Differentiating Nylon Samples with Visually Indistinguishable Fluorescence Using Principal Component Analysis and Common Dimension Partial Least Squares Linear Discriminant Analysis with Synchronous Fluorescence Spectroscopy.

Fluorescence spectroscopy is an attractive candidate for analyzing samples of nylon. Impurities within the polymers formed during the synthesis and processing of nylons give rise to the observed fluorescence, allowing for nylons to be analyzed based on their impurities. Nylons from the same source are expected to display similar fluorescence profiles, and nylons with different fluorescence are expected to be from different sources. This paper investigates an important case where different nylons displayed similar fluorescence, preventing easy discrimination. Samples of Nylon 6 and Nylon 6/12 had visually indistinguishable excitation-emission matrices (EEM), excitation spectra, fluorescence spectra, and synchronous fluorescence spectra at larger Δλ. By collecting synchronous fluorescence spectra at smaller Δλ, additional features in the fluorescence profiles were identified that allowed for some discrimination between the two nylons. Combining the EEM and synchronous fluorescence data with chemometric algorithms provided a clearer differentiation between the two nylons. parallel factor analysis, principal component analysis, and common dimension partial least squares (ComDim-PLS) showed two distinct clusters in the data, with ComDim-PLS providing the greatest distinction between the clusters. The loadings revealed the variables of interest to the ComDim-PLS were the 400 nm and 335 nm bands for all synchronous fluorescence spectra, the 460 nm and 310 nm bands for the Δλ = 20 nm and Δλ = 30 nm synchronous fluorescence spectra, and the 440 nm band for the Δλ = 20 nm synchronous fluorescence spectra. The linear discriminant analysis performed with the PLS data yielded a classification accuracy of 95% with the EEM data and 100% with the synchronous fluorescence data, displaying the power of this technique to differentiate two different nylons with visually indistinguishable fluorescence spectra.

Application of two- and multiway chemometric strategies for describing elementomic changes in pepper plants exposed to cadmium stress by multielement determination.

The objective of this work was to evaluate elemental changes in pepper exposed to Cd stress through different chemometric tools. For this purpose, pepper plants were grown under five different treatments with different Cd concentrations in the nutrient solution. Considering the hypothesis that pepper plants exposed to Cd stress during growth undergo changes in the macro- and microelemental distribution in leaves, stems, and roots, principal component analysis (PCA) and parallel factor (PARAFAC) analysis were applied to compare bidirectional and multivariate chemometric strategies to assess elemental changes in pepper plants. Since the number of variables and the data generated were large and complex, the application of chemometric tools was justified to facilitate the visualization and interpretation of results. The mineral composition, namely the Ca, Cd, Cu, Fe, K, Mg, Mn, N, and P contents, was assessed in 180 samples of leaves, stems, and roots of the cultivated peppers. Then, PCA and PARAFAC analysis were applied to compare bidirectional and multivariate chemometric strategies to assess elemental changes throughout pepper plants. The visualization of the trend on each sample and their intrinsic relationship with the variables were possible with the application of PCA. The use of PARAFAC analysis permitted the simultaneous study of all samples in a straightforward representation of the information that facilitated a quick and comprehensive understanding of the spatial distribution of elements in plants. Thus, macroelements (Ca, K, Mg, N, and P) that were found in higher concentrations in leaves did not present significant differences in the distribution along the plants under different treatment conditions. In contrast, a significant impact on the microelement (Cu, Fe, and Mn) distribution was produced between uncontaminated and contaminated samples. This analysis revealed a significant accumulation of Cd in roots and adverse effects on normal plant growth, demonstrating their level of phytotoxicity to pepper.

Prebiotic Potential of a New Sweetener Based on Galactooligosaccharides and Modified Mogrosides.

This study was conducted to investigate the sweetness intensity and the potential fecal microbiome modulation of galactooligosaccharides in combination with enzymatically modified mogrosides (mMV-GOS), both generated through a patented single-pot synthesis. Sweetness intensity was performed in vivo by trained sensory panelists. The impact on the human fecal microbiome was evaluated by in vitro pH-controlled batch fermentation, and bacterial populations and organic acid concentrations were measured by qPCR and GC-FID, respectively. Significant growth (p ≤ 0.05) during the fermentation at 10 h of bacterial populations includes Bifidobacterium (8.49 ± 0.44 CFU/mL), Bacteroides (9.73 ± 0.32 CFU/mL), Enterococcus (8.17 ± 0.42 CFU/mL), and Clostridium coccoides (6.15 ± 0.11 CFU/mL) as compared to the negative control counts for each bacterial group (7.94 ± 0.27, 7.84 ± 1.11, 7.52 ± 0.37, and 5.81 ± 0.08 CFU/mL, respectively) at the same time of fermentation. Likewise, the corresponding significant increase in production of SCFA in mMV-GOS at 10 h of fermentation, mainly seen in acetate (20.32 ± 2.56 mM) and propionate (9.49 ± 1.44 mM) production compared to a negative control at the same time (8.15 ± 1.97 and 1.86 ± 0.24 mM), is in line with a positive control (short-chain fructooligosaccharides; 46.74 ± 12.13 and 6.51 ± 1.91 mM, respectively) revealing a selective fermentation. In conclusion, these substrates could be considered as novel candidate prebiotic sweeteners, foreseeing a feasible and innovative approach targeting the sucrose content reduction in food. This new ingredient could provide health benefits when evaluated in human studies by combining sweetness and prebiotic fiber functionality.

Variable selection in the chemometric treatment of food data: A tutorial review.

Food analysis covers aspects of quality and detection of possible frauds to ensure the integrity of the food. The arsenal of analytical instruments available for food analysis is broad and allows the generation of a large volume of information per sample. But this instrumental information may not yet give the desired answer; it must be processed to provide a final answer for decision making. The possibility of discarding non-informative and/or redundant signals can lead to models of better accuracy, robustness, and chemical interpretability, in line with the principle of parsimony. Thus, in this tutorial review, we cover aspects of variable selection in food analysis, including definitions, theoretical aspects of variable selection, and case studies showing the advantages of variable selection-based models concerning the use of a wide range of non-informative and redundant instrumental information in the analysis of food matrices.

Greening Ultrasound-Assisted Extraction for Sorghum Flour Multielemental Determination by Microwave-Induced Plasma Optical Emission Spectrometry.

Sorghum is the fourth most important cereal produced in Argentina and the fifth worldwide. It has good agronomic characteristics and could be developed in arid areas, allowing a wide geographic distribution. Its starch content, higher than 70%, makes it possible to obtain a good yield of flours. Nutritionally, it should be noted that the grain does not have the protein fraction called prolamins, which makes it suitable for consumption by people with celiac disease. The multielemental composition constitutes an important indicator of the nutritional profile of the grains and allows, together with other parameters, to select the most suitable varieties for human consumption. In its determination, the preanalytical stage is decisive to obtain a reliable result. Organic samples are a challenge for sample introduction systems that use plasma-based techniques. As an alternative to conventional pretreatment with a microwave-assisted digestion (MWAD), a greener, quick, and simple treatment is proposed, using ultrasound-assisted extraction (UAE) in diluted acid media. The UAE method accelerates analysis times, improves performance and productivity, and was applied to sorghum samples cultivated in the province of La Pampa (Argentina). Microwave-induced plasma optical emission spectrometry (MIP OES) was employed for the determination of Cu, K, Mg, Mn, P, and Zn. The detection limits found ranged from 0.6 (Cu) to 89 (P) mg kg-1, and the precision expressed by the relative standard deviation (RSD) was ≤7.7% (Zn). For validation, a maize reference material (NCS ZC 73010) was evaluated. The principal component analysis revealed three different groupings related to the sorghum varieties' mineral profile.

D-optimal mixture design for the optimization of extraction induced by emulsion breaking for multielemental determination in edible vegetable oils by microwave-induced plasma optical emission spectrometry.

A sample pretreatment based on an extraction process by emulsion breaking for multi-element determination in edible oils was developed. The determination of eight trace elements (Al, Ba, Cu, Cr, P, Ni, Ti, and Zn) was carried out by microwave-induced plasma optical emission spectrometry (MIP OES) after the extraction procedure. A D-optimal mixture experimental design was used to obtain the best experimental conditions for the extraction induced by emulsion breaking (EIEB). The proportion of HNO3 solution, Triton X-100 solution and sample was evaluated in a multivariate manner. The best recovery efficiency was obtained with 1.0 mL of 30% (v/v) HNO3, 1.0 mL of 30% (w/v) Triton-X 100 and 3.0 mL of the sample. The precisions, established as the relative standard deviation (RSD, %), were better than 2.5% for all analytes. The developed method was applied to the analysis of commercial vegetable oils with low limits of detection and good precision.

Multi-level data fusion strategies for modeling three-way electrophoresis capillary and fluorescence arrays enhancing geographical and grape variety classification of wines.

Capillary electrophoresis with diode array detection (CE-DAD) and multidimensional fluorescence spectroscopy (EEM) second-order data were fused and chemometrically processed for geographical and grape variety classification of wines. Multi-levels data fusion strategies on three-way data were evaluated and compared revealing their advantages/disadvantages in the classification context. Straightforward approaches based on a series of data preprocessing and feature extraction steps were developed for each studied level. Partial least square discriminant analysis (PLS-DA) and its multi-way extension (NPLS-DA) were applied to CE-DAD, EEM and fused data matrices structured as two-way and three-way arrays, respectively. Classification results achieved on each model were evaluated through global indices such as average sensitivity non-error rate and average precision. Different degrees of improvement were observed comparing the fused matrix results with those obtained using a single one, clear benefits have been demonstrated when level of data fusion increases, achieving with the high-level strategy the best classification results.

Excitation-emission fluorescence as a tool to assess the presence of grape-must caramel in PDO wine vinegars.

A practice in wine vinegar production is the addition of grape-must caramel to correct and unify the final colour of different batches. Although current legislation allows it, the effect in vinegars' quality has not been studied yet and it can become a fraud when it is used to simulate the effect of a longer ageing. Therefore, the aim of this work was to assess multidimensional fluorescence as a cost-effective and fast technique for detecting and quantifying grape-must caramel in vinegars. Different amounts of grape-must caramel and multivariate data analysis, as Parallel Factor Analysis (PARAFAC), N-way partial least squares and partial least squares discrimination and regression (NPLS-DA, PLS-DA and NPLS) were studied. Triangle sensory test was also performed. Results demonstrated the ability of this methodology in the detection and quantification of grape-must caramel (low prediction errors, RMSEP ≈ 0.24) and the effects that grape-must caramel has upon a PDO vinegar's final quality.

Exploiting the synergistic effect of concurrent data signals: Low-level fusion of liquid chromatographic with dual detection data.

A low-level data fusion strategy was developed and implemented for data processing of second-order liquid chromatographic data with dual detection, i.e. absorbance and fluorescence monitoring. The synergistic effect of coupling individual information provided by two different detectors was evaluated by analyzing the results gathered after the application of a series of data preprocessing steps and chemometric resolution. The chemometric modeling involved data analysis by MCR-ALS, PARAFAC and N-PLS. Their ability to handle the new data block was assessed through the estimation of the analytical figures of merits achieved in the prediction of a validation set containing fifteen fluorescent and non-fluorescent veterinary active ingredients that can be found in poultry litter. Eventually, the feasibility of the application of the fusion strategy to real poultry litter samples containing the studied compounds was verified.

Nutritional analysis of Spirulina dietary supplements: Optimization procedure of ultrasound-assisted digestion for multielemental determination.

Arthrospira platensis and Arthrospira maxima are a type of blue-green microalga used as a dietary supplement (Spirulina). A low time-consuming ultrasound-assisted digestion (UAD) of Spirulina supplements for multielemental determination by microwave induced plasma atomic emission spectrometry (MPAES) was performed. Several parameters such as acid concentration (AC), thermostated water bath (TWB), digestion time (DT) and UAD - probe or bath - affecting the digestion process were evaluated through a full factorial design. Under the optimal conditions -100 °C for TWB, 5% for AC and 10 min for DT- and selecting the bath as the proper UAD system, the concentrations of 15 analytes (Al, Ba, Ca, Cd, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, P, V, Zn) were reported. The values are in accordance with the recommendation established by Food and Drug Administration (FDA) excepting for Cd. The knowledge of Spirulina multielemental composition contributes to an outstanding nutritional and toxicological report for human health.

Second-order capillary electrophoresis diode array detector data modeled with the Tucker3 algorithm: A novel strategy for Argentinean white wine discrimination respect to grape variety.

Data obtained by capillary electrophoresis with diode array detection (CE-DAD) were modeled with the purpose to discriminate Argentinean white wines samples produced from three grape varieties (Torrontés, Chardonnay, and Sauvignon blanc). Thirty-eight samples of commercial white wine from four wine-producing provinces of Argentina (Mendoza, San Juan, Salta, and Rio Negro) were analyzed. CE-DAD matrices with dimensions of 421 elution times (from 1.17 to 7.39 minutes) × 71 wavelengths (from 227 to 367 nm) were joined in a three way data array and decomposed by Tucker3 method under non-negativity constraint, employing 18, 18 and six factors in the modes 1, 2 and 3, respectively. Using the scores of Tucker model, it was possible to discriminate samples of Argentinean white wine by linear discriminant analysis and Kernel linear discriminant analysis. Core element analysis of the Tucker3 model allows identifying the loading profiles in spectral mode related to Argentinean white wine samples.

Single-step solubilization of milk samples with N,N-dimethylformamide for inductively coupled plasma-mass spectrometry analysis and classification based on their elemental composition.

A single-step procedure for trace elements analysis of milk samples is presented. Solubilization with small amounts of dymethylformamide (DMF) was assayed prior to inductively coupled plasma mass spectrometry (ICPMS) detection with a high efficiency sample introduction system. All main instrumental conditions were optimized in order to readily introduce the samples without matrix elimination. In order to assess and mitigate matrix effects in the determination of As, Cd, Co, Cu, Eu, Ga, Gd, Ge, Mn, Mo, Nb, Nd, Ni, Pb, Pr, Rb, Sm, S, Sr, Ta, Tb, V, Zn, and Zr, matrix matching calibration with (103)Rh as internal standard (IS) was performed. The obtained limits of detection were between 0.68 (Tb) and 30 (Zn) µg L(-1). For accuracy verification, certified Skim milk powder reference material (BCR 063R) was employed. The developed method was applied to trace elements analysis of commercially available milks. Principal components analysis was used to correlate the content of trace metals with the kind of milk, obtaining a classification according to adults, baby or baby fortified milks. The outcomes highlight a simple and fast approach that could be trustworthy for routine analysis, quality control and traceability of milks.

Modeling excitation-emission fluorescence matrices with pattern recognition algorithms for classification of Argentine white wines according grape variety.

This paper reports the modeling of excitation-emission matrices for classification of Argentinean white wines according to the grape variety employing chemometric tools for pattern recognition. The discriminative power of the data was first investigated using Principal Component Analysis (PCA) and Parallel Factor Analysis (PARAFAC). The score plots showed strong overlapping between classes. A forty-one samples set was partitioned into training and test sets by the Kennard-Stone algorithm. The algorithms evaluated were SIMCA, N- and U-PLS-DA and SPA-LDA. The fit of the implemented models was assessed by mean of accuracy, sensitivity and specificity. These models were then used to assign the type of grape of the wines corresponding to the twenty samples test set. The best results were obtained for U-PLS-DA and SPA-LDA with 76% and 80% accuracy.