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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]

On-site and sensitive analysis of phytoavailable heavy metals is the key to fast evaluate the pollution incidents, while the development of convenient and efficient sample introduction approaches against matrix interference is crucial to improve the performances of field-deployable instruments. Herein, trace phytoavailable heavy metals in soil are first extracted by 0.1 M NaNO 3 , afterwards adsorbed onto an activated carbon tip via electroextraction (EE), and finally analyzed by dielectric barrier discharge (DBD) microplasma optical emission spectrometry (OES) via in situ desorption. The activated carbon tip is not only able to extract heavy metals from alkali metals/alkaline earth metals matrix, resisting the interference of coexisting anions and non-electroactive species in saline soil extract, but also significantly improves the detection sensitivity of subsequent DBD-OES analysis by increasing loading amounts of analytes. Taking the key heavy metals pollution as model, the detection limits of Cd, Zn, Cu and Pb reach 0.8, 2.3, 6.0 and 4.5 μg kg -1 , respectively, and precisions are within 2.7-4.6%. The accuracy and practicability of the present miniaturized EE-DBD-OES device have been verified by measuring several certified reference materials and real soil samples, providing a promising tool for convenient and sensitive analysis of trace phytoavailable heavy metals in soil., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

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]

Single particle-inductively coupled plasma-mass spectrometry (SP-ICP-MS) was used in the analysis of single particles/cells. Although quadrupole mass analyzers are widely used, the long settling time restricts measurement to single elements in individual particles. Recently, dual-elemental analysis has successfully been developed with the assistance of oxygen gas in the collision cell. This simple approach greatly expands the capability of quadrupole-based ICP-MS. In this study, we adopted bandpass mode in the first quadrupole (Q1) to improve the limit of detection of single particles against spectral interference. A model was developed to explain the rationale behind the selection of quadrupole voltages. The quadrupole voltages were optimized systematically so that the mass bandwidth of Q1 allowed the transmission of two target analytes while the interference species were rejected. As a result, the signal from the polyatomic interference was reduced by 98% with no significant change in the analyte signal. The bandpass mode was further applied to accurately determine the isotope ratio of 109 Ag/ 107 Ag in 80-nm Ag nanoparticles, as well as the Ag content in AgSn alloy particles, under the polyatomic interference of 91 Zr 16 O originating from dissolved ions and particles. This technique was further applied to the determination of two Yb isotopes in algal cells with interference from Gd. Results indicate that this approach has great potential for assessing single particles and biological cells in the presence of severe interference from imaging agents, drugs, or biological fluids., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

The cellular metabolism of metals is highly critical to elucidate their potential cytotoxicity or cell protection mechanism. In this work, an asymmetric serpentine microfluidic device (ASMD) with high sampling efficiency and excellent focusing performance was developed for single-cell focusing. ASMD coupling with ICP-MS ensures single-cell assay to provide the information for trivalent arsenic (As(III)) uptake by HepG2 cells, which reveals the heterogeneity of cellular arsenic distribution, and elucidates the arsenic elimination behaviors in single HepG2 cells. Further, the metabolism and transformation of As(III) in HepG2 cells was tracked by hyphenating capillary electrophoresis (CE) separation with ICP-MS. The results for single-cell analysis and arsenic elimination kinetics illustrated that the half-life of arsenic elimination is 0.9 ± 0.04 h with the elimination constant of 0.77 ± 0.03, i.e., 77% of accumulated As in HepG2 cells may be eliminated per hour. Moreover, arsenobetaine (AsB) was identified to be the main metabolite and biotransformation species of As in HepG2 cells. ASMD-ICP-MS and CE-ICP-MS are powerful for tracking the fate of metals or metal drugs in single cells to comprehensively understand their metabolic pathway and transformation behaviors., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

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]

Cannabis has been at the center of scientific attention for some years now. Since its pharmacological potential has been highlighted, cannabis has become a hot topic in research laboratories, leading to the publication of many scientific studies. Focusing on analytical chemistry, an enormous number of analytical methods for cannabinoid (CNB) determination have been published, involving various techniques. However, no globally accepted reference method for CNB determination has yet been chosen. This review aims to identify very recent analytical methods developed to analyze phytocannabinoids in cannabis herbal samples. For certain techniques, stagnation in terms of employed operational conditions can be observed. In this context, a reference method of analysis should be proposed and accepted worldwide to standardize CNB determination. In contrast, for other techniques, we are witnessing a scientific ferment, which is resulting in the development of new interesting analytical options. In this regard, particular focus has been given to these niche techniques, which are now emerging in the analytical panorama of cannabis analysis, offering new important perspectives for the future of cannabis testing. Supercritical fluid chromatography and infrared spectroscopy showed tangible advantages when applied to CNB determination in herbal samples., (Copyright © 2021 Elsevier B.V. All rights reserved.)

A fast and cost-effective procedure based on Inductively Coupled Plasma - Mass Spectrometry (ICP-MS) for the selective determination of methylmercury (MeHg) is proposed and validated for fish tissue analysis. Selectivity for MeHg is achieved by simply inserting a strong anion exchange resin to block inorganic mercury as negatively charged chloride species, leaving MeHg unretained. The procedure features a 15 min extraction time followed by a 100 s analysis time achieving a limit of detection of 1.6 μg kg -1 on solid samples. The effect of the solution composition and inorganic mercury concentration were extensively studied to fully assess the selectivity of the procedure: Hg(II):MeHg ratios up to 50 are tolerated and cause systematic errors lower than 15%. The entire procedure was successfully validated by standard reference material from the marine food web, namely fish muscle and liver plus zooplankton. The method was finally applied to the detection of MeHg in the marine trophic web of Djibouti (Gulf of Aden): a trophic magnification factor of 13.5 proved the high risk associated with the biomagnification of methylmercury., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

This paper presents a novel approach, based on the standard addition method, for overcoming the matrix effects that often hamper the accurate characterization of nanoparticles (NPs) in complex samples via single particle inductively coupled plasma mass spectrometry (SP-ICP-MS). In this approach, calibration of the particle size is performed by two different methods: (i) by spiking a suspension of NPs standards of known size containing the analyte, or (ii) by spiking the sample with ionic standards; either way, the measured sensitivity is used in combination with the transport efficiency (TE) for sizing the NPs. Moreover, such transport efficiency can be readily obtained from the data obtained via both calibration methods mentioned above, so that the particle number concentration can also be determined. The addition of both ionic and NP standards can be performed on-line, by using a T-piece with two inlet lines of different dimensions. The smaller of the two is used for the standards, thus ensuring a constant and minimal sample dilution. As a result of the spiking of the samples, mixed histograms including the signal of the sample and that of the standards are obtained. However, the use of signal deconvolution approaches permits to extract the information, even in cases of signal populations overlapping. For proofing the concept, characterization of a 50 nm AuNPs suspension prepared in three different media (i.e., deionized water, 5% ethanol, and 2.5% tetramethyl ammonium hydroxide-TMAH) was carried out. Accurate results were obtained in all cases, in spite of the matrix effects detected in some media. Overall, the approach proposed offers flexibility, so it can be adapted to different situations, but it might be specially indicated for samples for which the matrix is not fully known and/or dilution is not possible/recommended., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Herein, we developed a novel microplasma-assisted vapor generation technique based on solution anode glow discharge (SAGD) as a substitute for the pneumatic nebulization (PN) routinely used in inductively coupled plasma-optical emission spectrometry (ICP-OES). Under the optimal state of the newly proposed SAGD-ICP-OES system, the detection limits (DLs) of Cd and Hg were reduced by some 12- and 90-fold respectively, and an average of 2 times for other elements (Ag, Bi, Pb, Tl and Zn) in comparison with conventional PN. This system not only obtained the good detection limits amenable to the ultra-trace determination of Cd (DL = 0.3 μg L -1 ) and Hg (DL = 0.2 μg L -1 ), but also retained the ability of multi-element analysis. Its accuracy was demonstrated by the determination of Cd and Hg using GBW07312 (stream sediments) and GBW10029 (fish) certified reference materials (CRMs) and spiked tap water samples. The results showed good consistency with the certified values and the values obtained using inductively coupled plasma-mass spectrometry., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)