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Reddy, P. Near-Infrared Spectroscopy Coupled to Hyperspectral Imaging. Encyclopedia. Available online: https://encyclopedia.pub/entry/20615 (accessed on 01 May 2024).
Reddy P. Near-Infrared Spectroscopy Coupled to Hyperspectral Imaging. Encyclopedia. Available at: https://encyclopedia.pub/entry/20615. Accessed May 01, 2024.
Reddy, Priyanka. "Near-Infrared Spectroscopy Coupled to Hyperspectral Imaging" Encyclopedia, https://encyclopedia.pub/entry/20615 (accessed May 01, 2024).
Reddy, P. (2022, March 16). Near-Infrared Spectroscopy Coupled to Hyperspectral Imaging. In Encyclopedia. https://encyclopedia.pub/entry/20615
Reddy, Priyanka. "Near-Infrared Spectroscopy Coupled to Hyperspectral Imaging." Encyclopedia. Web. 16 March, 2022.
Near-Infrared Spectroscopy Coupled to Hyperspectral Imaging
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This entry is adapted from the peer-reviewed paper 10.3390/s22051981

Near-infrared (800–2500 nm; NIR) spectroscopy coupled to hyperspectral imaging (NIR-HSI) has greatly enhanced its capability and thus widened its application and use across various industries. This non-destructive technique that is sensitive to both physical and chemical attributes of virtually any material can be used for both qualitative and quantitative analyses.

multispectral imaging NIR-HSI

1. Introduction

Near-infrared spectroscopy (NIRS) is a widely used technique for performing quality control in laboratories that are associated with pharmaceutical [1], petroleum [2] and agriculture. Now commonly used for various applications, NIR, which absorbs at wavelengths of 800–2500 nm (12,500–4000 cm−1) of the electromagnetic spectrum, was developed by Karl Norris, United States Department of Agriculture (USDA), in the 1960s for the purposes of quality assessment of agricultural products [3][4]. In 1962, Norris was already ahead of his time, using NIR data to build calibration models using advanced statistical methods [5][6][7]. The use of NIR has since been extended to applications such as quality assurance of agricultural products. For example, the dairy industry uses NIRS to perform measurements of lactose, protein and fat in milk [8][9][10]. There are also numerous reports of NIRS used in the grains industry, for example the discrimination among cultivars of wheat kernels [11], detection of fungal infestation [12] and prohibited additives [13][14]. Plant stress [15] and nutritive value [16] can also be measured using portable NIR spectrometers in the field for plant breeding programs. The high-throughput nature of these phenotyping imaging tools has allowed large-scale implementation in breeding by genomic selection [17] in agriculture. NIRS is also widely used in the meat industry for applications such as classification of poultry carcasses infected with disease in real time [18][19] and determination of sensory and texture characteristics of beef [20], as well as meat properties and chemical composition [20]. Applications in plant-based industries include fruit, grain and seed quality, particularly pathogen infestation and varietal purity, as well as chemical composition. The list of applications continues to grow rapidly [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37].
The main advantage of NIRS is that the methodology is non-destructive and high throughput. NIRS platforms provide spectral information, usually an average of a few selected points or the mean spectrum of a larger area. However, this is not so useful when objects are non-uniform and the features of interest are restricted to a relatively small but unknown part of the object [38]. When NIRS is coupled to imaging techniques, such as multispectral and hyperspectral techniques, both spatial and spectral information can be obtained. NIRS-multispectral imaging captures images within a small number of spectral bands, usually a maximum of 10 [39]. NIR-hyperspectral imaging (NIR-HSI), an extension of NIRS-multispectral imaging, captures hundreds of contiguous wavelength bands in the NIR region for each pixel. Coupling NIRS with an imaging technique was first introduced in the late 1990s [40] and the technology has advanced such that it is now routinely applied in fields such as military surveillance [41], astronomy [42], environmental monitoring [43] and agriculture.
The non-destructive nature of NIRS imaging allows quality control in agriculturally important fruit (e.g., apples, peaches and apricots) to be obtained, [44][45][46][47][48] particularly for detecting external defects. Typically, fresh fruits are graded into categories based on parameters that affect quality, such as external defects, size, shape and colour, to comply with the Organisation for Economic Co-operation and Development (OECD) standards [49]. External defects are the most difficult to distinguish in automated systems; however, with the advent of NIRS imaging and, more recently, hyperspectral imaging, applications for detecting surface defects in fruit have been rapidly increasing. For example, NIRS and hyperspectral models for detecting surface defects originating from fungal infestation, insect damage on oranges [50], mangoes [51] and peaches [46], as well as damaged almond nut [52], have been reported. More recently, fruit ripening and maturity are also being assessed using NIRS imaging such as ripeness in grapes and firmness and maturity in lime and mango by determination of the total soluble solid and titratable acidity [53]. NIRS for sorting fruit based on sweetness is in use in more than 1000 packing houses in Japan using single-point measurements [54] and conceivably superseded with developments driven in NIR imaging as a visualization technique for sugar content and distribution [54][55][56][57].
It has become increasingly evident that hyperspectral imaging can be applied to the non-destructive qualitative and quantitative determination of the desired features of selected samples, without contact [58]. Thus, it is very much suited for routine diagnostics such as food quality assessments and safety analyses [59]. A further advantage is that the NIR-HSI spectrum is collected at each pixel in the image, providing both the distribution and chemical composition of individual components [58], whereas NIRS only acquires a single spectrum for the sample [40]. This makes NIR-HSI suitable for heterogenous samples or for exploratory analyses, where the composition of the sample is largely unknown [38]. Despite the time and financial investment required for method development and the large data sets obtained, once a method is developed, it is inexpensive and routine with the benefits of reduced labour, turnaround and cost, compared to traditional methods used for inspecting and testing agricultural food products [60][61].

2. Hyperspectral Imaging Instruments

The components, configuration and design of a hyperspectral system are essential in the acquiring of reliable and high-quality images and data. A typical hyperspectral imaging system consists of a light source, wavelength dispersion device and an area detector (camera).
HSI systems are generally either active or passive systems. Active HSI systems are equipped with an active light source, such as those described in Table 1. A passive HSI system relies on ambient radiation, e.g., sunlight [62]. The light source is an important aspect in the excitation of the target sample and the quality of the images strongly depends on a well-balanced light intensity. Light sources include halogen lamps, light emitting diodes (LED), lasers and tuneable light sources. Fluorescent lamps are not recommended as there are inconsistencies in energy levels for different wavelengths [63].
Broadband light sources, including the halogen lamp, LED and tuneable light, are used with a wavelength dispersion device, such as a prism, grating, or filter, a key component in hyperspectral instruments [64]. It is placed between the detector and sample and it is used to disperse broadband light into different wavelengths. A filter is generally used for multispectral imaging systems whereas a prism and grating are widely used in hyperspectral systems. The final component of the system is the camera which has the role of quantifying the intensity of the light generated by the light source by converting photons into electrons. Charge-couple devices (CCDs) and complementary metal-oxide-semiconductors (CMOSs) are the two major cameras [64][65]. The CCD image sensor is superior to the CMOS device and generates high-quality image data.
The typical hyperspectral components utilised in the development of seed quality applications, include a halogen lamp light source, imaging spectrographs that covers VNIR, SW-NIR and NIR spectral ranges and, mostly, CCD-type image sensors.
Table 1. Advantages and disadvantages of using various light sources in hyperspectral imaging systems.
Light Source Application Advantages Disadvantages Example
References
Halogen lamps VIS
NIR
SW-NIR
Broadband white light		 Delivers smooth and continuous spectrum in the spectral range
High light intensity		 Short lifetime
High heat
Unstable 1 (operating voltage fluctuations)
Sensitive to vibration		 [66]
LED From UV to SW-NIR, while some LEDs emit light from LW-NIR to MIR
Broadband white light
Excitation mode (fluorescence)		 Small size
Low cost
Fast switching
Long lifetime
Minimal bulb replacement
Low heat generation
Low energy consumption
Robust		 Low spectral resolution
Sensitive to wide voltage fluctuations
High junction temperature
Low light intensity		 [62]
Laser excitation Emission of fluorescence and Raman
Narrowband pulsed light		 Composition detection at pixel level
High intensity light
Narrower bandwidth than LED
Signals are not interfered by carbon or water absorption		 Detection of weak Raman signals is challenging due to high-fluorescence background [67]
Tuneable light source (Quartz–Tungsten Halogen lamp) Near UV
VIS
NIR		 Area scanning
Weak illumination (using wavelength dispersion) reduces heat damage of samples		 No point or line scanning [68]
1 Low heat–load illumination is also available and provides an evenly distributed illumination line while emitting very low heat compared with the typical halogen lamp.

3. Image Acquisition Methods

Hyperspectral imaging collects information as images using well-defined spectral bands. The images are combined to form a three-dimensional (x, y, λ) hyperspectral data cube for processing and analysis, where x and y represent the two spatial dimensions of the scene and λ represents the spectral dimension (wavelengths) [69][70]. The resolution is characterized by the number of spectral bands of a range in the electromagnetic spectrum. Thus, multispectral images have low spectral resolution, given they have a maximum of 10 spectral bands, whereas HSI sensors have high resolution. A hyperspectral image comprises thousands or even millions of pixels. An ordinary image with 320 × 320 pixels contains 102,400 pixels. However, a hyperspectral image with 200 spectral bands has 20,480,000 pixels [71]. Spatial resolution, defined as the smallest distinguishable detail in an image [58], is also a factor; the size of the pixel or spatial point influences the signal-to-noise ratio [69][70].
Acquisition methods of a hyperspectral data cube include the following: (1) whisk-broom—one spectrum of a single point at a time; (2) push-broom—spectra of points of one spatial line at a time [72]; (3) tuneable filter—one waveband image at a time, much similar to a 2D photographic image (x, y); and (4) snapshot—full-waveband image at a time, much similar to capturing a photographic image with a third spectral dimension (x, y, λ). Push-broom is the most commonly used acquisition mode for applications in the food and agricultural industry. Push-broom imaging works either by the movement of the sample, for example, on a conveyer belt, or by directing the beam and detector to the region of interest [73].
Hyperspectral image acquisition is carried out in various sensing modes, including fluorescence [74], or one of four spectral modes typically utilized in the visible (400–700 nm) and NIR region—reflectance, absorbance, transmittance and interactance. These various imaging modes may be selected depending on the nature of the sample and the parameters being assessed. The light source for each optical mode is positioned differently to best capture the image based on the mode with minimal interference.
Hyperspectral Fluorescence Imaging (HSFI) detects chemical components that produce a fluorescence emission in the visible region (400–700 nm) when excited with short wavelengths (e.g., ultraviolet (UV) radiation or monochromatic laser light) [74]. These include components such as chlorophyll and some pigments in seed samples [75]. There are many examples of fluorescence imaging used in assessing physical and chemical quality parameters in food products [76], as well as faecal contamination in apples [77]. However, HSFI is unable to measure many of the food quality attributes that are detected by the optical modes (e.g., reflectance), such as soluble solid content, fruit pH and maturity discrimination [77].
Reflectance is the most common hyperspectral mode used in agriculture due to the ease in obtaining responses from higher light levels. Quality parameters such as size, shape, colour and surface defects are generally detected in reflectance mode [73][78].
Some applications show better calibration models and prediction outcomes in interactance and transmittance modes [79][80]. In transmittance mode, light is transmitted through the sample, with a detector opposite to the light source to capture the light that has passed through the sample; the strength is often weak and sample dependent but is considered to be a more valuable response in relation to internal components and defects [81]. In interactance mode, the light source and detector are located on the same side; however, the received light is sealed from the environment to prevent interference [82]. This particular mode is thought to be a combination of reflectance and transmittance as it can penetrate the sample, extracting more information than reflectance mode. The conformation of the light and detector is important to avoid refraction, specular reflectance and scattering in all three optical modes.
Another technique used in hyperspectral image acquisition is absorbance. Although there are limited reports of its use in agriculture, it is a preferred method for the quantitation of chemical constituents, for example, protein and oil contents of wheat grain [83].

References

  1. Klukkert, M.; Wu, J.X.; Rantanen, J.; Carstensen, J.M.; Rades, T.; Leopold, C.S. Multispectral UV imaging for fast and non-destructive quality control of chemical and physical tablet attributes. Eur. J. Pharm. Sci. Off. J. Eur. Fed. Pharm. Sci. 2016, 90, 85–95.
  2. Lutz, O.M.D.; Bonn, G.K.; Rode, B.M.; Huck, C.W. Reproducible quantification of ethanol in gasoline via a customized mobile near-infrared spectrometer. Anal. Chim. Acta 2014, 826, 61–68.
  3. Siesler, H.W.; Ozaki, Y.; Kawata, S.; Heise, H.M. Near-Infrared Spectroscopy: Principles, Instruments, Applications; John Wiley & Sons, Incorporated: Hoboken, Germany, 2002.
  4. Norris, K.H. History of NIR. J. Near Infrared Spectrosc. 1996, 4, 31–37.
  5. Hart, J.R.; Norris, K.H.; Golumbic, C. Determination of the moisture content of seeds by near-infrared spectrophotometry of their methanol extracts. Cereal Chem. 1962, 39, 94–99.
  6. Massie, D.R.; Norris, K.H. Spectral reflectance and transmittance properties of grain in the visible and near infrared. Trans. ASAE 1965, 8, 598–600.
  7. Finney, E.E.; Norris, K.H. Determination of moisture in corn kernels by near-infrared transmittance measurements. Trans. ASAE 1978, 21, 581–584.
  8. Woo, Y.A.; Terazawa, Y.; Chen, J.Y.; Iyo, C.; Terada, F.; Kawano, S. Development of a new measurement unit (MilkSpec-1) for rapid determination of fat, lactose, and protein in raw milk using near-infrared transmittance spectroscopy. Appl. Spectrosc. 2002, 56, 599–604.
  9. Šašić, S.; Ozaki, Y. Short-wave near-infrared spectroscopy of biological fluids. 1. Quantitative analysis of fat, protein, and lactose in raw milk by partial least-squares regression and band assignment. Anal. Chem. 2001, 73, 64–71.
  10. Muñiz, R.; Pérez, M.A.; De La Torre, C.; Carleos, C.E.; Corral, N.; Baro, J.A. Comparison of principal component regression (PCR) and partial least square (PLS) methods in prediction of raw milk composition by VIS-NIR spectrometry. Application to development of on-line sensors for fat, protein and lactose contents. In Proceedings of the 19th IMEKO World Congress 2009, Lisbon, Portugal, 6–11 September 2009; pp. 2498–2502.
  11. Archibald, D.; Thai, C.; Dowell, F. Development of Short-Wavelength Near-Infrared Spectral Imaging for Grain Color Classification; SPIE: Bellingham, WA, USA, 1999; Volume 3543.
  12. Delwiche, S.R.; Kim, M.S.; Dong, Y. Fusarium damage assessment in wheat kernels by Vis/NIR hyperspectral imaging. Sens. Instrum. Food Qual. Saf. 2011, 5, 63–71.
  13. Zhang, Z.Y.; Li, G.; Liu, H.X.; Lin, L.; Zhang, B.J.; Wu, X.R. Detection of benzoyl peroxide in wheat flour by NIR diffuse reflectance spectroscopy technique. Guang Pu Xue Yu Guang Pu Fen Xi/Spectrosc. Spectr. Anal. 2011, 31, 3260–3263.
  14. Wang, D.; Ma, Z.; Han, P.; Zhao, L.; Pan, L.; Li, X.; Wang, J. Research on detection of lime in wheat flour by NIR micro-imaging. Sens. Lett. 2012, 10, 252–257.
  15. Behrens, T.; Müller, J.; Diepenbrock, W. Optimizing a diode array VIS/NIR spectrometer system to detect plant stress in the field. J. Agron. Crop Sci. 2007, 193, 292–304.
  16. Smith, C.; Cogan, N.; Badenhorst, P.; Spangenberg, G.; Smith, K. Field spectroscopy to determine nutritive value parameters of individual ryegrass plants. Agronomy 2019, 9, 293.
  17. Sun, D.; Cen, H.; Weng, H.; Wan, L.; Hassan, A.A.M.; El-Manawy, A.I.; Zhu, Y.; Fu, H.; Shu, Q.; Liu, F.; et al. Hyperspectral imaging technology combined with genome-wide association study rapidly identifies more genes related to rice quality. In Proceedings of the ASABE 2018 Annual International Meeting, Detroit, MI, USA, 29 July–1 August 2018.
  18. Park, B.; Chen, Y.R.; Nguyen, M. Multi-spectral image analysis using neural network algorithm for inspection of poultry carcasses. J. Agric. Eng. Res. 1998, 69, 351–363.
  19. Chen, Y.-R. Classifying diseased poultry carcasses by visible and near-IR reflectance spectroscopy. In Proceedings of SPIE—The International Society for Optical Engineering; SPIE: Bellingham, WA, USA, 1993; pp. 46–55.
  20. Ripoll, G.; Alberti, P.; Panea, B.; Olleta, J.L.; Sanudo, C. Near-infrared reflectance spectroscopy for predicting chemical, instrumental and sensory quality of beef. Meat Sci. 2008, 80, 697–702.
  21. Senthilkumar, T.; Jayas, D.S.; White, N.D.G.; Fields, P.G.; Gräfenhan, T. Detection of ochratoxin A contamination in stored wheat using near-infrared hyperspectral imaging. Infrared Phys. Technol. 2017, 81, 228–235.
  22. Senthilkumar, T.; Jayas, D.S.; White, N.D.G.; Fields, P.G.; Gräfenhan, T. Detection of fungal infection and Ochratoxin A contamination in stored wheat using near-infrared hyperspectral imaging. J. Stored Prod. Res. 2016, 65, 30–39.
  23. Senthilkumar, T.; Jayas, D.S.; White, N.D.G. Detection of different stages of fungal infection in stored canola using near-infrared hyperspectral imaging. J. Stored Prod. Res. 2015, 63, 80–88.
  24. Sendin, K.; Williams, P.J.; Manley, M. Near infrared hyperspectral imaging in quality and safety evaluation of cereals. Crit. Rev. Food Sci. Nutr. 2018, 58, 575–590.
  25. Sendin, K.; Manley, M.; Baeten, V.; Fernández Pierna, J.A.; Williams, P.J. Near infrared hyperspectral imaging for white maize classification according to grading regulations. Food Anal. Methods 2019, 12, 1612–1624.
  26. Scholten, R.C.; Hill, J.; Werner, W.; Buddenbaum, H.; Dash, J.P.; Gomez Gallego, M.; Rolando, C.A.; Pearse, G.D.; Hartley, R.; Estarija, H.J.; et al. Hyperspectral VNIR-spectroscopy and imagery as a tool for monitoring herbicide damage in wilding conifers. Biol. Invasions 2019, 21, 3395–3413.
  27. Sagan, V.; Maimaitiyiming, M.; Fishman, J. Effects of ambient ozone on soybean biophysical variables and mineral nutrient accumulation. Remote Sens. 2018, 10, 562.
  28. Rodríguez-Pulido, F.J.; Lourdes González, M.M.; Heredia, F.J. Application of imaging techniques for the evaluation of phenolic maturity of grape seeds. Opt. Pura Y Apl. 2017, 50, 1–11.
  29. Rodríguez-Pulido, F.J.; Hernández-Hierro, J.M.; Nogales-Bueno, J.; Gordillo, B.; González-Miret, M.L.; Heredia, F.J. A novel method for evaluating flavanols in grape seeds by near infrared hyperspectral imaging. Talanta 2014, 122, 145–150.
  30. Rodríguez-Pulido, F.J.; Gil-Vicente, M.; Gordillo, B.; Heredia, F.J.; González-Miret, M.L. Measurement of ripening of raspberries (Rubus idaeus L) by near infrared and colorimetric imaging techniques. J. Food Sci. Technol. 2017, 54, 2797–2803.
  31. Reddy, K.R.; Zhao, D.; Kakani, V.G.; Read, J.J.; Sailaja, K. Estimating cotton growth and developmental parameters through remote sensing. In Proceedings of SPIE—The International Society for Optical Engineering; SPIE: Bellingham, WA, USA, 2003; pp. 277–288.
  32. Ravikanth, L.; Chelladurai, V.; Jayas, D.S.; White, N.D.G. Detection of broken kernels content in bulk wheat samples using near-infrared hyperspectral imaging. Agric. Res. 2016, 5, 285–292.
  33. Rajendran, K.; Patil, S.; Kumar, S. Phenotyping for problem soils. In Phenomics in Crop Plants: Trends, Options and Limitations; Springer: New Delhi, India, 2015; pp. 129–146.
  34. Rajah, P.; Odindi, J.; Abdel-Rahman, E.M.; Mutanga, O.; Modi, A. Varietal discrimination of common dry bean (Phaseolus vulgaris L.) grown under different watering regimes using multitemporal hyperspectral data. J. Appl. Remote Sens. 2015, 9, 096050.
  35. Rahman, A.; Faqeerzada, M.A.; Joshi, R.; Lohumi, S.; Kandpal, L.M.; Lee, H.; Mo, C.; Kim, M.S.; Cho, B.K. Quality analysis of stored bell peppers using near-infrared hyperspectral imaging. Trans. ASABE 2018, 61, 1199–1207.
  36. Rahman, A.; Faqeerzada, M.A.; Cho, B.K. Hyperspectral imaging for predicting the allicin and soluble solid content of garlic with variable selection algorithms and chemometric models. J. Sci. Food Agric. 2018, 98, 4715–4725.
  37. Rady, A.; Ekramirad, N.; Adedeji, A.A.; Li, M.; Alimardani, R. Hyperspectral imaging for detection of codling moth infestation in GoldRush apples. Postharvest Biol. Technol. 2017, 129, 37–44.
  38. Esquerre, C.; Gowen, A.A.; Downey, G.; O’Donnell, C.P. Wavelength selection for development of a near Infrared imaging system for early detection of bruise damage in mushrooms (Agaricus Bisporus). J. Near Infrared Spectrosc. 2012, 20, 537–546.
  39. Hagen, N.; Kudenov, M. Review of snapshot spectral imaging technologies. Opt. Eng. 2013, 52, 090901.
  40. Ozaki, Y.; Huck, C.; Beć, K. Near infrared spectroscopy and its applications. In Molecular and Laser Spectroscopy; Elsevier: Amsterdam, The Netherlands, 2017; pp. 11–38.
  41. Lavigne, D.A.; Breton, M.; Pichettea, M.; Laroehellea, V.; Simarda, J.R. Evaluation of active and passive polarimetrie electro-optic imagery for civilian and military targets discrimination. In Proceedings of SPIE—The International Society for Optical Engineering; SPIE: Bellingham, WA, USA, 2008.
  42. Hipwood, L.G.; Shorrocks, N.; Maxey, C.; Atkinson, D.; Bezawada, N. SWIR and NIR MCT arrays grown by MOVPE for astronomy applications. In Proceedings of SPIE—The International Society for Optical Engineering; SPIE: Bellingham, WA, USA, 2012.
  43. Demattê, J.A.M.; Horák-Terra, I.; Beirigo, R.M.; Terra, F.D.S.; Marques, K.P.P.; Fongaro, C.T.; Silva, A.C.; Vidal-Torrado, P. Genesis and properties of wetland soils by VIS-NIR-SWIR as a technique for environmental monitoring. J. Environ. Manag. 2017, 197, 50–62.
  44. Bellon, V.; Rabatel, G.; Guizard, C. Automatic sorting of fruit: Sensors for the future. Food Control 1992, 3, 49–54.
  45. Upchurch, B.L.; Throop, J.A.; Aneshansley, D.J. Influence of time, bruise-type, and severity on near-infrared reflectance from apple surfaces for automatic bruise detection. Trans. Am. Soc. Agric. Eng. 1994, 37, 1571–1575.
  46. Zhang, B.; Li, J.; Fan, S.; Huang, W.; Zhao, C.; Liu, C.; Huang, D. Hyperspectral imaging combined with multivariate analysis and band math for detection of common defects on peaches (Prunus persica). Comput. Electron. Agric. 2015, 114, 14–24.
  47. Miller, B.K.; Delwiche, M.J. Spectral analysis of peach surface defects. Trans. Am. Soc. Agric. Eng. 1991, 34, 2509–2575.
  48. Zwiggelaar, R.; Yang, Q.; Garcia-Pardo, E.; Bull, C.R. Use of spectral information and machine vision for bruise detection on peaches and apricots. J. Agric. Eng. Res. 1996, 63, 323–331.
  49. Leemans, V.; Destain, M.F. A real-time grading method of apples based on features extracted from defects. J. Food Eng. 2004, 61, 83–89.
  50. Li, J.; Rao, X.; Ying, Y. Detection of common defects on oranges using hyperspectral reflectance imaging. Comput. Electron. Agric. 2011, 78, 38–48.
  51. 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.
  52. Nakariyakul, S.; Casasent, D.P. Classification of internally damaged almond nuts using hyperspectral imagery. J. Food Eng. 2011, 103, 62–67.
  53. Hussain, A.; Pu, H.; Sun, D.W. Innovative nondestructive imaging techniques for ripening and maturity of fruits—A review of recent applications. Trends Food Sci. Technol. 2018, 72, 144–152.
  54. Kawano, S. Present condition of nondestructive quality evaluation techniques for fruits and vegetables. Food Preserv. Sci. 1998, 24, 193–200.
  55. Tsuta, M.; Sugiyama, J.; Sagara, Y. Near-infrared imaging spectroscopy using a hyper-spectral camera-Visualization of the sugar distribution in the flesh of melons. Kyokai Joho Imeji Zasshi/J. Inst. Image Inf. Telev. Eng. 2002, 56, 2037–2040.
  56. Sugiyama, J. Visualization of sugar content in the flesh of a melon by near-infrared imaging. J. Agric. Food Chem. 1999, 47, 2715–2718.
  57. Kalviainen, H.; Parkkinen, J.; Kaarna, A. (Eds.) Image Analysis: 14th Scandinavian Conference, SCIA 2005, Joensuu, Finland, June 19–22, 2005, Proceedings. In Lecture Notes in Computer Science; Springer: Berlin/Heidelberg, Germany, 2005.
  58. Khan, M.J.; Khan, H.S.; Yousaf, A.; Khurshid, K.; Abbas, A. Modern trends in hyperspectral image analysis: A Review. IEEE Access 2018, 6, 14118–14129.
  59. ElMasry, G.; Kamruzzaman, M.; Sun, D.W.; Allen, P. Principles and applications of hyperspectral imaging in quality evaluation of agro-food products: A Review. Crit. Rev. Food Sci. Nutr. 2012, 52, 999–1023.
  60. Faqeerzada, M.A.; Perez, M.; Lohumi, S.; Lee, H.; Kim, G.; Wakholi, C.; Joshi, R.; Cho, B.K. Online application of a hyperspectral imaging system for the sorting of adulterated almonds. Appl. Sci. 2020, 10, 6569.
  61. Zhao, Y.; Wang, Y.; Wei, D.; Mu, H.; Ning, T. Application of hyperspectral imaging in measurement real-time of seeds. In Proceedings of the 2016 IEEE International Conference on Smart Cloud (SmartCloud), New York, NY, USA, 18–20 November 2016; pp. 274–277.
  62. Wang, H.; Hu, Y.; Ma, X.; Sun, J.; Sun, X.; Chen, D.; Zheng, X.; Li, Q. An active hyperspectral imaging system based on a multi-LED light source. Rev. Sci. Instrum. 2019, 90, 026107.
  63. Zahavi, A.; Palshin, A.; Liyanage, D.C.; Tamre, M. Influence of illumination sources on hyperspectral imaging. In Proceedings of the 2019 20th International Conference on Research and Education in Mechatronics (REM), Wels, Austria, 23–24 May 2019.
  64. Zhang, B.; Huang, W.; Li, J.; Zhao, C.; Fan, S.; Wu, J.; Liu, C. Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review. Food Res. Int. 2014, 62, 326–343.
  65. Wu, D.; Sun, D.W. Advanced applications of hyperspectral imaging technology for food quality and safety analysis and assessment: A review—Part I: Fundamentals. Innov. Food Sci. Emerg. Technol. 2013, 19, 1–14.
  66. Ariana, D.P.; Lu, R. Quality evaluation of pickling cucumbers using hyperspectral reflectance and transmittance imaging—Part II. Performance of a prototype. Sens. Instrum. Food Qual. Saf. 2008, 2, 152–160.
  67. Gruber, F.; Wollmann, P.; Grählert, W.; Kaskel, S. Hyperspectral imaging using laser excitation for fast Raman and fluorescence hyperspectral imaging for sorting and quality control applications. J. Imaging 2018, 4, 110.
  68. Francis, R.P.; Zuzak, K.J.; Ufret-Vincenty, R. Hyperspectral retinal imaging with a spectrally tunable light source. In Proceedings of SPIE—The International Society for Optical Engineering; SPIE: Bellingham, WA, USA, 2011.
  69. Cogdill, R.; Hurburgh, C., Jr.; Rippke, G.; Bajic, S.; Jones, R.; McClelland, J.; Jensen, T.; Liu, J. Single-kernel maize analysis by near-infrared hyperspectral imaging. Trans. ASAE 2004, 47, 311–320.
  70. Williams, P.; Manley, M.; Fox, G.; Geladi, P. Indirect detection of Fusarium verticillioides in maize
  71. Jia, B.; Wang, W.; Ni, X.; Lawrence, K.C.; Zhuang, H.; Yoon, S.C.; Gao, Z. Essential processing methods of hyperspectral images of agricultural and food products. Chemom. Intell. Lab. Syst. 2020, 198, 103936.
  72. Ravikanth, L.; Jayas, D.S.; White, N.D.G.; Fields, P.G.; Sun, D.W. Extraction of spectral information from hyperspectral data and application of hyperspectral imaging for food and agricultural products. Food Bioprocess Technol. 2017, 10, 1–33.
  73. Mo, C.; Kim, G.; Lim, J. Online hyperspectral imaging system for evaluating quality of agricultural products. In Proceedings of SPIE—The International Society for Optical Engineering; SPIE: Bellingham, WA, USA, 2017.
  74. Zhang, R.; Ying, Y.; Rao, X.; Li, J. Quality and safety assessment of food and agricultural products by hyperspectral fluorescence imaging. J. Sci. Food Agric. 2012, 92, 2397–2408.
  75. Yao, H.; Hruska, Z.; Kincaid, R.; Brown, R.L.; Bhatnagar, D.; Cleveland, T.E. Detecting maize inoculated with toxigenic and atoxigenic fungal strains with fluorescence hyperspectral imagery. Biosyst. Eng. 2013, 115, 125–135.
  76. Noh, H.K.; Peng, Y.; Lu, R. Integration of hyperspectral reflectance and fluorescence imaging for assessing apple maturity. Trans. ASABE 2007, 50, 963–971.
  77. Lefcout, A.M.; Kim, M.S.; Chen, Y.-R.; Kang, S. Systematic approach for using hyperspectral imaging data to develop multispectral imagining systems: Detection of feces on apples. Comput. Electron. Agric. 2006, 54, 22–35.
  78. Shenk, J.S.; Workman, J.J., Jr.; Westerhaus, M.O. Application of NIR spectroscopy to agricultural products. In Handbook of Near-Infrared Analysis; Ciurczak, E.W., Burns, D.A., Eds.; CRC Press: Boca Raton, FL, USA, 2007; pp. 347–386.
  79. Wang, H.; Li, C.; Wang, M. Quantitative determination of onion internal quality using reflectance, interactance, and transmittance modes of hyperspectral imaging. Trans. ASABE 2013, 56, 1623–1635.
  80. Hu, M.H.; Dong, Q.L.; Liu, B.L. Classification and characterization of blueberry mechanical damage with time evolution using reflectance, transmittance and interactance imaging spectroscopy. Comput. Electron. Agric. 2016, 122, 19–28.
  81. Schaare, P.N.; Fraser, D.G. Comparison of reflectance, interactance and transmission modes of visible-near infrared spectroscopy for measuring internal properties of kiwifruit (Actinidia chinensis). Postharvest Biol. Technol. 2000, 20, 175–184.
  82. McGlone, V.A.; Kawano, S. Firmness, dry-matter and soluble-solids assessment of postharvest kiwifruit by NIR spectroscopy. Postharvest Biol. Technol. 1998, 13, 131–141.
  83. Mahesh, S.; Dsjayas; Paliwal, J.; White, N.D.G. Protein and oil contents determination in wheat using near-infrared (NIR) hyperspectral imaging. In Proceedings of the American Society of Agricultural and Biological Engineers Annual International Meeting 2008, ASABE, Providence, RI, USA, 29 June–2 July 2008; pp. 6660–6671.
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Subjects: Biochemical Research Methods; Agriculture, Dairy & Animal Science; Computer Science, Artificial Intelligence
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Priyanka Reddy
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