A Versatile near Infrared Imaging Spectrometer

Martinsen, Paul; Schaare, Peter; Andrews, Mark

doi:10.1255/jnirs.230

Cited by 18 publications

(10 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The hardware system includes an imaging spectrograph, which is often a PGP spectrograph, a NIR camera, motor controlled translation movement stage, and a frame-grabber with personal computer [45]. The sequential data acquisition can be accomplished by either moving the imaging hardware [46] or the target samples [47] in small steps. The wavelength tuning imaging approach sequentially captures entire spatial image at discrete scanning wavelengths to complete the threedimensional data cube.…”

Section: Hyperspectral Imaging Systemsmentioning

confidence: 99%

“…Spectrometric stability can be measured by observing the deviation of spectral response of the instrument within a specific time period [61]. Spectral range and its calibration can be carried out using a mercury argon lamp [46]. A pushbroom hyperspectral imaging system has to deal with two types of optical errors, which are called 'smile' and 'keystone' errors.…”

Section: System Calibrationmentioning

confidence: 99%

See 1 more Smart Citation

Near-infrared spectroscopy and imaging in food quality and safety

Wang

Paliwal

2007

Sens. & Instrumen. Food Qual.

143

View full text Add to dashboard Cite

Over the last two decades, near-infrared spectroscopy (NIRS) has established itself as a non-destructive analytical technique in a variety of disciplines. However, recent technological advancements in hardware design and data mining techniques have unleashed the potential of NIRS to become a tool of choice for routine analyses of agricultural products. The current paper synthesizes the status of NIRS in the agri-food industry in terms of hardware and software development as well as the direction in which the NIRS research is headed. An extensive review of literature reveals that the emphasis on hardware development is focused on developing compact, robust, and portable spectrometers and hyperspectral imaging (HSI) systems. The software development on the other hand is geared towards developing better preprocessing, analyses, and modeling techniques using chemometrics, support vector machines, and artificial neural networks. The four main agri-food sectors identified to be the beneficiaries of this research revolution are grain quality monitoring; postharvest handling of fruits and vegetables; identification of contaminants in animal produce and feed; and food safety and authenticity. Apart from discussing the aforementioned topics, the paper also provides food scientists some working knowledge on parameters crucial to the performance of spectral and imaging systems. It is expected that further development of NIRS will help agricultural and food scientists to enhance the quality and safety of our food.

show abstract

Section: Hyperspectral Imaging Systemsmentioning

confidence: 99%

Section: System Calibrationmentioning

confidence: 99%

Near-infrared spectroscopy and imaging in food quality and safety

Wang

Paliwal

2007

Sens. & Instrumen. Food Qual.

143

View full text Add to dashboard Cite

show abstract

“…To achieve this, a low cost near infrared spectral imaging instrument was built according to a tested design [10]. This instrumentation used a low cost silicon Charged Coupled Detector (CCD) that operated from 400nm to 1100nm and obtained image spectra.…”

Section: A Apparatusmentioning

confidence: 99%

“…3) to generate a data set of 120 spectral images. The measurements were taken in reflectance mode and the mathematical treatment of the spectral variables was log 10 (1/R) where R represents the reflectance value at a specified wavelength.…”

Section: B Spectral Dataset Collectionmentioning

confidence: 99%

NIR spectrometer used for material modeling with neural networks

Yee

2014

Asia-Pacific World Congress on Computer Science and Engineering

View full text Add to dashboard Cite

Abstract-Near infrared multi-spectral image analysis is a tool used for non-destructive determination of biological material properties.In this investigation a custom built imaging spectrometer is constructed and used for the image spectra instrumentation and tests are performed on this instrument to determine its spectral resolution and spectral range; a biological data set (moisture in potato crisps) is then captured using this instrument and this data set is modeled using near infrared multi-spectral image analysis. A common problem with near infrared multi-spectral quantitative image measurements is light scatter and light non-linearity resulting from sample shape contours/curvatures and optical aberrations from optical component selection/layout. In this paper we detail an imaging spectrometer and the use of orthogonal signal correction preprocessing combined with a neural network full spectrum model for measurement of material property.

show abstract

“…Hyperspectral imaging techniques have been already adapted in many scientific disciplines, from microscopic studies to airborne remote-sensing applications (Goetz et al, 1997;Martinsen et al, 1999;Borregaard et al, 2000). In general, these systems measure reflectance with spectral regions ranging from the visible (VIS) to short-wave infrared (SWIR) ranges of the solar spectrum.…”

Section: Hyperspectral Reflectance and Fluorescence Imaging System Fomentioning

confidence: 99%

Hyperspectral Reflectance and Fluorescence Imaging System for Food Quality and Safety

Kim¹,

Chen²,

Mehl³

2001

Transactions of the ASAE

122

View full text Add to dashboard Cite

The spectral range is from 430 to 930 nm with spectral resolution of approximately 10 nm (full width at half maximum) and spatial resolution better than 1 mm. Our system is capable of reflectance and fluorescence measurements with the use of dual illumination sources where fluorescence emissions are measured with ultraviolet (UV-A) excitation. We present the calibrations and image-correction procedures for the system artifacts and heterogeneous responses caused by the optics, sensor, and lighting conditions throughout the spectrum region for reflectance and fluorescence. The results of the fluorescence correction method showed that the system responses throughout the spectrum region were normalized to within 0.5% error. The versatility of the hyperspectral imaging system was demonstrated with sample fluorescence and reflectance images of a normal apple and an apple with fungal contamination and bruised spots. The primary use of the imaging system in our laboratory is to conduct food safety and quality research. However, we envision that this unique system can be used in a number of scientific applications.

show abstract

A Versatile near Infrared Imaging Spectrometer

Cited by 18 publications

References 8 publications

Near-infrared spectroscopy and imaging in food quality and safety

Near-infrared spectroscopy and imaging in food quality and safety

NIR spectrometer used for material modeling with neural networks

Hyperspectral Reflectance and Fluorescence Imaging System for Food Quality and Safety

Contact Info

Product

Resources

About