Analytical figures of merit: from univariate to multiway calibration

Fast and easy detection of hypochlorite by a smartphone-based fluorescent turn-on probe: Applications to water samples, zebrafish and plant imaging

We have developed a fluorescent probe DBT-Cl ((E)-2-(2-(4-(diphenylamino)benzylidene) hydrazinyl)-N,N,N-trimethyl-2-oxoethan-1-aminium chloride) for ClO- with an aggregation-induced emission (AIE) strategy depending on solvent polarity. DBT-Cl possessed a prominent solvatochromic emission property with intramolecular charge transfer (ICT) from the TPA (triphenylamine) to the amide group, which was studied by spectroscopic analysis and DFT calculations. These unique AIE properties of DBT-Cl led to the recognition of ClO- with high fluorescent selectivity. DBT-Cl quickly detected ClO- in less than 1 sec with a fluorescent color change from green to cyan. DBT-Cl had a low detection limit of 9.67 μM to ClO-. Detection mechanism of DBT-Cl toward ClO- was illustrated to be oxidative cleavage of DBT-Cl by 1H NMR titrations, ESI-mass, and DFT calculations. We established the viability for dependable detection of ClO- in actual water samples, as well as zebrafish and plant imaging. In particular, DBT-Cl was capable of easily monitoring ClO- through a smartphone application. Therefore, DBT-Cl assured a promising approach for a fast-responsive and multi-applicable ClO- probe in environmental and living organism systems.

Analytical Chemistry Strategies in the Use of Miniaturised NIR Instruments: An Overview

Miniaturized NIR instruments have been increasingly used in the last years, and they have become useful tools for many applications on a broad variety of samples. This review focuses on miniaturized NIR instruments from an analytical point of view, to give an overview of the analytical strategies used in order to help the reader to set up their own analytical methods, from the sampling to the data analysis. It highlights the uses of these instruments, providing a critical discussion including current and future trends.

Second-order calibration high-resolution continuum source graphite furnace molecular absorption spectrometry-based determination of bromine and fluorine

In this study, we report the simultaneous determination of bromine and fluorine using Second-Order Calibration High-Resolution Continuum Source Graphite Furnace Molecular Absorption Spectrometry (HR CS MAS). The instrumental data acquired correspond to the time versus wavelength matrix per sample that were analyzed using Parallel Factor Analysis (PARAFAC), along with Unfold and N-way Partial Least Squares combined with a post-calibration step known as Residual Bilinearization (U and N PLS/RBL). Despite the significant difference in sensitivity between bromine and fluorine, all approaches provided reasonably accurate results when predicting both analytes in synthetic mixtures within a controlled environment. The relative prediction error (REP) values for bromine were 29.8 % (PARAFAC), 23.6 % (N-PLS/RBL), and 13.1 % (U-PLS/RBL), while for fluorine, the REP values were 3.4 % (PARAFAC), 3.5 % (N-PLS/RBL), and 3.2 % (U-PLS/RBL). When applying this approach to predict unknown samples, a comparison was made between the estimated nominal concentrations of fluorine and bromine obtained using either a reference method or based on labeled values or spiked mass, and those obtained using the proposed method. It was observed that PARAFAC was unable to predict the samples accurately, whereas the REP values for the prediction of bromine and fluorine using N-PLS/RBL and U-PLS/RBL methods were 19.3 %/19.2 % and 13.6 %/13.1 %, respectively.

An optimized methodology for the determination of multiclass organic ultraviolet sunscreens and metabolites in human milk through chromatographic and chemometric resolution

Organic UV filters (UVFS) are used to mitigate the dermal effects associated with health risks from UV radiation, making them essential in personal care products. UVFS are frequently identified in environmental samples due to their high lipophilicity and persistence, underscoring the urgency of comprehensive assessments and regulatory measures aimed at safeguarding ecosystems and human health. The present study reports a multiclass analytical method for determining 16 UV sunscreens and metabolites in breast milk based on an ultrasound-assisted-dispersive liquid-liquid micro-extraction (UA-DLLME) with further chromatographic and chemometric resolution. The experimental conditions of the UA-DLLME were optimized through the implementation of the Design of Experiment tools. To model the responses, least-squares and artificial neural network methodologies were implemented. The optimal conditions were found by employing the desirability function. The samples were analyzed through reverse-phase liquid chromatographic separation, UV diode array, and fast-scanning fluorescence detection. The chromatographic analysis enabled the resolution of 16 analytes in a total time of 13.0 min. Multivariate curve resolution-alternating least-square (MCR-ALS) modelling was implemented to resolve analytes that were not fully resolved and to determine analytes that coeluted with endogenous components of the breast milk samples. An enrichment factor of 5-fold concentration was obtained with this methodology, reaching recoveries between 65 % and 105 % for 13 multiclass UV sunscreens and metabolites in breast milk samples with RSD % and REP % lower than 12 %.

Third-order calibration applied to process surfactant-modulated excitation-emission matrix four-way fluorescence data for the direct determination of four polycyclic aromatic hydrocarbons in oilfield produced water

Fluorescence spectroscopy is a powerful tool to determine polycyclic aromatic hydrocarbons (PAHs) owing to the strong endogenous fluorescence of these compounds. However, the presence of unknown interferences and overlapped spectra hinders the accurate determination of PAHs in oilfield produced water. Moreover, surfactants frequently coexist in oilfield produced water and will seriously affect the fluorescence signals of PAHs. Herein, a new methodology applying third-order calibration to process four-way (4D) fluorescence data was proposed to solve these problems and achieve accurate determination of pyrene, fluorene, phenanthrene, and fluoranthene as an example in oilfield produced water. The methodology is based on excitation-emission matrix fluorescence modulated by different concentrations of sodium dodecyl benzene sulfonate (SDBS) in the analyzed samples. The 4D fluorescence data were processed by third-order calibration methods including four-way parallel factor analysis (4-PARAFAC) and alternating weighted residue constraint quadrilinear decomposition (AWRCQLD), and the results were compared with those of second-order calibration methods. It was proved that third-order calibration was capable of accurately identifying and quantifying PAHs together with SDBS in oilfield produced water, which has better quantitative results and figures of merit compared to second-order calibration. This study provided a new approach to generating 4D fluorescence data and opened up an avenue for the accurate determination of PAHs in complex oilfield produced water with surfactants.

A fluorescence-based multivariate method for biodiesel quantification in undiluted diesel-biodiesel blends without sample preparation

In this work, excitation-emission matrices (EEMs) were used in association with parallel factor analysis (PARAFAC) to assess biodiesel content in undiluted diesel-biodiesel blends (DBBs) without pre-sample preparation. EEMs were decomposed using the PARAFAC (EEMs-PARAFAC), and the loading values of the PARAFAC component as a function of biodiesel content in the blends were used to build an analytical model to quantify the biodiesel content in DBBs. The proposed model presenting a limit of detection (LOD) and a limit of quantification (LOQ) of 2.5% and 11% w/w, respectively, successfully predicted the biodiesel content in the validation samples. The robustness of the model was confirmed by a close analysis of the root mean square error of prediction, standard error of prediction, relative standard deviation of prediction, and Bias. Therefore, an accurate and robust analytical model based on EEMs-PARAFAC was developed to quantify the biodiesel content in undiluted DBBs without sample preparation.

Extrapolation study for determining the time since injury in a rat subcutaneous hematoma model utilizing ATR-FTIR spectroscopy

The determination of the time of an injury has been a major problem in forensic science due to the lack of objective, reliable and portable methods. In this study, a subcutaneous hemorrhage model in rats was established over six days, and attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy coupled with chemometrics was used to determine the time since injury. Initial principal component analysis (PCA) showed variance among hematoma sites. Subsequently, spectral data were acquired to establish a dependable partial least square (PLS) regression model with predictive abilities. The root mean square error of cross-validation (RMSECV) and the root mean square error of prediction (RMSEP) values produced by a genetic algorithm (GA) were 0.64 d (R2 = 0.88) and 0.57 d (R2 = 0.90), respectively. Few variables were involved in the model, and significantly better results were obtained in comparison to the conventional full-spectrum PLS model. In combination with the results of variable importance in projection (VIP) scores, all components, including proteins, nucleic acids and phospholipids, provided inferences regarding the samples at different time points; additionally, amide I and II bands represented the secondary structure of proteins and provided the largest contribution. Based on our preliminary study, the combination of swift and nondamaging ATR-FTIR spectroscopy with chemometrics could prove to be an advantageous approach for gauging the age of an injury in the forensic field.

Comparison of Multiple NIR Spectrometers for Detecting Low-Concentration Nitrogen-Based Adulteration in Protein Powders

Protein adulteration is a common fraud in the food industry due to the high price of protein sources and their limited availability. Total nitrogen determination is the standard analytical technique for quality control, which is incapable of distinguishing between protein nitrogen and nitrogen from non-protein sources. Three benchtops and one handheld near-infrared spectrometer (NIRS) with different signal processing techniques (grating, Fourier transform, and MEM-micro-electro-mechanical system) were compared with detect adulteration in protein powders at low concentration levels. Whey, beef, and pea protein powders were mixed with a different combination and concentration of high nitrogen content compounds-namely melamine, urea, taurine, and glycine-resulting in a total of 819 samples. NIRS, combined with chemometric tools and various spectral preprocessing techniques, was used to predict adulterant concentrations, while the limit of detection (LOD) and limit of quantification (LOQ) were also assessed to further evaluate instrument performance. Out of all devices and measurement methods compared, the most accurate predictive models were built based on the dataset acquired with a grating benchtop spectrophotometer, reaching R2P values of 0.96 and proximating the 0.1% LOD for melamine and urea. Results imply the possibility of using NIRS combined with chemometrics as a generalized quality control tool for protein powders.

Chemometrics-assisted excitation-emission matrix fluorescence spectroscopy for real-time migration monitoring of multiple polycyclic aromatic hydrocarbons from plastic products to food simulants

Polycyclic aromatic hydrocarbons (PAHs), as a class of organic pollutants that have attracted much attention, are likely to be formed with the production and processing of plastic products, and they may migrate from contaminated plastic products to food, causing the risk of poisoning or cancer. In this study, migration tests were carried out on disposable plastic products for food contact, and a novel strategy that combines excitation-emission matrix (EEM) fluorescence spectroscopy with the advanced second-order calibration method based on the three-direction resection alternating trilinear decomposition (TDR-ATLD) algorithm was used to monitor the migration of three PAHs anthracene (ANT), pyrene (PYR), and phenanthrene (PHE) from the plastic products to food simulants in real-time. With the "second-order advantage", even if the fluorescence spectra of the target analytes overlapped seriously, and other unknown substances migrated from the plastic products to food simulants, accurate qualitative and quantitative results were still obtained by the proposed method. In the static system, the coefficient of determination (R2) of the three PAHs within the calibration range were all greater than 0.99, and the average spiked recoveries were 99.5-107.1%, with the standard deviation lower than 8.9%. The figures of merit (FOMs) and intra- or inter-day precision also showed good feasibility and reliability of the method. In the simulation study of the migration kinetic process, three PAHs can be quantified in real-time in complex matrix, then the related migration equations were established. The results indicate that the proposed method can be used for real-time migration quantitative monitoring of PAHs, providing a potential and available method for the study of the migration kinetics of hazardous substances from food contact materials to food or food simulants.

A rapid and direct method for dating blue pen ink in documents using multiset modeling of infrared spectroscopy and mass spectrometry data

The dating of documents is crucial in forensic chemistry, particularly for verifying their authenticity. This study aimed to develop a rapid and direct method for the dating of pen ink in documents, using a combination of Fourier transform infrared spectroscopy in attenuated reflectance mode (FTIR-ATR), desorption electrospray ionization mass spectrometry (DESI-MS) and multiple ensemble data modeling. Two sets of paper document samples containing writing in blue pen ink were investigated: (I) artificially aged documents and (II) real documents dating from 1960 to 2022. The FTIR-ATR spectra of both sets of samples showed a decrease in absorbance at ∼1584 cm-1, related to the chemical modification of the CN bond in the molecular structure of Basic Violet 3 (BV3), one of the main dyes used in blue pen ink. DESI-MS confirmed the presence of BV3 and its degradation by-products in all the samples, indicating its widespread utilization in blue pen ink production. Moreover, DESI-MS detected combinations of dyes within the ink composition. The models were built using the DESI-MS and FTIR-ATR data separately, but the error and trend were significantly reduced when both sets of data were used. The combination of DESI-MS and FTIR-ATR spectral information resulted in a final predictive model with low error for pen inks from real documents in writing from the years 1960 to 2022. These analyses proved to be effective for the dating of pen inks and are suitable for use in routine forensic analysis, providing a direct and rapid method that allows for accurate prediction.

Identification and Parametrization of Key Factors Affecting Levoglucosan Emission During Solid Fuel Burning

Levoglucosan (LG) is a pyrolysis product of cellulose and hemicellulose at low combustion temperatures. However, LG release cannot be determined only by considering the contents of cellulose and hemicellulose exclusively due to the complexity of combustion processes and the physical-chemical properties of the fuel. This study detected the emission factors (EFs) of LG from 22 different solid fuel samples (including coal and biomass) by considering 18 different fuel properties and five combustion parameters. The average LGEFs during solid fuel burning varied in a range of 0.03-136 mg kg-1, with a magnitude difference of 1-4 orders. While the variations in cellulose (59.5-368 mg g-1) and hemicellulose (73.5-165 mg g-1) contents of fuel samples were only one- to 6-fold. A short combustion duration (<150 min) and a medium combustion temperature (200-400 °C) influenced by volatile and ash contents are crucial for the generation and accumulation of LG. A random forest coupled with the Akaike information criterion stepwise regression model successfully explained 96% of the total LG emission variation using three variables (ash content, cellulose content, and modified combustion efficiency). The ash content promoted coke formation and LG chain cracking by increasing the pyrolysis temperature and is considered the most important factor. The alkali metal in ash can reduce the energy barrier of intramolecular ring contraction reactions and inhibit the dehydration reactions, which led to additional heat being utilized by the competitive pathways of LG formation. This study provided a method to address the parametrization and release mechanisms of combustion source emissions.

A micellar-enhanced fluorescence photoinduced four-way calibration method for the determination of multiclass pesticides in lemon juice

In this work, a four-way multivariate calibration method for the simultaneous determination of four pesticides - carbendazim (CBZ), thiabendazole (TBZ), pirimiphos-methyl (PMM), and clothianidin (CLT) - in lemon juice is presented. Third-order data were acquired by registering the photoinduced fluorescence of the analytes as excitation-emission matrices at different times of UV-light irradiation, in the presence of organized media (direct micelles) as fluorescence enhancers. The optimal experimental conditions (pH 11.5 and 32 mmol L-1 hexadecyltrimethylammonium chloride surfactant) were determined through a central composite design using the response surface methodology. The analytes were individually calibrated, except for TBZ and CBZ due to the inner filter effect of TBZ on CBZ. Test samples containing all analytes and imidacloprid (as potential interference) were analysed. PARAFAC was utilized to evaluate both the trilinearity and quadrilinearity of the third-order data and four-way arrays, respectively. PMM was successfully determined with quadrilinear PARAFAC decomposition, whereas CLT, TBZ, and CBZ were satisfactorily modelled using U-PLS/RTL due to the loss of quadrilinearity caused by different phenomena. The profitable applicability of the analytical method in the CBZ, TBZ, PMM, and CLT determination in lemon juice samples was demonstrated, achieving limits of detection below the maximum residue levels reported by the European Commission, and mean recoveries at 90 ± 5%.

Detection of adulteration in Eragrostis tef (Zucc.) Trotter flour using EDXRF and ComDim-MLR data fusion

The teff cereal gained worldwide attention because it is gluten-free and rich in iron; thus, its flour is subject to fraud. This study evaluated the ability of Energy Dispersive X-Ray Fluorescence Spectroscopy (EDXRF) to identify teff flours adulterated with rice, whole wheat, oat, and rye flours. The adulteration followed a {5,4} simplex-lattice design. After smoothing and pretreatments, 15 kV and 50 kV spectra were fused by Common Dimension Analysis (ComDim). Multiple Linear Regression (MLR) models using EDXRF-ComDim scores and percentage of teff were adjusted. The best model presented four common dimensions (CD), r2prediction = 0.8534, low RMSEP (0.0564), and absence of overfitting. The obtained model was robust to quantify adulteration in teff flour even with the differences in the intensity of EDXRF spectra of different crops. Therefore, EDXRF, in tandem with ComDim data fusion, was an efficient tool for the adulteration control of teff flours.

Front-face excitation-emission matrix fluorescence spectroscopy combined with interpretable deep learning for the rapid identification of the storage year of Ningxia wolfberry

Ningxia wolfberry stored for many years may be disguised as fresh wolfberry by unscrupulous traders and sold for huge profits. In this work, the front-face excitation-emission matrix (FF-EEM) fluorescence spectroscopy coupled with interpretable deep learning was proposed to identify the storage year of Ningxia wolfberry in a lossless, fast and accurate way. Alternating trilinear decomposition (ATLD) algorithm was used to decompose the three-way data array obtained by Ningxia wolfberry samples, extracting the chemically meaningful information. Meanwhile, a convolutional neural network (CNN) model for the identification of the storage year of Ningxia wolfberry, called EEMnet, was proposed. The model successfully classified wolfberry samples from different storage years by extracting the subtle feature differences of the spectra, and the correct classification rate of the training set, test set and prediction set was more than 98%. In addition, a series of interpretability analyses were implemented to break the "black box" of the deep learning model. These results indicated that the method based on FF-EEM fluorescence spectroscopy combined with EEMnet could quickly and accurately identify the year of Ningxia wolfberry in a green way, providing a new idea for the identification of the storage years of Chinese medicinal materials.

Interference-free quantitation of aromatic amino acids in two complex systems by three-way calibration with ultraviolet-visible spectrophotometer: Exploration of trilinear decomposition of spectrum-pH data

Aromatic amino acids play an extremely important role in life activities and participate in many biological processes. Their concentration levels are associated with a variety of diseases, such as phenylketonuria and colorectal cancer. Therefore, the quantification of aromatic amino acids is an important task. In the present work, a novel and rapid three-way analytical method was proposed to detect the levels of aromatic amino acids in prostate cancer cells (PC3 cells) and Dulbecco's modified minimal essential medium (DMEM cell culture), by using the affordable ultraviolet-visible spectrophotometer. First, spectrum-pH second-order data were designed per sample; Second, properties of the resulted spectrum-pH-sample three-way data were investigated by utilizing the parallel factor analysis (PARAFAC), alternating trilinear decomposition (ATLD), and constrained alternating trilinear decomposition (CATLD) algorithms, and a flexible scanning approach for determining the constraint parameters of CATLD was proposed; Third, a three-way calibration method based on the CATLD algorithm with the proposed scanning approach was developed for interference-free quantification of aromatic amino acids in these systems. The average relative predictive errors of validation (ARPEV) for phenylalanine, tyrosine, and tryptophan were 1.4%, 3.0%, and 0.7% in prostate cancer cells, and ARPEV for phenylalanine, tyrosine, and tryptophan were 4.1%, 1.2%, and 0.7% in DMEM cell culture. The predicted contents of tyrosine and tryptophan in DMEM cell culture were 64.2 ± 2.9 μg mL^-1, 5.6 ± 0.3 μg mL^-1, there are no significant differences in the concentrations between the developed analytical method and high performance liquid chromatography method. The proposed spectrum-pH-sample three-way calibration method based on CATLD algorithm can provide an interesting analytical strategy with high selectivity and accuracy for ultraviolet-visible spectrophotometer.

Kinetic Determination of Acetylsalicylic Acid Using a CdTe/AgInS2 Photoluminescence Probe and Different Chemometric Models

The combination of multiple quantum dots (QDs) in a multi-emitter nanoprobe can be envisaged as a promising sensing scheme, as it enables obtaining a collective response of individual emitters towards a given analyte and allows for achieving specific analyte-response profiles. The processing of these profiles using adequate chemometric methods empowers a more sensitive, reliable and selective determination of the target analyte. In this work, we developed a kinetic fluorometric method consisting of a dual CdTe/AgInS₂ quantum dots photoluminescence probe for the determination of acetylsalicylic acid (ASA). The fluorometric response was acquired as second-order time-based excitation/emission matrices that were subsequently processed using chemometric methods seeking to assure the second-order advantage. The data obtained in this work are considered second-order data as they have a three-dimensional size, I × J × K (where I represents the samples' number, J the fluorescence emission wavelength while K represents the time). In order to select the most adequate chemometric method regarding the obtained data structure, different chemometric models were tested, namely unfolded partial least squares (U-PLS), N-way partial least squares (N-PLS), multilayer feed-forward neural networks (MLF-NNs) and radial basis function neural networks (RBF-NNs).

Discrimination of the harvesting season of green tea by alcohol/salt-based aqueous two-phase systems combined with chemometric analysis

The flavor and aroma quality of green tea are closely related to the harvest season. The aim of this study was to identify the harvesting season of green tea by alcohol/salt-based aqueous two-phase system (ATPS) combined with chemometric analysis. In this paper, the single factor experiments (SFM) and response surface methodology (RSM) optimization were designed to investigate and select the optimal ATPS. A total of 180 green tea samples were studied in this work, including 86 spring tea and 94 autumn tea. After the active components in green tea samples were extracted by the optimal ethanol/(NH₄)₂SO₄ ATPS, the qualitative and quantitative analysis was realized based on HPLC-DAD combined with alternating trilinear decomposition-assisted multivariate curve resolution (ATLD-MCR) algorithm, with satisfactory spiked recoveries (86.00 %-112.45 %). The quantitative results obtained from ATLD-MCR model were subjected to chemometric pattern recognition analysis. The constructed partial least squares-discriminant analysis (PLS-DA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) models showed better results than the principal component analysis (PCA) model, and the R²X_cum values (>0.835) and R²Y_cum (>0.937) were close to 1, the Q²_cum values were greater than 0.75 (>0.933), and the differences between R²Y_cum and Q²_cum were not larger than 0.2, indicating excellent cross-validation prediction performance of the models. Furthermore, the classification results based on the hierarchical clustering analysis (HCA) were consistent with the PCA, PLS-DA and OPLS-DA results, establishing a good correlation between tea active components and the harvesting seasons of green tea. Overall, the combination of ATPS and chemometric methods is accurate, sensitive, fast and reliable for the qualitative and quantitative determination of tea active components, providing guidance for the quality control of green tea.

Calibration-free PAT: Locating selective crystallization or precipitation sweet spot in screenings with multi-way PARAFAC models

When developping selective crystallization or precipitation processes, biopharmaceutical modalities require empirical screenings and analytics tailored to the specific needs of the target molecule. The multi-way chemometric approach called parallel factor analysis (PARAFAC) coupled with ultraviolet visible light (UV/Vis) spectroscopy is able to predict specific concentrations and spectra from highly structured data sets without the need for calibration samples and reference analytics. These calculated models can provide exploratory information on pure species spectra and concentrations in all analyzed samples by representing one model component with one species. In this work, protein mixtures, monoclonal antibodies, and virus-like particles in chemically defined and complex solutions were investigated in three high-throughput crystallization or precipitation screenings with the aim to construct one PARAFAC model per case. Spectroscopic data sets of samples after the selective crystallization or precipitation, washing, and redissolution were recorded and arranged into a four-dimensional data set per case study. Different reference analytics and pure species spectra served as validation. Appropriate spectral preprocessing parameters were found for all case studies allowing even the application of this approach to the third case study in which quantitative concentration analytics are missing. Regardless of the modality or the number of species present in complex solutions, all models were able to estimate the specific concentration and find the optimal process condition regarding yield and product purity. It was shown that in complex solutions, species demonstrating similar phase behavior can be clustered as one component and described in the model. PARAFAC as a calibration-free approach coupled with UV/Vis spectroscopy provides a fast overview of species present in complex solution and of their concentration during selective crystallization or precipitation, washing, and redissolution.

High-Performance Liquid Chromatography–Diode Array Detection Combined with Chemometrics for Simultaneous Quantitative Analysis of Five Active Constituents in a Chinese Medicine Formula Wen-Qing-Yin

In this work, a simple analytical strategy combining high-performance liquid chromatography–diode array detection (HPLC-DAD) and the chemometric method was developed for the simultaneous quantification of 5-hydroxymethyl-2-furfural (HMF), paeoniflorin (PAE), ferulic acid (FER), baicalin (BAI), and berberine (BER) in a Chinese medicine formula Wen-Qing-Yin (WQY). The alternating trilinear decomposition (ATLD) algorithm and alternating trilinear decomposition assisted multivariate curve resolution (ATLD-MCR) algorithm were used to realize the separation and rapid determination of five target analytes under the presence of time shifts, solvent peaks, peak overlaps, and unknown interferences. All analytes were eluted within 10 min and the linear correlation coefficients of calibration sets were between 0.9969 and 0.9996. In addition, the average recoveries of the five active compounds obtained by ATLD and ATLD-MCR analysis were in the range of 91.8–112.5% and 88.6–101.6%, respectively. For investigating the accuracy and reliability of the proposed method, figures of merit including limit of detection (LOD), limit of quantitation (LOQ), sensitivity (SEN), and selectivity (SEL) were calculated. The proposed analytical strategy has the advantages of being fast, simple, and sensitive, and can be used for the qualitative and quantitative analysis of WQY, providing a feasible option for the quality monitoring of the traditional Chinese medicine formula.

Deep Learning-Based Sensor Array: 3D Fluorescence Spectra of Gold Nanoclusters for Qualitative and Quantitative Analysis of Vitamin B6 Derivatives

Vitamin B₆ derivatives (VB6Ds) are of great importance for all living organisms to complete their physiological processes. However, their excess in the body can cause serious problems. What is more, the qualitative and quantitative analysis of different VB6Ds may present significant challenges due to the high similarity of their chemical structures. Also, the transfer of deep learning model from one task to a similar task needs to be present more in the fluorescence-based biosensor. Therefore, to address these problems, two deep learning models based on the intrinsic fingerprint of 3D fluorescence spectra have been developed to identify five VB6Ds. The accuracy ranges of a deep neural network (DNN) and a convolutional neural network (CNN) were 94.44-97.77% and 97.77-100%, respectively. After that, the developed models were transferred for quantitative analysis of the selected VB6Ds at a broad concentration range (1-100 μM). The determination coefficient (R²) values of the test set for DNN and CNN were 93.28 and 97.01%, respectively, which also represents the outperformance of CNN over DNN. Therefore, our approach opens new avenues for qualitative and quantitative sensing of small molecules, which will enrich fields related to deep learning, analytical chemistry, and especially sensor array chemistry.

A tutorial on multi-way data processing of excitation-emission fluorescence matrices acquired from semiconductor quantum dots sensing platforms

This tutorial demonstrates how to exploit the second-order advantage on excitation-emission fluorescence matrices (EEFMs) acquired from sensing platforms based on analyte-triggered semiconductor quantum dots (QDs) fluorescence modulation (quenching/enhancing). The advantage in processing such second-order EEFMs data from complex samples, seeking successful quantification, is comprehensively addressed. It is worth emphasizing that, aiming to exploit the second-order advantage, the selection of the most appropriate advanced chemometric model should rely on the matching between the data structure and the physicochemical chemometric model assumption. In this sense, the achievement of second-order advantage after EEFMs' processing is extensively addressed throughout this tutorial taking into consideration three different analytical situations, each involving a specific data structure: i) parallel factor analysis (PARAFAC), which is applied in a real dataset stacked in a three-way data array containing a trilinear data structure acquired from QDs-based detection with non-selective species; ii) multivariate curve resolution - alternating least-squares (MCR-ALS), which is also employed in a real dataset arranged in an augmented data matrix containing non-trilinear data structure acquired from QDs-based detection with a single breaking mode caused by background signals; iii) unfolded partial least-squares with residual bilinearization (U-PLS/RBL), which is applied in a dataset containing non-trilinear data acquired from a classical fluorescence system with two breaking modes caused by inner filter effect (IFE) in both instrumental modes (excitation and emission). The latter challenging data structure can be acquired via fluorescence quenching from IFE-based sensing platforms and chemometrically handled in two main steps. First, a set of calibration EEFMs data is converted into an unfolded data matrix during the unfolding process, followed by applying U-PLS model. Second, a post-calibration procedure using RBL analysis is applied to a test sample of EEFM maintained in its matrix form, in order to handle potential interferents. In the last section, the state-of-the-art of second-order EEFMs data acquired from semiconductor QDs-based sensing platforms and coupled to multi-way fluorescence data processing to accomplish a successful quantification, even with substantial interfering species, is critically reviewed.

In situ Raman spectroscopy for real time detection of cysteine

Cysteine serves a wide range of important biological and chemical functions and may have an association to neurodegenerative disease and cancer. Rapid, accurate analytical methods for cysteine detection are thus highly desirable. In this work, we report an investigation into the utility of in situ Raman spectroscopy as a Process Analytical Technology (PAT) for real time monitoring of cysteine. Cysteine concentrations are tracked in real time using Raman spectroscopy across a range of pharmaceutically-relevant concentrations, demonstrating the capability of Raman spectroscopy detection for in situ cysteine monitoring. The concentration range over which this analytical methodology can be applied is successfully established. As such, the results herein serve as a proof-of-principle investigation to demonstrate and evaluate the capabilities of a real time Raman spectroscopic approach for in situ cysteine detection, thus informing the range of important chemical and biological processes to which this approach can be applied. To the best of our knowledge, this is the first report of in situ Raman spectroscopy for real time monitoring of dynamically changing cysteine process concentrations.

Intra-regional classification of Codonopsis Radix produced in Gansu province (China) by multi-elemental analysis and chemometric tools

Multi-elemental analysis is widely used to identify the geographical origins of plants. The purpose of this study was to explore the feasibility of combining chemometrics with multi-element analysis for classification of Codonopsis Radix from different producing regions of Gansu province (China). A total of 117 Codonopsis Radix samples from 7 counties of Gansu province were collected. Inductively coupled plasma mass spectrometry (ICP-MS) was used for the determination of 28 elements (³⁹ K, ²⁴ Mg, ⁴⁴Ca, ²⁷Al, ¹³⁷Ba, ⁵⁷Fe, ²³Na, ⁸⁸Sr, ⁵⁵Mn, ⁶⁶Zn, ⁶⁵Cu, ⁸⁵Rb, ⁶¹Ni, ⁵³Cr, ⁵¹ V, ⁷Li, ²⁰⁸Pb, ⁵⁹Co, ⁷⁵As, ¹³³Cs, ⁷¹ Ga, ⁷⁷Se, ²⁰⁵Tl, ¹¹⁴Cd, ²³⁸U, ¹⁰⁷Ag, ⁴Be and ²⁰²Hg). Among macro elements, ³⁹ K showed the highest level, whereas ²³Na was found to have the lowest content value. Micro elements showed the concentrations order of: ⁸⁸Sr > ⁵⁵Mn > ⁶⁶Zn > ⁸⁵Rb > ⁶⁵Cu. Among trace elements, ⁵³Cr and ⁶¹Ni showed higher content and ⁴Be was not detected in all samples. Intra-regions differentiation was performed by principal component analysis (PCA), cluster analysis (CA) and supervised learning algorithms such as linear discriminant analysis (LDA), k-nearest neighbors (k-NN), support vector machines (SVM), and random forests (RF). Among them, the RF model performed the best with an accuracy rate of 78.79%. Multi-elemental analysis combined with RF was a reliable method to identify the origins of Codonopsis Radix in Gansu province.

Second-order electrochemical data generation to quantify carvacrol in oregano essential oils

A novel analytical method using voltammetric second-order modeling based on multivariate curve resolution-alternating least-square (MCR-ALS) is presented for the first time for the quantitation of carvacrol (CAR) in oregano essential oils (OEO). The second-order cyclic voltammetry data were generated on the basis that CAR shows a diffusional system. Thus, the scan rate (v) was used as a second instrumental mode and cyclic voltammograms at different v were acquired for a single sample, generating the second-order data. CAR determination was performed in presence of thymol, included as a potential interferent. Results demonstrated that MCR-ALS successfully exploited the second-order advantage and the recoveries were not statistically different than 100%. The limits of detection and quantitation were estimated using the MCR-ALS which were 6.27 × 10^-5°mol°L^-1°and 1.90 × 10^-4°mol L^-1, respectively. Finally, the developed methodology was implemented to quantify of CAR in OEO samples.

Large-Area Interfaces for Single-Molecule Label-free Bioelectronic Detection

Bioelectronic transducing surfaces that are nanometric in size have been the main route to detect single molecules. Though enabling the study of rarer events, such methodologies are not suited to assay at concentrations below the nanomolar level. Bioelectronic field-effect-transistors with a wide (μm²-mm²) transducing interface are also assumed to be not suited, because the molecule to be detected is orders of magnitude smaller than the transducing surface. Indeed, it is like seeing changes on the surface of a one-kilometer-wide pond when a droplet of water falls on it. However, it is a fact that a number of large-area transistors have been shown to detect at a limit of detection lower than femtomolar; they are also fast and hence innately suitable for point-of-care applications. This review critically discusses key elements, such as sensing materials, FET-structures, and target molecules that can be selectively assayed. The amplification effects enabling extremely sensitive large-area bioelectronic sensing are also addressed.

A Lab-on-a-Chip Based Automatic Platform for Continuous Nitrites Sensing in Aquaculture

In aquaculture, the density of fish stock, use of feeding, and surrounding environmental conditions can easily result in an excessive concentration of harmful compounds that require continuous monitoring. Chemical sensors are available for most of these compounds, however, operative conditions and continuous monitoring in water make the development of sensors suitable for long and unattended deployments difficult. A possible solution is the development of engineered automatic labs where the uptake of sample and the contact with water is reduced and the use of a minimal quantity of reagents enables the implementation of reliable chemical assays. In this paper, a platform for automatic chemical assays is presented. The concept is demonstrated with the detection of nitrites based on the well-known colorimetric Griess reaction. The platform is centered around a lab-on-a-chip where reagents and water samples are mixed. The color of the reaction product is measured with low-cost optoelectronic components. Results show the feasibility of the approach with a minimum detectable concentration of about 0.1 mg/L which is below the tolerance level for aquaculture farms.

Quantitative analysis of carbaryl and thiabendazole in complex matrices using excitation-emission fluorescence matrices with second-order calibration methods

In this paper, a fast and efficient analytical strategy was proposed that chemometrics assisted with excitation-emission fluorescence matrices was used to quantify carbaryl (CAR) and thiabendazole (TBZ) in peach, soil and sewage. Even if there are serious overlapped peaks and unknown interferences in fluorescence analysis, the second-order calibration method based on alternating trilinear decomposition (ATLD) algorithm can be used to analyze CAR and TBZ in peach, soil and sewage. The recoveries of CAR and TBZ in peach are 110.4% and 99.7% and their standard deviations are lower than 2.1% and 0.3%, respectively. In addition, the accuracy of the method was assessed with figures of merit as well as intra-day and inter-day precision. The limit of detection, the limit of quantitation of CAR and TBZ in peach are 1.2 ng mL^-1 and 0.3 ng mL^-1, 3.5 ng mL^-1 and 0.8 ng mL^-1, respectively. And their root-mean-square error of prediction are 17.0 ng mL^-1 and 5.0 ng mL^-1 and there are high sensitivity and selectivity in this method. Meanwhile, the results obtained by ATLD algorithm were compared with those obtained by the self-weighted alternate trilinear decomposition algorithm (SWATLD) and the parallel factor analysis (PARAFAC) algorithm, and statistical methods such as the t-test, F-test and the elliptic joint confidence region were used to evaluate for analysis. There were no significant differences among these methods. At last, high performance liquid chromatography-fluorescence detector (HPLC-FLD) was used to evaluate the accuracy and reliability of the proposed method. These results are satisfactory and indicate that the proposed method can be used for accurate and rapid determination of pesticides in complex systems.

Data fusion of middle-resolution NMR spectroscopy and low-field relaxometry using the Common Dimensions Analysis (ComDim) to monitor diesel fuel adulteration

Medium-resolution (MR-NMR) and time-domain NMR relaxometry (TD-NMR) using benchtop and low-field NMR instruments are powerful tools to tackle fuel adulteration issues. In this work, for the first time, we investigate the possibility of enhancing the low-field NMR capability on fuel analysis using data fusion of MR and TD-NMR. We used the ComDim (Common Dimensions Analysis) multi-block analysis to join the data, which allowed exploration, classification, and quantification of common adulterations of diesel fuel by vegetable oils, biodiesel, and diesel of different sources as well as the sulfur content. After data exploration using ComDim, classification (applying linear discriminant analysis, LDA), and regression (applying multiple linear regression, MLR), models were built using ComDim scores as input variables on the LDA and MLR analyses. This approach enabled 100% of accuracy in classifying diesel fuel source (refinery), sulfur content (S10 or S500), vegetable oil, and biodiesel source. Moreover, in the quantification step, all MLR models showed a root mean square error of prediction (RMSEP) and the residual prediction deviation (RPD) values comparable to the literature for determining diesel, vegetable oil, and biodiesel contents.

Chemometric-assisted kinetic determination of oxytetracycline using AgInS2 quantum dots as PL sensing platforms

In this work a kinetic fluorometric methodology relying on the time-based monitoring of the photoluminescence quenching of AgInS₂ ternary quantum dots induced by oxytetracycline, was developed. The kinetic approach allowed not only to reduce the LOD and improve sensitivity and selectivity but also to collect second-order data that was explored for the quantification of the target analyte in the presence of uncalibrated interfering species. Upon processing the acquired second-order kinetic PL data by unfolded partial least-squares (U-PLS), oxytetracycline was quantified in commercially available pharmaceutical formulations. The obtained results, namely an R²_P higher than 0.99 and RE lower than 8%, proved the suitability and accuracy of the developed approach.

Chemometric analysis in Raman spectroscopy from experimental design to machine learning-based modeling

Raman spectroscopy is increasingly being used in biology, forensics, diagnostics, pharmaceutics and food science applications. This growth is triggered not only by improvements in the computational and experimental setups but also by the development of chemometric techniques. Chemometric techniques are the analytical processes used to detect and extract information from subtle differences in Raman spectra obtained from related samples. This information could be used to find out, for example, whether a mixture of bacterial cells contains different species, or whether a mammalian cell is healthy or not. Chemometric techniques include spectral processing (ensuring that the spectra used for the subsequent computational processes are as clean as possible) as well as the statistical analysis of the data required for finding the spectral differences that are most useful for differentiation between, for example, different cell types. For Raman spectra, this analysis process is not yet standardized, and there are many confounding pitfalls. This protocol provides guidance on how to perform a Raman spectral analysis: how to avoid these pitfalls, and strategies to circumvent problematic issues. The protocol is divided into four parts: experimental design, data preprocessing, data learning and model transfer. We exemplify our workflow using three example datasets where the spectra from individual cells were collected in single-cell mode, and one dataset where the data were collected from a raster scanning-based Raman spectral imaging experiment of mice tissue. Our aim is to help move Raman-based technologies from proof-of-concept studies toward real-world applications.

Being Uncertain in Chromatographic Calibration-Some Unobvious Details in Experimental Design

This is an introductory tutorial and review about the uncertainty problem in chromatographic calibration. It emphasizes some unobvious, but important details influencing errors in the calibration curve estimation, uncertainty in prediction, as well as the connections and dependences between them, all from various perspectives of uncertainty measurement. Nonuniform D-optimal designs coming from Fedorov theorem are computed and presented. As an example, all possible designs of 24 calibration samples (3–8, 4–6, 6–4, 8–3 and 12–2, both uniform and D-optimal) are compared in context of many optimality criteria. It can be concluded that there are only two independent (orthogonal, but slightly complex) trends in optimality of these designs. The conclusions are important, as the uniform designs with many concentrations are not the best choices, contrary to some intuitive perception. Nonuniform designs are visibly better alternative in most calibration cases.

Rapid determination of sulfamethoxazole and trimethoprim illegally added to health products using excitation-emission matrix fluorescence coupled with the second-order calibration method

In this work, a simple and fast analytical method based on a self-weighted alternating trilinear decomposition (SWATLD) algorithm coupled with excitation-emission matrix (EEM) fluorescence was developed for the simultaneous determination of sulfamethoxazole (SMZ) and trimethoprim (TMP) illegally added to health products. With the second-order advantage, the proposed method obtained satisfactory results in the presence of peak overlap and unknown interferences. The analysis time for a single sample is only 0.8 minutes. The average spiked recoveries of SMZ and TMP in three health product spiked samples were in the range of 91.0-106.2% and 86.8-107.8%, respectively. The relative standard deviations (RSDs) were lower than 8.6%. In addition, verification parameters including sensitivity (SEN), selectivity (SEL), the limit of detection (LOD), the limit of quantification (LOQ), intra-day precision, and inter-day precision were calculated, and the results show that the proposed method is feasible. The quantitative results of the proposed method were further confirmed by the LC-MS/MS method, which proved that the proposed method was efficient and green for drug-abuse monitoring of SMZ and TMP in health products.

Chromatographic Applications in the Multi-Way Calibration Field

In this review, recent advances and applications using multi-way calibration protocols based on the processing of multi-dimensional chromatographic data are discussed. We first describe the various modes in which multi-way chromatographic data sets can be generated, including some important characteristics that should be taken into account for the selection of an adequate data processing model. We then discuss the different manners in which the collected instrumental data can be arranged, and the most usually applied models and algorithms for the decomposition of the data arrays. The latter activity leads to the estimation of surrogate variables (scores), useful for analyte quantitation in the presence of uncalibrated interferences, achieving the second-order advantage. Recent experimental reports based on multi-way liquid and gas chromatographic data are then reviewed. Finally, analytical figures of merit that should always accompany quantitative calibration reports are described.