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Spectral intensity drift is a frequent issue in analytical processes, especially in long time excitation scanning for large size metal materials, which can significantly adversely impact the accuracy and stability of analysis results. Spectral intensity drift correction is the process of preprocessing spectral data using mathematical algorithms in order to facilitate the subsequent qualitative and quantitative analysis of spectra, especially in combination with stoichiometric methods. Up to now, spectral intensity drift correction within prolonged excitation has not been reported yet. We propose an intensity drift correction method for element content of large-size samples using the Spark Mapping Analysis for Large Samples (SMALS) technique. By considering the row-by-row and column-by-column mapping modes of the SMALS, this includes curve fitting baseline correction for in-row and in-column correction, as well as total average value correction for inter-row and inter-column correction. The final measurement values are derived by coupling rows with columns. The careful implementation of correction steps can enhance baseline correction performance, effectively reducing measurement errors a drift errors. Application of this method to characterize the cross and longitudinal sections of an oversized steel billet indicates high agreement with composition distribution obtained by micro-beam X-ray fluorescence (μ-XRF). The corrected longitudinal and cross-sectional data also exhibit strong alignment. Comparison of statistical analysis results pre- and post-correction demonstrates significant improvements in the clarity of elements segregation pattern. This intensity drift correction method not only enhances the spectral quality but also improves the accuracy and robustness of quantitative and qualitative spectral analysis. This study contributes to establishing a robust foundation for component characterization of large-size metal materials using the SMALS technique. The novel spectral intensity correction method shows theoretical significance and practical value for large-scale, long-duration excitation scanning analysis. [Display omitted] • The measurement error was eliminated by in-row/in-column, inter-row/inter-column correction and complementary coupling. • The intensity drift correction method significantly improves the accuracy and stability of analysis results of the SMALS. • This work laid a firm foundation of component characterization for large-size metal material using the SMALS. [ABSTRACT FROM AUTHOR]

The utilization of near-infrared (NIR) spectroscopy, in conjunction with chemometric techniques, has been widely used in a variety of sectors, including agricultural production and pharmaceutical production. Nevertheless, the laborious and arduous procedure of gathering samples and evaluating their physicochemical properties leads to relatively limited training set sizes for modeling. This problem severely limits the optimization and practical application of NIR spectrum analysis models. The Safer Active Semi-Supervised Sample Augmentation Learning Model (Safer-AS3A) proposed in this paper tries to address the problem by incorporating active learning (AL) and semi-supervised learning (SSL) techniques. Experiments were conducted on two sets of publicly available NIR spectral datasets, and the Safer-AS3A model was compared to other models with similar characteristics. The experimental results indicate that the Safer-AS3A model proposed in our study outperforms comparable models in terms of accuracy and robustness when dealing with scenarios having a limited number of labeled samples. Furthermore, after the training set was expanded with the Safer-AS3A model, the Partial least squares regression (PLSR), Bayesian ridge regression (BRR), and Support vector regression (SVR) models on the Diesel dataset improved their R 2 on the test set by 5.923 %, 3.018 %, and 7.331 %, respectively, compared to the models using only the labeled sample set. On the other hand, the Ridge regression (RR), BRR, and SVR models on the test set on the Shoot dataset improved the R 2 by 4.169 %, 4.449 %, and 11.597 %, respectively. Overall, the Safer-AS3A model can effectively expand the NIR spectral dataset and considerably improve the performance of the NIR spectral analysis model. Using the AL method, the SSL method, and the co-training method together, a novel and effective method is presented for generating high-quality pseudo-labels. This method opens up new avenues for enhancing the efficiency and precision of NIR spectrum analysis. It also provides novel perspectives on sample diversification and prospective applications in other disciplines. [Display omitted] • A deep active learning method is developed to analyze near-infrared spectroscopy data. • The stability principle and voting mechanism are added to the confidence evaluation of pseudo-labeling. • The co-training method effectively alleviates the problem of insufficient labeled spectral samples. [ABSTRACT FROM AUTHOR]

Near infrared spectroscopy (NIRS) is an analytical technique for determining the chemical composition or structure of a given sample. For several decades, NIRS has been a frequently used analysis tool in agriculture, pharmacology, medicine, and petrochemistry. The popularity of NIRS is constantly growing as new application areas are discovered. Contrary to mid infrared spectral region, the absorption bands in near infrared spectral region are often non-specific, broad, and overlapping. Analysis of NIR spectra requires multivariate methods which are highly subjective to noise arising from instrumentation, scattering effects, and measurement setup. NIRS measurements are also frequently performed outside of a laboratory which further contributes to the presence of noise. Therefore, preprocessing is a critical step in NIRS as it can vastly improve the performance of multivariate models. While extensive research regarding various preprocessing methods exists, selection of the best preprocessing method is often determined through trial-and-error. A more powerful approach for optimizing preprocessing in NIRS models would be to automatically compare a large number of preprocessing techniques (e.g., through grid-search or hyperparameter tuning). To enable this, we present, nippy, an open-source Python module for semi-automatic comparison of NIRS preprocessing methods (available at https://github.com/uef-bbc/nippy). We provide here a brief overview of the capabilities of nippy and demonstrate the typical usage through two examples with public datasets. Image 1 • Spectral preprocessing affects the performance of chemometric models. • Selection of best preprocessing strategy depends heavily on the intended application. • nippy module can rapidly explore different preprocessing combinations. • Semi-automatic preprocessing tuning for spectroscopic models. [ABSTRACT FROM AUTHOR]

We demonstrate a recognition and feature visualization method that uses a deep convolutional neural network for Raman spectrum analysis. The visualization is achieved by calculating important regions in the spectra from weights in pooling and fully-connected layers. The method is first examined for simple Lorentzian spectra, then applied to the spectra of pharmaceutical compounds and numerically mixed amino acids. We investigate the effects of the size and number of convolution filters on the extracted regions for Raman-peak signals using the Lorentzian spectra. It is confirmed that the Raman peak contributes to the recognition by visualizing the extracted features. A near-zero weight value is obtained at the background level region, which appears to be used for baseline correction. Common component extraction is confirmed by an evaluation of numerically mixed amino acid spectra. High weight values at the common peaks and negative values at the distinctive peaks appear, even though the model is given one-hot vectors as the training labels (without a mix ratio). This proposed method is potentially suitable for applications such as the validation of trained models, ensuring the reliability of common component extraction from compound samples for spectral analysis. Image 1 • A recognition and trained feature visualization that uses a CNN are demonstrated. • Raman peak extraction and near-zero feature value at background region are obtained. • Common component and extraordinary peak extraction from mixed spectra are confirmed. [ABSTRACT FROM AUTHOR]

The determination accuracy of alloying elements in high alloy steel is generally poor in laser-induced breakdown spectroscopy (LIBS) due to their matrix effect. To solve this problem, an image quantitative analysis (IQA) method was proposed and verified by determining nickel (Ni) in 17 stainless steel samples in this work. The results showed that the coefficient of determination (R2) was increased from 0.9833 of a conventional spectrum quantitative analysis (SQA) method to 0.9996 of the IQA method, and the average relative error of cross-validation (ARECV) and root mean squared error of cross-validation (RMSECV) were decreased from 56.80% and 1.0818 wt% to 15.93% and 0.9866 wt%, respectively. Besides, the determinations of chromium (Cr) and silicon (Si) demonstrated the generalization ability of the IQA. This study provides an effective approach to improving the quantitative performance of LIBS through the combination of image processing and computer vision technology. Image 1 • A new quantitative analysis method named IQA is first proposed in LIBS. • Extracting spectral features indirectly by image features from LIBS image. • The quantitative analysis performance of image features is proved. • Significantly reduce the influence of matrix effects in the quantitative analysis of stainless steels. [ABSTRACT FROM AUTHOR]

In a qualitative analysis of near-infrared spectroscopy (NIRS), when the samples to be analyzed are difficult to obtain or there are few counterexamples, the robustness of the models is poor, resulting in the decline of the generalization ability of the models. In this case, the effective method is to construct virtual samples to achieve the balance of categories. In this contribution, three virtual spectrum construction strategies including Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling (ADASYN), and Deep Convolutional Generative Adversarial Network (DCGAN) were explored to deal with the problem of insufficient or imbalanced sample numbers in NIRS analysis. The strategies were tested with the melamine and Yali pears two spectral datasets. The PLS-DA and Correct Recognition Rate (CRR) were used for discriminant model construction and accuracy evaluation, respectively. The results show that SMOTE, ADASYN, and DCGAN processing strategies can all improve the global CRR (CRR glob). The SMOTE and ADASYN can improve the CRR for majority class sample (CRR maj), but the CRR for minority class sample (CRR min) has decreased. For the DCGAN method, the CRR glob , CRR maj , and CRR min were all improved. The standard deviation of the results of the multiple parallel calculations demonstrates the robustness of DCGAN generation method. Therefore, the DCGAN method has good reliability and practicability, and can increase the robustness and generalization ability of the NIRS model. [Display omitted] • Exploring virtual spectrum construction strategies. • Evaluate the plausibility and diversity of the virtual spectrum. • Improve the robustness and generalization ability of the NIRS model. • Test model performance on public datasets and experimental data. [ABSTRACT FROM AUTHOR]

Abstract Protein-surfactant interactions have been explored for decades owing to their widespread application in the pharmaceutical, food, and cosmetics industries and their importance to biochemical systems. However, they require further study owing to their compositional complexity and the innate limitations of current analytical approaches. In this review, we briefly introduce a series of individual approaches used for the qualitative and quantitative investigation of protein-surfactant interactions, including absorbance- or emission-based spectroscopy, scattering-based spectroscopy, mass spectrometry, calorimetry, computation and microscopy. More importantly, we then compare and evaluate various combinations of these approaches and provide comprehensive critical assessments and comments regarding their application to the advanced study of protein-surfactant interactions at the molecular level. Graphical abstract Image 1 Highlights • Investigation of protein-surfactant interactions are important in chemistry. • Individual analytical approaches have limitations. • Overview of individual analytical approaches: cons and pros. • Combinations of current individual approaches to overcome their limitations. • Assessment of the efficiency and validity of combinations of analytical approaches. [ABSTRACT FROM AUTHOR]

Abstract The prospect of developing new bio-based products from lignin partially depends on the development of effective analytical techniques to characterize the end products from lignin degradation experiments. To date, the most utilized mass spectrometric technique for characterizing lignin oligomers has been negative ion mass spectrometry. Positive ion mass spectrometry remains a relatively unexplored approach for lignin sequencing. Here we report on the sequencing of lignin oligomers using lithium cationization positive ion electrospray followed by higher-energy collisional dissociation (HCD) tandem mass spectrometry on a high-resolution accurate-mass Orbitrap mass spectrometer. Thirteen synthetic lignin model oligomers containing β-O-4 and 4-O-α linkages were analyzed and sequenced based on their HCD tandem mass spectrometry fragmentation patterns. Proposed sequence-specific fragmentation pathways are provided. The method presented in this study provides a potential application of lithium cationization tandem mass spectrometry for the analysis of lignin degradation products. Graphical abstract Image 1 Highlights • Lithium forms stable adducts with lignin oligomers generating cations for positive ion mass spectrometry. • Tandem mass spectrometry of lithium-lignin precursors produces lithiated fragment ions. • The monolignol sequence of an oligomer can be determined from the tandem mass spectrum. • Lithium cationization provides an alternative means of lignin oligomer ionization that retains sequencing features. [ABSTRACT FROM AUTHOR]

Abstract In this review, an assessment of non-separative methods based on mass spectrometry used to analyse volatile organic compounds in the field of bioanalysis is performed. The use of non-separative methods based on mass spectrometry has been established as an attractive option for analysing compounds. These instrumental configurations are suitable for biomedical applications because of their versatility, rapid output of results, and the wide range of volatile organic compounds that can be determined. Here, techniques such as headspace sampling coupled to mass spectrometry, membrane introduction mass spectrometry, selected ion flow tube mass spectrometry, proton transfer reaction mass spectrometry, secondary electrospray ionization mass spectrometry and ion mobility mass spectrometry, are evaluated. Samples involving non-invasive methods of collection, such as urine, saliva, breath and sweat, are mainly considered. To the best of our knowledge, a comprehensive review of all the non-separative instrumental configurations applied to the analysis of gaseous samples from all matrices non-invasively collected has not yet been carried out. The assessment of non-separative techniques for the analysis of these type of samples can be considered a key issue for future clinical applications, as they allow real-time sample analysis, without patient suffering. Any contribution to the early diagnosis of disease can be considered a priority for the scientific community. Therefore, the identification and determination of volatile organic compounds related to particular diseases has become an important field or research. Graphical abstract Image 1 Highlights • Advantages and disadvantages of using different non-separative methods in bioanalysis are presented. • Potential of VOCs determination using methods based on mass spectrometry are discussed. • The use of samples involving non-invasive sample collection is a key issue for clinical applications. [ABSTRACT FROM AUTHOR]

The resolution and quantitation of pure spectra of minority components in measurements of chemical mixtures without prior knowledge of the mixture is a challenging problem. In this work, a combination of band target entropy minimization (BTEM) and target partial least squares (T-PLS) was used to obtain estimates for single pure component spectra and to calibrate those estimates in a true, one-at-a-time fashion. This approach allows for minor components to be targeted and their relative amounts estimated in the presence of other varying components in spectral data. The use of T-PLS estimation is an improvement to the BTEM method because it overcomes the need to identify all of the pure components prior to estimation. Estimated amounts from this combination were found to be similar to those obtained from a standard method, multivariate curve resolution-alternating least squares (MCR-ALS), on a simple, three component mixture dataset. Studies from two experimental datasets demonstrate where the combination of BTEM and T-PLS was used to model the pure component spectra and to obtain concentration profiles of minor components, but MCR-ALS could not. [ABSTRACT FROM AUTHOR]

Mass spectrometry imaging (MSI) is a technique that can map analyte spatial distribution directly onto a tissue section. This enables the spatial correlation of molecular entities with a tissue morphology to be investigated. Analyte annotation in MSI is intrinsically linked to the mass accuracy of the data. Mass accuracy and analyte identification are determined by such factors as the experimental set up and the data processing workflow. We present an MSI data processing workflow that uses a label-free approach to compensate for mass shifts. The algorithms developed were designed to perform efficiently even for datasets much larger than computer's memory. Herein, we present the application of the developed processing workflow to a dataset with more than 13.000 pixels and ∼50.000 mass channels. We assessed the overall mass accuracy in the range m/z 400 to 1200 using silver and gold sputtered nanolayers. With our novel processing workflow we were able to obtain mass errors as low as 5 ppm using a TOF instrument. [ABSTRACT FROM AUTHOR]

Principal Component Analysis (PCA) is widely used in analytical chemistry, to reduce the dimensionality of a multivariate data set in a few Principal Components (PCs) that summarize the predominant patterns in the data. An accurate estimate of the number of PCs is indispensable to provide meaningful interpretations and extract useful information. We show how existing estimates for the number of PCs may fall short for datasets with considerable coherence, noise or outlier presence. We present here how Angle Distribution of the Loading Subspaces (ADLS) can be used to estimate the number of PCs based on the variability of loading subspace across bootstrap resamples. Based on comprehensive comparisons with other well-known methods applied on simulated dataset, we show that ADLS (1) may quantify the stability of a PCA model with several numbers of PCs simultaneously; (2) better estimate the appropriate number of PCs when compared with the cross-validation and scree plot methods, specifically for coherent data, and (3) facilitate integrated outlier detection, which we introduce in this manuscript. We, in addition, demonstrate how the analysis of different types of real-life spectroscopic datasets may benefit from these advantages of ADLS. [ABSTRACT FROM AUTHOR]

Generative adversarial linear discriminant analysis (GALDA) is formulated as a broadly applicable tool for increasing classification accuracy and reducing overfitting in spectrochemical analysis. Although inspired by the successes of generative adversarial neural networks (GANs) for minimizing overfitting artifacts in artificial neural networks, GALDA was built around an independent linear algebra framework distinct from those in GANs. In contrast to feature extraction and data reduction approaches for minimizing overfitting, GALDA performs data augmentation by identifying and adversarially excluding the regions in spectral space in which genuine data do not reside. Relative to non-adversarial analogs, loading plots for dimension reduction showed significant smoothing and more prominent features aligned with spectral peaks following generative adversarial optimization. Classification accuracy was evaluated for GALDA together with other commonly available supervised and unsupervised methods for dimension reduction in simulated spectra generated using an open-source Raman database (Romanian Database of Raman Spectroscopy, RDRS). Spectral analysis was then performed for microscopy measurements of microsphereroids of the blood thinner clopidogrel bisulfate and in THz Raman imaging of common constituents in aspirin tablets. From these collective results, the potential scope of use for GALDA is critically evaluated relative to alternative established spectral dimension reduction and classification methods. [Display omitted] • Generative adversarial linear discriminant analysis (GALDA) was developed for spectral classification. • A theoretical foundation for implementing GALDA for spectral dimension reduction and classification was derived. • Simulations with known ground truth spectral classes supported the assessment of GALDA. • Application of GALDA improved classification accuracy in polymorph discrimination by conventional Raman spectroscopy. • Pixel-wise application of GALDA produced composition maps in good agreement with manual assignments for model system. [ABSTRACT FROM AUTHOR]

The modification of graphene with biomacromolecules like DNA, protein, peptide, and others extends the potential applications of graphene materials in various fields. The bound biomacromolecules could improve the biocompatibility and bio-recognition ability of graphene-based nanocomposites, therefore could greatly enhance their biosensing performances on both selectivity and sensitivity. In this review, we presented a comprehensive introduction and discussion on recent advance in the synthesis and biosensor applications of graphene-peptide nanocomposites. The biofunctionalization of graphene with specifically designed peptides, and the synthesis strategies of graphene-peptide (monomer, nanofibrils, and nanotubes) nanocomposites were demonstrated. On the other hand, the fabrication of graphene-peptide nanocomposite based biosensor architectures for electrochemical, fluorescent, electronic, and spectroscopic biosensing were further presented. This review includes nearly all the studies on the fabrication and applications of graphene-peptide based biosensors recently, which will promote the future developments of graphene-based biosensors in biomedical detection and environmental analysis. [ABSTRACT FROM AUTHOR]

Peak broadening in ion mobility (IM) is a relatively predictable process and abnormally broad peaks can be indicative of the presence of unresolved species. Here, we introduce a new ion mobility peak fitting (IM_FIT) software package for automated and systematic determination of traveling wave ion mobility (TWIM) unresolved species. To identify IM unresolved species, the IM_FIT software generates a trend line by plotting ions' mobility peak widths as a function of their arrival times. Utilizing user-defined thresholds, IM_FIT allows for automated and rapid detection of ions that deviate from the peak width trend line. To demonstrate the advantages of IM_FIT for automated detection of IM unresolved species, IM-mass spectrometry (IM-MS) data from a sample mixture containing polypropylene glycol and multiple peptides were analyzed. A total of 14 out of the 34 observed singly-charged IM peaks above 5% relative abundance ( i.e., signal-to-noise ratios above ∼200) were tagged as potentially co-eluting ions by IM_FIT. Subsequently, the 14 IM peaks tagged as potentially unresolved (presumably, peaks corresponding to co-eluting compounds), were further analyzed by automated IM deconvolution (AIMD), liquid chromatography-IM-MS (LC-IM-MS), and/or ultra-high resolution mass spectrometry. Using the aforementioned techniques, more than 85% of the tagged IM peaks (12 out of 14) were confirmed to contain co-eluting ions. As an additional new finding, IM_FIT facilitated the discovery of an unexpected sequence-scrambled y-type fragment ion. [ABSTRACT FROM AUTHOR]

Many chemometric tools are invaluable and have proven effective in data mining and substantial dimensionality reduction of highly multivariate data. This becomes vital for interpreting various physicochemical data due to rapid development of advanced analytical techniques, delivering much information in a single measurement run. This concerns especially spectra, which are frequently used as the subject of comparative analysis in e.g. forensic sciences. In the presented study the microtraces collected from the scenarios of hit-and-run accidents were analysed. Plastic containers and automotive plastics (e.g. bumpers, headlamp lenses) were subjected to Fourier transform infrared spectrometry and car paints were analysed using Raman spectroscopy. In the forensic context analytical results must be interpreted and reported according to the standards of the interpretation schemes acknowledged in forensic sciences using the likelihood ratio approach. However, for proper construction of LR models for highly multivariate data, such as spectra, chemometric tools must be employed for substantial data compression. Conversion from classical feature representation to distance representation was proposed for revealing hidden data peculiarities and linear discriminant analysis was further applied for minimising the within-sample variability while maximising the between-sample variability. Both techniques enabled substantial reduction of data dimensionality. Univariate and multivariate likelihood ratio models were proposed for such data. It was shown that the combination of chemometric tools and the likelihood ratio approach is capable of solving the comparison problem of highly multivariate and correlated data after proper extraction of the most relevant features and variance information hidden in the data structure. [ABSTRACT FROM AUTHOR]

Sweat has recently gained popularity as clinical sample in metabolomics analysis as it is a non-invasive biofluid the composition of which could be modified by certain pathologies, as is the case with cystic fibrosis that increases chloride levels in sweat. However, the whole composition of sweat is still unknown and there is a lack of analytical strategies for sweat analysis. The aim of the present study was to develop and validate a method for metabolomic analysis of human sweat by gas chromatography–time of flight/mass spectrometry (GC–TOF/MS) in high resolution mode. Thus, different sample preparation strategies were compared to check their effect on the profile of sweat metabolites. Sixty-six compounds were tentatively identified by the obtained MS information. Amino acids, dicarboxylic acids and other interesting metabolites such as myo-inositol and urocanic acid were identified. Among the tested protocols, methyoxiamination plus silylation after deproteinization was the most suited option to obtain a representative snapshot of sweat metabolome. The intra-day repeatability of the method ranged from 0.60 to 16.99% and the inter-day repeatability from 2.75 to 31.25%. As most of the identified metabolites are involved in key biochemical pathways, this study opens new possibilities to the use of sweat as a source of metabolite biomarkers of specific disorders. [ABSTRACT FROM AUTHOR]

The use of reference solutions dispersed on filter paper discs is proposed for the first time as an external calibration strategy for matrix matching and determination of As, Cd, Co, Cr, Cu, Mn, Ni, Pb, Sr, V and Zn in plants by laser ablation-inductively coupled plasma mass spectrometry (LA-ICP-MS). The procedure is based on the use of filter paper discs as support for aqueous reference solutions, which are further evaporated, resulting in solid standards with concentrations up to 250 μg g −1 of each element. The use of filter paper for calibration is proposed as matrix matched standards due to the similarities of this material with botanical samples, regarding to carbon concentration and its distribution through both matrices. These characteristics allowed the use of 13 C as internal standard (IS) during the analysis by LA-ICP-MS. In this way, parameters as analyte signal normalization with 13 C, carrier gas flow rate, laser energy, spot size, and calibration range were monitored. The calibration procedure using solution deposition on filter paper discs resulted in precision improvement when 13 C was used as IS. The method precision was calculated by the analysis of a certified reference material (CRM) of botanical matrix, considering the RSD obtained for 5 line scans and was lower than 20%. Accuracy of LA-ICP-MS determinations were evaluated by analysis of four CRM pellets of botanical composition, as well as by comparison with results obtained by ICP-MS using solution nebulization after microwave assisted digestion. Plant samples of unknown elemental composition were analyzed by the proposed LA method and good agreement were obtained with results of solution analysis. Limits of detection (LOD) established for LA-ICP-MS were obtained by the ablation of 10 lines on the filter paper disc containing 40 μL of 5% HNO 3 (v v −1 ) as calibration blank. Values ranged from 0.05 to 0.81 μg g −1 . Overall, the use of filter paper as support for dried aqueous standards showed to be a useful strategy for calibration and plant analysis by LA-ICP-MS. [ABSTRACT FROM AUTHOR]

Using HG – AFS as a powerful tool to study valence transformations of Te, we found that, in presence of HCl and at high temperature, Te can form volatile species and be lost during sample digestion and pre-reduction steps. It was also noticed that the chemical valences of Te can be modified under different chemical and digestion conditions and even by samples themselves with certain matrices. KBr can reduce Te(VI) to Te(IV) in 3.0 M HCl at 100 °C, but when HNO 3 was >5% (v/v) in solution, Br 2 was formed and caused serious interference to Te measurements. HCl alone can also pre-reduce Te(VI) to Te(IV), only when its concentration was ≥6.0 M (100 °C for 15min). Among 10 studied chemical elements, only Cu 2+ caused severe interference. Thiourea is an effective masking agent only when Cu 2+ concentration is equal or lower than 10 mg/L. Chemical reagents, chemical composition of sample, as well as the modes of digestion can greatly affect Te valences, reagent blanks and analytical precisions. A protocol of 2–step–digestion followed by an elimination of HF is proposed to minimize reagent blank and increase the signal/noise ratios. It is important to perform a preliminary test to confirm whether a pre-reduction step is necessary; this is especially true for samples with complex matrices such as those with high sulfide content. The analytical detection limits of this method in a pure solution and a solid sample were 100 ng/L and 0.10 ± 0.02 μg/g, respectively. [ABSTRACT FROM AUTHOR]

Near infrared spectroscopy is an overtone spectroscopy regarded as a quick and non-destructive method that provides analytical solutions for components that represent approximately 1% or more of the total mass of the investigated composite samples. Aquaphotomics offers the possibility for disentanglement of information remaining hidden in the spectra when conventional data evaluation methods are used, since this concept utilizes changes of the water structure induced by the measured solute as specific molecular vibrations at water bands. Here, near infrared technique and aquaphotomics are applied for non-destructive identification and quantification of mono- and di-saccharide solutes at 100–0.02 mM concentration that is accepted as unachievable with near infrared spectroscopy. The results presented in this study support the aquaphotomics' water molecular mirror concept that explores spectral changes related to water molecular rearrangements caused by minute changes of the solutes in the aqueous systems. The method provides quick and accurate alternative for classical analytical measurements of saccharides even at millimolar concentration levels. [ABSTRACT FROM AUTHOR]

Femtosecond laser-induced breakdown spectroscopy (fs-LIBS) has been used for the first time for quantitative determination of nutrients in plant materials from different crops. A highly heterogeneous population of 31 samples, previously analyzed by inductively coupled plasma optical emission spectroscopy, covering a wide range of matrices was interrogated. To tackle the analysis, laser-induced plasmas under argon atmosphere of pellets prepared from sieved cryogenically ground leaves were studied. Predictive functions based on univariate and multivariate modeling of optical emissions associated to macro- (Ca, Mg, and P) and micronutrients (Cu, Fe, Mn and Zn) were designed. Hierarchical cluster analysis was performed to select representative calibration ( n cal = 17) and validation ( n val = 14) datasets. The predictive performance of calibration functions over fs-LIBS data was compared with that attained on spectral information from nanosecond LIBS (ns-LIBS) operating at different wavelengths (1064 nm, 532 nm, and 266 nm). Findings established higher accuracy and less uncertainty on mass fractions quantification from fs-LIBS, whatever the modeling approach. Quality coefficients below 20% for the accuracy error on mass fractions’ prediction in unknown samples, and residual predictive deviations in general above 5, were obtained. In contrast, only multivariate modeling satisfactorily handled the non-linear variations of emissions in ns-LIBS, leading to 2-fold decrease in the root mean square error of prediction (RMSEP) of Ca, Mg, P, Cu, Fe, Mn and Zn in comparison with the univariate approach. But still, an averaged quality coefficient about 35% and residual predictive deviations below 3 were found. Similar predictive capabilities were observed when changing the laser wavelength. Although predicted values by ns-LIBS multivariate modeling exhibit better agreement with reference mass fractions as compared to univariate functions, fs-LIBS conducts better quantification of nutrients in plant materials since it is less dependent on the chemical composition of the matrices. [ABSTRACT FROM AUTHOR]

The successive projections algorithm (SPA) is widely used to select variables for multiple linear regression (MLR) modeling. However, SPA used only once may not obtain all the useful information of the full spectra, because the number of selected variables cannot exceed the number of calibration samples in the SPA algorithm. Therefore, the SPA-MLR method risks the loss of useful information. To make a full use of the useful information in the spectra, a new method named “consensus SPA-MLR” (C-SPA-MLR) is proposed herein. This method is the combination of consensus strategy and SPA-MLR method. In the C-SPA-MLR method, SPA-MLR is used to construct member models with different subsets of variables, which are selected from the remaining variables iteratively. A consensus prediction is obtained by combining the predictions of the member models. The proposed method is evaluated by analyzing the near infrared (NIR) spectra of corn and diesel. The results of C-SPA-MLR method showed a better prediction performance compared with the SPA-MLR and full-spectra PLS methods. Moreover, these results could serve as a reference for combination the consensus strategy and other variable selection methods when analyzing NIR spectra and other spectroscopic techniques. [ABSTRACT FROM AUTHOR]

A new side-reaction occurring during divergent synthesis of PAMAM dendrimers (generations G 0 –G 2 ) was revealed by mass spectrometric detection of defective molecules with a net gain of a single carbon atom as compared to expected compounds. Combining MS/MS experiments performed on different electrosprayed precursor ions (protonated molecules and lithiated adducts) with NMR analyses allowed the origin of these by-products to be elucidated. Modification of one ethylenediamine end-group of perfect dendrimers into a cyclic imidazolidine moiety was induced by formaldehyde present at trace level in the methanol solvent used as the synthesis medium. Dendrimers studied here were purposely constructed from a triethanolamine core to make them more flexible, as compared to NH 3 - or ethylenediamine-core PAMAM, and hence improve their interaction with DNA. Occurrence of this side-reaction would be favored by the particular flexibility of the dendrimer branches. [ABSTRACT FROM AUTHOR]

A critical step in multivariate calibration is wavelength selection, which is used to build models with better prediction performance when applied to spectral data. Up to now, many feature selection techniques have been developed. Among all different types of feature selection techniques, those based on swarm intelligence optimization methodologies are more interesting since they are usually simulated based on animal and insect life behavior to, e.g., find the shortest path between a food source and their nests. This decision is made by a crowd, leading to a more robust model with less falling in local minima during the optimization cycle. This paper represents a novel feature selection approach to the selection of spectroscopic data, leading to more robust calibration models. The performance of the firefly algorithm, a swarm intelligence paradigm, was evaluated and compared with genetic algorithm and particle swarm optimization. All three techniques were coupled with partial least squares (PLS) and applied to three spectroscopic data sets. They demonstrate improved prediction results in comparison to when only a PLS model was built using all wavelengths. Results show that firefly algorithm as a novel swarm paradigm leads to a lower number of selected wavelengths while the prediction performance of built PLS stays the same. [ABSTRACT FROM AUTHOR]

The selection of a training set is the key to determining the quality of the model. In the spectrum analysis, due to various interference factors, the quality of the collected spectral data of some samples has a serious deviation. If directly used in modeling, it will introduce bias to the establishment of the model. Therefore, to get the most representative samples, it is necessary to select samples before establishing the model. This paper proposes a two-dimensional sample selection (TDSS) method, which selects samples from two angles of spectral data quality and variable correlation. This method and Mahalanobis distance method were respectively applied to dynamic spectrum (DS) data to screen samples. The samples screened by the two methods were used for modeling. Finally, establish partial least squares (PLS) linear regression model with a quadratic nonlinear correction method to predict the target components. The experimental results show that the sample screening method significantly improved the accuracy and prediction performance of the model, and it is better than the Mahalanobis distance method. In the prediction of triglyceride and total cholesterol, the correlation coefficient can reach above 0.82. The experimental results fully prove the effectiveness of the sample selection method in this paper, and it has a remarkable effect on improving the accuracy and robustness of the model. This paper provides a new way for sample selection of modeling set in spectral analysis of complex solutions. [Display omitted] • Propose a new training set sample screening method. • Consider two dimensions: sample data quality and variable correlation. • Based on statistical and dynamic spectrum data. • Improve the accuracy of non-invasive blood component detection. • This method is a universal method for sample screening in spectral analysis. [ABSTRACT FROM AUTHOR]

Highlights: [•] The elemental analysis of biodiesel by the proposed method is simple and fast. [•] Two-nozzles Flow Blurring® nebulizer allow to reduce the spectral interferences. [•] Two-nozzles Flow Blurring® nebulizer avoid the formation of carbon deposits. [•] The analysis may be carried out without any sample pretreatment. [ABSTRACT FROM AUTHOR]

Highlights: [•] We present a comprehensive comparative study of nonlinear calibration techniques. [•] The methods are connected in terms of traditional calibration by ridge regression. [•] Three real-life near infrared (NIR) data sets are used for comparison of the methods. [•] Different practical aspects of the methods are discussed for spectral analysis. [ABSTRACT FROM AUTHOR]

Highlights: [•] Ambient mass spectrometry allows direct analysis of polymers and their additives. [•] Ambient sampling methods offer many advantages over classical mass spectrometry. [•] Capabilities, limitations, and future perspectives of each method are discussed. [Copyright &y& Elsevier]

Highlights: [•] The new method automatically determines the number of components used for a deconvolution-model. [•] The method is based on a cross-validation scheme. [•] Application to several different deconvolution models can be envisaged. [Copyright &y& Elsevier]

Highlights: [•] Quantitative fingerprinting and detailed profiling of food volatiles. [•] Adoption of a “comprehensive” analytical platform (HS-SPME–GC×GC–MS) for sensomics and flavoromics studies. [•] Quantitative headspace analysis for a complete and informative assessment of food volatiles. [•] Fully-integrated system of multiple dimensions of analysis: sample preparation-separation-identification/quantitation-advanced data processing. [ABSTRACT FROM AUTHOR]

Quantitative mass spectrometry analysis for multi-component gas phase reaction processes is a typical multi-input and multi-output (MIMO) nonlinear problem. Conventional calibration and analytical methods that are based on the common hypothesis of linearity of the detected signal and gas parameters, could result in misjudgment of the reaction mechanism and inaccuracy in the determination of the reaction kinetics. In the present work, theoretical derivations based on equivalent characteristic spectrum analysis (ECSA®), discrete mode experiments and continuous mode experiments were performed, and the nonlinearity of mass spectrometry was confirmed. It is only possible to determine the physical parameters such as flow rate and/or concentrations of gases by properly handling the nonlinearity of mass spectrometry. In such case comprehensive reaction mechanisms and even the kinetics of the process can be accurately characterized. Well-handled nonlinear mass spectrometry analysis ensures a reliable and highly accurate identification for the multi-component gas phase reaction processes, and ensures high signal-to-noise ratio for detecting the small-flow gases at a wide range of carrier gas flow. [Display omitted] • The MS nonlinearity between MI and MO signals is found and expounded firstly. • The MS nonlinearity is confirmed through theoretical derivation and experiments. • Taking the nonlinearity ensures the MS reliability, and high signal-to-noise ratio. • Using ECSA® it was possible to well interpret the nonlinearity. [ABSTRACT FROM AUTHOR]

Highlights: [•] Novel comprehensive determination of feasible solutions for 4-component systems. [•] The method works in the presence of noise. [•] Visualization of data structure in both concentration and spectral subspaces. [ABSTRACT FROM AUTHOR]

Highlights: [•] MALDI mass spectrometry used for shrimp species identification. [•] Peak database constructed from six commercially important shrimp species. [•] 97% of samples were correctly identified at the species level. [ABSTRACT FROM AUTHOR]

Highlights: [•] c-SWNTs have been determined in spiked river water samples by SERS spectroscopy. [•] Bare AuNPs are microfiltrated on a membrane forming the SERS active substrate. [•] Measured SERS intensity data were closely fit to a Langmuir isotherm. [•] The use of a portable Raman spectrometer enables in situ determination of SWNTs. [•] The limit of detection of the proposed methodology is 10μgL−1. [Copyright &y& Elsevier]

Highlights: [•] Inner filter effects (IFE) in salt marsh humic fluorescence spectra were studied. [•] IFE act differently depending on the type of salt marsh humic acids (SMHA). [•] The tryptophan (Trp) band of some SMHA is severally obscured by IFE. [•] IFE were studied in mixtures of model fluorophores for SMHA. [•] IFE in SMHA spectra were mathematically corrected. [Copyright &y& Elsevier]

Highlights: [•] Femtosecond transient absorption spectroscopy investigating a complex photodynamic scheme. [•] Combining experiments obtained from two photo-active systems with complementary pathways. [•] Multiset hybrid hard- and soft-multivariate curve resolution incorporating reaction quantum yields. [Copyright &y& Elsevier]

Highlights: [•] The concepts of warping and alignment are introduced. [•] The most important warping methods are critically reviewed and explained. [•] Reference selection, evaluation and place of warping in preprocessing are discussed. [•] Some pitfalls, especially for LC–MS and similar data, are addressed. [•] Examples are provided, together with programming scripts to rework and extend them. [Copyright &y& Elsevier]

Abstract: Extent-based kinetic identification is a kinetic modeling technique that uses concentration measurements to compute extents and identify reaction kinetics by the integral method of parameter estimation. This article considers the case where spectroscopic data are used together with a calibration model to predict concentrations. The calibration set is assumed to be constructed from reacting data that include pairs of concentration and spectral data. Alternatively, one can use the concentration- and spectral contributions of the reactions and mass transfers, which are obtained by pretreatment in reaction- and mass-transfer-variant form. The extent-based kinetic identification using concentrations predicted from spectroscopic data is illustrated through the simulation of both a homogeneous and a gas–liquid reaction system. [Copyright &y& Elsevier]

Abstract: Analytical methods for confirmation of food authenticity claims should be rapid, economic, non-destructive and should not require highly skilled personnel for their deployment. All such conditions are satisfied by spectroscopic techniques. In order to be extensively implemented in routine controls, an ideal method should also give a response independent of the particular equipment used. In the present study, near-infrared (NIR) spectroscopy was used for verifying authenticity of commercial olives in brine of cultivar Taggiasca. Samples were analysed in two laboratories with different NIR spectrometers and a mathematical spectral transfer correction – the boxcar signal transfer (BST) – was developed, allowing to minimise the systematic differences existing between signals recorded with the two instruments. Class models for the verification of olive authenticity were built by the unequal dispersed classes (UNEQ) method, after data compression by disjoint principal component analysis (PCA). Models were validated on an external test set. [Copyright &y& Elsevier]

Abstract: In biospectroscopy, suitably annotated and statistically independent samples (e.g. patients, batches, etc.) for classifier training and testing are scarce and costly. Learning curves show the model performance as function of the training sample size and can help to determine the sample size needed to train good classifiers. However, building a good model is actually not enough: the performance must also be proven. We discuss learning curves for typical small sample size situations with 5–25 independent samples per class. Although the classification models achieve acceptable performance, the learning curve can be completely masked by the random testing uncertainty due to the equally limited test sample size. In consequence, we determine test sample sizes necessary to achieve reasonable precision in the validation and find that 75–100 samples will usually be needed to test a good but not perfect classifier. Such a data set will then allow refined sample size planning on the basis of the achieved performance. We also demonstrate how to calculate necessary sample sizes in order to show the superiority of one classifier over another: this often requires hundreds of statistically independent test samples or is even theoretically impossible. We demonstrate our findings with a data set of ca. 2550 Raman spectra of single cells (five classes: erythrocytes, leukocytes and three tumour cell lines BT-20, MCF-7 and OCI-AML3) as well as by an extensive simulation that allows precise determination of the actual performance of the models in question. [Copyright &y& Elsevier]

Abstract: Artificial neural network (ANN) and a hybrid principal component analysis-artificial neural network (PCA-ANN) classifiers have been successfully implemented for classification of static time-of-flight secondary ion mass spectrometry (ToF-SIMS) mass spectra collected from complex Cu–Fe sulphides (chalcopyrite, bornite, chalcocite and pyrite) at different flotation conditions. ANNs are very good pattern classifiers because of: their ability to learn and generalise patterns that are not linearly separable; their fault and noise tolerance capability; and high parallelism. In the first approach, fragments from the whole ToF-SIMS spectrum were used as input to the ANN, the model yielded high overall correct classification rates of 100% for feed samples, 88% for conditioned feed samples and 91% for Eh modified samples. In the second approach, the hybrid pattern classifier PCA-ANN was integrated. PCA is a very effective multivariate data analysis tool applied to enhance species features and reduce data dimensionality. Principal component (PC) scores which accounted for 95% of the raw spectral data variance, were used as input to the ANN, the model yielded high overall correct classification rates of 88% for conditioned feed samples and 95% for Eh modified samples. [Copyright &y& Elsevier]

Abstract: In recent years, total synchronous fluorescence (TSF) spectroscopy has become popular for the analysis of multifluorophoric systems. Application of PARAFAC, a popular deconvolution tool, requires trilinear structure in the three-way data array. The present work shows that TSF based three-way array data set of dimension sample×wavelength×Δλ does not have trilinear structure and hence it should not be subjected to PARAFAC analysis. This work also proposes that a TSF data set can be converted to an excitation–emission matrix fluorescence (EEMF) like data set which has trilinear structure, so that PARAFAC analysis can be performed on it. This also enables the retrieval of PARAFAC-separated component TSF spectra. [Copyright &y& Elsevier]

Abstract: Many complex natural or synthetic products are analysed either by the GC–MS (gas chromatography–mass spectrometry) or HPLC–DAD (high performance liquid chromatography–diode-array detector) technique, each of which produces a one-dimensional fingerprint for a given sample. This may be used for classification of different batches of a product. GC–MS and HPLC–DAD analyses of complex, similar substances represented by the three common types of the TCM (traditional Chinese medicine), Rhizoma Curcumae were analysed in the form of one- and two-dimensional matrices firstly with the use of PCA (Principal component analysis), which showed a reasonable separation of the samples for each technique. However, the separation patterns were rather different for each analytical method, and PCA of the combined data matrix showed improved discrimination of the three types of object; close associations between the GC–MS and HPLC–DAD variables were observed. LDA (linear discriminant analysis), BP-ANN (back propagation-artificial neural networks) and LS-SVM (least squares-support vector machine) chemometrics methods were then applied to classify the training and prediction sets. For one-dimensional matrices, all training models indicated that several samples would be misclassified; the same was observed for each prediction set. However, by comparison, in the analysis of the combined matrix, all models gave 100% classification with the training set, and the LS-SVM calibration also produced a 100% result for prediction, with the BP-ANN calibration closely behind. This has important implications for comparing complex substances such as the TCMs because clearly the one-dimensional data matrices alone produce inferior results for training and prediction as compared to the combined data matrix models. Thus, product samples may be misclassified with the use of the one-dimensional data because of insufficient information. [Copyright &y& Elsevier]

Abstract: Chemical imaging is a rapidly emerging analytical method in pharmaceutical technology. Due to the numerous chemometric solutions available, characterization of pharmaceutical samples with unknown components present has also become possible. This study compares the performance of current state-of-the-art curve resolution methods (multivariate curve resolution-alternating least squares, positive matrix factorization, simplex identification via split augmented Lagrangian and self-modelling mixture analysis) in the estimation of pure component spectra from Raman maps of differently manufactured pharmaceutical tablets. The batches of different technologies differ in the homogeneity level of the active ingredient, thus, the curve resolution methods are tested under different conditions. An empirical approach is shown to determine the number of components present in a sample. The chemometric algorithms are compared regarding the number of detected components, the quality of the resolved spectra and the accuracy of scores (spectral concentrations) compared to those calculated with classical least squares, using the true pure component (reference) spectra. It is demonstrated that using appropriate multivariate methods, Raman chemical imaging can be a useful tool in the non-invasive characterization of unknown (e.g. illegal or counterfeit) pharmaceutical products. [Copyright &y& Elsevier]

Abstract: The objective of this study was to investigate the spectral behavior of the relationship between reflectance and chlorophyll content and to develop a technique for non-destructive chlorophyll estimation and distribution in leaves using hyperspectral imaging. The hyperspectral imaging data cube of cucumber (Cucumis sativus) leaves in the range of 450–850nm was investigated and preprocessed. Sixty optical signatures or indices as a function of the associated reflectance (R λ ) at the special wavelength (λ) nm which proposed in the literatures were used to predict the total chlorophyll content in cucumber leaves. Finally, R 710/R 760, (R 780 − R 710)/(R 780 − R 680), (R 750 − R 705)/(R 750 + R 705), (R 680 − R 430)/(R 680 + R 430), R 860/(R 550 × R 708), (R 695−705)−1 −(R 750−800)−1, and REP-LEM (a index based on red edge position and estimated with a linear extrapolation method) were identified as optimum indices. Red-edge waveband (680–780nm) appeared in all these optimum indices, indicating the importance of REP (red edge position) in chlorophyll estimation. When (R 695−705)−1 −(R 750−800)−1, the best index was applied to an independent validation set, chlorophyll content (r =0.8286) were reasonably well predicted, indicating model robustness. Depending on the sample, this technique enables to identify and characterize the relative content of various chlorophyll that distribution in the cucumber leaves. The map shows a relatively low level of chlorophyll at margins. Higher level can be noticed in the regions along the main veins and in some areas exhibiting dark green tissue. Our results indicate that hyperspectral imaging has considerable promise for predicting pigments in leaves and, the pigments can be detected in situ in living plant samples non-destructively. [Copyright &y& Elsevier]

Abstract: Fundamental research on non-spectroscopic interferences, also known as matrix effects, in inductively coupled plasma (ICP) mass spectrometry with sample introduction using nebulization is critically and exclusively examined in this review, starting with fundamental processes that may be a source of matrix effects during sample introduction, ion generation in the ICP, ion extraction through the interface, and ion transport through the ion optics to the detector. Various methods for attenuating matrix effects are then reviewed and illustrated with some examples. Instead of exhaustively reviewing the literature, representative references are used to comprehensively describe the main issues, several of which are also common to ICP optical emission spectrometry. [Copyright &y& Elsevier]

Abstract: The output of many instruments can be modeled as a convolution of an impulse response and a series of sharp spikes. Deconvolution considers the inverse problem: estimate the input spike train from an observed (noisy) output signal. We approach this task as a linear inverse problem, solved using penalized regression. We propose the use of an L 0 penalty and compare it with the more common L 2 and L 1 penalties. In all cases a simple and iterative weighted regression procedure can be used. The model is extended with a smooth component to handle drifting baselines. Application to three different data sets shows excellent results. [Copyright &y& Elsevier]

Abstract: In this paper a new model based on frequency self deconvolution (FSD) is proposed for the quantitative analysis of a near infrared (NIR) spectrum. The model couples FSD and partial least square regression (PLS). The grid search optimization method is used to select the optimal values of the full width at half height (FWHH) and the truncation point of the apodization function. The proposed FSD–PLS provides a significant improvement in the prediction ability of the PLS model. Furthermore, a modification of the new FSD–PLS method is introduced to enable the removal of the baseline variations from the NIR spectra. The proposed models were validated using absorbance spectra of mixtures composed from glucose, urea and triacetin in a phosphate buffer solution where the concentrations of the components are selected to be within their physiological range in blood. The whole experiments were carried out in a non-controlled environment to show that the model can suppress effectively most of the experimental variations. The results show that the standard error of prediction (SEP) decreases from 35.58mgdL−1 using 8 factors for the PLS model to 15.53mgdL−1 by using 12 factors for the modified FSD–PLS model. The proposed models are also shown to yield a slightly improved performance than a newly developed second derivative-PLS model without incurring the shortcoming associated with the derivative approach in not providing interpretable results and in degrading the SNR of the spectra at a faster rate. [Copyright &y& Elsevier]

Abstract: Sign of two-dimensional (2D) correlation peaks provides information on sequence of spectral events. This information is related to molecular mechanism of changes in a given system. Recently, few papers addressing the problems with interpretation of the sign of 2D correlation peaks have been published. To overcome these problems, a modification of the generalized 2D correlation method has been proposed. This method compares variations in the dynamic spectrum with a linear change at a reference point. The rates of spectral responses at individual wavenumbers are proportional to magnitudes of the peaks in the slice of asynchronous spectrum at the reference point. This way, analysis of complex 2D contour plots is replaced by a simple examination of one-dimensional (1D) slice spectrum. In spite of reduced ability of the resolution enhancement, in special cases the proposed method provides information not accessible from the classical 2D correlation analysis. At first, the principles of this method are shown with the synthetic data. Next, the influence of spectral separation, band width and position changes on the slice spectrum is evaluated. Finally, the proposed approach is applied to the experimental spectra of two hydrogen-bonded systems. [Copyright &y& Elsevier]

Abstract: The use of single gold nanoparticle plasmon-based spectroscopy for the sensitive, anion-selective detection of arsenate is described. The method is based on the selective formation of electrostatic complexes between arsenate and poly(allylamine hydrochloride) (PAH) and changes in the single particle plasmon in Rayleigh scattering profiles. PAH, when modified with gold nanoparticles, binds arsenate via its amine-functionalities. The scattering properties of the resulting selectively formed complexes are altered, leading to significant changes in the surface plasmon resonance wavelength. The limit of detection of the method was determined to be 10nM, which is ca. 13 times more sensitive than U.S. EPA regulation levels. The response is essentially linear in the concentration range of 50–300nM. The method also shows good selectivity for arsenate in the presence of other environmentally relevant anions, including H2PO4 −, SO4 2−, NO3 −, and Cl−. [Copyright &y& Elsevier]