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Optical Spectral Sensing and Imaging Technology
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Optical Spectral Sensing and Imaging Technology

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Optical Sensors".

Deadline for manuscript submissions: closed (15 November 2023) | Viewed by 41825

Special Issue Editor

Prof. Dr. Marc Brecht

E-Mail Website
Guest Editor
1. Process Analysis & Technology, Reutlingen Research Institute, Reutlingen University, Alteburgstraße 150, 72762 Reutlingen, Germany
2. Institute of Physical and Theoretical Chemistry, University of Tübingen, Auf der Morgenstelle 18, 72076 Tübingen, Germany
Interests: fluorescence and Raman imaging; sensor development; biophysics; process analytics; hyperspectral imaging; chemometrics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In optical spectral sensing and imaging technologies, spatial information is combined with spectroscopy. These methods offer fast and non-destructive methods which have evolved into powerful analysis tools for science and industry. In recent years, the related areas have developed rapidly. On the one hand, development is being driven forward both technically and methodically. Camera/imaging technology is developing very quickly—especially, the first hyperspectral chips have become commercially affordable. Additionally, their use in smartphones as sensors will not be long in coming. Another driving force is the ever more powerful computers and programs that enable us to visualize and analyze the enormous amounts of data in a reasonable amount of time using chemometric methods or AI applications.

This Special Issue will focus on (i) current state-of-the-art of optical sensors for spectral sensing and imaging, (ii) recent technological improvements in new devices/sensors, (iii) mathematical methods for data analysis, and (iv) scientific/industrial applications. Both original research papers and review articles describing the current state-of-the-art in this research field are welcome. The Editor intends with this SI to provide an overview of the present status as well as a future perspective of these topics.

The manuscripts should cover, but are not limited to, the following topics:

  • Existing methodology and instrumentation;
  • Emerging novel instrumentation and techniques;
  • Spectral/data unmixing;
  • Spectral variability;
  • Classification, segmentation, and compression;
  • Data fusion, information extraction, and simulation;
  • Target detection;
  • Hyperspectral image classification;
  • High performance computing;
  • AI applications and chemometric modeling;
  • Calibration transfer;
  • Scientific and industrial applications—all topics are welcome.

Prof. Dr. Marc Brecht
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (17 papers)

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14 pages, 1712 KiB  
Article
Star Image Registration Modeling and Parameter Calibration for a 3CMOS Star Sensor
by Yanzhao Niu, Xinguo Wei and Jian Li
Sensors 2024, 24(1), 259; https://doi.org/10.3390/s24010259 - 02 Jan 2024
Viewed by 654
Abstract
This paper presents an image registration method specifically designed for a star sensor equipped with three complementary metal oxide semiconductor (CMOS) detectors. Its purpose is to register the red-, green-, and blue-channel star images acquired from three CMOS detectors, assuring the precision of [...] Read more.
This paper presents an image registration method specifically designed for a star sensor equipped with three complementary metal oxide semiconductor (CMOS) detectors. Its purpose is to register the red-, green-, and blue-channel star images acquired from three CMOS detectors, assuring the precision of star image fusion and centroid extraction in subsequent stages. This study starts with a theoretical analysis aimed at investigating the effect of inconsistent three-channel imaging parameters on the position of feature points. Based on this analysis, this paper establishes a registration model for transforming the red- and blue-channel star images into the green channel’s coordinate system. Subsequently, the method estimates model parameters by finding a nonlinear least-squares solution. The experimental results demonstrate the correctness of the theoretical analysis and the proposed registration method. This method can achieve subpixel alignment accuracy in both the x and y directions, thus effectively ensuring the performance of subsequent operation steps in the 3CMOS star sensor. Full article
(This article belongs to the Special Issue Optical Spectral Sensing and Imaging Technology)
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15 pages, 1150 KiB  
Article
Three-Dimensional (3D) Visualization under Extremely Low Light Conditions Using Kalman Filter
by Hyun-Woo Kim, Myungjin Cho and Min-Chul Lee
Sensors 2023, 23(17), 7571; https://doi.org/10.3390/s23177571 - 31 Aug 2023
Cited by 1 | Viewed by 769
Abstract
In recent years, research on three-dimensional (3D) reconstruction under low illumination environment has been reported. Photon-counting integral imaging is one of the techniques for visualizing 3D images under low light conditions. However, conventional photon-counting integral imaging has the problem that results are random [...] Read more.
In recent years, research on three-dimensional (3D) reconstruction under low illumination environment has been reported. Photon-counting integral imaging is one of the techniques for visualizing 3D images under low light conditions. However, conventional photon-counting integral imaging has the problem that results are random because Poisson random numbers are temporally and spatially independent. Therefore, in this paper, we apply a technique called Kalman filter to photon-counting integral imaging, which corrects data groups with errors, to improve the visual quality of results. The purpose of this paper is to reduce randomness and improve the accuracy of visualization for results by incorporating the Kalman filter into 3D reconstruction images under extremely low light conditions. Since the proposed method has better structure similarity (SSIM), peak signal-to-noise ratio (PSNR) and cross-correlation values than the conventional method, it can be said that the visualization of low illuminated images can be accurate. In addition, the proposed method is expected to accelerate the development of autonomous driving technology and security camera technology. Full article
(This article belongs to the Special Issue Optical Spectral Sensing and Imaging Technology)
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15 pages, 2627 KiB  
Article
Design of a Dual-Mode Multispectral Filter Array
by Zhengnan Ye, Haisong Xu, Yiming Huang and Minhang Yang
Sensors 2023, 23(15), 6856; https://doi.org/10.3390/s23156856 - 01 Aug 2023
Viewed by 1204
Abstract
Multispectral imaging is valuable in many vision-related fields as it provides an additional modality to observe the world. Cameras equipped with multispectral filter arrays (MSFAs) are typically impractical for everyday use due to their intractable demosaicking and chromatic reproduction processes, which restrict their [...] Read more.
Multispectral imaging is valuable in many vision-related fields as it provides an additional modality to observe the world. Cameras equipped with multispectral filter arrays (MSFAs) are typically impractical for everyday use due to their intractable demosaicking and chromatic reproduction processes, which restrict their applicability beyond academic research. In this work, a novel MSFA design is proposed to enable dual-mode imaging for multispectral cameras. In addition to a conventional multispectral image, the camera is also able to produce a Bayer-formed RGB image from a single shot by grouping and merging adjacent pixels in the proposed MSFA, making it suitable for scenarios where display-ready RGB images are required. Furthermore, a two-stage optimization scheme is implemented to jointly optimize objective functions for both imaging modes. The evaluation results on multiple datasets suggest that the proposed MSFA design is able to simultaneously achieve competitive spectral reconstruction accuracy compared to elaborate multispectral cameras and chromatic accuracy compared to commercial RGB cameras. Full article
(This article belongs to the Special Issue Optical Spectral Sensing and Imaging Technology)
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13 pages, 1926 KiB  
Article
Machine Learning Analysis of Hyperspectral Images of Damaged Wheat Kernels
by Kshitiz Dhakal, Upasana Sivaramakrishnan, Xuemei Zhang, Kassaye Belay, Joseph Oakes, Xing Wei and Song Li
Sensors 2023, 23(7), 3523; https://doi.org/10.3390/s23073523 - 28 Mar 2023
Cited by 4 | Viewed by 2151
Abstract
Fusarium head blight (FHB) is a disease of small grains caused by the fungus Fusarium graminearum. In this study, we explored the use of hyperspectral imaging (HSI) to evaluate the damage caused by FHB in wheat kernels. We evaluated the use of [...] Read more.
Fusarium head blight (FHB) is a disease of small grains caused by the fungus Fusarium graminearum. In this study, we explored the use of hyperspectral imaging (HSI) to evaluate the damage caused by FHB in wheat kernels. We evaluated the use of HSI for disease classification and correlated the damage with the mycotoxin deoxynivalenol (DON) content. Computational analyses were carried out to determine which machine learning methods had the best accuracy to classify different levels of damage in wheat kernel samples. The classes of samples were based on the DON content obtained from Gas Chromatography–Mass Spectrometry (GC-MS). We found that G-Boost, an ensemble method, showed the best performance with 97% accuracy in classifying wheat kernels into different severity levels. Mask R-CNN, an instance segmentation method, was used to segment the wheat kernels from HSI data. The regions of interest (ROIs) obtained from Mask R-CNN achieved a high mAP of 0.97. The results from Mask R-CNN, when combined with the classification method, were able to correlate HSI data with the DON concentration in small grains with an R2 of 0.75. Our results show the potential of HSI to quantify DON in wheat kernels in commercial settings such as elevators or mills. Full article
(This article belongs to the Special Issue Optical Spectral Sensing and Imaging Technology)
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18 pages, 8736 KiB  
Article
Detection of Plastic Granules and Their Mixtures
by Roman-David Kulko, Alexander Pletl, Andreas Hanus and Benedikt Elser
Sensors 2023, 23(7), 3441; https://doi.org/10.3390/s23073441 - 24 Mar 2023
Viewed by 1487
Abstract
Chemically pure plastic granulate is used as the starting material in the production of plastic parts. Extrusion machines rely on purity, otherwise resources are lost, and waste is produced. To avoid losses, the machines need to analyze the raw material. Spectroscopy in the [...] Read more.
Chemically pure plastic granulate is used as the starting material in the production of plastic parts. Extrusion machines rely on purity, otherwise resources are lost, and waste is produced. To avoid losses, the machines need to analyze the raw material. Spectroscopy in the visible and near-infrared range and machine learning can be used as analyzers. We present an approach using two spectrometers with a spectral range of 400–1700 nm and a fusion model comprising classification, regression, and validation to detect 25 materials and proportions of their binary mixtures. one dimensional convolutional neural network is used for classification and partial least squares regression for the estimation of proportions. The classification is validated by reconstructing the sample spectrum using the component spectra in linear least squares fitting. To save time and effort, the fusion model is trained on semi-empirical spectral data. The component spectra are acquired empirically and the binary mixture spectra are computed as linear combinations. The fusion model achieves very a high accuracy on visible and near-infrared spectral data. Even in a smaller spectral range from 400–1100 nm, the accuracy is high. The visible and near-infrared spectroscopy and the presented fusion model can be used as a concept for building an analyzer. Inexpensive silicon sensor-based spectrometers can be used. Full article
(This article belongs to the Special Issue Optical Spectral Sensing and Imaging Technology)
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16 pages, 4147 KiB  
Article
Mutual Information-Driven Feature Reduction for Hyperspectral Image Classification
by Md Rashedul Islam, Boshir Ahmed, Md Ali Hossain and Md Palash Uddin
Sensors 2023, 23(2), 657; https://doi.org/10.3390/s23020657 - 06 Jan 2023
Cited by 8 | Viewed by 1794
Abstract
A hyperspectral image (HSI), which contains a number of contiguous and narrow spectral wavelength bands, is a valuable source of data for ground cover examinations. Classification using the entire original HSI suffers from the “curse of dimensionality” problem because (i) the image bands [...] Read more.
A hyperspectral image (HSI), which contains a number of contiguous and narrow spectral wavelength bands, is a valuable source of data for ground cover examinations. Classification using the entire original HSI suffers from the “curse of dimensionality” problem because (i) the image bands are highly correlated both spectrally and spatially, (ii) not every band can carry equal information, (iii) there is a lack of enough training samples for some classes, and (iv) the overall computational cost is high. Therefore, effective feature (band) reduction is necessary through feature extraction (FE) and/or feature selection (FS) for improving the classification in a cost-effective manner. Principal component analysis (PCA) is a frequently adopted unsupervised FE method in HSI classification. Nevertheless, its performance worsens when the dataset is noisy, and the computational cost becomes high. Consequently, this study first proposed an efficient FE approach using a normalized mutual information (NMI)-based band grouping strategy, where the classical PCA was applied to each band subgroup for intrinsic FE. Finally, the subspace of the most effective features was generated by the NMI-based minimum redundancy and maximum relevance (mRMR) FS criteria. The subspace of features was then classified using the kernel support vector machine. Two real HSIs collected by the AVIRIS and HYDICE sensors were used in an experiment. The experimental results demonstrated that the proposed feature reduction approach significantly improved the classification performance. It achieved the highest overall classification accuracy of 94.93% for the AVIRIS dataset and 99.026% for the HYDICE dataset. Moreover, the proposed approach reduced the computational cost compared with the studied methods. Full article
(This article belongs to the Special Issue Optical Spectral Sensing and Imaging Technology)
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11 pages, 3129 KiB  
Article
Investigation of the Hue–Wavelength Response of a CMOS RGB-Based Image Sensor
by Hyeon-Woo Park, Ji-Won Choi, Ji-Young Choi, Kyung-Kwang Joo and Na-Ri Kim
Sensors 2022, 22(23), 9497; https://doi.org/10.3390/s22239497 - 05 Dec 2022
Cited by 10 | Viewed by 4511
Abstract
In this study, a non-linear hue–wavelength (H-W) curve was investigated from 400 to 650 nm. To date, no study has reported on H-W relationship measurements, especially down to the 400 nm region. A digital camera mounted with complementary metal oxide semiconductor (CMOS) image [...] Read more.
In this study, a non-linear hue–wavelength (H-W) curve was investigated from 400 to 650 nm. To date, no study has reported on H-W relationship measurements, especially down to the 400 nm region. A digital camera mounted with complementary metal oxide semiconductor (CMOS) image sensors was used. The obtained digital images of the sample were based on an RGB-based imaging analysis rather than multispectral imaging or hyperspectral imaging. In this study, we focused on the raw image to reconstruct the H-W curve. In addition, several factors affecting the digital image, such as exposure time or international organization for standardization (ISO), were investigated. In addition, cross check of the H-W response using laser was performed. We expect that our method will be useful as an auxiliary method in the future for obtaining the fluor emission wavelength information. Full article
(This article belongs to the Special Issue Optical Spectral Sensing and Imaging Technology)
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35 pages, 13479 KiB  
Article
Irradiance Independent Spectrum Reconstruction from Camera Signals Using the Interpolation Method
by Yu-Che Wen, Senfar Wen, Long Hsu and Sien Chi
Sensors 2022, 22(21), 8498; https://doi.org/10.3390/s22218498 - 04 Nov 2022
Cited by 1 | Viewed by 1449
Abstract
The spectrum of light captured by a camera can be reconstructed using the interpolation method. The reconstructed spectrum is a linear combination of the reference spectra, where the weighting coefficients are calculated from the signals of the pixel and the reference samples by [...] Read more.
The spectrum of light captured by a camera can be reconstructed using the interpolation method. The reconstructed spectrum is a linear combination of the reference spectra, where the weighting coefficients are calculated from the signals of the pixel and the reference samples by interpolation. This method is known as the look-up table (LUT) method. It is irradiance-dependent due to the dependence of the reconstructed spectrum shape on the sample irradiance. Since the irradiance can vary in field applications, an irradiance-independent LUT (II-LUT) method is required to recover spectral reflectance. This paper proposes an II-LUT method to interpolate the spectrum in the normalized signal space. Munsell color chips irradiated with D65 were used as samples. Example cameras are a tricolor camera and a quadcolor camera. Results show that the proposed method can achieve the irradiance independent spectrum reconstruction and computation time saving at the expense of the recovered spectral reflectance error. Considering that the irradiance variation will introduce additional errors, the actual mean error using the II-LUT method might be smaller than that of the ID-LUT method. It is also shown that the proposed method outperformed the weighted principal component analysis method in both accuracy and computation speed. Full article
(This article belongs to the Special Issue Optical Spectral Sensing and Imaging Technology)
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23 pages, 31880 KiB  
Article
Automated Optical Inspection for Defect Identification and Classification in Actual Woven Fabric Production Lines
by Chung-Feng Jeffrey Kuo, Wei-Ren Wang and Jagadish Barman
Sensors 2022, 22(19), 7246; https://doi.org/10.3390/s22197246 - 24 Sep 2022
Cited by 1 | Viewed by 2621
Abstract
This paper presents a turnkey integrated system that can be operated in real time for real textile manufacturers. Eight types of defects in woven fabric, including stain, broken end, broken weft, hole, nep, double pick, kinky weft and float can be recognized and [...] Read more.
This paper presents a turnkey integrated system that can be operated in real time for real textile manufacturers. Eight types of defects in woven fabric, including stain, broken end, broken weft, hole, nep, double pick, kinky weft and float can be recognized and classified. First, an image is captured by a CMOS industrial camera with a pixel size of 4600 × 600 above the batcher at 20 m/min. After that, the four-stage image processing procedure is applied to detect defects and for classification. Stage 1 is image pre-processing; the filtration of the image noise is carried out by a Gaussian filter. The light source is corrected to reduce the uneven brightness resulting from halo formation. The improved mask dodging algorithm is used to reduce the standard deviation of the corrected original image. Afterwards, the background texture is filtered by an averaging filter, and the mean value is corrected for histogram shifting, so that this system is robust to the texture and color changes of woven fabric. The binary segmentation threshold is determined using the mean value and standard deviation of an image with a normal sample. Stage 2 uses adaptive binarization for separation of the background and defects and to filter the noise. In Stage 3, the morphological processing is used before the defect contour is circled, i.e., four features of each block, including the defect area, the aspect ratio of the defect, the average gray level of the defect and the defect orientation, which are calculated according to the range of contour. The image defect recognition dataset consists of 2246 images. The results show that the detection success rate is 96.44%, and the false alarm rate is 3.21%. In Stage 4, the defect classification is implemented. The support vector machine (SVM) is used for classification, 230 defect images are used as training samples, and 206 are used as test samples. The experimental results show that the overall defect recognition rate is 96.60%, providing that the software and hardware equipment designed in this study can implement defect detection and classification for woven fabric effectively. Full article
(This article belongs to the Special Issue Optical Spectral Sensing and Imaging Technology)
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25 pages, 8271 KiB  
Article
Laboratory Hyperspectral Image Acquisition System Setup and Validation
by Alejandro Morales, Pablo Horstrand, Raúl Guerra, Raquel Leon, Samuel Ortega, María Díaz, José M. Melián, Sebastián López, José F. López, Gustavo M. Callico, Ernestina Martel and Roberto Sarmiento
Sensors 2022, 22(6), 2159; https://doi.org/10.3390/s22062159 - 10 Mar 2022
Cited by 4 | Viewed by 4125
Abstract
Hyperspectral Imaging (HSI) techniques have demonstrated potential to provide useful information in a broad set of applications in different domains, from precision agriculture to environmental science. A first step in the preparation of the algorithms to be employed outdoors starts at a laboratory [...] Read more.
Hyperspectral Imaging (HSI) techniques have demonstrated potential to provide useful information in a broad set of applications in different domains, from precision agriculture to environmental science. A first step in the preparation of the algorithms to be employed outdoors starts at a laboratory level, capturing a high amount of samples to be analysed and processed in order to extract the necessary information about the spectral characteristics of the studied samples in the most precise way. In this article, a custom-made scanning system for hyperspectral image acquisition is described. Commercially available components have been carefully selected in order to be integrated into a flexible infrastructure able to obtain data from any Generic Interface for Cameras (GenICam) compliant devices using the gigabyte Ethernet interface. The entire setup has been tested using the Specim FX hyperspectral series (FX10 and FX17) and a Graphical User Interface (GUI) has been developed in order to control the individual components and visualise data. Morphological analysis, spectral response and optical aberration of these pushbroom-type hyperspectral cameras have been evaluated prior to the validation of the whole system with different plastic samples for which spectral signatures are extracted and compared with well-known spectral libraries. Full article
(This article belongs to the Special Issue Optical Spectral Sensing and Imaging Technology)
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12 pages, 2094 KiB  
Article
Rapid Quantitative Analysis of IR Absorption Spectra for Trace Gas Detection by Artificial Neural Networks Trained with Synthetic Data
by Jens Goldschmidt, Leonard Nitzsche, Sebastian Wolf, Armin Lambrecht and Jürgen Wöllenstein
Sensors 2022, 22(3), 857; https://doi.org/10.3390/s22030857 - 23 Jan 2022
Cited by 15 | Viewed by 3737
Abstract
Infrared absorption spectroscopy is a widely used tool to quantify and monitor compositions of gases. The concentration information is often retrieved by fitting absorption profiles to the acquired spectra, utilizing spectroscopic databases. In complex gas matrices an expanded parameter space leads to long [...] Read more.
Infrared absorption spectroscopy is a widely used tool to quantify and monitor compositions of gases. The concentration information is often retrieved by fitting absorption profiles to the acquired spectra, utilizing spectroscopic databases. In complex gas matrices an expanded parameter space leads to long computation times of the fitting routines due to the increased number of spectral features that need to be computed for each iteration during the fit. This hinders the capability of real-time analysis of the gas matrix. Here, an artificial neural network (ANN) is employed for rapid prediction of gas concentrations in complex infrared absorption spectra composed of mixtures of CO and N2O. Experimental data is acquired with a mid-infrared dual frequency comb spectrometer. To circumvent the experimental collection of huge amounts of training data, the network is trained on synthetically generated spectra. The spectra are based on simulated absorption profiles making use of the HITRAN database. In addition, the spectrometer’s influence on the measured spectra is characterized and included in the synthetic training data generation. The ANN was tested on measured spectra and compared to a non-linear least squares fitting algorithm. An average evaluation time of 303 µs for a single measured spectrum was achieved. Coefficients of determination were 0.99997 for the predictions of N2O concentrations and 0.99987 for the predictions of CO concentrations, with uncertainties on the predicted concentrations between 0.04 and 0.18 ppm for 0 to 100 ppm N2O and between 0.05 and 0.18 ppm for 0 to 60 ppm CO. Full article
(This article belongs to the Special Issue Optical Spectral Sensing and Imaging Technology)
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10 pages, 2077 KiB  
Article
Electromagnetic Field Enhancement of Nanostructured TiN Electrodes Probed with Surface-Enhanced Raman Spectroscopy
by Ibrahim Halil Öner, Christin David, Christine Joy Querebillo, Inez M. Weidinger and Khoa Hoang Ly
Sensors 2022, 22(2), 487; https://doi.org/10.3390/s22020487 - 09 Jan 2022
Cited by 6 | Viewed by 2006
Abstract
We present a facile approach for the determination of the electromagnetic field enhancement of nanostructured TiN electrodes. As model system, TiN with partially collapsed nanotube structure obtained from nitridation of TiO2 nanotube arrays was used. Using surface-enhanced Raman scattering (SERS) spectroscopy, the [...] Read more.
We present a facile approach for the determination of the electromagnetic field enhancement of nanostructured TiN electrodes. As model system, TiN with partially collapsed nanotube structure obtained from nitridation of TiO2 nanotube arrays was used. Using surface-enhanced Raman scattering (SERS) spectroscopy, the electromagnetic field enhancement factors (EFs) of the substrate across the optical region were determined. The non-surface binding SERS reporter group azidobenzene was chosen, for which contributions from the chemical enhancement effect can be minimized. Derived EFs correlated with the electronic absorption profile and reached 3.9 at 786 nm excitation. Near-field enhancement and far-field absorption simulated with rigorous coupled wave analysis showed good agreement with the experimental observations. The major optical activity of TiN was concluded to originate from collective localized plasmonic modes at ca. 700 nm arising from the specific nanostructure. Full article
(This article belongs to the Special Issue Optical Spectral Sensing and Imaging Technology)
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13 pages, 3456 KiB  
Article
UV Hyperspectral Imaging as Process Analytical Tool for the Characterization of Oxide Layers and Copper States on Direct Bonded Copper
by Mohammad Al Ktash, Mona Stefanakis, Tim Englert, Maryam S. L. Drechsel, Jan Stiedl, Simon Green, Timo Jacob, Barbara Boldrini, Edwin Ostertag, Karsten Rebner and Marc Brecht
Sensors 2021, 21(21), 7332; https://doi.org/10.3390/s21217332 - 04 Nov 2021
Cited by 5 | Viewed by 2499
Abstract
Hyperspectral imaging and reflectance spectroscopy in the range from 200–380 nm were used to rapidly detect and characterize copper oxidation states and their layer thicknesses on direct bonded copper in a non-destructive way. Single-point UV reflectance spectroscopy, as a well-established method, was utilized [...] Read more.
Hyperspectral imaging and reflectance spectroscopy in the range from 200–380 nm were used to rapidly detect and characterize copper oxidation states and their layer thicknesses on direct bonded copper in a non-destructive way. Single-point UV reflectance spectroscopy, as a well-established method, was utilized to compare the quality of the hyperspectral imaging results. For the laterally resolved measurements of the copper surfaces an UV hyperspectral imaging setup based on a pushbroom imager was used. Six different types of direct bonded copper were studied. Each type had a different oxide layer thickness and was analyzed by depth profiling using X-ray photoelectron spectroscopy. In total, 28 samples were measured to develop multivariate models to characterize and predict the oxide layer thicknesses. The principal component analysis models (PCA) enabled a general differentiation between the sample types on the first two PCs with 100.0% and 96% explained variance for UV spectroscopy and hyperspectral imaging, respectively. Partial least squares regression (PLS-R) models showed reliable performance with R2c = 0.94 and 0.94 and RMSEC = 1.64 nm and 1.76 nm, respectively. The developed in-line prototype system combined with multivariate data modeling shows high potential for further development of this technique towards real large-scale processes. Full article
(This article belongs to the Special Issue Optical Spectral Sensing and Imaging Technology)
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14 pages, 4437 KiB  
Article
Use of Hyperspectral Imaging for the Quantification of Organic Contaminants on Copper Surfaces for Electronic Applications
by Tim Englert, Florian Gruber, Jan Stiedl, Simon Green, Timo Jacob, Karsten Rebner and Wulf Grählert
Sensors 2021, 21(16), 5595; https://doi.org/10.3390/s21165595 - 19 Aug 2021
Cited by 3 | Viewed by 2147
Abstract
To correctly assess the cleanliness of technical surfaces in a production process, corresponding online monitoring systems must provide sufficient data. A promising method for fast, large-area, and non-contact monitoring is hyperspectral imaging (HSI), which was used in this paper for the detection and [...] Read more.
To correctly assess the cleanliness of technical surfaces in a production process, corresponding online monitoring systems must provide sufficient data. A promising method for fast, large-area, and non-contact monitoring is hyperspectral imaging (HSI), which was used in this paper for the detection and quantification of organic surface contaminations. Depending on the cleaning parameter constellation, different levels of organic residues remained on the surface. Afterwards, the cleanliness was determined by the carbon content in the atom percent on the sample surfaces, characterized by XPS and AES. The HSI data and the XPS measurements were correlated, using machine learning methods, to generate a predictive model for the carbon content of the surface. The regression algorithms elastic net, random forest regression, and support vector machine regression were used. Overall, the developed method was able to quantify organic contaminations on technical surfaces. The best regression model found was a random forest model, which achieved an R2 of 0.7 and an RMSE of 7.65 At.-% C. Due to the easy-to-use measurement and the fast evaluation by machine learning, the method seems suitable for an online monitoring system. However, the results also show that further experiments are necessary to improve the quality of the prediction models. Full article
(This article belongs to the Special Issue Optical Spectral Sensing and Imaging Technology)
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13 pages, 2506 KiB  
Article
Characterization of Pharmaceutical Tablets Using UV Hyperspectral Imaging as a Rapid In-Line Analysis Tool
by Mohammad Al Ktash, Mona Stefanakis, Barbara Boldrini, Edwin Ostertag and Marc Brecht
Sensors 2021, 21(13), 4436; https://doi.org/10.3390/s21134436 - 28 Jun 2021
Cited by 16 | Viewed by 3462
Abstract
A laboratory prototype for hyperspectral imaging in ultra-violet (UV) region from 225 to 400 nm was developed and used to rapidly characterize active pharmaceutical ingredients (API) in tablets. The APIs are ibuprofen (IBU), acetylsalicylic acid (ASA) and paracetamol (PAR). Two sample sets were [...] Read more.
A laboratory prototype for hyperspectral imaging in ultra-violet (UV) region from 225 to 400 nm was developed and used to rapidly characterize active pharmaceutical ingredients (API) in tablets. The APIs are ibuprofen (IBU), acetylsalicylic acid (ASA) and paracetamol (PAR). Two sample sets were used for a comparison purpose. Sample set one comprises tablets of 100% API and sample set two consists of commercially available painkiller tablets. Reference measurements were performed on the pure APIs in liquid solutions (transmission) and in solid phase (reflection) using a commercial UV spectrometer. The spectroscopic part of the prototype is based on a pushbroom imager that contains a spectrograph and charge-coupled device (CCD) camera. The tablets were scanned on a conveyor belt that is positioned inside a tunnel made of polytetrafluoroethylene (PTFE) in order to increase the homogeneity of illumination at the sample position. Principal component analysis (PCA) was used to differentiate the hyperspectral data of the drug samples. The first two PCs are sufficient to completely separate all samples. The rugged design of the prototype opens new possibilities for further development of this technique towards real large-scale application. Full article
(This article belongs to the Special Issue Optical Spectral Sensing and Imaging Technology)
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16 pages, 6262 KiB  
Article
Snapshot Imaging Spectrometer Based on Pixel-Level Filter Array (PFA)
by Yunqiang Xie, Chunyu Liu, Shuai Liu, Weiyang Song and Xinghao Fan
Sensors 2021, 21(7), 2289; https://doi.org/10.3390/s21072289 - 25 Mar 2021
Cited by 10 | Viewed by 2487
Abstract
Snapshot spectral imaging technology plays an important role in many fields. However, most existing snapshot imaging spectrometers have the shortcomings of a large volume or heavy computational burden. In this paper, we present a novel snapshot imaging spectrometer based on the pixel-level filter [...] Read more.
Snapshot spectral imaging technology plays an important role in many fields. However, most existing snapshot imaging spectrometers have the shortcomings of a large volume or heavy computational burden. In this paper, we present a novel snapshot imaging spectrometer based on the pixel-level filter array (PFA), which can simultaneously obtain both spectral and spatial information. The system is composed of a fore-optics, a PFA, a relay lens, and a monochromatic sensor. The incoming light first forms an intermediate image on the PFA through the fore-optics. Then, the relay lens reimages the spectral images on the PFA onto the monochromatic sensor. Through the use of the PFA, we can capture a three-dimensional (spatial coordinates and wavelength) datacube in a single exposure. Compared with existing technologies, our system possesses the advantages of a simple implementation, low cost, compact structure, and high energy efficiency by removing stacked dispersive or interferometric elements. Moreover, the characteristic of the direct imaging mode ensures the low computational burden of the system, thus shortening the imaging time. The principle and design of the system are described in detail. An experimental prototype is built and field experiments are carried out to verify the feasibility of the proposed scheme. Full article
(This article belongs to the Special Issue Optical Spectral Sensing and Imaging Technology)
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Review

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29 pages, 7989 KiB  
Review
Data Augmentation Techniques for Machine Learning Applied to Optical Spectroscopy Datasets in Agrifood Applications: A Comprehensive Review
by Ander Gracia Moisés, Ignacio Vitoria Pascual, José Javier Imas González and Carlos Ruiz Zamarreño
Sensors 2023, 23(20), 8562; https://doi.org/10.3390/s23208562 - 18 Oct 2023
Cited by 2 | Viewed by 1962
Abstract
Machine learning (ML) and deep learning (DL) have achieved great success in different tasks. These include computer vision, image segmentation, natural language processing, predicting classification, evaluating time series, and predicting values based on a series of variables. As artificial intelligence progresses, new techniques [...] Read more.
Machine learning (ML) and deep learning (DL) have achieved great success in different tasks. These include computer vision, image segmentation, natural language processing, predicting classification, evaluating time series, and predicting values based on a series of variables. As artificial intelligence progresses, new techniques are being applied to areas like optical spectroscopy and its uses in specific fields, such as the agrifood industry. The performance of ML and DL techniques generally improves with the amount of data available. However, it is not always possible to obtain all the necessary data for creating a robust dataset. In the particular case of agrifood applications, dataset collection is generally constrained to specific periods. Weather conditions can also reduce the possibility to cover the entire range of classifications with the consequent generation of imbalanced datasets. To address this issue, data augmentation (DA) techniques are employed to expand the dataset by adding slightly modified copies of existing data. This leads to a dataset that includes values from laboratory tests, as well as a collection of synthetic data based on the real data. This review work will present the application of DA techniques to optical spectroscopy datasets obtained from real agrifood industry applications. The reviewed methods will describe the use of simple DA techniques, such as duplicating samples with slight changes, as well as the utilization of more complex algorithms based on deep learning generative adversarial networks (GANs), and semi-supervised generative adversarial networks (SGANs). Full article
(This article belongs to the Special Issue Optical Spectral Sensing and Imaging Technology)
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