Classification of bloodstains deposited at different times on floor tiles using hierarchical modelling and a handheld NIR spectrometer.

Bloodstains are commonly encountered at crime scenes, especially on floor tiles, and can be deposited over different periods and intervals. Therefore, it is crucial to develop techniques that can accurately identify bloodstains deposited at different times. This study builds upon a previous investigation and aims to enhance the performance of three distinct hierarchical models (HMs) designed to differentiate and identify stains of human blood (HB), animal blood (AB), and common false positives (CFPs) on nine different types of floor tiles. Soft Independent Modeling Class Analogies (SIMCA), and Partial Least Squares-Discriminant Analysis (PLS-DA) were employed as decision rules in this process. The originally published model was constructed using a training set that included samples with a known time of deposit of six days. This model was then tested to predict samples with various deposition times, including human blood samples aged for 0, 1, 9, 20, 30, and 162 days, as well as animal blood samples aged for 0, 1, 10, 13, 20, 29, 105, and 176 days. To improve the identification of human blood, the models were modified by adding zero-day and one-day-old bloodstains to the original training set. All models showed improvement when fresher samples were included in the training set. The best results were achieved with the hierarchical model that used partial least squares-discriminant analysis as the second decision rule and incorporated one-day-old samples in the training set. This model yielded sensitivity values above 0.92 and specificity values above 0.7 for samples aged between zero and 30 days.

Colorimetric pH indicators based on well-defined amylose and amylopectin matrices enriched with anthocyanins from red cabbage.

This study aimed to prepare a novel colorimetric indicator film from virtually pure (99 %) amylose (AM) and anthocyanins extracted from red cabbage (RCA). The AM used was a unique engineered bulk material extracted from transgenic barley grains. Films produced by solution casting were compared to normal barely starch (NB) and pure barley amylopectin (AP), with amylose contents of 30 % and 0 %, respectively. The pH-indicator films were produced by incorporation of RCA into the different starch support matrices with different amylose contents. Barrier, thermal, and mechanical properties, photo degradation stability, and release behavior data revealed that RCA interact differently through the glucan matrices. Microstructural observations showed that RCA were evenly dispersed in the glucan matrix, and AM+RCA indicator films showed high UV-barrier and mechanical performance over normal starch. FTIR revealed that RCA was properly affected by the AM matrix. Moreover, the AM+RCA films showed sensitive color changes in the pH range (2-11) and a predominant Fickian diffusion release mechanism for RCA. This study provides for the first time data regarding AM films with RCA and their promising potential for application as support matrices in responsive food and other industrial biodegradable packaging materials.

Excitation-Emission Matrix Fluorescence Spectroscopy Coupled with PARAFAC Modeling for Viability Prediction of Cells.

Cell-based sensors and assays have great potential in bioanalysis, drug discovery screening, and biochemical mechanisms research. The cell viability tests should be fast, safe, reliable, and time- and cost-effective. Although methods stated as "gold standards", such as MTT, XTT, and LDH assays, usually fulfill these assumptions, they also show some limitations. They can be time-consuming, labor-intensive, and prone to errors and interference. Moreover, they do not enable the observation of the cell viability changes in real-time, continuously, and nondestructively. Therefore, we propose an alternative method of viability testing: native excitation-emission matrix fluorescence spectroscopy coupled with parallel factor analysis (PARAFAC), which is especially advantageous for cell monitoring due to its noninvasiveness and nondestructiveness and because there is no need for labeling and sample preparation. We demonstrate that our approach provides accurate results with even better sensitivity than the standard MTT test. With PARAFAC, it is possible to study the mechanism of the observed cell viability changes, which can be directly linked to increasing/decreasing fluorophores in the cell culture medium. The resulting parameters of the PARAFAC model are also helpful in establishing a reliable regression model for accurate and precise determination of the viability in A375 and HaCaT-adherent cell cultures treated with oxaliplatin.

PARASIAS: A new method for analyzing higher-order tensors with shifting profiles.

Higher-order tensor data analysis has been extensively employed to understand complicated data, such as multi-way GC-MS data in untargeted/targeted analysis. However, the analysis can be complicated when one of the modes shifts e.g., the elution profiles of specific compounds often with respect to retention time; something which violates the assumptions of more traditional models. In this paper, we introduce a new analysis method named PARASIAS for analyzing shifted higher-order tensor data by combining spectral transformation and the simple PARAFAC modeling. The proposed method is validated by applications on both simulated and real multi-way datasets. Compared to the state-of-art PARAFAC2 model, the results indicate that fitting of PARASIAS is 13 times faster on simulated datasets and more than eight times faster on average on the real datasets studied. PARASIAS has significant advantages in terms of model simplicity, convergence speed, the robustness to shift changes in the data, the ability to impose non-negativity constraint on the shift mode and the possibility of easily extending to data with multiple shift modes. However, the resolved profiles of PARASIAS model are always a little worse when the number of components in the data are larger than three and without using additional factors in PARASIAS model. In such cases, more components are necessary for PARASIAS to model the data than that would be needed e.g., by PARAFAC2. The reason for this is also discussed in this work.

Using fluorescence excitation-emission matrices to predict bitterness and pungency of virgin olive oil: A feasibility study.

Unlike other food products, virgin olive oil must undergo an organoleptic assessment that is currently based on a trained human panel, which presents drawbacks that might affect the efficiency and robustness. Therefore, disposing of instrumental methods that could serve as screening tools to support sensory panels is of paramount importance. The present work aimed to explore excitation-emission fluorescence spectroscopy (EEFS) to predict bitterness and pungency, since both attributes are related with fluorophore compounds, such as polar phenols. Bitterness and pungency intensities of 250 samples were provided by an official sensory panel and used to build and compare partial least squares regressions (PLSR) with the excitation-emission matrix. Both PARAFAC scores and two-way unfolded data led to successful PLSR. The most relevant PARAFAC scores agreed with virgin olive oil phenolic spectra, evidencing that EEFS would be the fit-for-purpose screening tool to support the sensory panel.

Trusting our machines: validating machine learning models for single-molecule transport experiments.

In this tutorial review, we will describe crucial aspects related to the application of machine learning to help users avoid the most common pitfalls. The examples we present will be based on data from the field of molecular electronics, specifically single-molecule electron transport experiments, but the concepts and problems we explore will be sufficiently general for application in other fields with similar data. In the first part of the tutorial review, we will introduce the field of single-molecule transport, and provide an overview of the most common machine learning algorithms employed. In the second part of the tutorial review, we will show, through examples grounded in single-molecule transport, that the promises of machine learning can only be fulfilled by careful application. We will end the tutorial review with a discussion of where we, as a field, could go from here.

Automatic and non-targeted analysis of the volatile profile of natural and alkalized cocoa powders using SBSE-GC-MS and chemometrics.

A total of 56 key volatile compounds present in natural and alkalized cocoa powders have been rapidly evaluated using a non-target approach using stir bar sorptive extraction gas chromatography mass spectrometry (SBSE-GC-MS) coupled to Parallel Factor Analysis 2 (PARAFAC2) automated in PARADISe. Principal component analysis (PCA) explained 80% of the variability of the concentration, in four PCs, which revealed specific groups of volatile characteristics. Partial least squares discriminant analysis (PLS-DA) helped to identify volatile compounds that were correlated to the different degrees of alkalization. Dynamics between compounds such as the acetophenone increasing and toluene and furfural decreasing in medium and strongly alkalized cocoas allowed its differentiation from natural cocoa samples. Thus, the proposed comprehensive analysis is a useful tool for understanding volatiles, e.g., for the quality control of cocoa powders with significant time and costs savings.

Hierarchical classification models and Handheld NIR spectrometer to human blood stains identification on different floor tiles.

One of the most important types of evidence in certain criminal investigations is traces of human blood. For a detailed investigation, blood samples must be identified and collected at the crime scene. The present study aimed to evaluate the potential of the identification of human blood in stains deposited on different types of floor tiles (five types of ceramics and four types of porcelain tiles) using a portable NIR instrument. Hierarchical models were developed by combining multivariate analysis techniques capable of identifying traces of human blood (HB), animal blood (AB) and common false positives (CFP). The spectra of the dried stains were obtained using a portable MicroNIR spectrometer (Viavi). The hierarchical models used two decision rules, the first to separate CFP and the second to discriminate HB from AB. The first decision rule, used to separate the CFP, was based on the Q-Residual criterion considering a PCA model. For the second rule, used to discriminate HB and AB, the Q-Residual criterion were tested as obtained from a PCA model, a One-Class SIMCA model, and a PLS-DA model. The best results of sensitivity and specificity, both equal to 100%, were obtained when a PLS-DA model was employed as the second decision rule. The hierarchical classification models built for these same training sets using a PCA or SIMCA model also obtained excellent sensitivity results for HB classification, with values above 94% and 78% of specificity. No CFP samples were misclassified. Hierarchical models represent a significant advance as a methodology for the identification of human blood stains at crime scenes.

Spider web biomonitoring: A cost-effective source apportionment approach for urban particulate matter.

Elevated levels of particulate matter (PM) in urban atmospheres are one of the major environmental challenges of the Anthropocene. To effectively lower those levels, identification and quantification of sources of PM is required. Biomonitoring methods are helpful tools to tackle this problem but have not been fully established yet. An example is the sampling and subsequent analysis of spider webs to whose adhesive surface dust particles can attach. For a methodical inspection, webs of orb-weaving spiders were sampled repeatedly from 2016 to 2018 at 22 locations in the city of Jena, Germany. Contents of Ag, Al, As, B, Ba, Ca, Cd, Co, Cr, Cs, Cu, Fe, K, La, Li, Mg, Mn, Mo, Na, Ni, P, Pb, Rb, S, Sb, Si, Sn, Sr, Th, Ti, V, Y, Zn and Zr were determined in the samples using inductively coupled plasma-mass spectrometry (ICP-MS) and inductively coupled plasma-optical emission spectroscopy (ICP-OES) after aqua regia digestion. Multivariate statistical methods were applied for a detailed evaluation. A combination of cluster analysis and principal component analysis allows for the clear identification of three main sources in the study area: brake wear from car traffic, abrasion of tram/train tracks and particles of geogenic origin. Quantitative source contributions reveal that high amounts of most of the metals are derived from a combination of brake wear and geogenic particles, the latter of which are likely resuspended by moving vehicles. This emphasizes the importance of non-exhaust particles connected to road traffic. Once a source identification has been performed for an area of interest, classification models can be applied to assess air quality for further samples from within the whole study area, offering a tool for air quality assessment. The general validity of this approach is demonstrated using samples from other locations.

A Metabolomic Approach to Beer Characterization.

The consumers' interest towards beer consumption has been on the rise during the past decade: new approaches and ingredients get tested, expanding the traditional recipe for brewing beer. As a consequence, the field of "beeromics" has also been constantly growing, as well as the demand for quick and exhaustive analytical methods. In this study, we propose a combination of nuclear magnetic resonance (NMR) spectroscopy and chemometrics to characterize beer. 1H-NMR spectra were collected and then analyzed using chemometric tools. An interval-based approach was applied to extract chemical features from the spectra to build a dataset of resolved relative concentrations. One aim of this work was to compare the results obtained using the full spectrum and the resolved approach: with a reasonable amount of time needed to obtain the resolved dataset, we show that the resolved information is comparable with the full spectrum information, but interpretability is greatly improved.

Can We Trust Score Plots?

In this paper, we discuss the validity of using score plots of component models such as partial least squares regression, especially when these models are used for building classification models, and models derived from partial least squares regression for discriminant analysis (PLS-DA). Using examples and simulations, it is shown that the currently accepted practice of showing score plots from calibration models may give misleading interpretations. It is suggested and shown that the problem can be solved by replacing the currently used calibrated score plots with cross-validated score plots.

Untargeted Metabolomic Profile for the Detection of Prostate Carcinoma-Preliminary Results from PARAFAC2 and PLS-DA Models.

Prostate-specific antigen (PSA) is the main biomarker for the screening of prostate cancer (PCa), which has a high sensibility (higher than 80%) that is negatively offset by its poor specificity (only 30%, with the European cut-off of 4 ng/mL). This generates a large number of useless biopsies, involving both risks for the patients and costs for the national healthcare systems. Consequently, efforts were recently made to discover new biomarkers useful for PCa screening, including our proposal of interpreting a multi-parametric urinary steroidal profile with multivariate statistics. This approach has been expanded to investigate new alleged biomarkers by the application of untargeted urinary metabolomics. Urine samples from 91 patients (43 affected by PCa; 48 by benign hyperplasia) were deconjugated, extracted in both basic and acidic conditions, derivatized with different reagents, and analyzed with different gas chromatographic columns. Three-dimensional data were obtained from full-scan electron impact mass spectra. The PARADISe software, coupled with NIST libraries, was employed for the computation of PARAFAC2 models, the extraction of the significative components (alleged biomarkers), and the generation of a semiquantitative dataset. After variables selection, a partial least squares-discriminant analysis classification model was built, yielding promising performances. The selected biomarkers need further validation, possibly involving, yet again, a targeted approach.

Multivariate interpretation of the urinary steroid profile and training-induced modifications. The case study of a Marathon runner.

The steroidal module of the athlete biological passport (ABP) introduced by the World Anti-Doping Agency (WADA) in 2014 includes six endogenous androgenic steroids and five of their concentration ratios, monitored in urine samples collected repeatedly from the same athlete, whose values are interpreted by a Bayesian model on the basis of intra-individual variability. The same steroid profile, plus dihydrotestosterone (DHT) and DHEA, was determined in 198 urine samples collected from an amateur marathon runner monitored over three months preceding an international competition. Two to three samples were collected each day and subsequently analyzed by a fully validated gas chromatography-mass spectrometry protocol. The objective of the study was to identify the potential effects of physical activity at different intensity levels on the physiological steroid profile of the athlete. The results were interpreted using principal component analysis and Hotelling's T2 vs Q residuals plots, and were compared with a profile model based on the samples collected after rest. The urine samples collected after activity of moderate or high intensity, in terms of cardiac frequency and/or distance run, proved to modify the basal steroid profile, with particular enhancement of testosterone, epitestosterone, and 5α-androstane-3α,17ß-diol. In contrast, all steroid concentration ratios were apparently not modified by intense exercise. The alteration of steroid profiles seemingly lasted for few hours, as most of the samples collected 6 or more hours after training showed profiles compatible with the "after rest" model. These observations issue a warning about the ABP results obtained immediately post-competition.

Using deep learning to evaluate peaks in chromatographic data.

Analysis of untargeted gas-chromatographic data is time consuming. With the earlier introduction of the PARAFAC2 (PARAllel FACtor analysis 2) based PARADISe (PARAFAC2 based Deconvolution and Identification System) approach in 2017, this task was made considerably more time-efficient. However, there are still a number of manual steps in the analysis which require data analytical expertise. One of these is the need to define whether or not each PARAFAC2 resolved component represents a peak suitable for integration. As the peaks may change in both shape and location on the elution time-axis, this presents a problem which cannot be readily solved by applying a linear classifier, such as PLS-DA (Partial Least Squares regression for Discriminant Analysis). As part of our ongoing efforts to further automate analysis of Gas Chromatography with Mass Spectrometry (GC-MS), we therefore explore a convolutional neural network classifier, capable of handling these shifts and variations in shape. The theory of convolutional neural networks and application on vector samples is briefly explained, and the performance is tested against a PLS-DA classifier, a shallow artificial neural network and a locally weighted regression model. The models are built on a training set with PARAFAC2 resolved components from eight different aroma related GC-MS runs with a total of over 70,000 elution profile samples, and validated using another, independent, GC-MS dataset. Based on Receiver Operating Characteristic curves (ROC) and manual analysis of the misclassified cases, it is shown that the convolutional network consistently outperforms the competing models, yielding an Area Under the Curve (AUC) value of 0.95 for peak classification. Examples are given illustrating that this new approach provides convincing means to automatically assess and evaluate modelled elution profiles of chromatographic data and thereby remove this laborious manual step.

Fused adjacency matrices to enhance information extraction: The beer benchmark.

Multivariate exploratory data analysis allows revealing patterns and extracting information from complex multivariate data sets. However, highly complex data may not show evident groupings or trends in the principal component space, e.g. because the variation of the variables are not grouped but rather continuous. In these cases, classical exploratory methods may not provide satisfactory results when the aim is to find distinct groupings in the data. To enhance information extraction in such situations, we propose a novel approach inspired by the concept of combining weak classifiers, but in the unsupervised context. The approach is based on the fusion of several adjacency matrices obtained by different distance measures on data from different analytical platforms. This paper is intended to present and discuss the potential of the approach through a benchmark data set of beer samples. The beer data were acquired using three spectroscopic techniques: Visible, near-Infrared and Nuclear Magnetic Resonance. The results of fusing the three data sets via the proposed approach are compared with those from the single data blocks (Visible, NIR and NMR) and from a standard mid-level data fusion methodology. It is shown that, with the suggested approach, groupings related to beer style and other features are efficiently recovered, and generally more evident.

Geometric search: A new approach for fitting PARAFAC2 models on GC-MS data.

PARAFAC2 is a powerful decomposition method which is ideally suited for modeling gas chromatography-mass spectrometry (GC-MS) data. However, the most widely used fitting algorithms (alternating least squares, ALS) are very slow which hinders use of the model. In this paper, an iterative method called geometric search is proposed to fit the PARAFAC2 model. This method models the PARAFAC2 loading parameters as geometric sequences with offsets during the ALS iterations. It extrapolates the optimal parameters from prior iterations to accelerate ALS convergence process. The performance of this method was evaluated by simulated datasets and two GC-MS datasets of wine and tobacco samples. This geometric search method proved an efficient way to fit PARAFAC2 models, compared with a standard ALS algorithm and two widely used line search algorithms in terms of convergence speed and fitting quality.

Forecasting Chronic Diseases Using Data Fusion.

Data fusion, that is, extracting information through the fusion of complementary data sets, is a topic of great interest in metabolomics because analytical platforms such as liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy commonly used for chemical profiling of biofluids provide complementary information. In this study, with a goal of forecasting acute coronary syndrome (ACS), breast cancer, and colon cancer, we jointly analyzed LC-MS, NMR measurements of plasma samples, and the metadata corresponding to the lifestyle of participants. We used supervised data fusion based on multiple kernel learning and exploited the linearity of the models to identify significant metabolites/features for the separation of healthy referents and the cases developing a disease. We demonstrated that (i) fusing LC-MS, NMR, and metadata provided better separation of ACS cases and referents compared with individual data sets, (ii) NMR data performed the best in terms of forecasting breast cancer, while fusion degraded the performance, and (iii) neither the individual data sets nor their fusion performed well for colon cancer. Furthermore, we showed the strengths and limitations of the fusion models by discussing their performance in terms of capturing known biomarkers for smoking and coffee. While fusion may improve performance in terms of separating certain conditions by jointly analyzing metabolomics and metadata sets, it is not necessarily always the best approach as in the case of breast cancer.

Gas chromatography - mass spectrometry data processing made easy.

Evaluation of GC-MS data may be challenging due to the high complexity of data including overlapped, embedded, retention time shifted and low S/N ratio peaks. In this work, we demonstrate a new approach, PARAFAC2 based Deconvolution and Identification System (PARADISe), for processing raw GC-MS data. PARADISe is a computer platform independent freely available software incorporating a number of newly developed algorithms in a coherent framework. It offers a solution for analysts dealing with complex chromatographic data. It allows extraction of chemical/metabolite information directly from the raw data. Using PARADISe requires only few inputs from the analyst to process GC-MS data and subsequently converts raw netCDF data files into a compiled peak table. Furthermore, the method is generally robust towards minor variations in the input parameters. The method automatically performs peak identification based on deconvoluted mass spectra using integrated NIST search engine and generates an identification report. In this paper, we compare PARADISe with AMDIS and ChromaTOF in terms of peak quantification and show that PARADISe is more robust to user-defined settings and that these are easier (and much fewer) to set. PARADISe is based on non-proprietary scientifically evaluated approaches and we here show that PARADISe can handle more overlapping signals, lower signal-to-noise peaks and do so in a manner that requires only about an hours worth of work regardless of the number of samples. We also show that there are no non-detects in PARADISe, meaning that all compounds are detected in all samples.

Using PAT to accelerate the transition to continuous API manufacturing.

Significant improvements can be realized by converting conventional batch processes into continuous ones. The main drivers include reduction of cost and waste, increased safety, and simpler scale-up and tech transfer activities. Re-designing the process layout offers the opportunity to incorporate a set of process analytical technologies (PAT) embraced in the Quality-by-Design (QbD) framework. These tools are used for process state estimation, providing enhanced understanding of the underlying variability in the process impacting quality and yield. This work describes a road map for identifying the best technology to speed-up the development of continuous processes while providing the basis for developing analytical methods for monitoring and controlling the continuous full-scale reaction. The suitability of in-line Raman, FT-infrared (FT-IR), and near-infrared (NIR) spectroscopy for real-time process monitoring was investigated in the production of 1-bromo-2-iodobenzene. The synthesis consists of three consecutive reaction steps including the formation of an unstable diazonium salt intermediate, which is critical to secure high yield and avoid formation of by-products. All spectroscopic methods were able to capture critical information related to the accumulation of the intermediate with very similar accuracy. NIR spectroscopy proved to be satisfactory in terms of performance, ease of installation, full-scale transferability, and stability to very adverse process conditions. As such, in-line NIR was selected to monitor the continuous full-scale production. The quantitative method was developed against theoretical concentration values of the intermediate since representative sampling for off-line reference analysis cannot be achieved. The rapid and reliable analytical system allowed the following: speeding up the design of the continuous process and a better understanding of the manufacturing requirements to ensure optimal yield and avoid unreacted raw materials and by-products in the continuous reactor effluent. Graphical Abstract Using PAT to accelerate the transition to continuous API manufacturing.

The effects of water and dairy drinks on dietary patterns in overweight adolescents.

The aim was to investigate the effects of increased water or dairy intake on total intake of energy, nutrients, foods and dietary patterns in overweight adolescents in the Milk Components and Metabolic Syndrome (MoMS) study (n=173). Participants were randomly assigned to consume 1l/d of skim milk, whey, casein or water for 12 weeks. A decrease in the dietary pattern called Convenience Food, identified by principal component analysis, was observed during the intervention both in the water and dairy groups. Total energy intake decreased by 990.9 kJ/d (236.8 kcal/d) in the water group but was unchanged in the dairy group during intervention. To conclude, an extra intake of fluid seems to favourably affect the rest of the diet by decreasing the intake of convenience foods, including sugar-sweetened beverages. A low energy drink, such as water, seems advantageous considering the total energy intake in these overweight adolescents. This study is registered at clinicaltrials.gov (NCT00785499).