Search

Hyperspectral remote sensing is currently underutilized in urban environments due to significant barriers concerning the existence, availability, and quality of urban hyperspectral reference spectra. This paper exposes these barriers by identifying, cataloging, and characterizing the contents of 23 spectral libraries, developing metrics to assess compliance with the Principles of Findability, Accessibility, Interoperability, and Reusability (FAIR), and evaluating existing resources using these criteria. Only 2931 urban spectral records were found within the 4 Global Spectral Libraries (0.61% of 476,592 published spectra). Within a further 19 Local Urban Spectral Libraries, 3862 additional urban spectra were found, but only 1662 (43%) were accessible without restriction. Content analysis revealed insufficient representation of urban material heterogeneity, imbalanced categories, and limited library interoperability, all of which further hinder effective data utilization. In response, this paper proposes a 14‐category metadataset, with specific considerations to overcome environmentally induced and inherent, intra‐material variability. In addition, material‐based spectral groupings and data resampling to common hyperspectral equipment specifications are recommended. These measures aim to enhance the utility of urban spectral libraries by improving FAIR compliance, thereby contributing to a more cohesive and enduring framework for hyperspectral reference data. [ABSTRACT FROM AUTHOR]

Satellite missions like EnMAP and PRISMA generate raster images that describe the Earth’s environment with hyperspectral resolution. Such imaging spectroscopy data is of high value for applications in, e.g., geological, vegetation, or hydrological research. Due to its high dimensionality, analyzing imaging spectroscopy data is still challenging and often requires the use of proprietary software. The motivation for the EnMAP-Box is to close this gap and to foster the use of imaging spectroscopy data with state-of-the art remote sensing methods. Developed as Python plugin for the QGIS geoinformation system, the EnMAP-Box integrates into a well-established, platform-independent, and free-and-open-source software ecosystem to analyze geospatial data. The EnMAP-Box offers advanced functionalities to visualize and process hyper- and multispectral, and multi-temporal remote sensing data, it implements novel spectral libraries concept, and provides easy access to published algorithms from different fields of environmental research. Already been widely used in the past, the EnMAP-Box can now unfold its full potential as first operational EnMAP data became available in 2022.

The 2017 Dagstuhl Seminar on Computational Proteomics provided an opportunity for a broad discussion on the current state and future directions of the generation and use of peptide tandem mass spectrometry spectral libraries. Their use in proteomics is growing slowly, but there are multiple challenges in the field that must be addressed to further increase the adoption of spectral libraries and related techniques. The primary bottlenecks are the paucity of high quality and comprehensive libraries and the general difficulty of adopting spectral library searching into existing workflows. There are several existing spectral library formats, but none captures a satisfactory level of metadata; therefore, a logical next improvement is to design a more advanced, Proteomics Standards Initiative-approved spectral library format that can encode all of the desired metadata. The group discussed a series of metadata requirements organized into three designations of completeness or quality, tentatively dubbed bronze, silver, and gold. The metadata can be organized at four different levels of granularity: at the collection (library) level, at the individual entry (peptide ion) level, at the peak (fragment ion) level, and at the peak annotation level. Strategies for encoding mass modifications in a consistent manner and the requirement for encoding high-quality and commonly seen but as-yet-unidentified spectra were discussed. The group also discussed related topics, including strategies for comparing two spectra, techniques for generating representative spectra for a library, approaches for selection of optimal signature ions for targeted workflows, and issues surrounding the merging of two or more libraries into one. We present here a review of this field and the challenges that the community must address in order to accelerate the adoption of spectral libraries in routine analysis of proteomics datasets.

Spectroscopic measurements of soil samples are reliable because they are highly repeatable and reproducible. They characterise the samples' mineral–organic composition. Estimates of concentrations of soil constituents are inevitably less precise than estimates obtained conventionally by chemical analysis. But the cost of each spectroscopic estimate is at most one‐tenth of the cost of a chemical determination. Spectroscopy is cost‐effective when we need many data, despite the costs and errors of calibration. Soil spectroscopists understand the risks of over‐fitting models to highly dimensional multivariate spectra and have command of the mathematical and statistical methods to avoid them. Machine learning has fast become an algorithmic alternative to statistical analysis for estimating concentrations of soil constituents from reflectance spectra. As with any modelling, we need judicious implementation of machine learning as it also carries the risk of over‐fitting predictions to irrelevant elements of the spectra. To use the methods confidently, we need to validate the outcomes with appropriately sampled, independent data sets. Not all machine learning should be considered 'black boxes'. Their interpretability depends on the algorithm, and some are highly interpretable and explainable. Some are difficult to interpret because of complex transformations or their huge and complicated network of parameters. But there is rapidly advancing research on explainable machine learning, and these methods are finding applications in soil science and spectroscopy. In many parts of the world, soil and environmental scientists recognise the merits of soil spectroscopy. They are building spectral libraries on which they can draw to localise the modelling and derive soil information for new projects within their domains. We hope our article gives readers a more balanced and optimistic perspective of soil spectroscopy and its future. Highlights: Spectroscopy is reliable because it is a highly repeatable and reproducible analytical technique.Spectra are calibrated to estimate concentrations of soil properties with known error.Spectroscopy is cost‐effective for estimating soil properties.Machine learning is becoming ever more powerful for extracting accurate information from spectra, and methods for interpreting the models exist.Large libraries of soil spectra provide information that can be used locally to aid estimates from new samples. [ABSTRACT FROM AUTHOR]

Microplastics are an emerging pollutant of concern, with environmental observations recorded across the world. Identifying the type of microplastic is challenging due to spectral similarities among the most common polymers, necessitating methods that can confidently distinguish plastic identities. In practice, a researcher chooses the reference vibrational spectrum that is most like the unknown spectrum, where the likeness between the two spectra is expressed numerically as the hit quality index (HQI). Despite the widespread use of HQI thresholds in the literature, acceptance of a spectral label often lacks any associated confidence. To address this gap, we apply a machine-learning framework called conformal prediction to output a set of possible labels that contain the true identity of the unknown spectrum with a user-defined probability (e.g., 90%). Microplastic reference libraries of environmentally aged and pristine polymeric materials, as well as unknown environmental plastic spectra, were employed to illustrate the benefits of this approach when used with two similarity metrics to compute HQI. We present an adaptable workflow using our open-access code to ensure spectral matching confidence for the microplastic community, reducing manual inspection of spectral matches and enhancing the robustness of quantification in the field.

Multiple endmember spectral mixture analysis (MESMA) is one of the leading approaches to perform spectral unmixing (SU) considering the variability of the endmembers (EMs). It represents each EM in the image using libraries of spectral signatures acquired a priori. However, existing spectral libraries are often small and unable to properly capture the variability of each EM in practical scenes, which compromises the performance of MESMA. In this letter, we propose a library augmentation strategy to increase the diversity of existing spectral libraries, thus improving their ability to represent the materials in real images. First, we leverage the power of deep generative models to learn the statistical distribution of the EMs based on the spectral signatures available in the existing libraries. Afterward, new samples can be drawn from the learned EM distributions and used to augment the spectral libraries, improving the overall quality of the SU process. Experimental results using synthetic and real data attest to the superior performance of the proposed method even under library mismatch conditions. [ABSTRACT FROM AUTHOR]

[Display omitted] • Authentication of mixed samples using proteomics Spectral library matching approach. • Estimating abundance of fish species in commercial seafood samples. • Spectra library approach successfully identifies fish in commercial products. • Other compounds and treatments do not affect results on commercial products. • Spectra library matching approach can be employed to combat food fraud. Seafood fraud has become a global issue, threatening food security and safety. Adulteration, substitution, dilution, and incorrect labeling of seafood products are fraudulent practices that violate consumer safety. In this context, developing sensitive, robust, and high-throughput molecular tools for food and feed authentication is becoming crucial for regulatory purposes. Analytical approaches such as proteomics mass spectrometry have shown promise in detecting incorrectly labeled products. For the application of these tools, genome information is crucial, but currently, for many marine species of commercial importance, such information is unavailable. However, when combining proteomic analysis with spectral library matching, commercially important fish species were successfully identified, differentiated, and quantified in pure muscle samples and mixtures, even when genome information was scarce. This study further tested the previously developed spectral library matching approach to differentiate between 29 fish species from the North Sea and examined samples including individual fish, laboratory-prepared mixtures and commercial products. For authenticating libraries generated from 29 fish species, fresh muscle samples from the fish samples were matched against the reference spectral libraries. Species of the fresh fish samples were correctly authenticated using the spectral library approach. The same result was obtained when evaluating the laboratory-prepared mixtures. Furthermore, processed commercial products containing mixtures of two or three fish species were matched against these reference spectral libraries to test the accuracy and robustness of this method for authentication of fish species. The results indicated that the method is suitable for the authentication of fish species from highly processed samples such as fish cakes and burgers. The study shows that current and future challenges in food and feed authentication can efficiently be tackled by reference spectral libraries method when prospecting new resources in the Arctic. [ABSTRACT FROM AUTHOR]

Quantitative proteomics via mass spectrometry can provide valuable insight into molecular and phenotypic characteristics of a living system. Recent mass spectrometry developments include data‐independent acquisition (SWATH/DIA‐MS), an accurate, sensitive and reproducible method for analysing the whole proteome. The main requirement for this method is the creation of a comprehensive spectral library. New technologies have emerged producing larger and more accurate species‐specific libraries leading to a progressive collection of proteome references for multiple molecular model species. Here, for the first time, we set out to compare different spectral library constructions using multiple tissues from a coral reef fish to demonstrate its value and feasibility for nonmodel organisms. We created a large spectral library composed of 12,553 protein groups from liver and brain tissues. Via identification of differentially expressed proteins under fish exposure to elevated pCO2 and temperature, we validated the application and usefulness of these different spectral libraries. Successful identification of significant differentially expressed proteins from different environmental exposures occurred using the library with a combination of data‐independent and data‐dependent acquisition methods as well as both tissue types. Further analysis revealed expected patterns of significantly up‐regulated heat shock proteins in a dual condition of ocean warming and acidification indicating the biological accuracy and relevance of the method. This study provides the first reference spectral library for a nonmodel organism. It represents a useful guide for future building of accurate spectral library references in nonmodel organisms allowing the discovery of ecologically relevant changes in the proteome. [ABSTRACT FROM AUTHOR]

Highlights • Regression-based fraction mapping requires mixed spectra for training. • Library-based training generates mixed training data efficiently and easily. • The proposed method outperforms map-based training approaches. • The method supports the development of generic fraction mapping workflows. • Application on Sentinel-2 image data for Brussels yields good accuracies. Abstract An improved trade-off between resolution, coverage and revisit time, makes Sentinel-2 multispectral imagery an interesting data source for mapping the composition and spatial-temporal dynamics of urban land cover. To fully realize the potential of Sentinel-2′s high amount of available data, efficient urban mapping workflows are required. Machine learning regression is a powerful approach to produce subpixel land cover fractions from remote sensing imagery, yet it requires mixed spectra for model training for which the fractions of the land cover classes present in the pixel are known. Typically, this data is obtained by sampling spectra from the image to be unmixed, and corresponding land-cover fractions from higher-resolution land cover reference data, i.e. map-based training. We propose synthetic mixing of library spectra as an alternative for producing land cover fraction training data for regression modelling, i.e. library-based training. The approach is applied to a Sentinel-2 image of the city of Brussels (Belgium) and part of its urban fringe for mapping Vegetation, Impervious, and Soil (VIS) fractions at 20 m resolution. VIS fraction maps obtained with library-based training have mean absolute errors below 0.1 for all three surface types. The composition of these three key surface categories and their spatial distribution is well represented for the entire area in resulting maps. As a proof of concept, library-based training is compared with the map-based training approach. The more flexible library-based training not only achieves similar mapping accuracies, but in most cases, outperforms the map-based training approach in terms of bias and magnitude of error. The outcome of the research suggests that use of spectral libraries and synthetic mixing may provide an efficient modelling framework for regression-based mapping from Sentinel-2 imagery in operational contexts. [ABSTRACT FROM AUTHOR]

Libraries of simulated lipid fragmentation spectra enable the identification of hundreds of unique lipids from complex lipid extracts, even when the corresponding lipid reference standards do not exist. Often, these in silico libraries are generated through expert annotation of spectra to extract and model fragmentation rules common to a given lipid class. Although useful for a given sample source or instrumental platform, the time-consuming nature of this approach renders it impractical for the growing array of dissociation techniques and instrument platforms. Here, we introduce Library Forge, a unique algorithm capable of deriving lipid fragment mass-to-charge (m/z) and intensity patterns directly from high-resolution experimental spectra with minimal user input. Library Forge exploits the modular construction of lipids to generate m/z transformed spectra in silico which reveal the underlying fragmentation pathways common to a given lipid class. By learning these fragmentation patterns directly from observed spectra, the algorithm increases lipid spectral matching confidence while reducing spectral library development time from days to minutes. We embed the algorithm within the preexisting lipid analysis architecture of LipiDex to integrate automated and robust library generation within a comprehensive LC-MS/MS lipidomics workflow. Graphical Abstract [ABSTRACT FROM AUTHOR]

Coastal sand dunes are highly dynamic aeolian landforms where different spatial patterns can be observed due to the complex interactions and relationships between landforms and land cover. Sediment distribution related to vegetation types is explored here on a single ridge dune system by using an airborne hyperspectral and light detection and ranging (LiDAR) remote sensing dataset. A correlation model is applied to describe the continuum of dune cover typologies, determine the class metrics from landscape ecology and the morphology parameters, and extract the relationship intensity among them. As a main result, the mixture of different vegetation types such as herbaceous, shrubs, and trees classes shows to be a key element for the sediment distribution pattern and a proxy for dune sediment retention capacity, and the anthropic fingerprints can play an even major role influencing both ecological and morphological features. The novelty of the approach is mostly based on the synergistic use of LiDAR with hyperspectral that allowed (i) the benefit from already existing processing methods to simplify the way to obtain thematic maps and coastal metrics and (ii) an improved detection of natural and anthropic landscape.

Future space-borne imaging spectrometers could enable global comparative analyses of urban composition. In particular, the high spectral resolution of imaging spectrometry could improve the discrimination of materials that have similar spectral signatures but are functionally dissimilar, such as turfgrass and trees. However, the amount of reflected energy needed to measure narrow-band reflectance with acceptable signal-to-noise ratios means that space-borne imaging spectrometry data will be collected at relatively coarse spatial resolutions, potentially limiting its usefulness for mapping urban composition. In this study, we use Airborne Visible Infra-Red Imaging Spectrometer-Classic and -Next Generation imaging spectrometry acquired in the summer of 2014 over the Santa Barbara, California area to quantify sub-pixel urban composition at fine (4 m) and coarse (18 m) spatial resolutions. We develop and compare spectral libraries of single- and multiple-resolution endmembers, and use Multiple Endmember Spectral Mixture Analysis to estimate sub-pixel fractions of spectrally dissimilar materials (vegetation, impervious, pervious) as well as pairs of spectrally similar materials (turfgrass and tree, paved and roof, non-photosynthetic vegetation and soil) at both resolutions. Fractions were validated using 1 m orthophotography. Overall, fractional accuracy was affected by the spatial resolution of the spectral library and image, and the (dis)similarity of the measured classes. Spectral libraries of multiple-resolution endmembers performed better than single-resolution libraries, likely because they increase within-class variance by capturing multiple levels of material variability that occur across spatial scales. A positive relationship was observed between pixel size and the number of sub-pixel materials, however significant pixel mixing occurred at 4 m resolution, with an average of 48% of all pixels modeled by more than one endmember. Fractional estimates produced by the best performing libraries at 4 m and 18 m resolution correlated with validation fractions, with mean R 2 > 0.89 for spectrally dissimilar classes and mean R 2 > 0.76 for spectrally similar classes. These results demonstrate the scalability of fractional estimates of urban materials using imaging spectrometry, suggesting its potential for future global urban analyses. [ABSTRACT FROM AUTHOR]

Semisupervised hyperspectral unmixing finds the ratio of spectral library members in the mixture of hyperspectral pixels to find the proportion of pure materials in a natural scene. The two main challenges are noise in observed spectral vectors and high mutual coherence of spectral libraries. To tackle these challenges, we propose a probabilistic sparse regression method for linear hyperspectral unmixing, which utilizes the implicit relations of neighboring pixels. We partition the hyperspectral image into rectangular patches. The sparse coefficients of pixels in each patch are assumed to be generated from a Laplacian scale mixture model with the same latent variables. These latent variables specify the probability of existence of endmembers in the mixture of each pixel. Experiments on synthetic and real hyperspectral images illustrate the superior performance of the proposed method over alternatives. [ABSTRACT FROM PUBLISHER]

A new method, based on shotgun spectral matching of peptide tandem mass spectra, was successfully applied to the identification of different food species. The method was demonstrated to work on raw as well as processed samples from 16 mammalian and 10 bird species by counting spectral matches to spectral libraries in a reference database with one spectral library per species. A phylogenetic tree could also be constructed directly from the spectra. Nearly all samples could be correctly identified at the species level, and 100% at the genus level. The method does not use any genomic information and unlike targeted methods, no prior knowledge of genetic variation within a genus or species is necessary. [ABSTRACT FROM AUTHOR]

In urban areas, information concerning very high resolution land cover and especially material maps are necessary for several city modelling or monitoring applications. That is to say, knowledge concerning the roofing materials or the different kinds of ground areas is required. Airborne remote sensing techniques appear to be convenient for providing such information at a large scale. However, results obtained using most traditional processing methods based on usual red-green-blue-near infrared multispectral images remain limited for such applications. A possible way to improve classification results is to enhance the imagery spectral resolution using superspectral or hyperspectral sensors. In this study, it is intended to design a superspectral sensor dedicated to urban materials classification and this work particularly focused on the selection of the optimal spectral band subsets for such sensor. First, reflectance spectral signatures of urban materials were collected from 7 spectral libraires. Then, spectral optimization was performed using this data set. The band selection workflow included two steps, optimising first the number of spectral bands using an incremental method and then examining several possible optimised band subsets using a stochastic algorithm. The same wrapper relevance criterion relying on a confidence measure of Random Forests classifier was used at both steps. To cope with the limited number of available spectra for several classes, additional synthetic spectra were generated from the collection of reference spectra: intra-class variability was simulated by multiplying reference spectra by a random coefficient. At the end, selected band subsets were evaluated considering the classification quality reached using a rbf svm classifier. It was confirmed that a limited band subset was sufficient to classify common urban materials. The important contribution of bands from the Short Wave Infra-Red (SWIR) spectral domain (1000 - 2400 nm) to material classification was also shown. [ABSTRACT FROM AUTHOR]

Data independent acquisition (DIA) proteomics techniques have matured enormously in recent years, thanks to multiple technical developments in, for example, instrumentation and data analysis approaches. However, there are many improvements that are still possible for DIA data in the area of the FAIR (Findability, Accessibility, Interoperability and Reusability) data principles. These include more tailored data sharing practices and open data standards since public databases and data standards for proteomics were mostly designed with DDA data in mind. Here we first describe the current state of the art in the context of FAIR data for proteomics in general, and for DIA approaches in particular. For improving the current situation for DIA data, we make the following recommendations for the future: (i) development of an open data standard for spectral libraries; (ii) make mandatory the availability of the spectral libraries used in DIA experiments in ProteomeXchange resources; (iii) improve the support for DIA data in the data standards developed by the Proteomics Standards Initiative; and (iv) improve the support for DIA datasets in ProteomeXchange resources, including more tailored metadata requirements., (© 2022 The Authors. Proteomics published by Wiley-VCH GmbH.)

In this paper, we explore the possibility of sparse regression, a new direction in unmixing, for vegetation and urban area classification. SUnSAL (Sparse unmixing via variable splitting and augmented Lagrangian) in both unconstrained and constrained forms (with the abundance non-negativity and abundance sum-to-one constraints) were used with a set of global endmembers (substrate, vegetation and dark objects) to unmix a set of computer simulated noise-free and noisy data (with Gaussian noise of different signal-to-noise ratio) in order to judge the robustness of the algorithm. The error in the fractional estimate was examined for varying noise power (variance): 2, 4, 8, 16, 32, 64, 128 and 256. In the second set of experiments, a spectrally diverse collection of 11 scenes of Level 1 terrain corrected, cloud free Landsat-5 TM data representing an agricultural setup in Fresno, California, USA were used. The corresponding ground data for validation were collected on the same days of satellite overpass. Finally in the third set of experiments, a clear sky Landsat-5 TM data for an area near the Golden Gate Bridge, San Francisco (an urbanized landscape), California, USA were used to assess the algorithm. The fractional estimates of the 30 m Landsat-5 TM data were compared with the fractional estimates of a high-resolution World View-2 data (2 m spatial resolution) obtained using a fully constrained least squares algorithm. The results were evaluated using descriptive statistics, correlation coefficient, RMSE, probability of success and bivariate distribution function, which showed that constrained model was better than unconstrained form. [ABSTRACT FROM AUTHOR]

One of the major issues with spectral mixture analysis remains the lack of ability to properly account for the spectral variability of endmembers (EMs). EM variability is most often addressed using large spectral libraries incorporating the variability present in the image. We propose a new geometric-based methodology to efficiently evaluate different binary EM combinations. Our approach selects the best EM combination prior to unmixing, building upon the equivalence between the reconstruction error in least squares unmixing and spectral angle minimization in geometric unmixing. This geometric approach is tested on both a simulated data set based on field measurements and a HyMap image. It is demonstrated that selecting the best EM combination for a pixel based on the angle minimization provided identical results compared with using the projection distance or reconstruction error. It also has the additional benefit of reducing the computation time due to the simplicity of the angle calculations. [ABSTRACT FROM PUBLISHER]

Spectral unmixing aims at finding the spectrally pure constituent materials (also called endmembers) and their respective fractional abundances in each pixel of a hyperspectral image scene. In recent years, sparse unmixing has been widely used as a reliable spectral unmixing methodology. In this approach, the observed spectral vectors are expressed as linear combinations of spectral signatures assumed to be known a priori and presented in a large collection, termed spectral library or dictionary, usually acquired in laboratory. Sparse unmixing has attracted much attention as it sidesteps two common limitations of classic spectral unmixing approaches, namely, the lack of pure pixels in hyperspectral scenes and the need to estimate the number of endmembers in a given scene, which are very difficult tasks. However, the high mutual coherence of spectral libraries, jointly with their ever-growing dimensionality, strongly limits the operational applicability of sparse unmixing. In this paper, we introduce a two-step algorithm aimed at mitigating the aforementioned limitations. The algorithm exploits the usual low dimensionality of the hyperspectral data sets. The first step, which is similar to the multiple signal classification array signal processing algorithm, identifies a subset of the library elements, which contains the endmember signatures. Because this subset has cardinality much smaller than the initial number of library elements, the sparse regression we are led to is much more well conditioned than the initial one using the complete library. The second step applies collaborative sparse regression, which is a form of structured sparse regression, exploiting the fact that only a few spectral signatures in the library are active. The effectiveness of the proposed approach, termed MUSIC-CSR, is extensively validated using both simulated and real hyperspectral data sets. [ABSTRACT FROM PUBLISHER]

Untargeted proteomics can contribute to composition and authenticity analyses of highly processed mixed food and feed products. Here, we present the setup of an analytical flow tandem mass spectrometry method (AF-HPLC HR-MS) for analysis of insect meal from five different species. Data acquired were compared with previously published data employing spectra matching and standard bottom-up proteomics bioinformatics analyses. In addition, data were screened for insect species marker peptides and common allergens, respectively. The results obtained indicate that the performance of the newly established AF-HPLC HR-MS workflow is in line with previously published methods for insect species differentiation. Data obtained in the present study, also lead to the discovery of novel markers for the development of targeted MS analyses of insect species in food- and feed-mixes and highlighted that known allergen such as arginine kinase or tropomyosin were consistently detected across all five species tested. [Display omitted] • Normal flow proteomics workflow developed for food and feed safety research. • Proteomics profiles of five insect species was analysed and reported. • QTOF and HR-MS spectral libraries for insect species authentication created. • Food allergens detected in the collected insect data. [ABSTRACT FROM AUTHOR]

Sparse unmixing has been recently introduced in hyperspectral imaging as a framework to characterize mixed pixels. It assumes that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance (e.g., spectra collected on the ground by a field spectroradiometer). Unmixing then amounts to finding the optimal subset of signatures in a (potentially very large) spectral library that can best model each mixed pixel in the scene. In this paper, we present a refinement of the sparse unmixing methodology recently introduced which exploits the usual very low number of endmembers present in real images, out of a very large library. Specifically, we adopt the collaborative (also called "multitask" or "simultaneous") sparse regression framework that improves the unmixing results by solving a joint sparse regression problem, where the sparsity is simultaneously imposed to all pixels in the data set. Our experimental results with both synthetic and real hyperspectral data sets show clearly the advantages obtained using the new joint sparse regression strategy, compared with the pixelwise independent approach. [ABSTRACT FROM AUTHOR]

In this review we will introduce the field of mass informatics, a branch of bioinformatics concerned with the analysis of data from mass spectrometry. As we shall demonstrate, this short definition hides a surprisingly diverse and challenging topic, driven by the remarkable versatility of the mass spectrometer. We first introduce the essential properties of the mass spectrum and highlight its key differences from the more common data types in high-throughput bioinformatics (sequence, microarray and image data). We then explore the breadth of biochemistry accessible through the associated algorithmic challenge of spectral identification. Finally, we demonstrate instances where data-mining techniques can be applied to large-scale spectral libraries, thereby extracting latent biological insight. [ABSTRACT FROM AUTHOR]

Abstract: The methods of linear and rank correlation and normalized coordinates (MNC) have been applied to the identification of organic solid materials with a very similar chemical composition by laser-induced breakdown spectroscopy (LIBS). The present study evaluated these three statistical methods using an Echelle spectrometer coupled with an intensified charge-coupled device (ICCD). Moreover, three instrumental parameters (laser pulse energy, delay time and integration time) were evaluated in terms of their influence on the signal-to-noise ratio of carbon and hydrogen emission lines. The probability of a right identification can be estimated by means the described methods in this paper. Methods of correlation provide better identification and discrimination than normalized coordinates at a 95% confidence level. [Copyright &y& Elsevier]

Abstract: Spectral library searching is an emerging approach in peptide identifications from tandem mass spectra, a critical step in proteomic data analysis. In spectral library searching, a spectral library is first meticulously compiled from a large collection of previously observed peptide MS/MS spectra that are conclusively assigned to their corresponding amino acid sequence. An unknown spectrum is then identified by comparing it to all the candidates in the spectral library for the most similar match. This review discusses the basic principles of spectral library building and searching, describes its advantages and limitations, and provides a primer for researchers interested in adopting this new approach in their data analysis. It will also discuss the future outlook on the evolution and utility of spectral libraries in the field of proteomics. [Copyright &y& Elsevier]

Abstract: In the literature of visible and near infrared diffuse reflectance spectroscopy (VNIR-DRS) for soil characterization, the effects of instruments and scanning environments on reflectance spectra and calibration models have not been well documented. To fill this knowledge gap, the goal of this study is to compare soil reflectance spectra and calibration models obtained with different spectrometers operated under different lab environments. Two sets of soil samples were used in this study. The first set (containing 180 samples collected from Quemado, Texas) was scanned by three spectrometers and no effort was provided to control the scanning protocol. The second set (containing 264 samples from Central Texas) was scanned by two spectrometers, and efforts were provided intentionally to control the scanning protocol. Partial least squares regression was applied to develop calibration models for soil organic carbon (OC) content using the first derivative spectra. Three calibration transfer methods (namely, slope and bias correction, direct standardization, and piecewise direct standardization) were used to transfer calibration models from one instrument to another. In the experiment where no scanning control was provided, significant differences were seen in mean soil spectra by different spectrometers. But cross-validation indicated that all three models can predict OC accurately. However, the OC models are quite dissimilar to each other in terms of the regression coefficients at each wavelength, and their application to the spectra measured by other instruments generally yielded poor results. All three calibration transfer methods provided a satisfactory application of the OC model calibrated on the primary instrument to secondary instruments. In the experiment where some controls were provided, mean soil spectra by different spectrometers matched each other well. The two OC models were quite similar, and model application to the spectra measured by the other instrument yielded satisfactory predictions. When scanning control was provided; however, model transfer methods improved the calibration model only marginally. All results indicate that VNIR-DRS calibration models are highly instrument/scanning environment dependent, and their extent of applicability could be highly limited. Provision of controls over the scanning protocol has the potential to remove a great deal of spectral variations that are related to extraneous effects due to multiple instruments/scanning environment. The results of this study have important implications on the future use of VNIR-DRS as a routine method for soil characterization, such as comparisons among VNIR prediction models derived from different soil labs and a global soil spectral library, where multiple instruments have to be involved. [Copyright &y& Elsevier]

Abstract: Traceability of animal products has become a priority for governments of the developed countries as a guarantee of food safety. Near infrared microscopy (NIRM) has been proposed as an alternative technology to detect and quantify banned ingredients in feedstuffs. The great advantage of this technique is its objectivity, whilst retaining the sensitivity of classic microscopy. The aim of this work was to build an NIRM reference spectral library on animal feed, consisting of samples of animal feed ingredients and possible contaminants, and to assess its ability to discriminate between ingredients using an internal cross-validation. A total of 48,899 spectra were measured on 229 samples representing 30 different ingredients. The method chosen for classification was K-nearest-neighbours (KNN) using first derivative spectra. Although the results showed an overall classification error of 35.88%, there was good discrimination between ingredients of animal and vegetable origin. There was some confusion between similar vegetable ingredients but this is unimportant. [Copyright &y& Elsevier]

Abstract: The management of crop residues (non-photosynthetic vegetation) in agricultural fields influences soil erosion and soil carbon sequestration. Remote sensing methods can efficiently assess crop residue cover and related tillage intensity over many fields in a region. Although the reflectance spectra of soils and crop residues are often similar in the visible, near infrared, and the lower part of the shortwave infrared (400–1900 nm) wavelength region, specific diagnostic chemical absorption features are evident in the upper shortwave infrared (1900–2500 nm) region. Two reflectance band height indices used for estimating residue cover are the Cellulose Absorption Index (CAI) and the Lignin-Cellulose Absorption (LCA) index, both of which use reflectances in the upper shortwave infrared (SWIR). Soil mineralogy and composition will affect soil spectral properties and may limit the usefulness of these spectral indices in certain areas. Our objectives were to (1) identify minerals and soil components with absorption features in the 2000 nm to 2400 nm wavelength region that would affect CAI and LCA and (2) assess their potential impact on remote sensing estimates of crop residue cover. Most common soil minerals had CAI values≤0.5, whereas crop residues were always >0.5, allowing for good contrast between soils and residues. However, a number of common soil minerals had LCA values>0.5, and, in some cases, the mineral LCA values were greater than those of the crop residues, which could limit the effectiveness of LCA for residue cover estimation. The LCA of some dry residues and live corn canopies were similar in value, unlike CAI. Thus, the Normalized Difference Vegetation Index (NDVI) or similar method should be used to separate out green vegetation pixels. Mineral groups, such as garnets and chlorites, often have wide ranges of CAI and LCA values, and thus, mineralogical analyses often do not identify individual mineral species required for precise CAI estimation. However, these methods are still useful for identifying mineral soils requiring additional scrutiny. Future advanced multi- and hyperspectral remote sensing platforms should include CAI bands to allow for crop residue cover estimation. [Copyright &y& Elsevier]

Abstract: Spectral libraries are commonly established as a means to archive representative signatures of natural materials. Such signatures can then be used to train feature extraction and classification algorithms applied to imagery, for comparison with unlabeled spectra. A number of spectral libraries are publicly available and widely used in the community. Disparities in viewing and illumination measurement configurations between libraries generally preclude the direct comparison of spectra for the same materials. Within libraries, measurements may be reported for varying sample properties, such as grain size in the case of powdered minerals or leaf or canopy structure in the case of vegetation. In such instances, use of the library and the selection of representative spectra to identify an unknown material may require a priori knowledge or an educated guess of the physical properties of the unknown material to conduct the comparison. This study demonstrates that continuous wavelet analysis can provide a new and useful representation of spectral libraries and minimize these disparities amongst libraries. In the context of spectral mixture analysis we suggest that the selection of representative endmember spectra from spectral libraries can be more readily defined in the wavelet domain than using reflectance data. In the context of sensing target compositional variability, for example changes in the chemistry of a given mineral, spectral differences due to distinct sample composition are more readily identified using wavelets. The examples provided in this paper are mainly for powdered mineral spectra because there are a number of widely known public spectral libraries of powdered minerals that have been in common use in the hyperspectral community but the principles apply to a range of natural materials including vegetation. [Copyright &y& Elsevier]

The aim of the present study was the rapid identification of alloys used in the manufacture of jewelry pieces with the help of a spectral library. The laser-induced breakdown spectra of 32 alloys were stored, with 25 of them chosen as library standards; the remaining seven spectra were used as samples. The composition of the alloys was obtained by flame atomic absorption spectrometry. A rank correlation method was applied for comparison between spectra, providing good correlation coefficients for the alloys studied. The composition of the samples was also predicted by partial least-squares regression to demonstrate the capability of this technique for the rapid analysis of this type of material. [Copyright &y& Elsevier]

Spectrum clustering is a powerful strategy to minimize redundant mass spectra by grouping them based on similarity, with the aim of forming groups of mass spectra from the same repeatedly measured analytes. Each such group of near-identical spectra can be represented by its so-called consensus spectrum for downstream processing. Although several algorithms for spectrum clustering have been adequately benchmarked and tested, the influence of the consensus spectrum generation step is rarely evaluated. Here, we present an implementation and benchmark of common consensus spectrum algorithms, including spectrum averaging, spectrum binning, the most similar spectrum, and the best-identified spectrum. We have analyzed diverse public data sets using two different clustering algorithms (spectra-cluster and MaRaCluster) to evaluate how the consensus spectrum generation procedure influences downstream peptide identification. The BEST and BIN methods were found the most reliable methods for consensus spectrum generation, including for data sets with post-translational modifications (PTM) such as phosphorylation. All source code and data of the present study are freely available on GitHub at https://github.com/statisticalbiotechnology/representative-spectra-benchmark.