Silvana Mariela Azcarate

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.

Classification of Argentinean Sauvignon Blanc Wines by UV Spectroscopy and Chemometric Methods

UNLABELLED Argentina is an important worldwide wine producer. In this country, there are several recognizable provinces that produce Sauvignon blanc wines: Neuquén, Río Negro, Mendoza, and San Juan. The analysis of the provenance of these white wines is complex and requires the use of expensive and time-consuming techniques. For this reason, this work discusses the determination of the provenance of Argentinean Sauvignon blanc wines by the use of UV spectroscopy and chemometric methods, such as principal component analysis (PCA), cluster analysis (CA), linear discriminant analysis (LDA), and partial least square discriminant analysis (PLS-DA). The proposed method requires low-cost equipment and short-time analysis in comparison with other techniques. The results are in very good agreement with results based on the geographical origin of Sauvignon blanc wines. PRACTICAL APPLICATION This manuscript describes a method to determine the geographical origin of Sauvignon wines from Argentina. The main advantage of this method is the use of nonexpensive techniques, such as UV-Vis spectroscopy.

Chemometric Characterization of Sunflower Seeds

The spectroscopic characterization of different varieties of sunflower seeds based on their oleic acid content is proposed. One hundred fifty samples of sunflower seeds from different places of Argentina were analyzed by near-infrared diffuse reflectance spectroscopy (NIRDRS). Seed samples were grounded and sieved without chemical treatment previous to the analysis. For the characterization, the used multivariate methods were: principal component analysis (PCA), cluster analysis (CA), linear discriminant analysis (LDA), and partial least square discriminant analysis (PLS-DA). By using PCA, CA, and LDA, and from the point of view of varieties of sunflower seeds, 2 groups were differentiated, based on the concentration of oleic acid: a low oleic group, which ranged from 15% to 25% w/w oleic acid; and the other one (mid-high oleic varieties) which ranged from 26% to 90% w/w oleic acid. However, by using the PLS-DA, 3 groups were correctly differentiated based on the concentration of oleic acid: low oleic (from 15% to 25% w/w oleic acid); mid oleic (26% to 76% w/w oleic acid); and high oleic (≥ than 77% w/w oleic acid), demonstrating the high classification ability of this method. This multivariate characterization of sunflower seed varieties did not require chromatographic analysis to generate the matrix of concentrations, and only direct measures of NIRDRS spectra were required. This characterization can be useful to quickly know the variety of sunflower seed in the grain market. Practical Applications: This manuscript describes a method to determine 3 varieties of sunflower seeds (high, mid, and low oleic) The advantage of this method is to avoid the use of techniques that require long-time analysis.

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.

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.

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.