ANOVA-Simultaneous Component analysis modelling of low-level-fused spectroscopic data: A food chemistry case-study.

Ripening is a crucial step to guarantee the high commercial value of cheddar cheese, one of the dairy products the European Union exports the most. Although several methods have lately been proposed to assess its ageing process from a chemical point of view, the majority of them is not particularly time-efficient and implies destructive analytical tests, thus, exhibiting limitations for, e.g., industrial applications. Here, a fast approach based on combining Raman and Mid-InfraRed (MIR) spectroscopy with ANOVA-Simultaneous Component Analysis (ASCA) is proposed in a low-level data fusion framework. This approach allowed to evaluate how storage temperature and time (as well as their interaction) influence cheddar ripening in a relatively cheap, rapid and green fashion.

Photochemical multivariate curve resolution models for the investigation of photochromic systems under continuous irradiation.

We propose a multivariate curve resolution approach for the investigation of photochromic systems using UV-Visible spectroscopy. The incorporation of photochemical hard-models as constraints in multivariate curve resolution alternating least squares (MCR-ALS) allows extracting reaction quantum yields in situations where a complete knowledge of the system is not available. We apply this approach to the study of the photochromism of CMTE (cis-1,2-dicyano-1,2-bis(2,4,5-trimethyl-3-thienyl)ethene) under continuous monochromatic irradiation. The mechanism, involving 3 species and 2 reversible reactions, is written and translated into a kinetic constraint that can be applied to the concentration profiles within ALS. First, ambiguity of the solution obtained for photochemical model(s) is calculated and discussed for single set analysis. Multiset analysis is then proposed combining data obtained under different irradiation wavelengths to provide more reliable results. Finally, the photochemical reactivity of CMTE is widely unraveled, and some description of the mechanism observed under irradiation at 365 nm is given.

Superhydrophobic polypyrene films to prevent Staphylococcus aureus and Pseudomonas aeruginosa biofilm adhesion on surfaces: high efficiency deciphered by fluorescence microscopy.

A blue luminescent and superhydrophobic coating based on an electropolymerized fluorinated-pyrene monomer and its planktonic bacteria and biofilm repellent properties are reported. Two different pathogenic bacterial strains (Gram-positive and Gram-negative) at two different incubation times (2 h planktonic bacterial and 24 h biofilm adhesion) were studied and monitored (analyzed) using multicolor scanning confocal fluorescence microscopy. The coating was proved to reduce bacterial adhesion by 65%. It is highly effective against biofilm attachment, with 90% reduction of bacteria surface coverage. This blue fluorescent surface provides a facile method to characterize the coating, observe the bacterial distribution and quantify the bacterial coverage rate by fluorescence imaging of different colors. Furthermore, the film does not show significant bacterial toxicity during the working incubation times.

Multivariate curve resolution: a review of advanced and tailored applications and challenges.

Multivariate curve resolution (MCR) is a widespread methodology for the analysis of process data in many different application fields. This article intends to propose a critical review of the recently published works. Particular attention will be paid to situations requiring advanced and tailored applications of multivariate curve resolution, dealing with improvements in preprocessing methods, multi-set data arrangements, tailored constraints, issues related to non-ideal noise structure and deviation to linearity. These analytical issues are tackling the limits of applicability of MCR methods and, therefore, they can be considered as the most challenging ones.

Hybrid hard- and soft-modelling applied to analyze ultrafast processes by femtosecond transient absorption spectroscopy: study of the photochromism of salicylidene anilines.

Multivariate curve resolution-alternating least squares (MCR-ALS) of multi-experiment data analysis was successfully applied to elucidate the photodynamics of the N-(3-methylsalicylidene)-3-methylaniline by analyzing UV-vis femtosecond transient absorption spectra. The two-way data obtained present some specific difficulties linked to the nature of the transient spectra collected and to the overlapping of the photodynamics of the solvent and other contributions at short time scale (below 1 ps). Advantage was taken from the flexibility of the hybrid hard-soft multivariate curve resolution (HS-MCR) approach to consider a non-absorbing contribution in the kinetic model and to provide a functional description of the solvent in soft-modelling. The results obtained confirm the existence of an intermediate excited state in the process, which is created just after the ESIPT. It was observed that this intermediate relaxes in a few hundreds of femtosecond to the S(1) fluorescent cis-keto excited state and a decay time constant of 219 fs was found. These results confirm other femtosecond time-resolved fluorescence studies on salicylidene aniline molecules. Previous hypothesis on the formation of the trans-keto photoproduct from the S(1) fluorescent cis-keto state (time constant 14 ps) is also confirmed.

Contribution made by multivariate curve resolution applied to gel permeation chromatography-Fourier transform infrared data for an in-depth characterization of styrene-butadiene rubber blends.

We evaluate the contribution made by multivariate curve resolution-alternating least squares (MCR-ALS) for resolving gel permeation chromatography-Fourier transform infrared (GPC-FT-IR) data collected on butadiene rubber (BR) and styrene butadiene rubber (SBR) blends in order to access in-depth knowledge of polymers along the molecular weight distribution (MWD). In the BR-SBR case, individual polymers differ in chemical composition but share almost the same MWD. Principal component analysis (PCA) gives a general overview of the data structure and attests to the feasibility of modeling blends as a binary system. MCR-ALS is then performed. It allows resolving the chromatographic coelution and validates the chosen methodology. For SBR-SBR blends, the problem is more challenging since the individual elastomers present the same chemical composition. Rank deficiency is detected from the PCA data structure analysis. MCR-ALS is thus performed on column-wise augmented matrices. It brings very useful insight into the composition of the analyzed blends. In particular, a weak change in the composition of individual SBR in the MWD's lowest mass region is revealed.

Genetic algorithm optimisation combined with partial least squares regression and mutual information variable selection procedures in near-infrared quantitative analysis of cotton-viscose textiles.

In this work, different approaches for variable selection are studied in the context of near-infrared (NIR) multivariate calibration of textile. First, a model-based regression method is proposed. It consists in genetic algorithm optimisation combined with partial least squares regression (GA-PLS). The second approach is a relevance measure of spectral variables based on mutual information (MI), which can be performed independently of any given regression model. As MI makes no assumption on the relationship between X and Y, non-linear methods such as feed-forward artificial neural network (ANN) are thus encouraged for modelling in a prediction context (MI-ANN). GA-PLS and MI-ANN models are developed for NIR quantitative prediction of cotton content in cotton-viscose textile samples. The results are compared to full-spectrum (480 variables) PLS model (FS-PLS). The model requires 11 latent variables and yielded a 3.74% RMS prediction error in the range 0-100%. GA-PLS provides more robust model based on 120 variables and slightly enhanced prediction performance (3.44% RMS error). Considering MI variable selection procedure, great improvement can be obtained as 12 variables only are retained. On the basis of these variables, a 12 inputs ANN model is trained and the corresponding prediction error is 3.43% RMS error.

Multivariate curve resolution of rapid-scan FTIR difference spectra of quinone photoreduction in bacterial photosynthetic membranes.

Photosynthetic reaction centres and membranes are systems of particular interest and are often taken as models to investigate the molecular mechanisms of selected bioenergetic reactions. In this work, a multivariate curve resolution by alternating least squares procedure is detailed for resolution of time-resolved difference FTIR spectra probing the evolution of quinone reduction in photosynthetic membranes from Rhodobacter sphaeroides under photoexcitation. For this purpose, different data sets were acquired in the same time range and spectroscopic domain under slightly different experimental conditions. To enable resolution and provide meaningful results the different data sets were arranged in an augmented matrix. This strategy enabled recovery of three different species despite rank-deficiency conditions. It also results in better definition (identity and evolution) of the contributions. From the resolved spectra, the species have been attributed to: 1. the formation of ubiquinol, more precisely the disappearance of Q/appearance of QH(2); 2. conformational change of the protein in the surrounding biological medium; 3. oxidation of diaminodurene, a redox mediator. Because, moreover, results obtained from augmented data sets strategies enable quantitative and qualitative interpretation of concentration profiles, other effects, for example the consequence of repeated light excitation of the same sample, choice of illumination power, or the number of spectra accumulated could be compared and discussed.

Quantitative analysis of cotton-polyester textile blends from near-infrared spectra.

Quantitative analysis of textile blends and textile fabrics is currently of particular interest in the industrial context. In this frame, this work investigates whether the use of Fourier transform (FT) near-infrared (NIR) spectroscopy and chemometrics is powerful for rapid and accurate quantitative analysis of cotton-polyester content in blend products. As samples of the same composition have many sources of variability that affect NIR spectra, indirect prediction is particularly challenging and a large sample population is required to design robust calibration models. Thus, a total of more than three-hundred cotton-polyester samples were selected covering the range from the 0% to 100% cotton and the corresponding NIR reflectance spectra were measured on raw fabrics. The data set obtained was used to develop multivariate models for quantitative prediction from reference measurements. A successful approach was found to rely on partial least squares (PLS) regression combined with genetic algorithms (GAs) for wavelength selection. It involved evaluating a set of calibration models considering different spectral regions. The results obtained considering 27.5% of the original variables yielded a prediction error (RMSEP) of 2.3 in percent cotton content. It demonstrates that FT-NIR spectroscopy has the potential to be used in the textile industry for the prediction of the composition of cotton-polyester blends. As a further consequence, it was observed that the spectral preprocessing and the complexity of the model are simplified compared to the full-spectrum approach. Also, the relevancy of the spectral intervals retained after variable selection can be discussed.

Time-resolved step-scan FT-IR spectroscopy: focus on multivariate curve resolution.

The present paper describes the application of step-scan FT-IR spectroscopy in combination with chemometric analysis of the spectral data for the study of the photocycle of bacteriorhodopsin. The focus is on the performance of this instrumentation for time-resolved experiments. Three-dimensional data-spectra recorded over time-are studied using various factor analysis techniques, e.g., singular values decomposition, evolving factor analysis, and multivariate curve resolution based on alternating least squares. Transient intermediates formed in the time domain ranging from 1 micros to 6.6 ms are clearly detected through reliable pure time evolving profiles. At the same time, pure difference absorbance spectra are provided. As a result, valuable information about transitions and dynamics of the protein can be extracted. We conclude first that step-scan FT-IR spectroscopy is a useful technique for the direct study of difficult photochemical systems. Second, and this is the essential motivation of this paper, chemometrics provide a step forward in the description of the photointermediates.

Multivariate curve resolution methods in imaging spectroscopy: influence of extraction methods and instrumental perturbations.

Imaging spectroscopy is becoming a key field of analytical chemistry. In the face of more and more complex samples, we actually need accurate microscopic insight. Nowadays, the methods used to produce concentration maps of the pure compounds from spectral data sets are based on the classical univariate approach although multivariate approaches are sometimes investigated. But in any case, the analytical quality of the chemical images thus provided cannot be discussed since no reference methods are at our disposal. Thus the proposed research focuses on the application of multivariate methods such as Orthogonal Projection Approach (OPA), SIMPLE-to-use Self-modeling Mixture Analysis (SIMPLISMA), Multivariate Curve Resolution - Alterning Least Squares (MCR-ALS), and Positive Matrix Factorization (PMF) for imaging spectroscopy. A systematic and quantitative characterization of the accuracy of spectra and images extraction is investigated on mid-infrared spectral data sets. Of special interest is the influence of instrumental perturbations such as noise and spectral shift on the extraction ability to access the algorithm's robustness.

Antibacterial activity of a pepsin-derived bovine hemoglobin fragment.

Peptic digestion of bovine hemoglobin yields a fragment with antibacterial activity. This peptide was purified to homogeneity by a two-step procedure including anion exchange chromatography and preparative reversed-phase HPLC. Mass determination and fragmentation indicated that this peptide corresponded to the 1-23 fragment of the alpha chain of hemoglobin. The minimum inhibitory concentration and mode of action of this peptide towards Micrococcus luteus strain A270 were determined. Hemolytic assay, interaction with liposomes, and study of its structure in solution were also performed.