Monitoring Polyurethane Foaming Reactions Using Near-Infrared Hyperspectral Imaging

Polyurethane (PU) foams are finding increasingly wider applications ranging from memory foams and mattresses to cushions and insulation materials. They are prepared by reactions between multifunctional isocyanates and polyols as the two main building blocks, along with other additives, including the blowing agents. A non-contact near-infrared (NIR) hyperspectral imaging (HSI) camera was used in this study to monitor PU foaming reactions between a polymeric methylene diphenyl diisocyanate, polyol, and water. Five foams were prepared with three process variables: water content, mixing time, and catalyst levels. Spectral changes characteristic of the PU reactions were observed and clear difference in kinetics could be effectively extracted from such NIR HSI results. The NIR HSI technology offers two substantial advantages over the conventional Fourier transform- (FT-) NIR systems: (i) faster spectral acquisition time and (ii) higher spatial resolution of line images rather than the point measurement. Examples are provided to illustrate these two advantages. The potential to acquire chemical images of PU foams is also demonstrated.

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Ultraviolet-visible/near infrared spectroscopy and hyperspectral imaging to study the different types of raw cotton

Journal of Spectral Imaging ◽

10.1255/jsi.2020.a18 ◽

2020 ◽

Author(s):

Mohammad Al Ktash ◽

Otto Hauler ◽

Edwin Ostertag ◽

Marc Brecht

Keyword(s):

Hyperspectral Imaging ◽

Large Scale ◽

Near Infrared ◽

Dominant Role ◽

Principal Component ◽

Industrial Applications ◽

Acquisition Time ◽

Hydroxyl Groups ◽

Short Acquisition Time ◽

Different Types

Different types of raw cotton were investigated by a commercial ultraviolet-visible/near infrared (UV-Vis/NIR) spectrometer (210–2200 nm) as well as on a home-built setup for NIR hyperspectral imaging (NIR-HSI) in the range 1100–2200 nm. UV-Vis/NIR reflection spectroscopy reveals the dominant role proteins, hydrocarbons and hydroxyl groups play in the structure of cotton. NIR-HSI shows a similar result. Experimentally obtained data in combination with principal component analysis (PCA) provides a general differentiation of different cotton types. For UV-Vis/NIR spectroscopy, the first two principal components (PC) represent 82 % and 78 % of the total data variance for the UV-Vis and NIR regions, respectively. Whereas, for NIR-HSI, due to the large amount of data acquired, two methodologies for data processing were applied in low and high lateral resolution. In the first method, the average of the spectra from one sample was calculated and in the second method the spectra of each pixel were used. Both methods are able to explain ≥90 % of total variance by the first two PCs. The results show that it is possible to distinguish between different cotton types based on a few selected wavelength ranges. The combination of HSI and multivariate data analysis has a strong potential in industrial applications due to its short acquisition time and low-cost development. This study opens a novel possibility for a further development of this technique towards real large-scale processes.

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Mid-Infrared Compressive Hyperspectral Imaging

Remote Sensing ◽

10.3390/rs13040741 ◽

2021 ◽

Vol 13 (4) ◽

pp. 741

Author(s):

Shuowen Yang ◽

Xiang Yan ◽

Hanlin Qin ◽

Qingjie Zeng ◽

Yi Liang ◽

...

Keyword(s):

Hyperspectral Imaging ◽

Near Infrared ◽

Side Information ◽

Computational Imaging ◽

Hyperspectral Data ◽

Hyperspectral Images ◽

Acquisition Time ◽

Reconstruction Method ◽

Mid Infrared ◽

Spectral Channels

Hyperspectral imaging (HSI) has been widely investigated within the context of computational imaging due to the high dimensional challenges for direct imaging. However, existing computational HSI approaches are mostly designed for the visible to near-infrared waveband, whereas less attention has been paid to the mid-infrared spectral range. In this paper, we report a novel mid-infrared compressive HSI system to extend the application domain of mid-infrared digital micromirror device (MIR-DMD). In our system, a modified MIR-DMD is combined with an off-the-shelf infrared spectroradiometer to capture the spatial modulated and compressed measurements at different spectral channels. Following this, a dual-stage image reconstruction method is developed to recover infrared hyperspectral images from these measurements. In addition, a measurement without any coding is used as the side information to aid the reconstruction to enhance the reconstruction quality of the infrared hyperspectral images. A proof-of-concept setup is built to capture the mid-infrared hyperspectral data of 64 pixels × 48 pixels × 100 spectral channels ranging from 3 to 5 μm, with the acquisition time within one minute. To the best of our knowledge, this is the first mid-infrared compressive hyperspectral imaging approach that could offer a less expensive alternative to conventional mid-infrared hyperspectral imaging systems.

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Pork Tissue Recognition Using Visible and Near Infrared Hyperspectral Imaging

2020 International Computer Symposium (ICS) ◽

10.1109/ics51289.2020.00084 ◽

2020 ◽

Author(s):

Chih-Cheng Pai ◽

Yang-Chu Chen ◽

Keng-Hao Liu ◽

Yuan-Hsun Tsai ◽

Po-Chi Hu ◽

...

Keyword(s):

Hyperspectral Imaging ◽

Near Infrared

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Integrated Optical Phased Arrays for Beam Forming and Steering

Applied Sciences ◽

10.3390/app11094017 ◽

2021 ◽

Vol 11 (9) ◽

pp. 4017

Author(s):

Yongjun Guo ◽

Yuhao Guo ◽

Chunshu Li ◽

Hao Zhang ◽

Xiaoyan Zhou ◽

...

Keyword(s):

Near Infrared ◽

Phased Arrays ◽

Mechanical Stability ◽

Building Blocks ◽

High Yield ◽

Beam Forming ◽

Integrated Optical ◽

Optical Phased Arrays ◽

Infrared Spectral ◽

Spectral Ranges

Integrated optical phased arrays can be used for beam shaping and steering with a small footprint, lightweight, high mechanical stability, low price, and high-yield, benefiting from the mature CMOS-compatible fabrication. This paper reviews the development of integrated optical phased arrays in recent years. The principles, building blocks, and configurations of integrated optical phased arrays for beam forming and steering are presented. Various material platforms can be used to build integrated optical phased arrays, e.g., silicon photonics platforms, III/V platforms, and III–V/silicon hybrid platforms. Integrated optical phased arrays can be implemented in the visible, near-infrared, and mid-infrared spectral ranges. The main performance parameters, such as field of view, beamwidth, sidelobe suppression, modulation speed, power consumption, scalability, and so on, are discussed in detail. Some of the typical applications of integrated optical phased arrays, such as free-space communication, light detection and ranging, imaging, and biological sensing, are shown, with future perspectives provided at the end.

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Identification of peat type and humification by laboratory VNIR/SWIR hyperspectral imaging of peat profiles with focus on fen-bog transition in aapa mires

Plant and Soil ◽

10.1007/s11104-020-04775-y ◽

2020 ◽

Author(s):

L. Granlund ◽

M. Keinänen ◽

T. Tahvanainen

Keyword(s):

Hyperspectral Imaging ◽

Near Infrared ◽

Support Vector ◽

High Resolution Imaging ◽

Factors Affecting ◽

Vector Machines ◽

Resolution Imaging ◽

Shortwave Infrared ◽

Type Classification ◽

Wide Potential

Abstract Aims Hyperspectral imaging (HSI) has high potential for analysing peat cores, but methodologies are deficient. We aimed for robust peat type classification and humification estimation. We also explored other factors affecting peat spectral properties. Methods We used two laboratory setups: VNIR (visible to near-infrared) and SWIR (shortwave infrared) for high resolution imaging of intact peat profiles with fen-bog transitions. Peat types were classified with support vector machines, indices were developed for von Post estimation, and K-means clustering was used to analyse stratigraphic patterns in peat quality. With separate experiments, we studied spectral effects of drying and oxidation. Results Despite major effects, oxidation and water content did not impede robust HSI classification. The accuracy between Carex peat and Sphagnum peat in validation was 80% with VNIR and 81% with SWIR data. The spectral humification indices had accuracies of 82% with VNIR and 56%. Stratigraphic HSI patterns revealed that 36% of peat layer shifts were inclined by over 20 degrees. Spectral indices were used to extrapolate visualisations of element concentrations. Conclusions HSI provided reliable information of basic peat quality and was useful in visual mapping, that can guide sampling for other analyses. HSI can manage large amounts of samples to widen the scope of detailed analysis beyond single profiles and it has wide potential in peat research beyond the exploratory scope of this paper. We were able to confirm the capacity of HSI to reveal shifts of peat quality, connected to ecosystem-scale change.

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