Chemometrically assisted differential pulse voltammetry for simultaneous and interference-free quantification of gallic and caffeic acids.

This article explores the application of chemometric tools including multivariate curve resolution with alternating least squares for the simultaneous determination of gallic and caffeic acids on the surface of a glassy carbon electrode without additional modification. Gallic and caffeic acids are primary polyphenols, the most abundant in red wines produced in Argentina, and are often used as quality markers for them. These polyphenols significantly contribute to the organoleptic properties of wines from this origin, but their electrochemical signals overlap significantly, making simultaneous quantification challenging without additional experiments such as electrode modification or alternative analytical techniques beyond differential pulse voltammetry. This study successfully quantified these compounds in complex mixtures by generating second-order data from differential pulse voltammetry experiments conducted at various potential steps and subsequently applying multivariate curve resolution with alternating least squares. The use of constraints during optimization prevented rotational ambiguities common in this modeling, leading to unique results in validation samples. The limits of detection (LOD) found for gallic and caffeic acids were 1.6 and 7.6 mg L-1, which are in excellent agreement with the expected concentrations of these compounds in red wines. The concentration ranges analyzed showed a linear dependency (between the LOD and 300 mg L-1) with the signals estimated by the model for both analytes. Advantages such as simplicity, low cost, and high speed, as well as not requiring electrode modification, combined with excellent results obtained for real samples, make it a promising alternative for polyphenol analysis in the wine industry.

Removal of metals and inorganics from rendered fat using polyamine-modified cellulose nanocrystals.

Meatpacking and poultry operations produce an enormous amount of co-products including offal, fat, blood, feathers etc. that are collected and processed by the rendering industry into value-added materials such as various protein meals and rendered fat products. Rendered fats (mainly composed of triglycerides from the adipose tissue of animals or used cooking oil from the restaurant industry) are sold for a variety of applications including animal feed formulations. Nonetheless, in the current context of energy scarcity, their use as feedstocks for the generation of renewable fuels including biodiesel and renewable diesel represents a growing market. The diverse composition of the source material can impose significant challenges in terms of compliance, requiring the control (and reduction) of the concentration of elements such as phosphorus, sulfur, calcium, magnesium, sodium, potassium, and other undesirable metals that can otherwise interfere with critical aspects of the refining process or contaminate the renewable fuel products. To address this critical need, we describe the application of poly(ethylenimine)-modified cellulose nanocrystals as a low-cost material for the removal of unwanted metal/inorganic cations from rendered fat. A total of 28 real samples including poultry, white pork grease, and beef tallow were analyzed. Test results showed that the approach can effectively decrease the concentration of the target elements by 95 ± 2%, suggesting that this treatment protocol could dramatically improve the application of rendered fat products for renewable fuel refining.

Correction: Removal of metals and inorganics from rendered fat using polyamine-modified cellulose nanocrystals.

[This corrects the article DOI: 10.1039/D3SU00116D.].

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.

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.

Oxidomics on the omega-3 volatile degradation pattern to determine differences between vegetable and marine oils.

In this work, oxidation assessment of vegetable and marine oils was performed based on their quantitative volatile profile and data analysis by 3-way partial least square chemometrics. Classification models were obtained using broad-spectrum isotopically labelled standards on the analysis of 25 volatile compounds from omega-3 fatty acid (FA) degradation by headspace solid phase microextraction gas chromatography time-of-flight mass spectrometry. Our oxidomic approach was performed on edible oils that differed in their origin (marine or vegetable) and in their omega-3 FA profile. In order to achieve a 3D matrix, every oil was oxidized at 6 different time-points. The obtained models classified edible oils according to their volatile degradation pattern. Oxidation of eicosapentaenoic/docosahexaenoic FA was mainly related to 2-propenal, butanal and 2-ethylfuran while α-linolenic acid oxidation was linked to 1-hydroxy-2-butanone and 5-ethyl-2(5H)-furanone. The present research provides valuable information on the degradation differences of omega-3 oils and proposes specific oxidation markers that could be used to ensure their quality assurance and avoid intentional adulterations.

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.

Multielemental analysis and classification of amaranth seeds according to their botanical origin.

The characterization of amaranth seeds (Amaranthus spp.) was developed for Amaranthus hypochondriacus, Amaranthus cruentus, and Amaranthus dubius. The elemental concentrations were determined by inductively coupled plasma optic atomic spectroscopy. Pattern recognition methods were used for the characterization of seed samples: nonsupervised methods included principal components analysis and cluster analysis; supervised methods were linear discriminant analysis and partial least squares discriminant analysis (PLS-DA). Informed are the concentrations of the following elements: Ag, Al, Ba, Ca, Co, Cr, Cu, Fe, K, La, Li, Mg, Mn, Mo, Na, Ni, P, S, Sr, V, Zn, and Zr. The lowest mineral content was found in A. hypochondriacus, and the highest one was found in A. dubius. For the classification, selected variables for all multivariate methods were Ba, Cr, Li, Mn, Ni, S, and Sr. Nonsupervised methods allowed us to distinguish between the three species of amaranth; however, PLS-DA supervised methods showed the best prediction ability.

Simultaneous determination of amoxicillin and diclofenac in pharmaceutical formulations using UV spectral data and the PLS chemometric method.

A new method for the simultaneous determination of amoxicillin trihydrate (AMT) and sodium diclofenac (DIC) in pharmaceutical formulations is proposed. The resolution of binary mixtures of these drugs has been accomplished by using partial least squares (PLS) regression analysis. The model was obtained from UV spectral data and validated by internal cross-validation; it was used to find the concentration of analytes in some commercial samples. The method was applied in the concentration ranges of 0-120.0 mg L(-1) for AMT and 0-16.0 mg L(-1) for DIC, allowing a rapid, accurate and precise simultaneous estimation of the concentration of both analytes of interest in the presence of small amounts of different, unmodelled, absorbing excipients, in spite of their important spectral overlapping. The accuracy, precision and figures of merit (FOM) for AMT and DIC were calculated. This new method proved to be useful for a fast and simultaneous determination of AMT and DIC in pharmaceutical formulations.

Characterisation of two South American food and medicinal plants by chemometric methods based on their multielemental composition.

INTRODUCTION: The chemometric characterisation of two plants frequently used as food and medicinal species, Achyrocline satureioides and Achyrocline venosa (Asteraceae: Gnaphalieae), was carried out based on their mineral composition. Both species, known by the common name of 'marcelas', are very similar in their morphological features but they have different medicinal and food properties. OBJECTIVE: To develop multivariate models for the classification of A. satureiodes and A. venosa based on their mineral content. METHODOLOGY: The analytic determinations were made by means of inductively coupled plasma optical emission spectrometry from aerial parts of the plants. An internal standard was used to evaluate the accuracy in the sample treatment and the recovery of toxic elements was studied. The multivariate methods used include principal components analysis, cluster analysis and linear discriminant analysis. RESULTS: Classification for both A. satureioides and A. venosa was successful in all cases using only four variables: aluminium, iron, magnesium and sulphur content. The concentrations of the following elements were determined: Al, As, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, La, Mg, Mn, Mo, Na, Ni, P, Pb, S, Sr, Ti, V, Y and Zn. CONCLUSIONS: This method is useful to identify both species in raw material in order to detect eventual errors of selection.

Determination of oleic acid in sunflower seeds by infrared spectroscopy and multivariate calibration method.

A method for the determination of oleic acid in sunflower seeds is proposed. One hundred samples of sunflower seeds were analyzed by near-infrared diffuse reflectance spectroscopy (NIRDRS). The direct measures were realized in ground and sifted seeds. The PLS multivariate calibration model was obtained using first derivate absorbance values as response matrix, while the oleic acid concentration matrix was obtained analyzing the seed samples by gas chromatography with a flame ionization detector (GC-FID). The NIRDRS-PLS model was validated externally using unknown samples of sunflower seeds. The accuracy and precision of the method was evaluated using GC as reference method. The following figures of merit (FOM) were obtained: LOD=3.4% (w/w); LOQ=11.3% (w/w); SEN=8x10(-5); SEL=0.15; analytical sensibility (gamma)=1.5 and linear range (LR)=18.1-89.2% (w/w). This method is useful for the fast determination of oleic acid in sunflower seeds and for quality control of raw materials.

Simultaneous determination of saccharin and aspartame in commercial noncaloric sweeteners using the PLS-2 multivariate calibration method and validation by capillary electrophoresis.

A new method to determine a mixture for sweetener sodium saccharin and aspartame in commercial noncaloric sweeteners is proposed. A classical full factorial design for standards was used in the calibration step to build the partial least-squares (PLS-2) model. Instrumental data were obtained by means of UV-visible spectrophotometry. Salicylic acid was used as an internal standard to evaluate the adjustment of the real samples to the PLS model. The concentration of analytes in the commercial samples was evaluated using the obtained model by UV spectral data. The PLS-2 method was validated by capillary zone electrophoresis (CZE), finding in all cases a relative error of less than 11% between the PLS-2 and the CZE methods. The proposed procedure was applied successfully to the determination of saccharin and aspartame in noncaloric commercial sweeteners.

Simultaneous determination of sorbic and benzoic acids in commercial juices using the PLS-2 multivariate calibration method and validation by high performance liquid chromatography.

A new method to determine a mixture for preserving sorbic and benzoic acids in commercial juices is proposed. The PLS-2 model was obtained preparing 40 standard solutions adding concentration of sorbic and benzoic acid to filtered natural juices of apple, lemon, orange and grapefruit. The concentration of analytes in the commercial samples was evaluated using the obtained model by UV spectral data. The PLS-2 method was validated by high performance liquid chromatography (HPLC), finding a relative error less than 12% between the PLS-2 and HPLC methods in all cases.