Is independent component analysis appropriate for multivariate resolution in analytical chemistry?

Visual characterization of microplastics in corn flour by near field molecular spectral imaging and data mining

As potential hazard to human health, microplastics have attracted increasing attention. Most current studies have addressed the characterization of microplastics from the environment. For microplastics in food, most detections focused on liquid systems such as alcohol, beverages, etc., while there has been quite rare research on microplastics in solid foods with complex matrices. Thus, this study attempted to use three molecular spectral imaging approaches, namely, Fourier transform infrared (FTIR), optical photothermal resonance infrared (O-PTIR), and confocal Raman spectral imaging, combined with chemometrics to characterize the presence of microplastics in corn flour. The results demonstrated that O-PTIR imaging can rapidly sense the presence of microplastics, but its data integrity and visualization were limited. By decomposing the image, FTIR and Raman acquired a more integral distribution. Wherein, microplastics were well depicted by Raman imaging coupled with independent component analysis. Moreover, O-PTIR imaging can quickly detect contaminants at low concentrations but with a low detection rate. Raman imaging underperformed in low-concentration samples but provided a better visualization in mid-concentration samples. Overall, the results confirmed that the visual detection of microplastics in powdered food can be realized by molecular spectral imaging coupled with data mining, which can provide a reference for the detection of microplastics in other foods.

Vis-NIR hyperspectral imaging coupled with independent component analysis for saffron authentication

In the present contribution, visible-near infrared hyperspectral imaging (Vis-NIR-HSI) combined with a novel chemometric approach based on mean-filed independent component analysis (MF-ICA) followed by multivariate classification techniques is proposed for saffron authentication and adulteration detection. First, MF-ICA was used to exploit pure spatial and spectral profiles of the components. Then, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to find patterns of authentic samples based on their distribution maps. Then, detection of five common plant-derived adulterants of saffron including safflower, saffron style, calendula, rubia and turmeric were investigated. For this purpose, partial least squares-discriminant analysis (PLS-DA) for supervised classification to find a boundary between authentic and adulterated saffron samples. Classification accuracies for all models for calibration and prediction sets were 100 %. Finally, a mixed dataset was prepared and analyzed using the proposed strategy which again 100 % of accuracies for calibration and prediction sets were obtained. At the end, data driven soft independent modelling of class analogy (dd-SIMCA) was used to evaluate model for class modeling. Sensitivity was 95% for authentic class and specificities for all adulterants were 100%.

Improve the performance of independent component analysis by mapping the spectrum to an orthogonal space

Independent Component Analysis (ICA) has attracted chemists recently, for its charm can separate the independent signals from a mixed system and does not need prior knowledge. However, its dissatisfactory performance for the chemical measured signal is still blocking the practicability. Thus, this paper summarized the ICA processing path from the establishment of rectangular coordinates in linear space to the determination of the corresponding relation between the coordinate system and real components. The primary cause of the deviation between the ICA results and the chemical measurements is that the measuring signal was subject to uncertainty. Besides, uncertainty made the deviation of source signal from the statistical independence assumption, or in other words, it appeared to be nonorthogonal. For this key, it proposed to map the measured value to the high-order derivative space, use the derivative to narrow the peak width, reduce the influence of uncertainty, and improve the separation performance of ICA to chemical measurement signal, such as the spectrum. Actual cases of this paper showed that when up to 6th order, the separating results had been perfect for IR spectra, and even for homologs isomers.

Independent components analysis (ICA) at the "cocktail-party" in analytical chemistry

Independent components analysis (ICA) is a probabilistic method, whose goal is to extract underlying component signals, that are maximally independent and non-Gaussian, from mixed observed signals. Since the data acquired in many applications in analytical chemistry are mixtures of component signals, such a method is of great interest. In this article recent ICA applications for quantitative and qualitative analysis in analytical chemistry are reviewed. The following experimental techniques are covered: fluorescence, UV-VIS, NMR, vibrational spectroscopies as well as chromatographic profiles. Furthermore, we reviewed ICA as a preprocessing tool as well as existing hybrid ICA-based multivariate approaches. Finally, further research directions are proposed. Our review shows that ICA is starting to play an important role in analytical chemistry, and this will definitely increase in the future.

Matrix augmentation as an efficient method for resolving interaction of bromocriptine with human serum albumin: trouble shooting and simultaneous resolution

This work reports the results of an interesting study related to the investigation of interactions of bromocriptine (BCP) with human serum albumin (HSA) by mathematicall modelling of voltammetric and spectroscopic data into an augmented data matrix and its resolution by multivariate curve resolution-alternating least squares (MCR-ALS). The quality of the results obtained by MCR-ALS was examined by MCR-BANDS and its outputs confirmed the absence of rotational ambiguities in the MCR-ALS results. BCP-HSA interactions were also modeled by molecular docking methods to verify the results obtained from experimental sections and fortunately, they were compatible. Hard modeling of the experimental data by EQUISPEC helped us to calculate the binding constant of the complex formed from BCP-HSA interactions which was in a good agreement with that of calculated from direct analysis of the experimental data. Finally, with the help of two different amperometric measurements based on BCP-HSA interactions a novel electroanalytical method was developed for biosensing of HSA in serum samples.

Application of Akaike information criterion assisted probabilistic latent semantic analysis on non-trilinear total synchronous fluorescence spectroscopic data sets: Automatizing fluorescence based multicomponent mixture analysis

The lack of trilinear structure is known to limit the application of chemometric techniques on the total synchronous fluorescence spectroscopy (TSFS) data sets. To overcome this limitation, the present work successfully proposes application of Akaike information criterion (AIC) assisted probabilistic latent semantic analysis (pLSA) algorithm on TSFS data sets. The present work also discusses various practical and theoretical aspects that need to be considered while applying AIC assisted pLSA algorithm on TSFS data sets.

Evaluation of olive oils from the Mediterranean region by UV-Vis spectroscopy and Independent Component Analysis

Extra-virgin olive oil (EVOO) from Mediterranean were analyzed by Ultraviolet-Visible (UV-Vis) spectroscopy and Independent Component Analysis (ICA). The use of ICA resolution provided information over dienes (primary oxidation compound), polyphenolic compounds, tocopherol, carotenoids and chlorophylls when EVOO was evaluated by UV-Vis spectroscopy. Based on these data, ICA could be used to determine the contribution of chemical compounds to the composition of EVOO produced in different regions from Mediterranean. The results suggest that the combination of UV-Vis measurements and ICA makes possible the EVOO evaluation, and can contribute to suggesting that a foodstuff comes from an alleged origin. The proposed methodology is a low cost, fast and sample preparation free methodology to highlights the EVOO characteristics produced in the Mediterranean region.

Independent component analysis for rectal bleeding prediction following prostate cancer radiotherapy

BACKGROUND AND PURPOSE

To evaluate the benefit of independent component analysis (ICA)-based models for predicting rectal bleeding (RB) following prostate cancer radiotherapy.

MATERIALS AND METHODS

A total of 593 irradiated prostate cancer patients were prospectively analyzed for Grade ≥2 RB. ICA was used to extract two informative subspaces (presenting RB or not) from the rectal DVHs, enabling a set of new pICA parameters to be estimated. These DVH-based parameters, along with others from the principal component analysis (PCA) and functional PCA, were compared to "standard" features (patient/treatment characteristics and DVH bins) using the Cox proportional hazards model for RB prediction. The whole cohort was divided into: (i) training (N = 339) for ICA-based subspace identification and Cox regression model identification and (ii) validation (N = 254) for RB prediction capability evaluation using the C-index and the area under the receiving operating curve (AUC), by comparing predicted and observed toxicity probabilities.

RESULTS

In the training cohort, multivariate Cox analysis retained pICA and PC as significant parameters of RB with 0.65 C-index. For the validation cohort, the C-index increased from 0.64 when pICA was not included in the Cox model to 0.78 when including pICA parameters. When pICA was not included, the AUC for 3-, 5-, and 8-year RB prediction were 0.68, 0.66, and 0.64, respectively. When included, the AUC increased to 0.83, 0.80, and 0.78, respectively.

CONCLUSION

Among the many various extracted or calculated features, ICA parameters improved RB prediction following prostate cancer radiotherapy.

In Quest of the Alanine R3 Radical: Multivariate EPR Spectral Analyses of X-Irradiated Alanine in the Solid State

The amino acid l-α-alanine is the most commonly used material for solid-state electron paramagnetic resonance (EPR) dosimetry, due to the formation of highly stable radicals upon irradiation, with yields proportional to the radiation dose. Two major alanine radical components designated R1 and R2 have previously been uniquely characterized from EPR and electron-nuclear double resonance (ENDOR) studies as well as from quantum chemical calculations. There is also convincing experimental evidence of a third minor radical component R3, and a tentative radical structure has been suggested, even though no well-defined spectral signature has been observed experimentally. In the present study, temperature dependent EPR spectra of X-ray irradiated polycrystalline alanine were analyzed using five multivariate methods in further attempts to understand the composite nature of the alanine dosimeter EPR spectrum. Principal component analysis (PCA), maximum likelihood common factor analysis (MLCFA), independent component analysis (ICA), self-modeling mixture analysis (SMA), and multivariate curve resolution (MCR) were used to extract pure radical spectra and their fractional contributions from the experimental EPR spectra. All methods yielded spectral estimates resembling the established R1 spectrum. Furthermore, SMA and MCR consistently predicted both the established R2 spectrum and the shape of the R3 spectrum. The predicted shape of the R3 spectrum corresponded well with the proposed tentative spectrum derived from spectrum simulations. Thus, results from two independent multivariate data analysis techniques strongly support the previous evidence that three radicals are indeed present in irradiated alanine samples.

Identification of trace additives in polymer materials by attenuated total reflection Fourier transform infrared mapping coupled with multivariate curve resolution

Although multivariate curve resolution (MCR) has been applied to the analysis of Fourier transform infrared (FTIR) imaging, it is still problematic to determine the number of components. The reported methods at present tend to cause the components of low concentration missed. In this paper a new idea was proposed to resolve this problem. First, MCR calculation was repeated by increasing the number of components sequentially, then each retrieved pure spectrum of as-resulted MCR component was directly compared with a real-world pixel spectrum of the local high concentration in the corresponding MCR map. One component was affirmed only if the characteristic bands of the MCR component had been included in its pixel spectrum. This idea was applied to attenuated total reflection (ATR)/FTIR mapping for identifying the trace additives in blind polymer materials and satisfactory results were acquired. The successful demonstration of this novel approach opens up new possibilities for analyzing additives in polymer materials.

Constraint randomised non-negative factor analysis (CRNNFA): an alternate chemometrics approach for analysing the biochemical data sets

The present work introduces an alternate chemometrics approach constraint randomised non-negative factor analysis (CRNNFA) for analysing the bioanalytical data sets.

A study of human colonic motility in healthy and constipated subjects using the wireless capsule

Constipation is a common and distressing condition that has been linked to major morbidity, burdens the health care system, and impacts patients׳ quality of life. However, there is no perfect method for diagnosing and treating constipation. The purpose of this paper is to develop an automatic algorithm to identify patients with constipation from healthy subjects. Data from 12 healthy subjects and 10 patients with constipation were analyzed. The key challenges for data processing were data filtering, feature extraction, information evaluation, and providing the reference conclusion; these were resolved by employing the phase space reconstruction (PSR), independent component analysis (ICA), dynamic feature extraction algorithm, and the Wilcoxon rank sum test. The contractile frequency (Fr), motility index per unit time (MIU), average peak of peristaltic wave (Pave) and variance (Var) were extracted as dynamic parameters and analyzed. Results between groups were compared with the Wilcoxon rank sum test. There were statistically significant differences between healthy subjects and patients with constipation for Fr and MIU (P<0.05), whereas there was no statistically difference for Var. Moreover, the Fr and MIU of patients with normal transit constipation (NTC) are significantly lower compared to healthy subjects, whereas patients with slow transit constipation (STC) did not show significant differences. The proposed algorithms were able to differentiate between healthy subjects and patients with constipation based on the colonic motility profiles.

Fast identification and quantification of BTEX coupling by Raman spectrometry and chemometrics

Monoaromatic hydrocarbons (MAHs) monitoring is of environmental interest since these chemical pollutants are omnipresent.

Mechanism of rhodamine 6G molecular aggregation in montmorillonite colloid

Stopped-flow mixing device and visible absorption spectroscopy were used for the analysis of dye rhodamine 6G (R6G) molecular aggregation in the colloids based on Na-saturated montmorillonite. Two stages of the reaction were identified: The first stage was very short and taking only several seconds, involving the adsorption of R6G cations and their initial aggregation on the surface of colloid particles. The initially formed J-aggregates exhibited similar spectral properties as monomeric form of R6G. In the second stage, initially formed aggregates converted to sandwich-type H-aggregates absorbing light at significantly lower wavelengths and adsorbed monomers. The aggregate rearrangement took several hours. Monomers, with the spectral properties identical to R6G solution, were also identified as a component in complex spectra using principal component analysis (PCA) and multivariate curve resolution (MCR). Partial bleaching of the dye was also proven. Reaction kinetics of the rearrangement of the aggregates followed the model considering a complex mechanism of the molecular aggregation. Data fits using stretched-exponential function led to the determination of rate constants, which had been in the range 10−3−4×10−3s−1.

Electronic nose based on independent component analysis combined with partial least squares and artificial neural networks for wine prediction

The aim of this work is to propose an alternative way for wine classification and prediction based on an electronic nose (e-nose) combined with Independent Component Analysis (ICA) as a dimensionality reduction technique, Partial Least Squares (PLS) to predict sensorial descriptors and Artificial Neural Networks (ANNs) for classification purpose. A total of 26 wines from different regions, varieties and elaboration processes have been analyzed with an e-nose and tasted by a sensory panel. Successful results have been obtained in most cases for prediction and classification.