A tutorial on the validation of qualitative methods: From the univariate to the multivariate approach

NMR Longitudinal Rotating Frame Relaxation Time (T1ρ) with a Weak Spin Locking Field as an Approach to Characterize Solid-State Active Pharmaceutical Ingredients: Proof of Concept

Nuclear magnetic resonance (NMR) longitudinal rotating frame relaxation time (T1ρ), rarely used in low-field NMR, can be more effective than conventional T1 and T2 relaxation times to differentiate polymorphic forms of solid pharmaceuticals. This could be attributed to T1ρ sensibility to structural and molecular dynamics that can be enhanced by changing the strength of the oscillating magnetic field (B1) of spinlock pulses. Here, we compared the capacity of T1, T2, and T1ρ to differentiate inactive (A) and active (C) crystalline forms of the World Health Organization essential drug Mebendazole. The results showed that T1 and T2 values of both forms were statistically identical at 0.47 T. Conversely, T1ρ of both forms measured with weak spinlock B1 fields, ranging from 0.08 to 0.80 mT were statistically different in the same spectrometer. The T1ρ also has the limit of detection to detect the presence of at least 10% of inactive A form in the active C form. Therefore, T1ρ, measured with weak spinlock B1 fields can be an effective, streamlined, and complementary approach for characterizing not only solid active pharmaceutical ingredients but other solid-state materials as well.

Multiphase electroextraction of malachite green from surface water and its determination using digital imaging and chemometric tools

This study introduces a novel method for the quantification of malachite green (MG), a pervasive cationic dye, in surface water by synergizing multiphase electroextraction (MPEE) with digital image analysis (DIA) and partial least square discriminant analysis. Aimed at addressing the limitations of conventional DIA methods in terms of quantitation limits and selectivity, this study achieves a significant breakthrough in the preconcentration of MG using magnesium silicate as a novel sorbent. Demonstrating exceptional processing efficiency, the method allows for the analysis of 10 samples within 20 min, exhibiting remarkable sensitivity and specificity (over 0.95 and 0.90, respectively) across 156 samples in both training and test sets. Notably, the method detects MG at low concentrations (0.2 µg L-1) in complex matrices, highlighting its potential for broader application in environmental monitoring. This approach not only underscores the method's cost-effectiveness and simplicity but also its precision, making it a valuable tool for the preliminary testing of MG in surface waters. This study underscores the synergy among MPEE, DIA, and chemometric tools, presenting a cost-efficient and reliable alternative for the sensitive detection of water contaminants.

Authentication and discrimination of new Brazilian Canephora coffees with geographical indication using a miniaturized near-infrared spectrometer

New Brazilian Canephora coffees (Conilon and Robusta) of high added value from specific origins have been protected by geographical indication to guarantee their origin and quality. Recently, benchtop near-infrared (NIR) spectroscopy combined with chemometrics has demonstrated its usefulness to discriminate them. It was the first study, however, and therefore the possibility exists to develop a new portable NIR method for this purpose. This work assessed a miniaturized NIR as a cheaper spectrometer to discriminate and authenticate new Brazilian Canephora coffees with certified geographical origins and to differentiate them from specialty Arabica. Discriminant chemometric and class modeling techniques have been applied and have obtained good predictive ability on external test sets. In addition, models with similar classification purpose were compared with those obtained in previous research carried out with benchtop NIR for the same samples, obtaining comparable results. In this context, the portable method was used as a laboratory technique and has the advantage of being cheaper than benchtop NIR spectrometer. Furthermore, it brings a high possibility to be implemented in small coffee cooperatives, industries or control agencies in the future that do not have high economic resources.

Evaluating the sensitivity, stability, and cross-reactivity of commercial fentanyl immunoassay test strips

Illicit fentanyl has flooded the United States' drug market, increasing the risk of overdose and poisonings throughout the general population and accidental exposure among law enforcement officers confiscating the increasing number of seizures. Fentanyl test strips (FTS) are used to obtain presumptive information about the presence of fentanyl in a suspected sample. However, their adoption by law enforcement personnel and seized-drug analysts has been limited because most products are advertised for urine testing, not for assays using water solutions. This study presents an evaluation of four commercial FTS: Rapid Response from BTNX, Inc.; T-Dip Fentanyl (FTY) Urine Dip Cards obtained from Amazon.com; Premier BioDip FYL10 from Premier Biotech Inc.; and MobileDetect Fentanyl strips from DetectaChem, Inc. Performance characteristics curves were used to compare the products' sensitivity, showing that all can reliably detect fentanyl in aqueous solutions at concentrations below 1 μg/mL, with some of the tests able to reliably detect the drug at 200 ng/mL. A stability study demonstrates the performance of all four FTS brands was only slightly affected after 30 days of storage at two extreme environmental conditions. Fentanyl-related substances are also evaluated using the Rapid Response FTS, which showed high cross-reactivity with para-fluorofentanyl and acetylfentanyl, but lower with ortho-chlorofentanyl, carfentanil, and 4-ANPP. Users should be aware that FTS may give false-negative results even when potentially dangerous levels of carfentanil are present. When testing other common drugs, adulterants, and diluents frequently encountered in seized tablets, concentration-dependent results were obtained and multiple instances of false positives were recorded.

A mid-infrared spectroscopy-random forest system for the origin tracing of Chinese geographical indication Aconiti Lateralis Radix Praeparata

Reliable origin certification methods are essential for the protection of high-value genuine medicinal material with designated origins and geographical indication (GI) products. Aconiti Lateralis Radix Praeparata (Fuzi), one well-known traditional Chinese medicine and geographical indication products have remarkable efficacy and wide clinical application, with high demand in domestic and international markets. The efficacy and price of Fuzi from different origins vary, and it is difficult for the general public to accurately identify them through traditional experience. The mass spectrometry detection technology based on the plant metabolomics is tedious and lengthy in test sample preparation, complicated in operation, long in detection time, and low in reproducibility. As a sophisticated, green, fast, and low-loss detection technique, infrared spectroscopy is integrated by machine learning to bring new ways for quality regulation and control of traditional Chinese medicines. An analytical method based on mid-infrared spectroscopy combined with a random forest algorithm was developed to verify the geographical origin of authentic herbs and/or GI products. The method successfully predicted and classified three varieties of Chinese GI Fuzi and four varieties of non-GI Fuzi. In this study, an environment-friendly traceability strategy with fast analysis, low sample loss and high precision was used to provide a new strategy for identifying the origin of Fuzi.

Optimization and validation of a protein phosphatase inhibition assay for accessible microcystin detection

The presence of cyanobacterial toxins in freshwater constitutes an increasing public health concern, especially affecting developing countries where the high cost of available methods makes monitoring programs difficult. The phosphatase inhibition assay (PPIA) is a sensitive method with low instrument requirements that allows the quantification of the most frequent cyanotoxins, microcystins (MCs). In this work, we implemented a PPIA, starting from Protein Phosphatase 1 (PP1) expression up to the validation with samples of algal blooms from Argentina. To do this, we optimized the expression and lyophilization of PP1, and the assay conditions. Also, we included robustness and possible interference analysis. We evaluated the most widely used cyanobacterial lysis methods and determined that heating for 15 min at 95 °C is simple and adequate for this assay. Then, we performed MC spikes recovery assays on water samples from three dams from Argentina, resulting in a recovery ranging from 77 to 115%. The limit of detection (LOD) was 0.4 μg/L and the linear range is 0.4 μg/L - 5 μg/L. Finally, we evaluated 65 environmental samples where MCs was measured by ELISA test containing from 0 μg/L to 625 μg/L. The PPIA showed excellent correlation (Pearson correlation coefficient = 0.967), no false negative and no false positives above the 1 μg/L WHO guideline (0.11 false positive rate). In conclusion, we optimized and validated a PPIA to be an effective and accessible alternative to available commercial tests.

Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends

Surface-enhanced Raman spectroscopy (SERS) has gained increasing attention because it provides rich chemical information and high sensitivity, being applicable in many scientific fields including medical diagnosis, forensic analysis, food control, and microbiology. Although SERS is often limited by the lack of selectivity in the analysis of samples with complex matrices, the use of multivariate statistics and mathematical tools has been demonstrated to be an efficient strategy to circumvent this issue. Importantly, since the rapid development of artificial intelligence has been promoting the implementation of a wide variety of advanced multivariate methods in SERS, a discussion about the extent of their synergy and possible standardization becomes necessary. This critical review comprises the principles, advantages, and limitations of coupling SERS with chemometrics and machine learning for both qualitative and quantitative analytical applications. Recent advances and trends in combining SERS with uncommonly used but powerful data analysis tools are also discussed. Finally, a section on benchmarking and tips for selecting the suitable chemometric/machine learning method is included. We believe this will help to move SERS from an alternative detection strategy to a general analytical technique for real-life applications.

From fundamentals in calibration to modern methodologies: A tutorial for small molecules quantification in liquid chromatography-mass spectrometry bioanalysis

Over the last two decades, liquid chromatography coupled to mass-spectrometry (LC‒MS) has become the gold standard to perform qualitative and quantitative analyses of small molecules. When quantitative analysis is developed, an analyst usually refers to international guidelines for analytical method validation. In this context, the design of calibration curves plays a key role in providing accurate results. During recent years and along with instrumental advances, strategies to build calibration curves have dramatically evolved, introducing innovative approaches to improve quantitative precision and throughput. For example, when a labeled standard is available to be spiked directly into the study sample, the concentration of the unlabeled analog can be easily determined using the isotopic pattern deconvolution or the internal calibration approach, eliminating the need for multipoint calibration curves. This tutorial aims to synthetize the advances in LC‒MS quantitative analysis for small molecules in complex matrices, going from fundamental aspects in calibration to modern methodologies and applications. Different work schemes for calibration depending on the sample characteristics (analyte and matrix nature) are distinguished and discussed. Finally, this tutorial outlines the importance of having international guidelines for analytical method validation that agree with the advances in calibration strategies and analytical instrumentation.

One-class model with two decision thresholds for the rapid detection of cashew nuts adulteration by other nuts

A green screening method to determine cashew nut adulteration with Brazilian nut, pecan nut, macadamia nut and peanut was proposed. The method was based on the development of a one-class soft independent modelling of class analogy (SIMCA) model for non-adulterated cashew nuts using near-infrared (NIR) spectra obtained with portable equipment. Once the model is established, the assignment of unknown samples depends on the threshold established for the authentic class, which is a key aspect in any screening approach. The authors propose innovatively to define two thresholds: lower model distance limit and upper model distance limit. Samples with distances below the lower threshold are assigned as non-adulterated with a 100% probability; samples with distance values greater than the upper threshold are assigned as adulterated with a 100% probability; and samples with distances within these two thresholds will be considered uncertain and should be submitted to a confirmatory analysis. Thus, the possibility of error in the sample assignment significantly decreases. In the present study, when just one threshold was defined, values greater than 95% for the optimized threshold were obtained for both selectivity and specificity. When two class thresholds were defined, the percentage of samples with uncertain assignment changes according to the adulterant considered, highlighting the case of peanuts, in which 0% of uncertain samples was obtained. Considering all adulterants, the number of samples that were submitted to a confirmatory analysis was quite low, 5 of 224 adulterated samples and 3 of 56 non-adulterated samples.

Synchronous fluorescence spectroscopy and multivariate classification for the discrimination of cachaças and rums

Although cachaça and rum are distilled beverages obtained from the same raw material, they present differences in their chemical compositions. In this study, synchronous fluorescence spectroscopy was used combined with supervised classification models based on the partial least squares discriminant analysis to develop a rapid and low-cost model for discriminating between 50 cachaça and 40 rum samples. Partial least squares discriminant analysis models were constructed using synchronous fluorescence spectra recorded at wavelength differences of 10-100 nm. Initially, spectra were preprocessed by the first derivative with the Savitzky-Golay smoothing, and filter width and polynomial order were selected through face-centered central composite designs. For the construction and validation models, the spectra data were split into two datasets: the training and the test sets containing 60 (C, n = 33; R, n = 27) and 30 (C, n = 17; R, n = 13) samples, respectively. The best discrimination was achieved using fluorescence spectra recorded at wavelength difference 10 nm, allowing the discrimination of cachaça and rum with a classification efficiency of 98%. These results indicate that synchronous fluorescence spectroscopy offers a promising approach for the authentication of cachaças and rums.

PLS-DA and data fusion of visible Reflectance, XRF and FTIR spectroscopy in the classification of mixed historical pigments

In this work samples of historical pigments of green hue were brushed on a canvas and studied by Visible Reflectance, X-Ray Fluorescence and Fourier Transform Infrared Spectroscopy. One hundred samples were investigated, all with green hue, these prepared from pigments themselves green, such as chromium oxide (Cr₂O₃) or from a mixture of pigments that result in green, for example, chrome yellow (PbCrO₄) and Prussian blue (Fe₄[Fe(CN)₆]₃). Because every sample investigated through the spectroscopic techniques were of green hue, the characterization of the pigments present in the mixtures through the visual inspection of spectra has become a complex task in some cases, also, due the large number of recorded spectra. In this work, classification models were developed using the multivariate statistical method Partial Least Squares Discriminant Analysis (PLS-DA) to automate the characterization of the pigments present in the mixtures. The models were developed to classify chromium oxide (Cr₂O₃), chrome yellow (PbCrO₄), cerulean blue (CoO.nSnO₂) and yellow ochre (Fe₂O₃·H₂O + clay + silica). The models were developed from the fusion of data from the three spectroscopic techniques. However, before data fusion, pre-treatments of the spectral data were tested for their influence on the PLS-DA models. The models developed with data from the three techniques made it possible to classify the pigments of interest in the samples with up to 100% effectiveness. The results also indicate that fusion of the data from the three techniques allows to obtain fingerprints of the pigments of interest, which is not always possible using data from only one or two of the techniques applied in this work.

A proper and systematic qualitative method validation procedure and its application to gas chromatography-mass spectrometry analysis of Chemical Weapons Convention related chemicals

Performing a detailed qualitative validation, which is carried out by many laboratories in the forensic community, has been the main goal of this study. In this study, a proper and systematic qualitative method validation procedure was proposed, and its application was shown on the analysis of Chemical Weapon Convention (CWC) related compounds in organic samples. All validation steps were described in detail. The study was carried out in pump oil and dichloromethane (DCM). The limit of detection values were determined for each compound and were found in the range of 0.5-2.0 µg mL^-1 in pump oil and 0.08-1.5 µg mL^-1 in DCM. The validation parameters were calculated, such as the rates of sensitivity, selectivity, false-negative, false-positive, also accordance and concordance. The predicted and obtained results were compared by using Cohen's Kappa Coefficient Test, and the compatibility of the results was found as "very good". After the validation procedure, all of the validation results were evaluated, and the proposed method was confirmed as appropriate for the analysis of CWC-related compounds in organic samples. The applicability of the validated method was proved by determining the CWC-related compounds in organic samples provided by the Organization for the Prohibition of Chemical Weapons during proficiency tests.

Detection of fraud in lime juice using pattern recognition techniques and FT-IR spectroscopy

The lime juice is one of the products that has always fallen victim to fraud by manufacturers for reducing the cost of products. The aim of this research was to determine fraud in distributed lime juice products from different factories in Iran. In this study, 101 samples were collected from markets and also prepared manually and finally derived into 5 classes as follows: two natural classes (Citrus limetta, Citrus aurantifolia), including 17 samples, and three reconstructed classes, including 84 samples (made from Spanish concentrate, Chinese concentrate, and concentrate containing adulteration compounds). The lime juice samples were freeze-dried and analyzed using FT-IR spectroscopy. At first, principal component analysis (PCA) was applied for clustering, but the samples were not thoroughly clustered with respect to their original groups in score plots. To enhance the classification rates, different chemometric algorithms including variable importance in projection (VIP), partial least square-discriminant analysis (PLS-DA), and counter propagation artificial neural networks (CPANN) were used. The best discriminatory wavenumbers related to each class were selected using the VIP-PLS-DA algorithm. Then, the CPANN algorithm was used as a nonlinear mapping tool for classification of the samples based on their original groups. The lime juice samples were correctly designated to their original groups in CPANN maps and the overall accuracy of the model reached up to 0.96 and 0.87 for the training and validation procedures. This level of accuracy indicated the FT-IR spectroscopy coupled with VIP-PLS-DA and CPANN methods can be used successfully for detection of authenticity of lime juice samples.

Screening-confirmation strategy for nanomaterials involving spectroscopic analytical techniques and its application to the control of silver nanoparticles in pastry samples

The full characterization of nanomaterials (NMs), which requires a range of different and expensive equipment, is not always necessary to meet certain demands of information. Qualitative analytical methods are ideal alternatives when only a piece of information is required. In this work, a qualitative method for the screening of NMs has been developed and statistically validated for the first time, with silver nanoparticles (AgNPs) as a proof of concept. It is based on the generation of chemiluminescence of the luminol/Ag⁺ system in alkaline media in the presence of AgNPs. Measurements are obtained in a short time with a simple instrument. Probability of detection (POD) curves were constructed at three cut-off values next to the limit of detection of the chemiluminescent method. The unreliability region (UR) was from 0.50 to 1.82 μg L^-1. Currently, no regulation on AgNPs exists, but the present method was successfully tested for a hypothetical threshold of 2.5 μg L^-1 of 40 nm AgNPs. The method was applied to silver colored pearls, with silver (E-174) or aluminum (E-173) coating, used for decoration of pastry. Performance parameters such as false negative and positive rates as well as specificity, sensitivity and reliability rates were calculated for validation. The results of the screening method were confirmed by asymmetric flow field flow fractionation coupled to inductively coupled plasma mass spectrometry (AF4-ICP-MS). The proposed screening method is simple, fast, economic, and easy to transfer to routine laboratories in the field of food safety.

Portable NIR spectrometer for quick identification of fat bloom in chocolates

Cocoa butter provides desirable sensory properties to chocolates; however, the exposure of chocolate to temperature variations during transportation and/or storage can lead to changes in the polymorphic form of butter, with the appearance of a dull-white film on the chocolate surface, known as fat bloom. This study investigated the use of a portable NIR spectrometer combined with chemometric tools to discriminate milk chocolate, white chocolate, 40% cocoa chocolate, and 70% cocoa chocolate samples, which were subjected to temperature abuse for 6 hours. The PCA allowed separating the samples into three classes: control at 20 °C, chocolate subjected to 35 °C, and chocolate subjected to 40 °C, for each type of chocolate studied. The PLS-DA models provided sensibility, specificity, and accuracy values in the range of 80 to 100%, and allowed identifying the wavelengths associated with the different chocolates that most impacted the construction of the models.

ROC curves for the optimization of one-class model parameters. A case study: Authenticating extra virgin olive oil from a Catalan protected designation of origin

This paper proposes a ROC curve-based methodology to find optimal classification model parameters. ROC curves are implemented to set the optimal number of PCs to build a one-class SIMCA model and to set the threshold class value that optimizes both the sensitivity and specificity of the model. The authentication of the geographical origin of extra-virgin olive oils of Arbequina botanical variety is presented. The model was developed for samples from Les Garrigues, target class, Samples from Siurana were used as the non-target class. Samples were measured by FT-Raman with no pretreatment. PCA was used as exploratory technique. Spectra underwent pre-treatment and variables were selected based on their VIP score values. ROC curve and others already known criteria were applied to set the threshold class value. The results were better when the ROC curve was used, obtaining performance values higher than 82%, 75% and 77% for sensitivity, specificity and efficiency, respectively.

The occurrence: A meaningful parameter to be considered in the validation of multivariate classification-based screening methods - Application for authenticating virgin olive oil

The development of multivariate screening analytical methods in the analytical chemistry field focused particularly on food authentication is growing in recent years, which is evidenced by the increase of scientific publications. Currently there are several guides and technical reports about how -univariate qualitative methods should be properly validated to produce reliable and accurate (fitted-for-purpose) results. Nevertheless, this is not the case when multivariate methods are considered. Aimed at redressing this untenable disadvantage, this paper proposes some guidelines for the validation of multivariate classification-based screening methods. As an application example, the detection of adulteration of virgin olive oil with any other edible vegetal oils is showed. The analytical techniques employed are liquid chromatography coupled to diode array detector (LC-DAD) and gas chromatography coupled to flame ionization detector (GC-FID). For the correct validation of the multivariate screening method a new parameter which never considered before, named occurrence, is accounted. Also, it has been developed two new applicability indicators of the multivariate screening methods: the assignation error index (I_ERROR) and the index saving (I_SAVING) to establish the validation requirements. Then the validation parameters of the methods: precision (or target predictive value), sensitivity, non-target predictive value, specificity and accuracy were estimated. The main conclusion of the work has been the need to take accounts the occurrence value to establish the specific validation requirements to apply the multivariate screening method in a particular scenario.

Differential diagnosis of hereditary hemolytic anemias in a single multiscreening test by TGA/chemometrics

A multi-screening test based on the coupling of thermogravimetry and chemometrics was optimized for the differential diagnosis of hereditary hemolytic anemias.

Differentiation of avocados according to their botanical variety using liquid chromatographic fingerprinting and multivariate classification tree

BACKGROUND

The oil content, composition and marketing threshold value of an avocado depends on the cultivar hence, identifying the cultivar of the avocado fruit is desirable. However, analytical methods have not been reported with this aim.

RESULTS

A multivariate classification tree method was proposed to discriminate three commercial botanical varieties of avocado: Hass, Fuerte and Bacon, using high-performance liquid chromatography coupled to a charged aerosol detector (HPLC-CAD). Prior to the chromatographic analysis the avocados were lyophilized and then the oil fraction was extracted using a pressurized liquid extraction system. Normal and reverse phase liquid chromatography were applied in order to obtain the chromatographic fingerprint for each sample. Soft independent modelling of class analogies (SIMCA) and partial least-squares discriminant analysis (PLS-DA) were applied. Classification quality metrics were determined to evaluate the performance of the classification. Several strategies to develop the classification models were employed. Finally, the useful application of 'classification trees' methodology, which has been scarcely applied in the field of analytical food control, was evaluated to perform a multiclass classification.

CONCLUSION

Discrimination of the three botanical varieties was achieved. The best classification was obtained when the PLS-DA is applied on the normal-phase chromatographic fingerprints. Classification trees are showed to be useful tools that provide complementary information to single concatenated models showing different results from the same prediction sample set. © 2019 Society of Chemical Industry.

Selection and characterization of botanical natural products for research studies: a NaPDI center recommended approach

Covering: up to the end of 2018 Dietary supplements, which include botanical (plant-based) natural products, constitute a multi-billion-dollar industry in the US. Regulation and quality control for this industry is an ongoing challenge. While there is general agreement that rigorous scientific studies are needed to evaluate the safety and efficacy of botanical natural products used by consumers, researchers conducting such studies face a unique set of challenges. Botanical natural products are inherently complex mixtures, with composition that differs depending on myriad factors including variability in genetics, cultivation conditions, and processing methods. Unfortunately, many studies of botanical natural products are carried out with poorly characterized study material, such that the results are irreproducible and difficult to interpret. This review provides recommended approaches for addressing the critical questions that researchers must address prior to in vitro or in vivo (including clinical) evaluation of botanical natural products. We describe selection and authentication of botanical material and identification of key biologically active compounds, and compare state-of-the-art methodologies such as untargeted metabolomics with more traditional targeted methods of characterization. The topics are chosen to be of maximal relevance to researchers, and are reviewed critically with commentary as to which approaches are most practical and useful and what common pitfalls should be avoided.

Chemometrics in forensic chemistry - Part I: Implications to the forensic workflow

The forensic literature shows a clear trend towards increasing use of chemometrics (i.e. multivariate analysis and other statistical methods). This can be seen in different disciplines such as drug profiling, arson debris analysis, spectral imaging, glass analysis, age determination, and more. In particular, current chemometric applications cover low-dimensional (e.g. drug impurity profiles) and high-dimensional data (e.g. Infrared and Raman spectra) and are therefore useful in many forensic disciplines. There is a dominant and increasing need in forensic chemistry for reliable and structured processing and interpretation of analytical data. This is especially true when classification (grouping) or profiling (batch comparison) is of interest. Chemometrics can provide additional information in complex crime cases and enhance productivity by improving the processes of data handling and interpretation in various applications. However, the use of chemometrics in everyday work tasks is often considered demanding by forensic scientists and, consequently, they are only reluctantly used. This article and following planned contributions are dedicated to those forensic chemists, interested in applying chemometrics but for any reasons are limited in the proper application of statistical tools - usually made for professionals - or the direct support of statisticians. Without claiming to be comprehensive, the literature reviewed revealed a sufficient overview towards the preferably used data handling and chemometric methods used to answer the forensic question. With this basis, a software tool will be designed (part of the EU project STEFA-G02) and handed out to forensic chemist with all necessary elements of data handling and evaluation. Because practical casework is less and less accompanied from the beginning to the end out of the same hand, more and more interfaces are built in through specialization of individuals. This article presents key influencing elements in the forensic workflow related to the most meaningful chemometric application and evaluation.

SWATH-MS screening strategy for the determination of food dyes in spices by UHPLC-HRMS

A multi-class wide-scope screening method for the detection and identification of artificial colours and illegal dyes in spices was developed for regulatory purposes. The screening was carried out by ultra-high performance liquid chromatography hyphenated with a quadrupole/time-of-flight mass spectrometry (UHPLC-QTOF-MS) with sequential window acquisition of all theoretical fragment-ion spectra (SWATH) and was validated with forty-one compounds by spiking experiments in curry and paprika extracts. In order to detect and identify the compounds with a high level of confidence, a home-made tandem mass spectrometry (QTOF-MS/MS) database of approximately one hundred illegal dyes and artificial colours was created. The procedure was then used to screen field samples of spices and spice blends purchased from Swiss markets. Sudan IV, Sudan I, bixin (E160b) and Ponceau 4R (E124) were all detected among the eight non-compliant samples.

Mesoporosity based classification of ZSM-5 nano catalysts using DRIFT spectroscopy and chemometrics

DRIFT spectra were used for classification of ZSM-5 catalysts according to their mesopore volumes. The spectra were pretreated by Savitzky-Golay smoothing and standard normal variate (SNV) algorithms prior to outlier detection by Hotelling T² statistic technique. Supervised classification was applied to the spectra using partial least squares-discriminant analysis (PLS-DA) and soft independent modelling of class analogies (SIMCA) algorithms. The samples were classified into three classes related to their mesopore volumes by the proposed method and the results were in accordance with N₂ physisorption textural analysis using Brunauer-Emmett-Teller (BET) model. The confusion matrix and classification efficiency parameters including sensitivity, specificity, accuracy and precision were calculated. Classification accuracy of 96% and error rate of 2% was obtained using PLS-DA algorithm while SIMCA algorithm by providing 100% classification accuracy and zero error rate proved better performance in classification of ZSM-5 catalysts.