Hyperspectral image analysis. A tutorial

Rapid detection of colored and colorless macro- and micro-plastics in complex environment via near-infrared spectroscopy and machine learning

To better understand the migration behavior of plastic fragments in the environment, development of rapid non-destructive methods for in-situ identification and characterization of plastic fragments is necessary. However, most of the studies had focused only on colored plastic fragments, ignoring colorless plastic fragments and the effects of different environmental media (backgrounds), thus underestimating their abundance. To address this issue, the present study used near-infrared spectroscopy to compare the identification of colored and colorless plastic fragments based on partial least squares-discriminant analysis (PLS-DA), extreme gradient boost, support vector machine and random forest classifier. The effects of polymer color, type, thickness, and background on the plastic fragments classification were evaluated. PLS-DA presented the best and most stable outcome, with higher robustness and lower misclassification rate. All models frequently misinterpreted colorless plastic fragments and its background when the fragment thickness was less than 0.1mm. A two-stage modeling method, which first distinguishes the plastic types and then identifies colorless plastic fragments that had been misclassified as background, was proposed. The method presented an accuracy higher than 99% in different backgrounds. In summary, this study developed a novel method for rapid and synchronous identification of colored and colorless plastic fragments under complex environmental backgrounds.

Visible and near-infrared spectral imaging combined with robust regression for predicting firmness, fatness, and compositional properties of fresh pork bellies

Belly is a widely consumed pork product with very variable properties. Meat industry needs real-time quality assessment for maintaining superior pork quality throughout the production. This study explores the potential of using visible and near-infrared (VNIR,386-1015 nm) spectral imaging for predicting firmness, fatness and chemical compositional properties in pork belly samples, offering robust spectral calibrations. A total of 182 samples with wide variations in firmness and compositional properties were analysed using common laboratory analyses, whereas spectral images were acquired with a VNIR spectral imaging system. Exploratory analysis of the studied properties was performed, followed by a robust regression approach called iterative reweighted partial least-squares regression to model and predict these belly properties. The models were also used to generate spatial maps of predicted chemical compositional properties. Chemical properties such as fat, dry matter, protein, ashes, iodine value, along with firmness measures as flop distance and angle, were predicted with excellent, very good and fair models, with a ratio prediction of standard deviation (RPD) of 4.93, 3.91, 2.58, 2.54, 2.41, 2.53 and 2.51 respectively. The methodology developed in this study showed that a short wavelength spectral imaging system can yield promising results, being a potential benefit for the pork industry in automating the analysis of fresh pork belly samples. VNIR spectral imaging emerges as a non-destructive method for pork belly characterization, guiding process optimization and marketing strategies. Moreover, future research can explore advanced data analytics approaches such as deep learning to facilitate the integration of spectral and spatial information in joint modelling.

Differentiation of Listeria monocytogenes serotypes using near infrared hyperspectral imaging

Among the severe foodborne illnesses, listeriosis resulting from the pathogen Listeria monocytogenes exhibits one of the highest fatality rates. This study investigated the application of near infrared hyperspectral imaging (NIR-HSI) for the classification of three L. monocytogenes serotypes namely serotype 4b, 1/2a and 1/2c. The bacteria were cultured on Brain Heart Infusion agar, and NIR hyperspectral images were captured in the spectral range 900-2500 nm. Different pre-processing methods were applied to the raw spectra and principal component analysis was used for data exploration. Classification was achieved with partial least squares discriminant analysis (PLS-DA). The PLS-DA results revealed classification accuracies exceeding 80 % for all the bacterial serotypes for both training and test set data. Based on validation data, sensitivity values for L. monocytogenes serotype 4b, 1/2a and 1/2c were 0.69, 0.80 and 0.98, respectively when using full wavelength data. The reduced wavelength model had sensitivity values of 0.65, 0.85 and 0.98 for serotype 4b, 1/2a and 1/2c, respectively. The most relevant bands for serotype discrimination were identified to be around 1490 nm and 1580-1690 nm based on both principal component loadings and variable importance in projection scores. The outcomes of this study demonstrate the feasibility of utilizing NIR-HSI for detecting and classifying L. monocytogenes serotypes on growth media.

Innovations in forensic science: Comprehensive review of hyperspectral imaging for bodily fluid analysis

Hyperspectral imaging (HSI) has become a crucial innovation in forensic science, particularly for analysing bodily fluids. This advanced technology captures both spectral and spatial data across a wide spectrum of wavelengths, offering comprehensive insights into the composition and distribution of bodily fluids found at crime scenes. In this review, we delve into the forensic applications of HSI, emphasizing its role in detecting, identifying, and distinguishing various bodily fluids such as blood, saliva, urine, vaginal fluid, semen, and menstrual blood. We examine the benefits of HSI compared to traditional methods, noting its non-destructive approach, high sensitivity, and capability to differentiate fluids even in complex mixtures. Additionally, we discuss recent advancements in HSI technology and their potential to enhance forensic investigations. This review highlights the importance of HSI as a valuable tool in forensic science, opening new pathways for improving the accuracy and efficiency of crime scene analyses.

Efficient microplastic identification by hyperspectral imaging: A comparative study of spatial resolutions, spectral ranges and classification models to define an optimal analytical protocol

Microplastics (MPs) pollution is a global and challenging issue, necessitating the development of efficient analytical strategies for their detection to monitor their environmental impact. This study aims to define an optimal analytical protocol for characterizing MPs by hyperspectral imaging (HSI), comparing different setups based on spatial resolution, spectral range and classification models. The investigated MPs include polymers commonly found in the environment, such as polystyrene (PS), polypropylene (PP) and high-density polyethylene (HDPE), subdivided in three size classes (1000-2000 μm, 500-1000 μm, 250-500 μm). Furthermore, MP particles with diameters ranging from 30 to 250 μm were assessed to determine the limit of detection (LOD) in the different configurations. Hyperspectral images were acquired with two spatial resolutions, 150 and 30 μm/pixel, and two spectral ranges, 1000-1700 nm (NIR) and 1000-2500 nm (SWIR). Three classification models, Partial Least Square-Discriminant Analysis (PLS-DA), Error Correction Output Coding-Support Vector Machine (ECOC-SVM) and Neural Network Pattern Recognition (NNPR) were tested on the acquired images. The correctness of these models was evaluated by prediction maps and statistical parameters (Recall, Specificity and Accuracy). The results demonstrated that for MP particles larger than 250 μm, the optimal setup is a spatial resolution of 150 μm/pixel and a spectral range of 1000-1700 nm, utilizing a linear classification model like PLS-DA. This approach offers accurate predictions while being time- and cost-efficient. For MPs smaller than 250 μm, a higher spatial resolution of 30 μm/pixel with a spectral range of 1000-2500 nm and a non-linear classification method like ECOC-SVM is preferable. The LOD is 250 μm for the 150 μm/pixel resolution and ranges from 100 to 200 μm for the 30 μm/pixel resolution. These findings provide a valuable guide for selecting the appropriate HSI acquisition conditions and data processing methods to optimally characterize MPs of different sizes.

Stochastic image spectroscopy: a discriminative generative approach to hyperspectral image modelling and classification

This paper introduces a new latent variable probabilistic framework for representing spectral data of high spatial and spectral dimensionality, such as hyperspectral images. We use a generative Bayesian model to represent the image formation process and provide interpretable and efficient inference and learning methods. Surprisingly, our approach can be implemented with simple tools and does not require extensive training data, detailed pixel-by-pixel labeling, or significant computational resources. Numerous experiments with simulated data and real benchmark scenarios show encouraging image classification performance. These results validate the unique ability of our framework to discriminate complex hyperspectral images, irrespective of the presence of highly discriminative spectral signatures.

Hyperspectral Detection of the Fluorescence Shift between Chirality-Sorted Empty and Water-Filled Single-Wall Carbon Nanotube Enantiomers

Single-wall carbon nanotubes (SWCNTs) have extraordinary electronic and optical properties that depend strongly on their exact chiral structure and their interaction with their inner and outer environment. The fluorescence (PL) of semiconducting SWCNTs, for instance, will shift depending on the molecules with which the SWCNT's hollow core is filled. These interaction-induced shifts are challenging to resolve on the ensemble level in samples containing a mixture of different filling contents due to the relatively large inhomogeneous line width of the ensemble SWCNT PL compared to the size of these shifts. To circumvent this inhomogeneous broadening, single-tube spectroscopy and hyperspectral imaging are often applied, which until now required time-consuming statistical studies. Here, we present hyperspectral PL microscopy combined with automated SWCNT segmenting based on either principal component analysis or a convolutional neural network, capable of both spatially and spectrally resolving the PL along the length of many individual SWCNTs at the same time and automatically fitting peak positions and line widths of individual SWCNTs. The methodology is demonstrated by accurately determining the emission shifts and line widths of thousands of left- and right-handed empty and water-filled SWCNTs coated with a chiral surfactant, resulting in four statistical distributions which cannot be resolved in ensemble spectroscopy of unsorted samples. The results demonstrate a robust method to quickly probe ensemble properties with single-enantiomer spectral resolution. Moreover, it promises to be an absolute quantitative method to characterize the relative abundances of SWCNTs with different handedness or filling content in macroscopic samples, simply by counting individual species.

Deposition of silica in sorghum root endodermis modifies the chemistry of associated lignin

Silica aggregates at the endodermis of sorghum roots. Aggregation follows a spotted pattern of locally deposited lignin at the inner tangential cell walls. Autofluorescence microscopy suggests that non-silicified (-Si) lignin spots are composed of two distinct concentric regions of varied composition. To highlight variations in lignin chemistry, we used Raman microspectroscopy to map the endodermal cell wall and silica aggregation sites in sorghum roots grown hydroponically with or without Si amendment. In +Si samples, the aggregate center was characterized by typical lignin monomer bands surrounded by lignin with a low level of polymerization. Farther from the spot, polysaccharide concentration increased and soluble silicic acid was detected in addition to silica bands. In -Si samples, the main band at the spot center was assigned to lignin radicals and highly polymerized lignin. Both +Si and -Si loci were enriched by aromatic carbonyls. We propose that at silica aggregation sites, carbonyl rich lignin monomers are locally exported to the apoplast. These monomers are radicalized and polymerized into short lignin polymers. In the presence of silicic acid, bonds typically involved in lignin extension, bind to silanols and nucleate silica aggregates near the monomer extrusion loci. This process inhibits further polymerization of lignin. In -Si samples, the monomers diffuse farther in the wall and crosslink with cell wall polymers, forming a ring of dense lignified cell wall around their export sites.

Time-frequency analysis reveals an association between the specific nuclear magnetic resonance (NMR) signal properties of serum samples and arteriosclerotic lesion progression in a diabetes mouse model

Diabetes causes arteriosclerosis, primarily due to persistent hyperglycemia, subsequently leading to various cardiovascular events. No method has been established for directly detecting and evaluating arteriosclerotic lesions from blood samples of diabetic patients, as the mechanism of arteriosclerotic lesion formation, which involves complex molecular biological processes, has not been elucidated. "NMR modal analysis" is a technology that enables visualization of specific nuclear magnetic resonance (NMR) signal properties of blood samples. We hypothesized that this technique could be used to identify changes in blood status associated with the progression of arteriosclerotic lesions in the context of diabetes. The study aimed to assess the possibility of early detection and evaluation of arteriosclerotic lesions by NMR modal analysis of serum samples from diabetes model mice. Diabetes model mice (BKS.Cg db/db) were bred in a clean room and fed a normal diet. Blood samples were collected and centrifuged. Carotid arteries were collected for histological examination by hematoxylin and eosin staining on weeks 10, 14, 18, 22, and 26. The serum was separated and subjected to NMR modal analysis and biochemical examination. Mice typically show hyperglycemia at an early stage (8 weeks old), and pathological findings of a previous study showed that more than half of mice had atheromatous plaques at 18 weeks old, and severe arteriosclerotic lesions were observed in almost all mice after 22 weeks. Partial least squares regression analysis was performed, which showed that the mice were clearly classified into two groups with positive and negative score values within 18 weeks of age. The findings of this study revealed that NMR modal properties of serum are associated with arteriosclerotic lesions. Thus, it may be worth exploring the possibility that the risk of cardiovascular events in diabetic patients could be assessed using serum samples.

Laser-based analytical techniques in cultural heritage science - Tutorial review

This tutorial review combines the fundamentals of the design and operation of lasers with their usage in applications related to conservation and cultural heritage (CH) science - as components of analytical devices for the study of the chemical composition of materials. The development of laser instruments and their fundamental physical background, including a short explanation of their properties and parameters, are briefly summarised, and an overview of different laser-based analytical techniques is given. The analytical techniques covered in this tutorial are divided into three groups based on their technical aspects and properties: (1) vibrational spectroscopy, (2) elemental analysis, and (3) different molecular mass spectrometric techniques.

Asbestos detection in construction and demolition waste by different classification methods applied to short-wave infrared hyperspectral images

In this study, different multivariate classification methods were applied to hyperspectral images acquired, in the short-wave infrared range (SWIR: 1000-2500 nm), to define and evaluate quality control actions applied to construction and demolition waste (C&DW) flow streams, with particular reference to the detection of hazardous material as asbestos. Three asbestos fibers classes (i.e., amosite, chrysotile and crocidolite) inside asbestos-containing materials (ACM) were investigated. Samples were divided into two groups: calibration and validation datasets. The acquired hyperspectral images were first explored by Principal Component Analysis (PCA). The following multivariate classification methods were selected in order to verify and compare their efficiency and robustness: Hierarchical Partial Least Squares-Discriminant Analysis (Hi-PLSDA), Principal Component Analysis k-Nearest Neighbors (PCA-kNN) and Error Correcting Output Coding with Support Vector Machines (ECOC-SVM). The classification results obtained for the three models were evaluated by prediction maps and the values of performance parameters (Sensitivity and Specificity). Micro-X-ray fluorescence (micro-XRF) maps confirmed the correctness of classification results. The results demonstrate how SWIR-HSI technology, coupled with multivariate analysis modelling, is a promising approach to develop both "off-line" and "online" fast, reliable and robust quality control strategies, finalized to perform a quick assessment of ACM presence.

Analysis of cellular response to drugs with a microfluidic single-cell platform based on hyperspectral imaging

BACKGROUND

Cellular response to pharmacological action of drugs is significant for drug development. Traditional detection method for cellular response to drugs normally rely on cell proliferation assay and metabolomics examination. In principle, these analytical methods often required cell labeling, invasion analysis, and hours of co-culture with drugs, which are relatively complex and time-consuming. Moreover, these methods can only indicate the drug effectiveness on cell colony rather than single cells. Thus, to meet the requirements of personal precision medicine, the development of drug response analysis on the high resolution of single cell is demanded.

RESULTS

To provide precise result for drug response on single-cell level, a microfluidic platform coupled with the label-free hyperspectral imaging was developed. With the help of horizontal single-cell trapping sieves, hundreds of single cells were trapped independently in microfluidic channels for the purposes of real-time drug delivery and single-cell hyperspectral image recording. To significantly identify the cellular hyperspectral change after drug stimulation, the differenced single-cell spectrum was proposed. Compared with the deep learning classification method based on hyperspectral images, an optimal performance can be achieved by the classification strategy based on differenced spectra. And the cellular response to different reagents, for example, K+, Epidermal Growth Factor (EGF), and Gefitinib at different concentrations can be accurately characterized by the differenced single-cell spectra analysis.

SIGNIFICANCE AND NOVELTY

The high-throughput, rapid analysis of cellular response to drugs at the single-cell level can be accurately performed by our platform. After systematically analyzing the materials and the structures of the single-cell microfluidic chip, the optimal single-cell trapping method was proposed to contribute to the further application of hyperspectral imaging on microfluidic single-cell analysis. And the hyperspectral characterization of single-cell with cancer drug stimulation proved the application potential of our method in personal cancer medication.

Raman developmental markers in root cell walls are associated with lodging tendency in tef

MAIN CONCLUSION

Using Raman micro-spectroscopy on tef roots, we could monitor cell wall maturation in lines with varied genetic lodging tendency. We describe the developing cell wall composition in root endodermis and cylinder tissue. Tef [Eragrostis tef (Zucc.) Trotter] is an important staple crop in Ethiopia and Eritrea, producing gluten-free and protein-rich grains. However, this crop is not adapted to modern farming practices due to high lodging susceptibility, which prevents the application of mechanical harvest. Lodging describes the displacement of roots (root lodging) or fracture of culms (stem lodging), forcing plants to bend or fall from their vertical position, causing significant yield losses. In this study, we aimed to understand the microstructural properties of crown roots, underlining tef tolerance/susceptibility to lodging. We analyzed plants at 5 and 10 weeks after emergence and compared trellised to lodged plants. Root cross sections from different tef genotypes were characterized by scanning electron microscopy, micro-computed tomography, and Raman micro-spectroscopy. Lodging susceptible genotypes exhibited early tissue maturation, including developed aerenchyma, intensive lignification, and lignin with high levels of crosslinks. A comparison between trellised and lodged plants suggested that lodging itself does not affect the histology of root tissue. Furthermore, cell wall composition along plant maturation was typical to each of the tested genotypes independently of trellising. Our results suggest that it is possible to select lines that exhibit slow maturation of crown roots. Such lines are predicted to show reduction in lodging and facilitate mechanical harvest.

Optical sensors (optodes) for multiparameter chemical imaging: classification, challenges, and prospects

Chemical gradients and uneven distribution of analytes are common in natural and artificial systems. As a result, the ability to visualize chemical distributions in two or more dimensions has gained significant importance in recent years. This has led to the integration of chemical imaging techniques into all domains of analytical chemistry. In this review, we focus on the use of optical sensors, so-called optodes, to obtain real-time and multidimensional images of two or more parameters simultaneously. It is important to emphasize that multiparameter imaging in this context is not confined solely to multiple chemical parameters (analytes) but also encompasses physical (e.g., temperature or flow) or biological (e.g., metabolic activity) parameters. First, we discuss the technological milestones that have paved the way for chemical imaging using optodes. Later, we delve into various strategies that can be taken to enable multiparameter imaging. The latter spans from developing novel receptors that enable the recognition of multiple parameters to chemometrics and machine learning-based techniques for data analysis. We also explore ongoing trends, challenges, and prospects for future developments in this field. Optode-based multiparameter imaging is a rapidly expanding field that is being fueled by cutting-edge technologies. Chemical imaging possesses the potential to provide novel insights into complex samples, bridging not only across various scientific disciplines but also between research and society.

Multi-level color classification of post-consumer plastic packaging flakes by hyperspectral imaging for optimizing the recycling process

In a circular economy perspective, the development of fast and efficient sensor-based recognition strategies of plastic waste, not only by polymer but also by color, plays a crucial role for the production of high quality secondary raw materials in recycling plants. In this work, mixed colored flakes of high-density polyethylene (HDPE) from packaging waste were simultaneously classified by hyperspectral imaging working in the visible range (400-750 nm), combined with machine learning. Two classification models were built and compared: (1) Partial Least Square-Discriminant Analysis (PLS-DA) for 6 HDPE macro-color classes identification (i.e., white, blue, green, red, orange and yellow) and (2) hierarchical PLS-DA for a more accurate discrimination of the different HDPE color tones, providing as output 14 color classes. The obtained classification results were excellent for both models, with values of Recall, Specificity, Accuracy, and F-score in prediction close to 1. The proposed methodological approach can be utilized as sensor-based sorting logic in plastic recycling plants, tuning the output based on the required needs of the recycling plant, allowing to obtain a high-quality recycled HDPE of different colors, optimizing the plastic recycling process, in agreement with the principles of circular economy.

Multicomponent hyperspectral grade evaluation of ilmenite using spectral-spatial joint features

Obtaining a comprehensive understanding of ore grade information is of significant importance for evaluating the value of ore. However, the real-time detection of multicomponent grade needs more effective online methods. This study proposes a novel approach utilizing hyperspectral imaging (HSI) to evaluate the grade information of nine major ilmenite components by integrating spectral and spatial data. Four multivariate input-output models were developed to mitigate variable interference to predict each component's grade. The results demonstrated that the backpropagation neural network (BPNN) model built from iPLS-VCPA-IRIV feature selection spectral data worked best (RP2 = 0.9935, RMSEP = 0.1364, RPD = 12.8986, and RPIQ = 21.4871, with a computational time of approximately 0.8 s). Furthermore, applying the best optimal combination algorithm for multicomponent grade inversion yielded highly accurate results, in which 97% of the component inversion residuals were less than 1. This investigation affirms that HSI enables rapid and accurate prediction and inversion of the multicomponent grade of ilmenite, thereby presenting a promising alternative to online analysis in the mineral field.

Raman spectroscopy and mass spectrometry identifies a unique group of epidermal lipids in active discoid lupus erythematosus

Discoid lupus erythematosus (DLE) is the most common form of cutaneous lupus1. It can cause permanent scarring. The pathophysiology of is not fully understood. Plasmacytoid dendritic cells are found in close association with apoptotic keratinocytes inferring close cellular signalling. Matrix Associated Laser Desorption Ionisation (MALDI) combined with Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR-MS) is an exquisitely sensitive combination to examine disease processes at the cellular and molecular level. Active areas of discoid lupus erythematosus were compared with normal perilesional skin using MALDI combined with FT-ICR-MS. A unique set of biomarkers, including epidermal lipids is identified in active discoid lupus. These were assigned as sphingomyelins, phospholipids and ceramides. Additionally, increased levels of proteins from the keratin, and small proline rich family, and aromatic amino acids (tryptophan, phenylalanine, and tyrosine) in the epidermis are observed. These techniques, applied to punch biopsies of the skin, have shown a distinctive lipid profile of active discoid lupus. This profile may indicate specific lipid signalling pathways. Lipid rich microdomains (known as lipid rafts) are involved in cell signalling and lipid abnormalities have been described with systemic lupus erythematosus which correlate with disease activity.

Comparing the Performance of Raman and Near-Infrared Imaging in the Prediction of the In Vitro Dissolution Profile of Extended-Release Tablets Based on Artificial Neural Networks

In this work, the performance of two fast chemical imaging techniques, Raman and near-infrared (NIR) imaging is compared by utilizing these methods to predict the rate of drug release from sustained-release tablets. Sustained release is provided by adding hydroxypropyl methylcellulose (HPMC), as its concentration and particle size determine the dissolution rate of the drug. The chemical images were processed using classical least squares; afterwards, a convolutional neural network was applied to extract information regarding the particle size of HPMC. The chemical images were reduced to an average HPMC concentration and a predicted particle size value; these were used as inputs in an artificial neural network with a single hidden layer to predict the dissolution profile of the tablets. Both NIR and Raman imaging yielded accurate predictions. As the instrumentation of NIR imaging allows faster measurements than Raman imaging, this technique is a better candidate for implementing a real-time technique. The introduction of chemical imaging in the routine quality control of pharmaceutical products would profoundly change quality assurance in the pharmaceutical industry.

Analysis of Hyperspectral Data to Develop an Approach for Document Images

Hyperspectral data analysis is being utilized as an effective and compelling tool for image processing, providing unprecedented levels of information and insights for various applications. In this manuscript, we have compiled and presented a comprehensive overview of recent advances in hyperspectral data analysis that can provide assistance for the development of customized techniques for hyperspectral document images. We review the fundamental concepts of hyperspectral imaging, discuss various techniques for data acquisition, and examine state-of-the-art approaches to the preprocessing, feature extraction, and classification of hyperspectral data by taking into consideration the complexities of document images. We also explore the possibility of utilizing hyperspectral imaging for addressing critical challenges in document analysis, including document forgery, ink age estimation, and text extraction from degraded or damaged documents. Finally, we discuss the current limitations of hyperspectral imaging and identify future research directions in this rapidly evolving field. Our review provides a valuable resource for researchers and practitioners working on document image processing and highlights the potential of hyperspectral imaging for addressing complex challenges in this domain.

Assessment of Nitrite Content in Vienna Chicken Sausages Using Near-Infrared Hyperspectral Imaging

Sodium nitrite is a food additive commonly used in sausages, but legally, the unsafe levels of nitrite in sausage should be less than 80 mg/kg, since higher levels can be harmful to consumers. Consumers must rely on processors to conform to these levels. Therefore, the determination of nitrite content in chicken sausages using near infrared hyperspectral imaging (NIR-HSI) was investigated. A total of 140 chicken sausage samples were produced by adding sodium nitrite in various levels. The samples were divided into a calibration set (n = 94) and a prediction set (n = 46). Quantitative analysis, to detect nitrate in the sausages, and qualitative analysis, to classify nitrite levels, were undertaken in order to evaluate whether individual sausages had safe levels or non-safe levels of nitrite. NIR-HSI was preprocessed to obtain the optimum conditions for establishing the models. The results showed that the model from the partial least squares regression (PLSR) gave the most reliable performance, with a coefficient of determination of prediction (R_p) of 0.92 and a root mean square error of prediction (RMSEP) of 15.603 mg/kg. The results of the classification using the partial least square-discriminant analysis (PLS-DA) showed a satisfied accuracy for prediction of 91.30%. It was therefore concluded that they were sufficiently accurate for screening and that NIR-HSI has the potential to be used for the fast, accurate and reliable assessment of nitrite content in chicken sausages.

Batch-wise versus continuous dough mixing of Danish butter cookies. A near infrared hyperspectral imaging study

The Danish buttered cookie is a famous confectionery product. Its success makes manufacturing of the large volumes required challenging, introducing the need for different strategies to increase production while maintaining a high-quality standard. Two manufacturing lines used are batch-wise and continuous dough mixing. Despite the recipe being the same, the outcome of the two production types differs in texture and external appearance. While this does not infringe on the quality, changes in texture are observable. This manuscript analyses the physicochemical differences of the cookies after baking using Near Infrared hyperspectral imaging and Chemometrics. The study demonstrates that the changes in texture between batch and continuous production are mostly due to the difference in crystalline sucrose emerging in invisible spots on or near the surface of the cookies and a higher tendency of migrated butter-fat spots on the surface of the cookies for the continuous manufacturing procedure.

Trends in in ovo sexing technologies: insights and interpretation from papers and patents

Numerous researchers and institutions have been developing in ovo sexing technologies to improve animal welfare by identifying male embryos in an early embryonic stage and disposing of them before pain perception. This review gives a complete overview of the technological approaches reported in papers and patents by performing a thorough search using Web of Science and Patstat/Espacenet databases for papers and patents, respectively. Based on a total of 49 papers and 115 patent families reported until May 2023 worldwide, 11 technology categories were defined: 6 non-optical and 5 optical techniques. Every category was described for its characteristics while assessing its potential for application. Next, the dynamics of the publications of in ovo sexing techniques in both paper and patent fields were described through growth curves, and the interest or actual status was visualized using the number of paper citations and the actual legal status of the patents. When comparing the reported technologies in papers to those in patents, scientific gaps were observed, as some of the patented technologies were not reported in the scientific literature, e.g., ion mobility and mass spectrometry approaches. Generally, more diverse approaches in all categories were found in patents, although they do require more scientific evidence through papers or industrial adoption to prove their robustness. Moreover, although there is a recent trend for non-invasive techniques, invasive methods like analyzing DNA through PCR or hormones through immunosensing are still being reported (and might continue to be) in papers and patents. It was also observed that none of the technologies complies with all the industry requirements, although 5 companies already entered the market. On the one hand, more research and harmony between consumers, industry, and governments is necessary. On the other hand, close monitoring of the market performance of the currently available techniques will offer valuable insights into the potential and expectations of in ovo sexing techniques in the poultry industry.

Developing a Clean Labelled Snack Bar Rich in Protein and Fibre with Dry-Fractionated Defatted Durum Wheat Cake

The shift towards a vegetarian, vegan, or flexitarian diet has increased the demand for vegetable protein and plant-based foods. The defatted cake generated during the extraction of lipids from durum wheat (Triticum turgidum L. var. durum) milling by-products is a protein and fibre-containing waste, which could be upcycled as a food ingredient. This study aimed to exploit the dry-fractionated fine fraction of defatted durum wheat cake (DFFF) to formulate a vegan, clean labelled, cereal-based snack bar. The design of experiments (DoEs) for mixtures was applied to formulate a final product with optimal textural and sensorial properties, which contained 10% DFFF, 30% glucose syrup, and a 60% mix of puffed/rolled cereals. The DFFF-enriched snack bar was harder compared to the control without DFFF (cutting stress = 1.2 and 0.52 N/mm², and fracture stress = 12.9 and 9.8 N/mm² in the DFFF-enriched and control snack bar, respectively), due to a densifying effect of DFFF, and showed a more intense yellow hue due to the yellow-brownish colour of DFFF. Another difference was in the caramel flavour, which was more intense in the DFFF-enriched snack bar. The nutritional claims "low fat" and "source of fibre" were applicable to the DFFF-enriched snack bar according to EC Reg. 1924/06.

Determination of Freshness of Mackerel (Scomber japonicus) Using Shortwave Infrared Hyperspectral Imaging

Shortwave infrared (SWIR) hyperspectral imaging was applied to classify the freshness of mackerels. Total volatile basic nitrogen (TVB-N) and acid values, as chemical compounds related to the freshness of mackerels, were also analyzed to develop a prediction model of freshness by combining them with hyperspectral data. Fresh mackerels were divided into three groups according to storage periods (0, 24, and 48 h), and hyperspectral data were collected from the eyes and whole body, separately. The optimized classification accuracies were 81.68% using raw data from eyes and 90.14% using body data by multiple scatter correction (MSC) pretreatment. The prediction accuracy of TVB-N was 90.76%, and the acid value was 83.76%. These results indicate that hyperspectral imaging, as a nondestructive method, can be used to verify the freshness of mackerels and predict the chemical compounds related to the freshness.

Robust near-infrared-based plastic classification with relative spectral similarity pattern

Sensor-based material flow characterization techniques, particularly hyperspectral imaging in the near-infrared (NIR) range, can recognize materials quickly, accurately, and economically. When identifying materials using NIR hyperspectral imaging, extracting influential features from high-dimensional wavelength information is essential for effective recognition. However, spectral noise from the rough and contaminated surfaces of objects (especially un-shredded waste) degrades the feature-extraction performance, which in turn deteriorates the material classification performance. In this study, we propose a real-time feature-extraction method, named relative spectral similarity pattern color mapping (RSSPCM), to robustly classify materials in noisy environments, such as plastic waste sorting facilities. RSSPCM compares relative intra- and inter-class spectral similarity patterns, instead of individual similarity, to class-representative spectra alone. Recognition targets have similar chemical makeups that are applied to feature extraction as an intra-class similarity ratio. The proposed model is robust owing to the remaining relative similarity trends found in a contaminated spectrum. We evaluated the effectiveness of the proposed method using noisy samples obtained from a waste-management facility. The results were compared with two spectral groups obtained at different noise levels. Both results showed high accuracy as there was an increased number of true positives for low-reflectance regions. The average F1-score values were 0.99 and 0.96 for low- and high-noise sets, respectively. Furthermore, the proposed method showed minimal F1-score variations between classes (standard deviation of 0.026 for the high-noise set).

Hyperspectral imaging in forensic science: an overview of major application areas

Analysis of evidence is a challenge. Crime scene materials are complex, diverse, sometimes of an unknown nature. Forensic science provides the most critical applications for their examination. Chemical tests, analytical methods, and techniques to process the evidence must be carefully selected by the forensic scientist. Ideally, it may be interpreted, analyzed, and judged in the original context of the crime scene. In this sense, hyperspectral imaging (HSI) has been employed as an analytical tool that maintains the integrity of the samples/objects for multiple and sequential analysis and for counter-proof exams. This paper is an overview of forensic science trends for the application of HSI techniques in the last ten years (2011-2021). The examination of documents was the main area of exploration, followed by bloodstain analysis aging process; trace analysis of explosives and gunshot residue. Chemometric tools were also addressed since they are crucial to obtain the most important information from the samples. There are great challenges in applying HSI in forensic science, but there have been clear technological and scientific advances, and a solid foundation has been built for the use of HSI in real-life cases.

In vivo grading of lipids in fatty liver by near-infrared autofluorescence and reflectance

The prevalence of nonalcoholic fatty liver (NAFLD) is rapidly increasing worldwide. When untreated, it may lead to complications such as liver cirrhosis or hepatocarcinoma. The diagnosis of NAFLD is usually obtained by ultrasonography, a technique that can underestimate its prevalence. For this reason, physicians aspire for an accurate, cost-effective, and noninvasive method to determine both the presence and the specific stage of the NAFLD. In this paper, we report an integrated approach for the quantitative estimation of the density of triglycerides in the liver based on the use of autofluorescence and reflectance signals generated by the abdomen of obese C57BL6/J mice. Singular value decomposition is applied to the generated spectra and its corresponding regression model provided a determination coefficient of 0.99 and a root mean square error of 240 mg/dl. This, in turn, enabled the quantitative imaging of triglycerides density in the livers of mice under in vivo conditions.

Improvement of pixel classification by the simultaneous use of spectral and spatial information in the framework of spectroscopic imaging

Hyperspectral imaging technology is developing in a very fast way. We find it today in many analytical developments using different spectroscopies for sample classification purposes. Instrumental developments allow us to acquire more and more data in shorter and shorter periods of time while improving their quality. Therefore, we are going in the right direction as far as the measure is concerned. On the other hand, we can make a more mixed assessment for the hyperspectral imaging data processing. Indeed, the data acquired in spectroscopic imaging have the particularity of encoding both spectral and spatial information. Unfortunately, in chemometrics, almost all classification approaches today only use spectral information from three-dimensional hyperspectral data arrays. To be more precise, an approach encompassing the unfolding/refolding of such arrays is often applied beforehand because the majority of algorithms for analysing these data are not capable of handling them in their original structure. Spatial information is therefore lost during the chemometric exploration. The study of the spectral part of the acquired data array alone is clearly a limitation that we propose to overcome in this work. 2-D Stationary Wavelet Transform will be used in the data preprocessing phase to ensure the joint use of spectral and spatial information. Two spectroscopic datasets will then be used to evaluate the potential of our approach in the context of supervised classification.

A User-Friendly Platform for Single-Cell Raman Spectroscopy Analysis

The optimization of Raman instruments greatly expands our understanding of single-cell Raman spectroscopy. The improvement in the speed and sensitivity of the instrument and the implementation of advanced data mining methods help to reveal the complex chemical and biological information within the Raman spectral data. Here we introduce a new Matlab Graphical User-Friendly Interface (GUI), named "CELL IMAGE" for the analysis of cellular Raman spectroscopy data. The three main steps of data analysis embedded in the GUI include spectral processing, pattern recognition and model validation. Various well-known methods are available to the user of the GUI at each step of the analysis. Herein, a new subsampling optimization method is integrated into the GUI to estimate the minimum number of spectral collection points. The introduction of the signal-to-noise ratio (SNR) of the analyte in the binomial statistical model means the new subsampling model is more sophisticated and suitable for complicated Raman cell data. These embedded methods allow "CELL IMAGE" to transform spectral information into biological information, including single-cell visualization, cell classification and biomolecular/ drug quantification.

A biophotonic platform for quantitative analysis in the spatial, spectral, polarimetric, and goniometric domains

Advanced instrumentation and versatile setups are needed for understanding light interaction with biological targets. Such instruments include (1) microscopes and 3D scanners for detailed spatial analysis, (2) spectral instruments for deducing molecular composition, (3) polarimeters for assessing structural properties, and (4) goniometers probing the scattering phase function of, e.g., tissue slabs. While a large selection of commercial biophotonic instruments and laboratory equipment are available, they are often bulky and expensive. Therefore, they remain inaccessible for secondary education, hobbyists, and research groups in low-income countries. This lack of equipment impedes hands-on proficiency with basic biophotonic principles and the ability to solve local problems with applied physics. We have designed, prototyped, and evaluated the low-cost Biophotonics, Imaging, Optical, Spectral, Polarimetric, Angular, and Compact Equipment (BIOSPACE) for high-quality quantitative analysis. BIOSPACE uses multiplexed light-emitting diodes with emission wavelengths from ultraviolet to near-infrared, captured by a synchronized camera. The angles of the light source, the target, and the polarization filters are automated by low-cost mechanics and a microcomputer. This enables multi-dimensional scatter analysis of centimeter-sized biological targets. We present the construction, calibration, and evaluation of BIOSPACE. The diverse functions of BIOSPACE include small animal spectral imaging, measuring the nanometer thickness of a bark-beetle wing, acquiring the scattering phase function of a blood smear and estimating the anisotropic scattering and the extinction coefficients, and contrasting muscle fibers using polarization. We provide blueprints, component list, and software for replication by enthusiasts and educators to simplify the hands-on investigation of fundamental optical properties in biological samples.

Towards recycling of challenging waste fractions: Identifying flame retardants in plastics with optical spectroscopic techniques

The use of plastics is rapidly rising around the world causing a major challenge for recycling. Lately, a lot of emphasis has been put on recycling of packaging plastics, but, in addition, there are high volume domains with low recycling rate such as automotive, building and construction, and electric and electronic equipment. Waste plastics from these domains often contain additives that restrict their recycling due to the hazardousness and challenges they bring to chemical and mechanical recycling. As such, the first step for enabling the reuse of these fractions is the identification of these additives in the waste plastics. This study compares the ability of different optical spectroscopy technologies to detect two different plastic additives, fire retardants ammonium polyphosphate and aluminium trihydrate, inside polypropylene plastic matrix. The detection techniques near-infrared (NIR), Fourier-transform infrared (FTIR) and Raman spectroscopy as well as hyperspectral imaging (HSI) in the short-wavelength infrared (SWIR) and mid-wavelength infrared (MWIR) range were evaluated. The results indicate that Raman, NIR and SWIR HSI have the potential to detect these additives inside the plastic matrix even at relatively low concentrations. As such, utilising these methods has the possibility to facilitate sorting and recycling of as of yet unused plastic waste streams, although more research is needed in applying them in actual waste sorting facilities.

Comparison of preprocessing techniques to reduce nontissue-related variations in hyperspectral reflectance imaging

SIGNIFICANCE

Hyperspectral reflectance imaging can be used in medicine to identify tissue types, such as tumor tissue. Tissue classification algorithms are developed based on, e.g., machine learning or principle component analysis. For the development of these algorithms, data are generally preprocessed to remove variability in data not related to the tissue itself since this will improve the performance of the classification algorithm. In hyperspectral imaging, the measured spectra are also influenced by reflections from the surface (glare) and height variations within and between tissue samples.

AIM

To compare the ability of different preprocessing algorithms to decrease variations in spectra induced by glare and height differences while maintaining contrast based on differences in optical properties between tissue types.

APPROACH

We compare eight preprocessing algorithms commonly used in medical hyperspectral imaging: standard normal variate, multiplicative scatter correction, min-max normalization, mean centering, area under the curve normalization, single wavelength normalization, first derivative, and second derivative. We investigate conservation of contrast stemming from differences in: blood volume fraction, presence of different absorbers, scatter amplitude, and scatter slope-while correcting for glare and height variations. We use a similarity metric, the overlap coefficient, to quantify contrast between spectra. We also investigate the algorithms for clinical datasets from the colon and breast.

CONCLUSIONS

Preprocessing reduces the overlap due to glare and distance variations. In general, the algorithms standard normal variate, min-max, area under the curve, and single wavelength normalization are the most suitable to preprocess data used to develop a classification algorithm for tissue classification. The type of contrast between tissue types determines which of these four algorithms is most suitable.

Recent Advances in Counterfeit Art, Document, Photo, Hologram, and Currency Detection Using Hyperspectral Imaging

Forgery and tampering continue to provide unnecessary economic burdens. Although new anti-forgery and counterfeiting technologies arise, they inadvertently lead to the sophistication of forgery techniques over time, to a point where detection is no longer viable without technological aid. Among the various optical techniques, one of the recently used techniques to detect counterfeit products is HSI, which captures a range of electromagnetic data. To aid in the further exploration and eventual application of the technique, this study categorizes and summarizes existing related studies on hyperspectral imaging and creates a mini meta-analysis of this stream of literature. The literature review has been classified based on the product HSI has used in counterfeit documents, photos, holograms, artwork, and currency detection.

A Tunable Hyperspectral Imager for Detection and Quantification of Marine Biofouling on Coated Surfaces

Fouling control coatings (FCCs) are used to prevent the accumulation of marine biofouling on, e.g., ship hulls, which causes increased fuel consumption and the global spread of non-indigenous species. The standards for performance evaluations of FCCs rely on visual inspections, which induce a degree of subjectivity. The use of RGB images for objective evaluations has already received interest from several authors, but the limited acquired information restricts detailed analyses class-wise. This study demonstrates that hyperspectral imaging (HSI) expands the specificity of biofouling assessments of FCCs by capturing distinguishing spectral features. We developed a staring-type hyperspectral imager using a liquid crystal tunable filter as the wavelength selective element. A novel light-emitting diode illumination system with high and uniform irradiance was designed to compensate for the low-filter transmittance. A spectral library was created from reflectance-calibrated optical signatures of representative biofouling species and coated panels. We trained a neural network on the annotated library to assign a class to each pixel. The model was evaluated on an artificially generated target, and global accuracy of 95% was estimated. The classifier was tested on coated panels (exposed at the CoaST Maritime Test Centre) with visible intergrown biofouling. The segmentation results were used to determine the coverage percentage per class. Although a detailed taxonomic description might be complex due to spectral similarities among groups, these results demonstrate the feasibility of HSI for repeatable and quantifiable biofouling detection on coated surfaces.

Classification and distribution of freshwater microplastics along the Italian Po river by hyperspectral imaging

In this work, freshwater microplastic samples collected from four different stations along the Italian Po river were characterized in terms of abundance, distribution, category, morphological and morphometrical features, and polymer type. The correlation between microplastic category and polymer type was also evaluated. Polymer identification was carried out developing and implementing a new and effective hierarchical classification logic applied to hyperspectral images acquired in the short-wave infrared range (SWIR: 1000-2500 nm). Results showed that concentration of microplastics ranged from 1.89 to 8.22 particles/m3, the most abundant category was fragment, followed by foam, granule, pellet, and filament and the most diffused polymers were expanded polystyrene followed by polyethylene, polypropylene, polystyrene, polyamide, polyethylene terephthalate and polyvinyl chloride, with some differences in polymer distribution among stations. The application of hyperspectral imaging (HSI) as a rapid and non-destructive method to classify freshwater microplastics for environmental monitoring represents a completely innovative approach in this field.

Theoretical Principles and Perspectives of Hyperspectral Imaging Applied to Sediment Core Analysis

Hyperspectral imaging is a recent technology that has been gaining popularity in the geosciences since the 1990s, both in remote sensing and in the field or laboratory. Indeed, it allows the rapid acquisition of a large amount of data that are spatialized on the studied object with a low-cost, compact, and automatable sensor. This practical article aims to present the current state of knowledge on the use of hyperspectral imaging for sediment core analysis (core logging). To use the full potential of this type of sensor, many points must be considered and will be discussed to obtain reliable and quality data to extract many environmental properties of sediment cores. Hyperspectral imaging is used in many fields (e.g., remote sensing, geosciences and artificial intelligence) and offers many possibilities. The applications of the literature will be reviewed under five themes: lake and water body trophic status, source-to-sink approaches, organic matter and mineralogy studies, and sedimentary deposit characterization. Afterward, discussions will be focused on a multisensor core logger, data management, integrated use of these data for the selection of sample areas, and other opportunities. Through this practical article, we emphasize that hyperspectral imaging applied to sediment cores is still an emerging tool and shows many possibilities for refining the understanding of environmental processes.

Non-destructive assessment of quality parameters of white button mushrooms (Agaricus bisporus) using image processing techniques

Considering that appearance of white button mushroom (WBM) as the trigger for registering its quality, this study was aimed at analyzing the visual cues by the application of image processing tools. While L-a-b colour space and skewness was used for estimating chromatic and morphological characteristics; onset of discolouration of WBM was predicted by hyperspectral image analysis. Undamaged (UD) and damaged (D) mushrooms were stored under refrigerated conditions (3-5 °C and 90% R_h). RGB and hyperspectral images were acquired on alternate storage days 1, 3, 5, 7 and 9. Weight loss, texture and moisture content of stored mushrooms were also recorded during the storage period. Colour changes in stored UD and D were found to be in b (21.55) and a (2399) value, respectively. Browning index in D was 83-212% higher than UD mushrooms across the storage period. Weight and firmness losses in D were higher by 65.9 and 31.4%, respectively than UD. Morphological characteristic in terms of aspect ratio and roundness were not found to vary significantly over the storage period for both UD and D mushrooms. Chemometrics revealed that multiplicative scatter correction was the best pre-processing tool and that onset on discolouration is conspicuous in the spectral region of 520-800 nm. k-NN fared better than PLS-DA for correct classification (100%) of UD and D mushrooms.

Leaf microscopy applications in photosynthesis research: identifying the gaps

Leaf imaging via microscopy has provided critical insights into research on photosynthesis at multiple junctures, from the early understanding of the role of stomata, through elucidating C4 photosynthesis via Kranz anatomy and chloroplast arrangement in single cells, to detailed explorations of diffusion pathways and light utilization gradients within leaves. In recent decades, the original two-dimensional (2D) explorations have begun to be visualized in three-dimensional (3D) space, revising our understanding of structure-function relationships between internal leaf anatomy and photosynthesis. In particular, advancing new technologies and analyses are providing fresh insight into the relationship between leaf cellular components and improving the ability to model net carbon fixation, water use efficiency, and metabolite turnover rate in leaves. While ground-breaking developments in imaging tools and techniques have expanded our knowledge of leaf 3D structure via high-resolution 3D and time-series images, there is a growing need for more in vivo imaging as well as metabolite imaging. However, these advances necessitate further improvement in microscopy sciences to overcome the unique challenges a green leaf poses. In this review, we discuss the available tools, techniques, challenges, and gaps for efficient in vivo leaf 3D imaging, as well as innovations to overcome these difficulties.

Deep generative neural networks for spectral image processing

An artificial intelligence approach based on deep generative neural networks for spectral imaging processing was proposed. The key idea was to treat different spectral image processing operations such as segmentation, regression, and classification as image-to-image translation tasks. For the image-to-image translation, the conditional generative adversarial networks were used. As a baseline comparison, the traditional chemometric approach based on pixels wise modelling was demonstrated. The analysis was presented with two real data sets related to fruit property prediction and kernel and shell classification of walnuts. The presented artificial intelligence approach for spectral image processing can provide benefits for any field of science where spectral imaging and processing is widely performed.