Hyperspectral imaging for food applications

Marshall, Stephen; Kelman, Timothy; Qiao, Tong; Murray, Paul; Zabalza, Jaime

doi:10.1109/eusipco.2015.7362906

Cited by 24 publications

(15 citation statements)

References 15 publications

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“…During testing the test samples is taken and it will compare the values stored in the database. Based on the values stored in the database we can tell whether the sample is healthy, burnt or blast [4]. And recognizing the nearness of disease, another road of research is to recognize diverse maladies to distinguish particular pathogens.…”

Section: System Designmentioning

confidence: 99%

Leaf Quality :Hyperspectral Imaging Technology

A¹,

Thippeswamy²

2018

IJET

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India is a rural nation 70% of the populace relies upon horticulture. Farmers are the backbone of our nation.70% of the economic growth depends on agriculture. The major food crops of India are wheat, corn, rice, barley, sorghum. In this project the concentration is on paddy as it is a main food crop of our state. This project helps us to find whether the leaf is diseased or not and also helps us to find the type of disease in paddy leaf. The agribusiness research of programmed leaf sickness recognition is fundamental research point as it might demonstrate benefits in checking substantial fields of products, and subsequently naturally recognize manifestations of illness when they show up on plant takes off. Digital image processing Advanced is a procedure utilized for improvement of the picture. To enhance farming items programmed recognition of side effects is useful.

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Section: System Designmentioning

confidence: 99%

Leaf Quality :Hyperspectral Imaging Technology

A¹,

Thippeswamy²

2018

IJET

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“…In the past, applications of HSI are in the field of remote sensing such as precision agriculture (Datt, McVicar, Van Niel, Jupp, & Pearlman, 2003) , land cover analysis (Tong Qiao, Ren, Sun, Zheng, & Marshall, 2014) or military target detection (Manolakis & Shaw, 2002;Young, Marshall, & Gray, 2016). Due to recent advances in imaging technology in the last decades, HSI became more popular for lab-based applications such as food quality monitoring (Marshall, Kelman, Qiao, Murray, & Zabalza, 2015;Sun, 2010), medical applications (Lu & Fei, 2014) and even artwork inspection (Polak et al, 2016). The popularity derives from the nondestructive nature of HSI, where samples can be analysed chemometrically without altering their physical integrity.…”

Section: Introductionmentioning

confidence: 99%

Potential of UV and SWIR hyperspectral imaging for determination of levels of phenolic flavour compounds in peated barley malt

et al. 2019

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In this study, ultra-violet (UV) and short-wave infra-red (SWIR) hyperspectral imaging (HSI) was used to measure the concentration of phenolic flavour compounds on malted barley that are responsible for smoky aroma of Scotch whisky. UV-HSI is a relatively unexplored technique that has the potential to detect specific absorptions of phenols. SWIR-HSI has proven to detect phenols in previous applications. Support Vector Machine Classification and Regression was applied to classify malts with ten different concentration levels of the compounds of interest, and to estimate the concentration respectively. Results reveal that UV-HSI is at its current development stage unsuitable for this task whereas SWIR-HSI is able to produce robust results with a classification accuracy of 99.8% and a squared correlation coefficient of 0.98 with a Root Mean Squared Error of 0.32 ppm for regression. The results indicate that with further testing and development, HSI may potentially be exploited in an industrial production environment.

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“…Spectral filters have a wide range of sensing applications ranging from environmental (hazardous waste, oil, etc) to surveillance. [1][2][3][4][5][6][7][8][9][10] In sensing, detectors are sensitive to anywhere from several to hundreds of electromagnetic bands. Based on the number of bands and bandwidth, these systems are separated into multispectral and hyperspectral imaging systems with the former capturing under 10 bands and the latter -hundreds to thousands of bands of narrow width (around 10-20 nm) that allow for a continuous measurement across a full spectrum.…”

Section: Introductionmentioning

confidence: 99%

Fabrication of ultra-thin si nanopillar arrays for polarization-independent spectral filters in the near-IR

Greer

Fountaine

2018

Photonic and Phononic Properties of Engineered Nanostructures VIII

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Sub-wavelength arrays have garnered significant interest for many potential optoelectronics applications. We fabricated sub-wavelength silicon nanopillar arrays with a ratio of radius, r and a center-to-center distance, a, of r/a ≈ 0.2 that were fully embedded in SiO 2 for narrow stopband filters that are compact and straightforward to fabricate compared to conventional Bragg stack reflectors. These arrays are well-suited for hyperspectral filtering applications in the infrared. They are ultra-thin (<0.1λ), polarization-independent, and attain greater efficiencies enabled by low loss compared to plasmonic-based designs. The choice of Si as the nanopillar material stems from its low cost, high index of refraction, and a band gap of 1.1 eV near the edge of the visible. These arrays exhibit narrow near-unity reflectivity resonances that arise from coupling of an incident wave into a leaky waveguide mode via a grating vector that is subsequently reradiated, also known as guided mode resonances (GMRs). Simulations reveal reflectivities of >99% with full width at half maxima (FWHM) of ≈0.01 μm. We demonstrate a fabrication route for obtaining nanopillar arrays that exhibit these GMRs. We experimentally observed a GMR with an amplitude of ~0.8 for filter arrays fabricated on silicon on insulator (SOI) substrates, combined with Fabry-Perot interference that stems from the underlying silicon layer.

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Hyperspectral imaging for food applications

Cited by 24 publications

References 15 publications

Leaf Quality :Hyperspectral Imaging Technology

Leaf Quality :Hyperspectral Imaging Technology

Potential of UV and SWIR hyperspectral imaging for determination of levels of phenolic flavour compounds in peated barley malt

Fabrication of ultra-thin si nanopillar arrays for polarization-independent spectral filters in the near-IR

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