Concept: The Unscrambler
BACKGROUND: Static posture, repetitive movements and lack of physical variation are known risk factors for work-related musculoskeletal disorders, and thus needs to be properly assessed in occupational studies. The aims of this study were (i) to investigate the effectiveness of a conventional exposure variation analysis (EVA) in discriminating exposure time lines and (ii) to compare it with a new cluster-based method for analysis of exposure variation. METHODS: For this purpose, we simulated a repeated cyclic exposure varying within each cycle between “low” and “high” exposure levels in a “near” or “far” range, and with “low” or “high” velocities (exposure change rates). The duration of each cycle was also manipulated by selecting a “small” or “large” standard deviation of the cycle time. Theses parameters reflected three dimensions of exposure variation, i.e. range, frequency and temporal similarity.Each simulation trace included two realizations of 100 concatenated cycles with either low (rho = 0.1), medium (rho = 0.5) or high (rho = 0.9) correlation between the realizations. These traces were analyzed by conventional EVA, and a novel cluster-based EVA (C-EVA). Principal component analysis (PCA) was applied on the marginal distributions of 1) the EVA of each of the realizations (univariate approach), 2) a combination of the EVA of both realizations (multivariate approach) and 3) C-EVA. The least number of principal components describing more than 90% of variability in each case was selected and the projection of marginal distributions along the selected principal component was calculated. A linear classifier was then applied to these projections to discriminate between the simulated exposure patterns, and the accuracy of classified realizations was determined. RESULTS: C-EVA classified exposures more correctly than univariate and multivariate EVA approaches; classification accuracy was 49%, 47% and 52% for EVA (univariate and multivariate), and C-EVA, respectively (p < 0.001). All three methods performed poorly in discriminating exposure patterns differing with respect to the variability in cycle time duration. CONCLUSION: While C-EVA had a higher accuracy than conventional EVA, both failed to detect differences in temporal similarity. The data-driven optimality of data reduction and the capability of handling multiple exposure time lines in a single analysis are the advantages of the C-EVA.
Saffron is one of the oldest and most expensive spices, which is often target of fraudulent activities. In this research, a new strategy of saffron authentication based on metabolic fingerprinting was developed. In the first phase, a solid liquid extraction procedure was optimized, the main aim was to isolate as maximal representation of small molecules contained in saffron as possible. In the second step, a detection method based on liquid chromatography coupled with high-resolution mass spectrometry was developed. Initially, principal component analysis (PCA) revealed clear differences between saffron cultivated and packaged in Spain, protected designation of origin (PDO), and saffron packaged in Spain of unknown origin, labeled Spanish saffron. Afterwards, orthogonal partial least square discriminant analysis (OPLS-DA) was favorably used to discriminate between Spanish saffron. The tentative identification of markers showed glycerophospholipids and their oxidized lipids were significant markers according to their origin.
A direct, sensitive and rapid method for the detection of smokeless powder components, from five different types of ammunition, is demonstrated using laser electrospray mass spectrometry (LEMS). Common components found in powder, such as ethyl centralite, methyl centralite, dibutyl phthalate and dimethyl phthalate, are detected under atmospheric conditions without additional sample preparation. LEMS analysis of the powders revealed several new mass spectral features that have not been identified previously. Offline principal component analysis and discrimination of the LEMS mass spectral measurements resulted in perfect classification of the smokeless powder with respect to manufacturer.
The use of multivariate analysis (MVA) methods in the processing of time-of-flight secondary ion mass spectrometry (ToF-SIMS) data has become increasingly more common. MVA presents a powerful set of tools to aid the user in processing data from complex, multicomponent surfaces such as biological materials and biosensors. When properly used, MVA can help the user identify the major sources of differences within a sample or between samples, determine where certain compounds exist on a sample, or verify the presence of compounds that have been engineered into the surface. Of all the MVA methods, principal component analysis (PCA) is the most commonly used and forms an excellent starting point for the application of many of the other methods employed to process ToF-SIMS data. Herein we discuss the application of PCA and other MVA methods to multicomponent ToF-SIMS data and provide guidelines on their application and use.
In this work, a simple model developed for the authentication of monofloral Yemeni Sidr honey using UV spectroscopy together with chemometric techniques of Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA), and Soft Independent Modeling of Class Analogy (SIMCA) is described. The model was constructed using 13 genuine Sidr honey samples and challenged with 25 honey samples of different botanical origins. HCA and PCA were successfully able to present a preliminary clustering pattern to segregate the genuine Sidr samples from the lower priced local polyfloral and non-Sidr samples. The SIMCA model presented a clear demarcation of the samples and was used to identify genuine Sidr honey samples as well as detect admixture with lower priced polyfloral honey by detection limits higher than 10%. The constructed model presents a simple and efficient method of analysis and may serve as a basis for the authentication of other honey types worldwide.
This work proposes the use of near infrared (NIR) spectroscopy in diffuse reflectance mode and multivariate statistical process control (MSPC) based on principal component analysis (PCA) for real-time monitoring of the coffee roasting process. The main objective was the development of a MSPC methodology able to early detect disturbances to the roasting process resourcing to real-time acquisition of NIR spectra. A total of fifteen roasting batches were defined according to an experimental design to develop the MSPC models. This methodology was tested on a set of five batches where disturbances of different nature were imposed to simulate real faulty situations. Some of these batches were used to optimize the model while the remaining was used to test the methodology. A modelling strategy based on a time sliding window provided the best results in terms of distinguishing batches with and without disturbances, resourcing to typical MSPC charts: Hotelling’s T2and squared predicted error statistics. A PCA model encompassing a time window of four minutes with three principal components was able to efficiently detect all disturbances assayed. NIR spectroscopy combined with the MSPC approach proved to be an adequate auxiliary tool for coffee roasters to detect faults in a conventional roasting process in real-time.
We have detected differences in metabolite levels between doped athletes, clean athletes, and volunteers (non athletes). This outcome is obtained by comparing results of measurements from two analytical platforms: UHPLC-QTOF/MS and FT-ICR/MS. Twenty-seven urine samples tested positive for glucocorticoids or beta-2-agonists and twenty samples coming from volunteers and clean athletes were analyzed with the two different mass spectrometry approaches using both positive and negative electrospray ionization modes. Urine is a highly complex matrix containing thousands of metabolites having different chemical properties and a high dynamic range. We used multivariate analysis techniques to unravel this huge data set. Thus, the several groups we created were studied by Principal Components Analysis (PCA) and Partial Least Square regression (PLS-DA and OPLS) in the search of discriminating m/z values. The selected variables were annotated and placed on pathway by using MassTRIX.
The photodecomposition mechanism of trans,trans,trans-[Pt(N3)2(OH)2(py)2] (1, py = pyridine), an anticancer prodrug candidate, was probed using complementary Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR), transient electronic absorption and UV-Vis spectroscopy. Data fitting using Principal Component Analysis (PCA) and multi-curve resolution alternating least squares, suggests the formation of a trans-[Pt(N3)(py)2(OH/H2O)] intermediate and trans [Pt(py)2(OH/H2O)2] as the final product upon 420 nm irradiation of 1 in water. Rapid disappearance of the hydroxido ligand stretching vibration upon irradiation is correlated with a -10 cm-1 shift to the anti-symmetric azido vibration, suggesting a possible second intermediate. Experimental proof of subsequent dissociation of azido ligands from platinum is presented, where at least one hydroxyl radical is formed in the reduction of Pt(IV) to Pt(II). Additionally, the photoinduced reaction of 1 with 5'-guanosine monophosphate was studied, and the identity of key photoproducts was assigned with the help of ATR FTIR spectroscopy, mass spectrometry and DFT calculations. The identification of marker bands for photoproducts, e.g. trans-[Pt(N3)(py)2(5'-GMP)] and trans-[Pt(py)2(5'-GMP)2], will aid elucidation of the chemical and biological mechanism of anticancer action of 1. In general, these studies demonstrate the potential of vibrational spectroscopic techniques as promising tools for studying such metal complexes.
Understanding the neural and metabolic correlates of fluid intelligence not only aids scientists in characterizing cognitive processes involved in intelligence, but it also offers insight into intervention methods to improve fluid intelligence. Here we use magnetic resonance spectroscopic imaging (MRSI) to measureN-acetyl aspartate (NAA), a biochemical marker of neural energy production and efficiency. We use principal components analysis (PCA) to examine how the distribution of NAA in the frontal and parietal lobes relates to fluid intelligence. We find that a left lateralized frontal-parietal component predicts fluid intelligence, and it does so independently of brain size, another significant predictor of fluid intelligence. These results suggest that the left motor regions play a key role in the visualization and planning necessary for spatial cognition and reasoning, and we discuss these findings in the context of the Parieto-Frontal Integration Theory of intelligence.
In this study, direct ionization mass spectrometry (DI-MS) has been developed for rapid differentiation of Ganoderma (known as Lingzhi in Chinese), a very popular and valuable herbal medicine. Characteristic mass spectra can be generated by DI-MS directly from the raw herbal medicines with the application of a high voltage and solvents. Rapid differentiation of the Ganoderma species that are officially stated in the Chinese pharmacopoeia from easily confused Ganoderma species could be achieved based on this method, as the acquired DI-MS spectra showed that ganoderic acids, the major active components of Ganoderma, could be found only in the official Ganoderma species but not in the confused Ganoderma species. In addition, classification of wild and cultivated Ganoderma and potential differentiation of Ganoderma from different geographical locations could be accomplished based on principal component analysis (PCA) or hierarchical clustering analysis (HCA). The method is rapid, simple and reproducible, and can be further extended to analysis of other herbal medicines.