Spectral Imaging Technique Research Articles

Research Progress of Spectral Imaging Techniques in Plant Phenotype Studies

Spectral imaging technique has been widely applied in plant phenotype analysis to improve plant trait selection and genetic advantages. The latest developments and applications of various optical imaging techniques in plant phenotypes were reviewed, and their advantages and applicability were compared. X-ray computed tomography (X-ray CT) and light detection and ranging (LiDAR) are more suitable for the three-dimensional reconstruction of plant surfaces, tissues, and organs. Chlorophyll fluorescence imaging (ChlF) and thermal imaging (TI) can be used to measure the physiological phenotype characteristics of plants. Specific symptoms caused by nutrient deficiency can be detected by hyperspectral and multispectral imaging, LiDAR, and ChlF. Future plant phenotype research based on spectral imaging can be more closely integrated with plant physiological processes. It can more effectively support the research in related disciplines, such as metabolomics and genomics, and focus on micro-scale activities, such as oxygen transport and intercellular chlorophyll transmission.

Improving LIBS-based mineral identification with raman imaging and spectral knowledge distillation

Combining data from different sensing modalities has been a promising research topic for building better and more reliable data-driven models. In particular, it is known that multimodal spectral imaging can improve the analytical capabilities of standalone spectroscopy techniques through fusion, hyphenation, or knowledge distillation techniques. In this manuscript, we focus on the latter, exploring how one can increase the performance of a Laser-induced Breakdown Spectroscopy system for mineral classification problems using additional spectral imaging techniques. Specifically, focusing on a scenario where Raman spectroscopy delivers accurate mineral classification performance, we show how to deploy a knowledge distillation pipeline where Raman spectroscopy may act as an autonomous supervisor for LIBS. For a case study concerning a challenging Li-bearing mineral identification of spodumene and petalite, our results demonstrate the advantages of this method in improving the performance of a single-technique system. LIBS trained with labels obtained by Raman presents an enhanced classification performance. Furthermore, leveraging the interpretability of the model deployed, the workflow opens opportunities for the deployment of assisted feature discovery pipelines, which may impact future academic and industrial applications.

Anthropogenic modification of a giant ground sloth tooth from Brazil supported by a multi-disciplinary approach

Identifying evidence of human modification of extinct animal remains, such as Pleistocene megafauna, is challenging due to the similarity of anthropogenic and non-anthropogenic taphonomic features observed under optical microscopy. Here, we re-investigate a Late Pleistocene ground sloth tooth from northeast Brazil, previously suggested as human-modified based only on optical observation. To characterize the macro- and micro-morphological characteristics of the marks preserved in this tooth and evaluate potential human modification, we used stereomicroscope and scanning electron microscopy (SEM) supplemented by energy dispersive spectroscopy (EDS), UV photoluminescence (UV/PL), synchrotron-based X-ray fluorescence (SR-XRF), and synchrotron micro-computed tomography (SR-µCT). These methods allowed us to discriminate non-anthropogenic taphonomic features (root and sedimentary damage), anthropogenic marks, and histological features. The latter shows the infiltration of exogenous elements into the dentine from the sediments. Our evidence demonstrates the sequence of anthropogenic and non-anthropogenic taphonomic modification of this tooth and supports its initial intentional modification by humans. We highlight the benefits of emerging imaging and spectral imaging techniques to investigate and diagnose human modification in fossil and archaeological records and propose that human modification of tooth tissues should be further considered when studying possibly anthropogenically altered fossil remains.

Studying microbially induced corrosion on glass using ToF-SIMS.

Microbially induced corrosion (MIC) is an emerging topic that has huge environmental impacts, such as long-term evaluation of microbial interactions with radioactive waste glass, environmental cleanup and disposal of radioactive material, and weathering effects of microbes. Time-of-flight secondary ion mass spectrometry (ToF-SIMS), a powerful mass spectral imaging technique with high surface sensitivity, mass resolution, and mass accuracy, can be used to study biofilm effects on different substrates. Understanding how to prepare biofilms on MIC susceptible substrates is critical for proper analysis via ToF-SIMS. We present here a step-by-step protocol for preparing bacterial biofilms for ToF-SIMS analysis, comparing three biofilm preparation techniques: no desalination, centrifugal spinning (CS), and water submersion (WS). Comparisons of two desalinating methods, CS and WS, show a decrease in the media peaks up to 99% using CS and 55% using WS, respectively. Proper desalination methods also can increase biological signals by over four times for fatty acids using WS, for example. ToF-SIMS spectral results show chemical compositional changes of the glass exposed in a Paenibacillus polymyxa SCE2 biofilm, indicating its capability to probe microbiologically induced corrosion of solid surfaces. This represents the proper desalination technique to use without significantly altering biofilm structure and substrate for ToF-SIMS analysis. ToF-SIMS spectral results showed chemical compositional changes of the glass exposed by a Paenibacillus bacterial biofilm over 3-month inoculation. Possible MIC products include various phosphate phase molecules not observed in any control samples with the highest percent increases when experimental samples were compared with biofilm control samples.

The Study of Pigments in Cultural Heritage: A Review Using Machine Learning

In this review, topic modeling—an unsupervised machine learning tool—is employed to analyze research on pigments in cultural heritage published from 1999–2023. The review answers the following question: What are topics and time trends in the past three decades in the analytical study of pigments within cultural heritage (CH) assets? In total, 932 articles are reviewed, ten topics are identified and time trends in the share of these topics are revealed. Each topic is discussed in-depth to elucidate the community, purpose and tools involved in the topic. The time trend analysis shows that dominant topics over time include T1 (the spectroscopic and microscopic study of the stratigraphy of painted CH assets) and T5 (X-ray based techniques for CH, conservation science and archaeometry). However, both topics have experienced a decrease in attention in favor of other topics that more than doubled their topic share, enabled by new technologies and methods for imaging spectroscopy and imaging processing. These topics include T6 (spectral imaging techniques for chemical mapping of painting surfaces) and T10 (the technical study of the pigments and painting methods of historical and contemporary artists). Implications for the field are discussed in conclusion.

From sensor fusion to knowledge distillation in collaborative LIBS and hyperspectral imaging for mineral identification

Multimodal spectral imaging offers a unique approach to the enhancement of the analytical capabilities of standalone spectroscopy techniques by combining information gathered from distinct sources. In this manuscript, we explore such opportunities by focusing on two well-known spectral imaging techniques, namely laser-induced breakdown spectroscopy, and hyperspectral imaging, and explore the opportunities of collaborative sensing for a case study involving mineral identification. In specific, the work builds upon two distinct approaches: a traditional sensor fusion, where we strive to increase the information gathered by including information from the two modalities; and a knowledge distillation approach, where the Laser Induced Breakdown spectroscopy is used as an autonomous supervisor for hyperspectral imaging. Our results show the potential of both approaches in enhancing the performance over a single modality sensing system, highlighting, in particular, the advantages of the knowledge distillation framework in maximizing the potential benefits of using multiple techniques to build more interpretable models and paving for industrial applications.

Neural‐Optic Co‐Designed Polarization‐Multiplexed Metalens for Compact Computational Spectral Imaging

AbstractIn the expanding fields of mobile technology and augmented reality, there is a growing demand for compact, high‐fidelity spectral imaging systems. Traditional spectral imaging techniques face limitations due to their size and complexity. Diffractive optical elements (DOEs), although helpful in reducing size, primarily modulate the phase of light. Here, an end‐to‐end computational spectral imaging framework based on polarization‐multiplexed metalens is introduced. A distinguishing feature of this approach lies in its capacity to simultaneously modulate orthogonal polarization channels. When harnessed in conjunction with a neural network, it facilitates the attainment of high‐fidelity spectral reconstruction. Importantly, the framework is intrinsically fully differentiable, a feature that permits the joint optimization of both the metalens structure and the parameters governing the neural network. The experimental results presented herein validate the exceptional spatial‐spectral reconstruction performance, underscoring the efficacy of this system in practical, real‐world scenarios. This innovative approach transcends the traditional boundaries separating hardware and software in the realm of computational imaging and holds the promise of substantially propelling the miniaturization of spectral imaging systems.

Snapshot spectral imaging: from spatial-spectral mapping to metasurface-based imaging

Abstract Snapshot spectral imaging technology enables the capture of complete spectral information of objects in an extremely short period of time, offering wide-ranging applications in fields requiring dynamic observations such as environmental monitoring, medical diagnostics, and industrial inspection. In the past decades, snapshot spectral imaging has made remarkable breakthroughs with the emergence of new computational theories and optical components. From the early days of using various spatial-spectral data mapping methods, they have evolved to later attempts to encode various dimensions of light, such as amplitude, phase, and wavelength, and then computationally reconstruct them. This review focuses on a systematic presentation of the system architecture and mathematical modeling of these snapshot spectral imaging techniques. In addition, the introduction of metasurfaces expands the modulation of spatial-spectral data and brings advantages such as system size reduction, which has become a research hotspot in recent years and is regarded as the key to the next-generation snapshot spectral imaging techniques. This paper provides a systematic overview of the applications of metasurfaces in snapshot spectral imaging and provides an outlook on future directions and research priorities.

Evaluation of Leaf Chlorophyll Content from Acousto-Optic Hyperspectral Data: A Multi-Crop Study

Chlorophyll plays a crucial role in the process of photosynthesis and helps to regulate plants’ growth and development. Timely and accurate evaluation of leaf chlorophyll content provides valuable information about the health and productivity of plants as well as the effectiveness of agricultural treatments. For non-contact and high-performance chlorophyll content mapping in plants, spectral imaging techniques are the most widely used. Due to agility and rapid random-spectral-access tuning, acousto-optical imagers seem to be very attractive for the detection of vegetation indices and chlorophyll content assessment. This laboratory study demonstrates the capabilities of an acousto-optic imager for evaluation of leaf chlorophyll content in six crops with different biophysical properties: Ribes rubrum, Betula populifolia, Hibiscus rosa-sinensis, Prunus padus, Hordeum vulgare and Triticum aestivum. The experimental protocol includes plant collecting, reference spectrophotometric measurements, hyperspectral imaging data acquisition, processing and analysis and building a multi-crop chlorophyll model. For 90 inspected samples of plant leaves, the optimal vegetation index and model were found. Obtained values of chlorophyll concentrations correlate well with reference values (determination coefficient of 0.89 and relative error of 15%). Applying a multi-crop model to each pixel, we calculated chlorophyll content maps across all plant samples. The results of this study demonstrate that acousto-optic imagery is very promising for fast chlorophyll content assessment and other laboratory spectral-index-based measurements.

Secondary Ion Mass Spectral Imaging of Metals and Alloys.

Secondary Ion Mass Spectrometry (SIMS) is an outstanding technique for Mass Spectral Imaging (MSI) due to its notable advantages, including high sensitivity, selectivity, and high dynamic range. As a result, SIMS has been employed across many domains of science. In this review, we provide an in-depth overview of the fundamental principles underlying SIMS, followed by an account of the recent development of SIMS instruments. The review encompasses various applications of specific SIMS instruments, notably static SIMS with time-of-flight SIMS (ToF-SIMS) as a widely used platform and dynamic SIMS with Nano SIMS and large geometry SIMS as successful instruments. We particularly focus on SIMS utility in microanalysis and imaging of metals and alloys as materials of interest. Additionally, we discuss the challenges in big SIMS data analysis and give examples of machine leaning (ML) and Artificial Intelligence (AI) for effective MSI data analysis. Finally, we recommend the outlook of SIMS development. It is anticipated that in situ and operando SIMS has the potential to significantly enhance the investigation of metals and alloys by enabling real-time examinations of material surfaces and interfaces during dynamic transformations.

A novel optical emission spectroscopy method for diagnostics of contribution of different ionization mechanisms and flux of ions in different valences in discharge channel of a Hall thruster

The mass application of Hall thrusters poses the need for a diagnostic method of ionization mechanism in the discharge channel to boost the iteration and optimization of thruster design. This work presents an Optical Emission Spectroscopy (OES) method for diagnostics of the contribution of different ionization mechanisms and the flux of ions in different valences in the discharge channel of a Hall thruster. The emission spectra in the discharge channel are analyzed by jointly utilizing a collisional-radiative model, an ionization-excitation model, and a flux-conservation model. It is found that the intensities of some spectral lines can be converted into the reaction rates of collision processes, e.g., electron-induced excitation and ionization processes. The latter can further be used to determine the evolutions of particle fluxes by utilizing the conservation law of matter. The novel method is demonstrated on a kilo-watt Hall thruster. The evolutions of several parameters are determined using this method along the discharge channel, including the ionization rates of different mechanisms, particle fluxes, particle densities, and particle velocities. This novel method can be further developed by being jointly utilized with spectral imaging and tomography techniques, enabling diagnostics of multi-dimensional distributions of the above-mentioned parameters in the discharge channel and near-field plume.

Manifold projection image segmentation for nano-XANES imaging

As spectral imaging techniques are becoming more prominent in science, advanced image segmentation algorithms are required to identify appropriate domains in these images. We present a version of image segmentation called manifold projection image segmentation (MPIS) that is generally applicable to a broad range of systems without the need for training because MPIS uses unsupervised machine learning with a few physically motivated hyperparameters. We apply MPIS to nanoscale x-ray absorption near edge structure (XANES) imaging, where XANES spectra are collected with nanometer spatial resolution. We show the superiority of manifold projection over linear transformations, such as the commonly used principal component analysis (PCA). Moreover, MPIS maintains accuracy while reducing computation time and sensitivity to noise compared to the standard nano-XANES imaging analysis procedure. Finally, we demonstrate how multimodal information, such as x-ray fluorescence data and spatial location of pixels, can be incorporated into the MPIS framework. We propose that MPIS is adaptable for any spectral imaging technique, including scanning transmission x-ray microscopy, where the length scale of domains is larger than the resolution of the experiment.

Dual-Channel Switchable Metasurface Filters for Compact Spectral Imaging with Deep Compressive Reconstruction.

Spectral imaging technology, which aims to capture images across multiple spectral channels and create a spectral data cube, has been widely utilized in various fields. However, conventional spectral imaging systems face challenges, such as slow acquisition speed and large size. The rapid development of optical metasurfaces, capable of manipulating light fields versatilely and miniaturizing optical components into ultrathin planar devices, offers a promising solution for compact hyperspectral imaging (HSI). This study proposes a compact snapshot compressive spectral imaging (SCSI) system by leveraging the spectral modulations of metasurfaces with dual-channel switchable metasurface filters and employing a deep-learning-based reconstruction algorithm. To achieve compactness, the proposed system integrates dual-channel switchable metasurface filters using twisted nematic liquid crystals (TNLCs) and anisotropic titanium dioxide (TiO2) nanostructures. These thin metasurface filters are closely attached to the image sensor, resulting in a compact system. The TNLCs possess a broadband linear polarization conversion ability, enabling the rapid switching of the incidence polarization state between x-polarization and y-polarization by applying different voltages. This polarization conversion facilitates the generation of two groups of transmittance spectra for wavelength-encoding, providing richer information for spectral data cube reconstruction compared to that of other snapshot compressive spectral imaging techniques. In addition, instead of employing classic iterative compressive sensing (CS) algorithms, an end-to-end residual neural network (ResNet) is utilized to reconstruct the spectral data cube. This neural network leverages the 2-frame snapshot measurements of orthogonal polarization channels. The proposed hyperspectral imaging technology demonstrates superior reconstruction quality and speed compared to those of the traditional compressive hyperspectral image recovery methods. As a result, it is expected that this technology will have substantial implications in various domains, including but not limited to object detection, face recognition, food safety, biomedical imaging, agriculture surveillance, and so on.

Medical imaging applied to heritage.

The use of imaging has transformed the study of cultural heritage artefacts in the same way that medical imaging has transformed medicine. X-ray-based techniques are common in both medical and heritage imaging. Optical imaging, including scientific photography and spectral imaging techniques, is also common in both domains. Some common medical imaging methods such as ultrasound and MRI have not yet found routine application in heritage, whereas other methods such as imaging with charged and uncharged particles and 3D surface imaging are more common in heritage. Here, we review the field of heritage imaging from the point of view of medical imaging and include some classic challenges of heritage imaging such as reading the text on burnt scrolls, identifying underdrawings in paintings, and CT scanning of mummies, an ancient calculating device and sealed documents. We show how hyperspectral imaging can offer insight into the drawing techniques of Leonardo da Vinci and explain how laparoscopy has identified the method of construction of a 500-year-old pop-up anatomical text book.

Spectral CT imaging versus conventional CT post-processing technique in differentiating malignant and benign renal tumors.

Quantitative comparison of the diagnostic value of spectral CT imaging and conventional CT post-processing technique in differentiating malignant and benign renal tumors. A total of 209 patients with renal tumors who had undergone CT enhancement were assigned to three groups-clear cell renal cell carcinoma (ccRCC, n = 106), non-ccRCC (n = 60), and benign renal tumor (n = 43) groups. Parametric CT enhancement of each tumor based on spectral CT and conventional CT was performed using in-house software, and the iodine concentration, water content, slope, and density values among the three groups were compared. The receiver operating characteristic (ROC) curve analysis was performed to determine the optimum diagnostic thresholds, the area under the ROC curve (AUC), sensitivity, specificity, and accuracy of the above parameters. The iodine concentration, slope, and density values were higher in the ccRCCs group compared to the non-ccRCCs and benign renal tumor groups (p < 0.05). Moreover, the iodine concentration, slope, and density values were higher in benign renal tumors compared to non-ccRCCs (p < 0.05). According to the ROC curve analysis, iodine concentration presented the highest diagnostic efficacy in differentiating ccRCCs/non-ccRCCs from benign renal tumors. The pairwise comparisons of the ROC curves and the diagnostic efficacies revealed that ROI-based CT enhancement was worse than the spectral CT imaging analysis in terms of density (p < 0.05). Iodine concentration presented the highest diagnostic efficacy in differentiating ccRCCs/non-ccRCCs from benign renal tumors. 1. The iodine concentration, slope, and density values were higher for the ccRCCs compared to non-ccRCCs and benign renal tumors.2. Iodine concentration presented the highest diagnostic efficacy in differentiating ccRCCs/non-ccRCCs from benign renal tumors.3. Spectral CT imaging analysis performed better than conventional CT in differentiating malignant and benign renal tumors.

Combining Vis-NIR and NIR Spectral Imaging Techniques with Data Fusion for Rapid and Nondestructive Multi-Quality Detection of Cherry Tomatoes.

Firmness, soluble solid content (SSC) and titratable acidity (TA) are characteristic substances for evaluating the quality of cherry tomatoes. In this paper, a hyper spectral imaging (HSI) system using visible/near-infrared (Vis-NIR) and near-infrared (NIR) was proposed to detect the key qualities of cherry tomatoes. The effects of individual spectral information and fused spectral information in the detection of different qualities were compared for firmness, SSC and TA of cherry tomatoes. Data layer fusion combined with multiple machine learning methods including principal component regression (PCR), partial least squares regression (PLSR), support vector regression (SVR) and back propagation neural network (BP) is used for model training. The results show that for firmness, SSC and TA, the determination coefficient R2 of the multi-quality prediction model established by Vis-NIR spectra is higher than that of NIR spectra. The R2 of the best model obtained by SSC and TA fusion band is greater than 0.9, and that of the best model obtained by the firmness fusion band is greater than 0.85. It is better to use the spectral bands after information fusion for nondestructive quality detection of cherry tomatoes. This study shows that hyperspectral imaging technology can be used for the nondestructive detection of multiple qualities of cherry tomatoes, and the method based on the fusion of two spectra has a better prediction effect for the rapid detection of multiple qualities of cherry tomatoes compared with a single spectrum. This study can provide certain technical support for the rapid nondestructive detection of multiple qualities in other melons and fruits.

Prior Image Guided Snapshot Compressive Spectral Imaging.

Spectral images with rich spatial and spectral information have wide usage, however, traditional spectral imaging techniques undeniably take a long time to capture scenes. We consider the computational imaging problem of the snapshot spectral spectrometer, i.e., the Coded Aperture Snapshot Spectral Imaging (CASSI) system. For the sake of a fast and generalized reconstruction algorithm, we propose a prior image guidance-based snapshot compressive imaging method. Typically, the prior image denotes the RGB measurement captured by the additional uncoded panchromatic camera of the dual-camera CASSI system. We argue that the RGB image as a prior image can provide valuable semantic information. More importantly, we design the Prior Image Semantic Similarity (PIDS) regularization term to enhance the reconstructed spectral image fidelity. In particular, the PIDS is formulated as the difference between the total variation of the prior image and the recovered spectral image. Then, we solve the PIDS regularized reconstruction problem by the Alternating Direction Method of Multipliers (ADMM) optimization algorithm. Comprehensive experiments on various datasets demonstrate the superior performance of our method.

Investigation of Small-Molecule Constituents in Voacanga africana Seeds and Mapping of Their Spatial Distributions Using Laser Ablation Direct Analysis in Real-Time Imaging-Mass Spectrometry (LADI-MS).

Plant seeds are a renewable resource that can furnish access to medicinal natural products that can only otherwise be isolated from aerial or root parts, the harvest of which may be destructive to the plant or threaten its viability. However, optimization of the isolation of such compounds from seeds would be greatly assisted if the spatial distribution of the molecules of interest within the plant tissue were known. For example, iboga alkaloids that hold promise for the treatment of opioid use disorder are typically isolated from the leaves, bark, or roots of Tabernanthe or Voacanga spp. trees, but it would be more environmentally sustainable to isolate such compounds from their seeds. Here, we leveraged the unique capabilities of the ambient mass spectral imaging technique termed laser ablation direct analysis in real-time imaging-mass spectrometry (LADI-MS) to reveal the spatial distributions of a range of molecules, including alkaloids within V. africana seeds. In addition to six compounds previously reported in these seeds, namely, tetradecanoic acid, n-hexadecanoic acid, (Z,Z)-9,12-octadecadienoic acid, (Z)-9-octadecenoic acid, octadecanoic acid, and Δ14-vincamine, an additional 31 compounds were newly identified in V. africana seeds. The compound classes included alkaloids, terpenes, and fatty acids. The ion images showed that the fatty acids were localized in the embryo of the seed. The alkaloids, which were mainly localized in the seed endosperm, included strictamine, akuammidine, polyneruidine, vobasine, and Δ14-vincamine. This information can be exploited to enhance the efficiency of secondary metabolite isolation from V. africana seeds while eliminating the destruction of other plant parts.

Nonlinear model prediction of needle chlorophyll content of Picea koraiensis Nakai at different needle ages based on hyperspectral features

Pigment content is a critical assessment indicator in the study of plant physiological metabolism, stress resistance, ornamental characteristics, and forest health. Spectral imaging technology is widely used for rapid and non-destructive determination of plant physicochemical parameters. To address the shortcomings of previous models of spectral reflectance prediction of chlorophyll content of needles only from the perspective of traditional algorithms and ignoring physical models, this research integrates variable complexity and refined classification of physical models to validate the increased accuracy of both the conventional partial least squares (PLS) method and the traditional neural network algorithm. The results of the conifer chlorophyll models of Picea koraiensis Nakai with different needle ages based on spectral reflectance and vegetation index parameters showed that the improved nonlinear state transition algorithm-backpropagation (STA-BP) neural network model approach (R2 of 0.73–0.89) and the nonlinear Stacking partial least squares (Stacking-PLS) model approach (R2 of 0. 67–0.85) is slightly more robust than the traditional algorithms nonlinear BP model (R2 of 0.63–0.82) and linear PLS model (R2 of 0.60–0.76). This finding suggests that the nonlinear fitting of chlorophyll content in needles of different needle ages in P. koraiensis Nakai surpasses the traditional linear model fitting methodology. Furthermore, the model fitting of chlorophyll content in conifers of different needle ages outperforms the mixed P. koraiensis Nakai model, suggesting that chlorophyll models using needle refinement classification help to improve model robustness. This study provides data and theoretical support for rapid and non-invasive characterization of physiological and biochemical properties of needles of different needle ages using spectral imaging techniques to predict growth and community structure productivity of forest trees in the coming years.

A Review of Recent Advances for the Detection of Biological, Chemical, and Physical Hazards in Foodstuffs Using Spectral Imaging Techniques.

Traditional methods for detecting foodstuff hazards are time-consuming, inefficient, and destructive. Spectral imaging techniques have been proven to overcome these disadvantages in detecting foodstuff hazards. Compared with traditional methods, spectral imaging could also increase the throughput and frequency of detection. This study reviewed the techniques used to detect biological, chemical, and physical hazards in foodstuffs including ultraviolet, visible and near-infrared (UV-Vis-NIR) spectroscopy, terahertz (THz) spectroscopy, hyperspectral imaging, and Raman spectroscopy. The advantages and disadvantages of these techniques were discussed and compared. The latest studies regarding machine learning algorithms for detecting foodstuff hazards were also summarized. It can be found that spectral imaging techniques are useful in the detection of foodstuff hazards. Thus, this review provides updated information regarding the spectral imaging techniques that can be used by food industries and as a foundation for further studies.