 11 AOAC Official method 972.32, Light filth (pre-and post-milling) in flour (white). Official Methods of Analysis of Association of Official Analytical Chemists International 1996, 18.
   Google Scholar
• Karunakaran, C.; Jayas, D.; White, N. Soft X–Ray Inspection of Wheat Kernels Infested by Sitophilus Oryzae. Trans. ASAE 2003, 46, 739.
   Google Scholar
• Toews, M. D.; Pearson, T. C.; Campbell, J. F. Imaging and Automated Detection of Sitophilus oryzae (Coleoptera: Curculionidae) Pupae in Hard Red Winter Wheat. J. Econ. Entomol. 2006, 99, 583–592.
   PubMed Web of Science ®Google Scholar
• Quinn, F. A.; Burkholder, W.; Kitto, B. G. Immunological Technique for Measuring Insect Contamination of Grain. J. Econ. Entomol. 1992, 85, 1463–1470.
   Web of Science ®Google Scholar
• Rotariu, L.; Lagarde, F.; Jaffrezic-Renault, N.; Bala, C. Electrochemical Biosensors for Fast Detection of Food Contaminants–Trends and Perspective. TrAC Trends Anal. Chem. 2016, 79, 80–87.
   Web of Science ®Google Scholar
• Ghasemi-Varnamkhasti, M.; Lozano, J. Electronic Nose as an Innovative Measurement System for the Quality Assurance and Control of Bakery Products: A Review. Eng. Agric. Environ. Food 2016, 9, 365–374.
   Google Scholar
• Mishra, G.; Srivastava, S.; Panda, B. K.; Mishra, H. Sensor Array Optimization and Determination of Rhyzopertha dominica Infestation in Wheat Using Hybrid Neuro-Fuzzy-Assisted Electronic Nose Analysis. Anal. Methods 2018, 10, 5687–5695.
   Web of Science ®Google Scholar
• Ridgway, C.; Chambers, J. Detection of External and Internal Insect Infestation in Wheat by Near‐Infrared Reflectance Spectroscopy. J. Sci. Food Agric. 1996, 71, 251–264.
   Web of Science ®Google Scholar
• Ghaedian, A. R.; Wehling, R. L. Discrimination of Sound and Granary-Weevil-Larva-Infested Wheat Kernels by Near-Infrared Diffuse Reflectance Spectroscopy. J. AOAC Int. (USA) 1997, 80, 997–1005.
   Web of Science ®Google Scholar
• Baker, J. E.; Dowell, F. E.; Throne, J. E. Detection of Parasitized Rice Weevils in Wheat Kernels with Near-Infrared Spectroscopy1. Biol. Control 1999, 16, 88–90.
   Web of Science ®Google Scholar
• Ridgway, C.; Chambers, J.; Cowe, I. A. Detection of Grain Weevils inside Single Wheat Kernels by a Very Near Infrared Two-Wavelength Model. J. Near Infrared Spectrosc. 1999, 7, 213–221.
   Web of Science ®Google Scholar
• Cheewapramong, P.; Wehling, R. A Simplified near–infrared method for detecting internal insect infestation in wheat kernels. In 2001 AACC Annual Meeting, AACC: Charlotte, North Carolina, 2001.
   Google Scholar
• Perez-Mendoza, J.; Throne, J. E.; Dowell, F. E.; Baker, J. E. Detection of Insect Fragments in Wheat Flour by Near-Infrared Spectroscopy. J. Stored Prod. Res. 2003, 39, 305–312.
   Web of Science ®Google Scholar
• Maghirang, E. B.; Dowell, F.; Baker, J.; Throne, J. E. Automated Detection of Single Wheat Kernels Containing Live or Dead Insects Using Near–Infrared Reflectance Spectroscopy. Trans. ASAE 2003, 46, 1277.
   Google Scholar
• Maghirang, E. B.; Dowell, F. E.; Baker, J. E.; Throne, J. E. Detecting Single Wheat Kernels containing Live or Dead Insects using Near-Infrared Reflectance Spectroscopy. In 2002 ASAE Annual International Meeting/CIGR XVth World Congress. ASAE: Chicago, Illinois, USA, 2002.
   Google Scholar
• Paliwal, J.; Wang, W.; Symons, S.; Karunakaran, C. Insect Species and Infestation Level Determination in Stored Wheat Using Near-Infrared Spectroscopy. Can. Biosyst. Eng. 2004, 46, 17–24.
   Google Scholar
• Perez-Mendoza, J.; Throne, J. E.; Maghirang, E. B.; Dowell, F. E.; Baker, J. E. Insect Fragments in Flour: Relationship to Lesser Grain Borer (Coleoptera: Bostrichidae) Infestation Level in Wheat and Rapid Detection Using Near-Infrared Spectroscopy. J. Econ. Entomol. 2005, 98, 2282–2291.
   PubMed Web of Science ®Google Scholar
• Toews, M. D.; Perez-Mendoza, J.; Throne, J. E.; Dowell, F. E.; Maghirang, E.; Arthur, F. H.; Campbell, J. F. Rapid Assessment of Insect Fragments in Flour Milled from Wheat Infested with Known Densities of Immature and Adult Sitophilus Oryzae (Coleoptera: Curculionidae). J. Econ. Entomol. 2007, 100, 1714–1723.
   PubMed Web of Science ®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. J. Stored Prod. Res. 2009, 45, 151–158.
   Web of Science ®Google Scholar
• Singh, C. B.; Jayas, D. S.; Paliwal, J.; White, N. D. G. Identification of Insect-Damaged Wheat Kernels Using Short-Wave Near-Infrared Hyperspectral and Digital Colour Imaging. Comput. Electron. Agric. 2010, 73, 118–125.
   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. J. Stored Prod. Res. 2013, 52, 107–111.
   Web of Science ®Google Scholar
• Agha, M. K.; Lee, W.; Wang, C.; Mankin, R.; Blount, A.; Bucklin, R.; Bliznyuk, N. Detection and Prediction of Sitophilus Oryzae Infestations in Triticale via Visible and near-Infrared Spectral Signatures. J. Stored Prod. Res. 2017, 72, 1–10.
   Web of Science ®Google Scholar
• Jarruwat, P.; Choomjaihan, P. Applicability of Near Infrared Spectroscopy for Detecting Post-Fumigated Weevils in Packaged Rice. J. Near Infrared Spectrosc. 2017, 25, 72–81.
   Web of Science ®Google Scholar
• Mishra, G.; Srivastava, S.; Panda, B. K.; Mishra, H. N. Rapid Assessment of Quality Change and Insect Infestation in Stored Wheat Grain Using FT-NIR Spectroscopy and Chemometrics. Food Anal. Methods 2018, 11, 1189–1198.
   Web of Science ®Google Scholar
• Srivastava, S.; Mishra, G.; Mishra, H. N. FTNIR-A Robust Diagnostic Tool for the Rapid Detection of Rhyzopertha dominica and Sitophilus oryzae Infestation and Quality Changes in Stored Rice Grains. Food Bioprocess. Technol. 2018, 11, 785–796.
   Web of Science ®Google Scholar
• Srivastava, S.; Mishra, G.; Mishra, H. N. Identification and Differentiation of Insect Infested Rice Grains Varieties with FTNIR Spectroscopy and Hierarchical Cluster Analysis. Food Chem. 2018, 268, 402–410.
   PubMed Web of Science ®Google Scholar
• Armstrong, P.; Maghirang, E.; Ozulu, M. Determining Damage Levels in Wheat Caused by Sunn Pest (Eurygaster integriceps) Using Visible and Near-Infrared Spectroscopy. J. Cereal Sci. 2019, 86, 102–107.
   Web of Science ®Google Scholar
• Biancolillo, A.; Firmani, P.; Bucci, R.; Magrì, A.; Marini, F. Determination of Insect Infestation on Stored Rice by near Infrared (NIR) Spectroscopy. Microchem. J. 2019, 145, 252–258.
   Web of Science ®Google Scholar
• Santos, P. M.; Simeone, M. L. F.; Pimentel, M. A. G.; Sena, M. M. Non-Destructive Screening Method for Detecting the Presence of Insects in Sorghum Grains Using Near Infrared Spectroscopy and Discriminant Analysis. Microchem. J. 2019, 149, 104057.
   Web of Science ®Google Scholar
• Chelladurai, V.; Karuppiah, K.; Jayas, D.; Fields, P.; White, N. Detection of Callosobruchus maculatus (F.) Infestation in Soybean Using Soft X-Ray and NIR Hyperspectral Imaging Techniques. J. Stored Prod. Res. 2014, 57, 43–48.
   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. Sens. Instrum. Food Qual. 2008, 2, 161–167.
   Google Scholar
• Xing, J.; Guyer, D. Detecting Internal Insect Infestation in Tart Cherry Using Transmittance Spectroscopy. Postharvest Biol. Technol. 2008, 49, 411–416.
   Web of Science ®Google Scholar
• Xing, J.; Guyer, D. Comparison of Transmittance and Reflectance to Detect Insect Infestation in Montmorency Tart Cherry. Comput. Electron. Agric. 2008, 64, 194–201.
   Web of Science ®Google Scholar
• Wang, J.; Nakano, K.; Ohashi, S. Nondestructive Detection of Internal Insect Infestation in Jujubes Using Visible and Near-Infrared Spectroscopy. Postharvest Biol. Technol. 2011, 59, 272–279.
   Web of Science ®Google Scholar
• Wang, J.; Nakano, K.; Ohashi, S.; Takizawa, K.; He, J. G. Comparison of Different Modes of Visible and Near-Infrared Spectroscopy for Detecting Internal Insect Infestation in Jujubes. J. Food Eng. 2010, 101, 78–84.
   Web of Science ®Google Scholar
• Peshlov, B. N.; Dowell, F. E.; Drummond, F. A.; Donahue, D. W. Comparison of Three near Infrared Spectrophotometers for Infestation Detection in Wild Blueberries Using Multivariate Calibration Models. J. Near Infrared Spectrosc. 2009, 17, 203–212.
   Web of Science ®Google Scholar
• Moscetti, R.; Haff, R. P.; Stella, E.; Contini, M.; Monarca, D.; Cecchini, M.; Massantini, R. Feasibility of NIR Spectroscopy to Detect Olive Fruit Infested by Bactrocera oleae. Postharvest Biol. Technol. 2015, 99, 58–62.
   Web of Science ®Google Scholar
• Saranwong, S.; Thanapase, W.; Suttiwijitpukdee, N.; Rittiron, R.; Kasemsumran, S.; Kawano, S. Applying near Infrared Spectroscopy to the Detection of Fruit Fly Eggs and Larvae in Intact Fruit. J. Near Infrared Spectrosc. 2010, 18, 271–280.
   Web of Science ®Google Scholar
• Haff, R. P.; Saranwong, S.; Thanapase, W.; Janhiran, A.; Kasemsumran, S.; Kawano, S. Automatic Image Analysis and Spot Classification for Detection of Fruit Fly Infestation in Hyperspectral Images of Mangoes. Postharvest Biol. Technol. 2013, 86, 23–28.
   Web of Science ®Google Scholar
• Lu, R.; Ariana, D. P. Detection of Fruit Fly Infestation in Pickling Cucumbers Using a Hyperspectral Reflectance/Transmittance Imaging System. Postharvest Biol. Technol. 2013, 81, 44–50.
   Web of Science ®Google Scholar
• Toyoshima, S.; Nakamoto, Y.; Takanashi, M.; Masuda, T. An Experiment to Detect Apples Infested by the Peach Fruit Moth, Carposina sasakii Matsumura (Lepidoptera: Carposinidae), Using Near-Infrared Spectroscopy. Bull. National Inst. Fruit Tree Sci. 2006, 5, 87–94.
   Google Scholar
• Tigabu, M.; Odén, P. C. Multivariate Classification of Sound and Insect-Infested Seeds of a Tropical Multipurpose Tree, Cordia africana, with Near Infrared Reflectance Spectroscopy. J. Near Infrared Spectrosc. 2002, 10, 45–51.
   Web of Science ®Google Scholar
• Tigabu, M.; Odén, P. Near Infrared Spectroscopy-Based Method for Separation of Sound and Insect-Damaged Seeds of Albizia schimperiana, a Multipurpose Legume. Seed Sci. Technol. 2003, 31, 317–328.
   Web of Science ®Google Scholar
• Tigabu, M.; Odén, P. C.; Shen, T. Y. Application of Near-Infrared Spectroscopy for the Detection of Internal Insect Infestation in Picea abies Seed Lots. Can. J. For. Res. 2004, 34, 76–84.
   Google Scholar
• Upchurch, B.; Thai, C. Spectral Characterization of Pecan Weevil Larvae and Pecan Nutmeat Using Multispectral Imaging. In 2000 ASAE Annual International meeting, Milwaukee, Wisconsin, USA, July 9–12, 2000; p 1–9.
   Google Scholar
• Shah, C. P.; Weckler, P. R.; Maness, N. O. Detection of Pecan Weevil Larvae in Pecan Nutmeat using Multispectral Imaging. In 2006 ASAE Annual Meeting, American Society of Agricultural and Biological Engineers, Portland, Oregon, 9-12 July 2006; p 1.
   Google Scholar
• Nansen, C.; Zhang, X.; Aryamanesh, N.; Yan, G. Use of Variogram Analysis to Classify Field Peas with and without Internal Defects Caused by Weevil Infestation. J. Food Eng. 2014, 123, 17–22.
   Web of Science ®Google Scholar
• Huang, M.; Wan, X.; Zhang, M.; Zhu, Q. Detection of Insect-Damaged Vegetable Soybeans Using Hyperspectral Transmittance Image. J. Food Eng. 2013, 116, 45–49.
   Web of Science ®Google Scholar
• Ma, Y.; Huang, M.; Yang, B.; Zhu, Q. Automatic Threshold Method and Optimal Wavelength Selection for Insect-Damaged Vegetable Soybean Detection Using Hyperspectral Images. Comput. Electron. Agric. 2014, 106, 102–110.
   Web of Science ®Google Scholar
• Dowell, F.; Parker, A.; Benedict, M.; Robinson, A.; Broce, A.; Wirtz, R. Sex Separation of Tsetse Fly Pupae Using near-Infrared Spectroscopy. Bull. Entomol. Res. 2005, 95, 249–257.
   PubMed Web of Science ®Google Scholar
• Vreysen, M. J.; Saleh, K. M.; Ali, M. Y.; Abdulla, A. M.; Zhu, Z.-R.; Juma, K. G.; Dyck, V. A.; Msangi, A. R.; Mkonyi, P. A.; Feldmann, H. U. Glossina austeni (Diptera: Glossinidae) Eradicated on the Island of Unguja, Zanzibar, Using the Sterile Insect Technique. J. Econ. Entomol. 2000, 93, 123–135.
   PubMed Web of Science ®Google Scholar
• Aw, W. C.; Dowell, F. E.; Ballard, J. W. O. Using near-Infrared Spectroscopy to Resolve the Species, Gender, Age, and the Presence of Wolbachia Infection in Laboratory-Reared Drosophila. G3 2012, 2, 1057–1065.
   Web of Science ®Google Scholar
• Liebman, K.; Swamidoss, I.; Vizcaino, L.; Lenhart, A.; Dowell, F.; Wirtz, R. The Influence of Diet on the Use of near-Infrared Spectroscopy to Determine the Age of Female Aedes aegypti Mosquitoes. Am. J. Trop. Med. Hyg. 2015, 92, 1070–1075.
   PubMed Web of Science ®Google Scholar
• Watts, D. M.; Burke, D. S.; Harrison, B. A.; Whitmire, R. E.; Nisalak, A. Effect of Temperature on the Vector Efficiency of Aedes aegypti for Dengue 2 Virus. Am. J. Trop. Med. Hyg. 1987, 36, 143–152.
   PubMed Web of Science ®Google Scholar
• Boorman, J.; Porterfield, J. A Simple Technique for Infection of Mosquitoes with Viruses. Transmission of Zika Yirus. Trans. Royal Soc. Trop. Med. Hyg. 1956, 50, 238.
   PubMedGoogle Scholar
• Dubrulle, M.; Mousson, L.; Moutailler, S.; Vazeille, M.; Failloux, A.-B. Chikungunya Virus and Aedes Mosquitoes: Saliva Is Infectious as Soon as Two Days after Oral Infection. PloS One 2009, 4, e5895.
   PubMed Web of Science ®Google Scholar
• Cook, P. E.; Hugo, L. E.; Iturbe-Ormaetxe, I.; Williams, C. R.; Chenoweth, S. F.; Ritchie, S. A.; Ryan, P. A.; Kay, B. H.; Blows, M. W.; O'Neill, S. L. The Use of Transcriptional Profiles to Predict Adult Mosquito Age under Field Conditions. Proc. Natl. Acad. Sci. 2006, 103, 18060–18065.
   PubMed Web of Science ®Google Scholar
• Perez-Mendoza, J.; Dowell, F. E.; Broce, A. B.; Throne, J. E.; Wirtz, R. A.; Xie, F.; Fabrick, J. A.; Baker, J. E. Chronological Age-Grading of House Flies by Using near-Infrared Spectroscopy. J. Med. Entomol. 2002, 39, 499–508.
   PubMed Web of Science ®Google Scholar
• Lehane, M.; Mail, T. Determining the Age of Adult Male and Female Glossina morsitans morsitans Using a New Technique. Ecol. Entomol. 1985, 10, 219–224.
   Web of Science ®Google Scholar
• Caputo, B.; Dani, F. R.; Horne, G. L.; Petrarca, V.; Turillazzi, S.; Coluzzi, M.; Priestman, A. A.; della Torre, A. Identification and Composition of Cuticular Hydrocarbons of the Major Afrotropical Malaria Vector Anopheles gambiae ss (Diptera: Culicidae): Analysis of Sexual Dimorphism and Age‐Related Changes. J. Mass Spectrom. 2005, 40, 1595–1604.
   PubMed Web of Science ®Google Scholar
• Perez-Mendoza, J.; Throne, J.; Baker, J. Ovarian Physiology and Age-Grading in the Rice Weevil, Sitophilus oryzae (Coleoptera: Curculionidae). J. Stored Prod. Res. 2004, 40, 179–196.
   Web of Science ®Google Scholar
• Gillies, M. A Modified Technique for the Age-Grading of Populations of Anopheles gambiae. Ann. Trop. Med. Parasitol. 1958, 52, 261–273.
   PubMedGoogle Scholar
• Lambert, B.; Sikulu-Lord, M. T.; Mayagaya, V. S.; Devine, G.; Dowell, F.; Churcher, T. S. Monitoring the Age of Mosquito Populations Using near-Infrared Spectroscopy. Sci. Rep. 2018, 8, 5274.
   PubMed Web of Science ®Google Scholar
• Perez-Mendoza, J.; Throne, J. E.; Dowell, F. E.; Baker, J. E. Chronological Age-Grading of Three Species of Stored-Product Beetles by Using near-Infrared Spectroscopy. J. Econ. Entomol. 2004, 97, 1159–1167.
   PubMed Web of Science ®Google Scholar
• Reeves, W.; Peiris, K.; Scholte, E. J.; Wirtz, R.; Dowell, F. Age-Grading the Biting Midge Culicoides sonorensis Using near‐Infrared Spectroscopy. Med. Vet. Entomol. 2010, 24, 32–37.
   PubMed Web of Science ®Google Scholar
• Sikulu, M.; Killeen, G. F.; Hugo, L. E.; Ryan, P. A.; Dowell, K. M.; Wirtz, R. A.; Moore, S. J.; Dowell, F. E. Near-Infrared Spectroscopy as a Complementary Age Grading and Species Identification Tool for African Malaria Vectors. Parasites Vectors 2010, 3, 49.
   PubMed Web of Science ®Google Scholar
• Sikulu, M.; Dowell, K. M.; Hugo, L. E.; Wirtz, R. A.; Michel, K.; Peiris, K. H.; Moore, S.; Killeen, G. F.; Dowell, F. E. Evaluating RNA Later® as a Preservative for Using near-Infrared Spectroscopy to Predict Anopheles gambiae Age and Species. Malar. J. 2011, 10, 186.
   PubMed Web of Science ®Google Scholar
• Dowell, F. E.; Noutcha, A. E.; Michel, K. The Effect of Preservation Methods on Predicting Mosquito Age by Near Infrared Spectroscopy. Am. J. Trop. Med. Hyg. 2011, 85, 1093–1096.
   PubMed Web of Science ®Google Scholar
• Ntamatungiro, A. J.; Mayagaya, V. S.; Rieben, S.; Moore, S. J.; Dowell, F. E.; Maia, M. F. The Influence of Physiological Status on Age Prediction of Anopheles arabiensis Using Near Infra-Red Spectroscopy. Parasites Vectors 2013, 6, 298.
   PubMed Web of Science ®Google Scholar
• Sikulu, M. T.; Majambere, S.; Khatib, B. O.; Ali, A. S.; Hugo, L. E.; Dowell, F. E. Using a near-Infrared Spectrometer to Estimate the Age of Anopheles Mosquitoes Exposed to Pyrethroids. PLoS One 2014, 9, e90657.
   PubMed Web of Science ®Google Scholar
• Mayagaya, V. S.; Ntamatungiro, A. J.; Moore, S. J.; Wirtz, R. A.; Dowell, F. E.; Maia, M. F. Evaluating Preservation Methods for Identifying Anopheles gambiae ss and Anopheles Arabiensis Complex Mosquitoes Species Using near Infra-Red Spectroscopy. Parasites Vectors 2015, 8, 60.
   PubMed Web of Science ®Google Scholar
• Sikulu-Lord, M. T.; Milali, M. P.; Henry, M.; Wirtz, R. A.; Hugo, L. E.; Dowell, F. E.; Devine, G. J. Near-Infrared Spectroscopy, a Rapid Method for Predicting the Age of Male and Female Wild-Type and Wolbachia Infected Aedes aegypti. PLoS Negl. Trop. Dis. 2016, 10, e0005040.
   PubMed Web of Science ®Google Scholar
• Krajacich, B. J.; Meyers, J. I.; Alout, H.; Dabiré, R. K.; Dowell, F. E.; Foy, B. D. Analysis of Near Infrared Spectra for Age-Grading of Wild Populations of Anopheles gambiae. Parasites Vectors 2017, 10, 552.
   PubMed Web of Science ®Google Scholar
• Sikulu-Lord, M. T.; Devine, G. J.; Hugo, L. E.; Dowell, F. E. First Report on the Application of near-Infrared Spectroscopy to Predict the Age of Aedes albopictus Skuse. Sci. Rep. 2018, 8, 9590.
   PubMed Web of Science ®Google Scholar
• Milali, M. P.; Sikulu-Lord, M. T.; Kiware, S. S.; Dowell, F. E.; Corliss, G. F.; Povinelli, R. J. Age Grading An. gambiae and An. arabiensis Using near Infrared Spectra and Artificial Neural Networks. PloS One 2019, 14, e0209451.
   PubMed Web of Science ®Google Scholar
• Wittmann, E.; Mellor, P.; Baylis, M. Effect of Temperature on the Transmission of Orbiviruses by the Biting Midge, Culicoides sonorensis. Med. Vet. Entomol. 2002, 16, 147–156.
   PubMed Web of Science ®Google Scholar
• Mandrioli, M.; Borsatti, F.; Mola, L. Factors Affecting DNA Preservation from Museum-Collected Lepidopteran Specimens. Entomol. Exper. Appl. 2006, 120, 239–244.
   Web of Science ®Google Scholar
• Bisanti, M.; Ganassi, S.; Mandrioli, M. Comparative Analysis of Various Fixative Solutions on Insect Preservation for Molecular Studies. Entomol. Exp. Appl. 2009, 130, 290–296.
   Web of Science ®Google Scholar
• Quicke, D. L.; Lopez‐Vaamonde, C.; Belshaw, R. Preservation of Hymenopteran Specimens for Subsequent Molecular and Morphological Study. Zool. Scr. 1999, 28, 261–267.
   Web of Science ®Google Scholar
• Cook, P. E.; Hugo, L. E.; Iturbe-Ormaetxe, I.; Williams, C. R.; Chenoweth, S. F.; Ritchie, S. A.; Ryan, P. A.; Kay, B. H.; Blows, M. W.; O'neill, S. L. Predicting the Age of Mosquitoes Using Transcriptional Profiles. Nat. Protoc. 2007, 2, 2796.
   PubMed Web of Science ®Google Scholar
• Milali, M. P.; Sikulu-Lord, M. T.; Kiware, S. S.; Dowell, F. E.; Povinelli, R. J.; Corliss, G. F. Do NIR Spectra Collected from Laboratory-Reared Mosquitoes Differ from Those Collected from Wild Mosquitoes? PloS One 2018, 13, e0198245.
   PubMed Web of Science ®Google Scholar
• Cook, L. G.; Edwards, R.; Crisp, M.; Hardy, N. Need Morphology Always Be Required for New Species Descriptions? Invert. Syst. 2010, 24, 322–326.
   Web of Science ®Google Scholar
• Wheeler, Q. D. Undisciplined Thinking: Morphology and Hennig’s Unfinished Revolution. Syst. Entomol. 2008, 33, 2–7.
   Web of Science ®Google Scholar
• Yeates, D. K.; Seago, A.; Nelson, L.; Cameron, S. L.; Joseph, L.; Trueman, J. W. Integrative Taxonomy, or Iterative Taxonomy? Syst. Entomol. 2011, 36, 209–217.
   Web of Science ®Google Scholar
• Schlick-Steiner, B. C.; Steiner, F. M.; Seifert, B.; Stauffer, C.; Christian, E.; Crozier, R. H. Integrative Taxonomy: A Multisource Approach to Exploring Biodiversity. Annu. Rev. Entomol. 2010, 55, 421–438.
   PubMed Web of Science ®Google Scholar
• Raupach, M. J.; Amann, R.; Wheeler, Q. D.; Roos, C. The Application of “-Omics” Technologies for the Classification and Identification of Animals. Org. Divers. Evol. 2016, 16, 1–12.
   Web of Science ®Google Scholar
• Jones, O. A.; Maguire, M. L.; Griffin, J. L.; Dias, D. A.; Spurgeon, D. J.; Svendsen, C. Metabolomics and Its Use in Ecology. Austral. Ecol. 2013, 38, 713–720.
   Web of Science ®Google Scholar
• Cvačka, J.; Jiroš, P.; Šobotník, J.; Hanus, R.; Svatoš, A. Analysis of Insect Cuticular Hydrocarbons Using Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry. J. Chem. Ecol. 2006, 32, 409–434.
   PubMed Web of Science ®Google Scholar
• Feltens, R.; Görner, R.; Kalkhof, S.; Gröger-Arndt, H.; von Bergen, M. Discrimination of Different Species from the Genus Drosophila by Intact Protein Profiling Using Matrix-Assisted Laser Desorption Ionization Mass Spectrometry. BMC Evol. Biol. 2010, 10, 95.
   PubMed Web of Science ®Google Scholar
• Hoppenheit, A.; Murugaiyan, J.; Bauer, B.; Steuber, S.; Clausen, P.-H.; Roesler, U. Identification of Tsetse (Glossina Spp.) Using Matrix-Assisted Laser Desorption/Ionisation Time of Flight Mass Spectrometry. PLoS Negl. Trop. Dis. 2013, 7, e2305.
   PubMed Web of Science ®Google Scholar
• Kaufmann, C.; Ziegler, D.; Schaffner, F.; Carpenter, S.; Pfluger, V.; Mathis, A. Evaluation of Matrix-Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry for Characterization of Culicoides nubeculosus Biting Midges. Med Vet Entomol. 2011, 25, 32–38.
   PubMed Web of Science ®Google Scholar
• Munck, L. A New Holistic Exploratory Approach to Systems Biology by near Infrared Spectroscopy Evaluated by Chemometrics and Data Inspection. J. Chemom. 2007, 21, 406–426.
   Web of Science ®Google Scholar
• Dowell, F. E.; Throne, J. E.; Wang, D.; Baker, J. Identifying Stored-Grain Insects Using Near-Infrared Spectroscopy. J. Econ. Entomol. 1999, 92, 165–169.
   Web of Science ®Google Scholar
• Klarica, J.; Bittner, L.; Pallua, J.; Pezzei, C.; Huck-Pezzei, V.; Dowell, F.; Schied, J.; Bonn, G. K.; Huck, C.; Schlick-Steiner, B. C.; et al. Near-Infrared Imaging Spectroscopy as a Tool to Discriminate Two Cryptic Tetramorium Ant Species. J. Chem. Ecol. 2011, 37, 549.
   PubMed Web of Science ®Google Scholar
• Kinzner, M.-C.; Wagner, H. C.; Peskoller, A.; Moder, K.; Dowell, F. E.; Arthofer, W.; Schlick-Steiner, B. C.; Steiner, F. M. A near-Infrared Spectroscopy Routine for Unambiguous Identification of Cryptic Ant Species. PeerJ 2015, 3, e991.
   PubMed Web of Science ®Google Scholar
• Wang, Y.; Nansen, C.; Zhang, Y. Integrative Insect Taxonomy Based on Morphology, Mitochondrial DNA, and Hyperspectral Reflectance Profiling. Zool. J. Linn. Soc. 2016, 177, 378–394.
   Web of Science ®Google Scholar
• Nansen, C.; Coelho, A.; Vieira, J. M.; Parra, J. R. P. Reflectance-Based Identification of Parasitized Host Eggs and Adult Trichogramma Specimens. J. Exp. Biol. 2014, 217, 1187–1192.
   PubMed Web of Science ®Google Scholar
• de Azevedo, R. A.; de Morais, J. W.; Lang, C.; de Sales Dambros, C. Discrimination of Termite Species Using Near-Infrared Spectroscopy (NIRS). Eur. J. Soil Biol. 2019, 93, 103084.
   Web of Science ®Google Scholar
• Aw, W. C.; Ballard, J. W. O. Near-Infrared Spectroscopy for Metabolite Quantification and Species Identification. Ecol. Evol. 2019, 9, 1336–1343.
   PubMed Web of Science ®Google Scholar
• Cao, Y.; Zhang, C.; Chen, Q.; Li, Y.; Qi, S.; Tian, L.; Ren, Y. Identification of Species and Geographical Strains of Sitophilus oryzae and Sitophilus zeamais Using the Visible/Near-Infrared Hyperspectral Imaging Technique. Pest Manage. Sci. 2015, 71, 1113–1121.
   PubMed Web of Science ®Google Scholar
• Siegwart, M.; Bouvier, F.; Maugin, S.; Lecomte, A.; Lavigne, C. Differentiating Oriental Fruit Moth and Codling Moth (Lepidoptera: Tortricidae) Larvae Using Near-Infrared Spectroscopy. J. Econ. Entomol. 2015, 108, 219–227.
   PubMed Web of Science ®Google Scholar
• Barbosa, T. M.; de Lima, L. A.; dos Santos, M. C.; Vasconcelos, S. D.; Gama, R. A.; Lima, K. M. A Novel Use of Infra-Red Spectroscopy (NIRS and ATR-FTIR) Coupled with Variable Selection Algorithms for the Identification of Insect Species (Diptera: Sarcophagidae) of Medico-Legal Relevance. Acta Trop. 2018, 185, 1–12.
   PubMed Web of Science ®Google Scholar
• Cole, T.; Ram, M.; Dowell, F.; Omwega, C.; Overholt, W.; Ramaswamy, S. Near-Infrared Spectroscopic Method to Identify Cotesia flavipes and Cotesia sesamiae (Hymenoptera: Braconidae). Ann. Entomol. Soc. Am. 2003, 96, 865–869.2.0.CO;2]
   Web of Science ®Google Scholar
• Hall, M. H.; Dutro, S. M.; Klowden, M. J. Determination by near-Infrared Reflectance Spectroscopy of Mosquito (Diptera: Culicidae) Bloodmeal Size. J. Med. Entomol. 1990, 27, 76–79.
   PubMed Web of Science ®Google Scholar
• Dowell, F. E.; Broce, A. B.; Xie, F.; Throne, J. E.; Baker, J. E. Detection of Parasitised Fly Puparia Using Near Infrared Spectroscopy. J. Near Infrared Spectrosc. 2000, 8, 259–265.
   Web of Science ®Google Scholar
• Webster, T. C.; Dowell, F. E.; Maghirang, E. B.; Thacker, E. M. Visible and Near-Infrared Spectroscopy Detects Queen Honey Bee Insemination. Apidologie 2009, 40, 565–569.
   Web of Science ®Google Scholar
• Sikulu-Lord, M. T.; Maia, M. F.; Milali, M. P.; Henry, M.; Mkandawile, G.; Kho, E. A.; Wirtz, R. A.; Hugo, L. E.; Dowell, F. E.; Devine, G. J. Rapid and Non-Destructive Detection and Identification of Two Strains of Wolbachia in Aedes aegypti by Near-Infrared Spectroscopy. PLoS Negl. Trop. Dis. 2016, 10, e0004759.
   PubMed Web of Science ®Google Scholar
• Fernandes, J. N.; Dos Santos, L. M.; Chouin-Carneiro, T.; Pavan, M. G.; Garcia, G. A.; David, M. R.; Beier, J. C.; Dowell, F. E.; Maciel-de-Freitas, R.; Sikulu-Lord, M. T. Rapid, Noninvasive Detection of Zika Virus in Aedes aegypti Mosquitoes by Near-Infrared Spectroscopy. Sci. Adv. 2018, 4, eaat0496.
   PubMed Web of Science ®Google Scholar
• Esperança, P. M.; Blagborough, A. M.; Da, D. F.; Dowell, F. E.; Churcher, T. S. Detection of Plasmodium berghei Infected Anopheles stephensi Using near-Infrared Spectroscopy. Parasites Vectors 2018, 11, 377.
   PubMed Web of Science ®Google Scholar
• Koljonen, J.; Nordling, T. E. M.; Alander, J. T. A Review of Genetic Algorithms in Near Infrared Spectroscopy and Chemometrics: Past and Future. J. Near Infrared Spectrosc. 2008, 16, 189–197.
   Web of Science ®Google Scholar
• Wang, J.-H.; Jiang, J.-H.; Yu, R.-Q. Robust Back Propagation Algorithm as a Chemometric Tool to Prevent the Overfitting to Outliers. Chemom. Intell. Lab. Syst. 1996, 34, 109–115.
   Web of Science ®Google Scholar
• Roggo, Y.; Chalus, P.; Maurer, L.; Lema-Martinez, C.; Edmond, A.; Jent, N. A Review of near Infrared Spectroscopy and Chemometrics in Pharmaceutical Technologies. J. Pharm. Biomed. Anal. 2007, 44, 683–700.
   PubMed Web of Science ®Google Scholar
• Rácz, A.; Bajusz, D.; Héberger, K.; Chemometrics in Analytical Chemistry. In Applied Chemoinformatics: Achievements and Future Opportunities; Engel, T., Gasteiger, J., Eds. Wiley; 2018, Ch 9, 471–499.
   Google Scholar
• Cozzolino, D.; Power, A.; Chapman, J. J. F. A. M. Interpreting and Reporting Principal Component Analysis in Food Science Analysis and Beyond. Food Anal. Methods 2019, 1–5.
   Web of Science ®Google Scholar
• Adams, M. Chemometrics in Analytical Spectroscopy, 2nd ed.; Barnett, N. W., Ed. The Royal Society of Chemistry: Cambridge, UK, 2004.
   Google Scholar
• Haenlein, M.; Kaplan, AMJUs. A Beginner's Guide to Partial Least Squares Analysis. Understanding Stat. 2004, 3, 283–297.
   Google Scholar
• Leardi, R. Nature-inspired Methods in Chemometrics: Genetic Algorithms and Artificial Neural Networks. In Data Handling in Science and Technology; Leardi, R., Ed. Elsevier: Amsterdam, The Netherlands, 2003; Vol. 23.
   Google Scholar
• Wold, S.; Sjostrom, M. SIMCA: A Method for Analyzing Chemical Data in Terms of Similarity and Analogy. Chemom. Theory Appl. 1977, 52, 243–282.
   Google Scholar
• Marini, F.; Bucci, R.; Magrì, A.; Magrì, A. Artificial Neural Networks in Chemometrics: History, Examples and Perspectives. Microchem. J. 2008, 88, 178–185.
   Web of Science ®Google Scholar
• Cogdill, R.; Dardenne, P. Least-Squares Support Vector Machines for Chemometrics: An Introduction and Evaluation. J. Near Infrared Spectrosc. 2004, 12, 93–100.
   Web of Science ®Google Scholar
• Brereton, R. G.; Lloyd, G. R. Support Vector Machines for Classification and Regression. Analyst 2010, 135, 230–267.
   PubMed Web of Science ®Google Scholar
• Small, G. W. Chemometrics and Near-Infrared Spectroscopy: Avoiding the Pitfalls. TrAC Trends Anal. Chem. 2006, 25, 1057–1066.
   Web of Science ®Google Scholar
• Dreiseitl, S.; Ohno-Machado, L. Logistic Regression and Artificial Neural Network Classification Models: A Methodology Review. J. Biomed. Inf. 2002, 35, 352–359.
   PubMed Web of Science ®Google Scholar
• Blanco, M.; Coello, J.; Iturriaga, H.; Maspoch, S.; Pages, J. Calibration in Non-Linear near Infrared Reflectance Spectroscopy: A Comparison of Several Methods. Anal. Chim. Acta 1999, 384, 207–214.
   Web of Science ®Google Scholar