José Manuel Camiña

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.

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.

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.

Chemometric tools for the characterisation of honey produced in La Pampa, Argentina, from their elemental content, using inductively coupled plasma optical emission spectrometry (ICP-OES)

Summary Thirty two samples of natural honey produced in the province of La Pampa (Argentina) were characterised on the basis of their phosphorous, aluminium, iron, calcium, magnesium and sodium contents. Analytical determinations were carried out using inductive coupled plasma optical emission spectrometry (ICP-OES). For characterisation, chemometric methods used were Principal Components Analysis (PCA) and Cluster Analysis (CA), while for classification, Linear Discriminant Analysis (LDA) was used. The results show that samples obtained within 50 km from the centre of the province (around Santa Rosa, the capital city), were different from those coming from the rest of the province, generating a classification based on geographical origin. Phosphorous content was the most significant variable in the classification of the PCA model.

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.

Elemental Analysis of Amaranth, Chia, Sesame, Linen, and Quinoa Seeds by ICP-OES: Assessment of Classification by Chemometrics

In this work, 26 elements (Ag, Al, As, B, Ba, Ca, Cd, Co, Cr, Cs, Cu, Ga, Fe, K, Mg, Mn, Na, P, Pb, Rb, Se, Si, Sr, Ti, V, and Zn) were analyzed by inductively coupled plasma optical emission spectroscopy (ICP-OES) in seed samples: amaranth, chia, sesame, linen, and quinoa. Elemental analysis showed the presence of toxic elements (As, Cd, and Pb), as well as high mineral content (Fe, Na, P, K, and Mg): The concentrations of toxic elements were below the permissible limits by the WHO. Chemometrics was performed by cluster analysis (CA), principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA). In all cases, a correct classification of each type of seed was obtained. The combination of elemental analysis (major, minor, and toxic elements) and chemometrics was useful to determine nutritional quality in the studied seeds, as well as to classify them according to the botanical origin. For that, this method can be useful for the analysis of raw material as quality control in food factories and government control labs.

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.

Assessment of the effect of silicon on antioxidant enzymes in cotton plants by multivariate analysis

Silicon has been extensively researched in relation to the response of plants to biotic and abiotic stress, as an element triggering defense mechanisms which activate the antioxidant system. Furthermore, in some species, adding silicon to unstressed plants modifies the activity of certain antioxidant enzymes participating in detoxifying processes. Thus, in this study, we analyzed the activity of antioxidant enzymes in leaves and roots of unstressed cotton plants fertilized with silicon (Si). Cotton plants were grown in hydroponic culture and added with increasing doses of potassium silicate; then, the enzymatic activity of catalase (CAT), guaiacol peroxidase (GPOX), ascorbate peroxidase (APX), and lipid peroxidation were determined. Using multivariate analysis, we found that silicon altered the activity of GPOX, APX, and CAT in roots and leaves of unstressed cotton plants, whereas lipid peroxidation was not affected. The analysis of these four variables in concert showed a clear differentiation among Si treatments. We observed that enzymatic activities in leaves and roots changed as silicon concentration increased, to stabilize at 100 and 200 mg Si L(-1) treatments in leaves and roots, respectively. Those alterations would allow a new biochemical status that could be partially responsible for the beneficial effects of silicon. This study might contribute to adjust the silicon application doses for optimal fertilization, preventing potential toxic effects and unnecessary cost.

Fluorescent fingerprints of edible oils and biodiesel by means total synchronous fluorescence and Tucker3 modeling

The present work proposes the use of total synchronous fluorescence spectroscopy (TSFS) as a discrimination methodology for fluorescent compounds in edible oils, which are preserved after the transesterification processes in the biodiesel production. In the same way, a similar study is presented to identify fluorophores that do not change in expired vegetal oils, to associate physicochemical parameters to fluorescent measures, as contribution to a fingerprint for increasing the chemical knowledge of these products. The fluorescent fingerprints were obtained by Tucker3 decomposition of a three-way array of the total synchronous fluorescence matrices. This chemometric method presents the ability for modeling non-bilinear data, as Total Synchronous Fluorescence Spectra data, and consists in the decomposition of the three way data arrays (samples×Δλ×λ excitation), into four new data matrices: A (scores), B (profile in Δλ mode), C (profile in spectra mode) and G (relationships between A, B and C). In this study, 50 samples of oil from soybean, corn and sunflower seeds before and after its expiration time, as well as 50 biodiesel samples obtained by transesterification of the same oils were measured by TSFS. This study represents an immediate application of chemical fingerprint for the discrimination of non-expired and expired edible oils and biodiesel. This method does not require the use of reagents or laborious procedures for the chemical characterization of samples.