A digital image smartphone-based approach to Slovak Tokaj wine authentication chemometric assisted.

The authentication of Slovak wines in comparison to other similar wines from various geographical regions, namely Hungary, France, Austria, and Ukraine, was conducted using the OC-PLS, DD-SIMCA, and PLS-DM models, all of them operating in rigorous way. The study involved 63 samples, of which 41 originated from Slovakia, covering diverse wine types such as varietal wines, cuvée selections (different "putnový"), and essence. To capture digital images under controlled conditions, a custom-made cardboard box with white inner surfaces was devised and equipped with a smartphone. During the training phase, sensitivities of 96%, 100%, and 96% were attained for OC-PLS, DD-SIMCA, and PLS-DM, respectively. In the subsequent stages of validation and testing for DD-SIMCA and PLS-DM, the proposed methods displayed optimal efficiency, achieving both sensitivity and specificity rates of 100%. However, such results were not achieved in the case of OC-PLS, which exhibited efficiency levels of 90% in validation and 80% in testing.

Second-order calibration high-resolution continuum source graphite furnace molecular absorption spectrometry-based determination of bromine and fluorine.

In this study, we report the simultaneous determination of bromine and fluorine using Second-Order Calibration High-Resolution Continuum Source Graphite Furnace Molecular Absorption Spectrometry (HR CS MAS). The instrumental data acquired correspond to the time versus wavelength matrix per sample that were analyzed using Parallel Factor Analysis (PARAFAC), along with Unfold and N-way Partial Least Squares combined with a post-calibration step known as Residual Bilinearization (U and N PLS/RBL). Despite the significant difference in sensitivity between bromine and fluorine, all approaches provided reasonably accurate results when predicting both analytes in synthetic mixtures within a controlled environment. The relative prediction error (REP) values for bromine were 29.8 % (PARAFAC), 23.6 % (N-PLS/RBL), and 13.1 % (U-PLS/RBL), while for fluorine, the REP values were 3.4 % (PARAFAC), 3.5 % (N-PLS/RBL), and 3.2 % (U-PLS/RBL). When applying this approach to predict unknown samples, a comparison was made between the estimated nominal concentrations of fluorine and bromine obtained using either a reference method or based on labeled values or spiked mass, and those obtained using the proposed method. It was observed that PARAFAC was unable to predict the samples accurately, whereas the REP values for the prediction of bromine and fluorine using N-PLS/RBL and U-PLS/RBL methods were 19.3 %/19.2 % and 13.6 %/13.1 %, respectively.

Discrimination of whole grape juice using fluorescence spectroscopy data with linear discriminant analysis coupled to genetic and ant colony optimisation algorithms.

In this study, a new approach was developed for classifying grape juices produced in Brazil using unfolded excitation-emission matrix (EEM) fluorescence spectroscopy and chemometrics, with respect to the agricultural production system, namely the conventional or organic agricultural one. Linear discriminant analysis (LDA) coupled to ant colony optimisation (ACO) and the genetic algorithm (GA) were used to select a more effective subset of variables to discriminate grape juice samples. The best results demonstrated highly efficient classification of grape juice samples according to a conventional or organic production process with an accuracy rate of up to 97% for the models and 94% in the prediction of these classes for samples external to the model. The models showed high selectivity and sensitivity with a rate of up to 100% for the training and test datasets, in addition to determining the most significant variables that explain the separation of classes. The proposed method proves to be viable, as it is fast and requires minimal sample preparation, allowing quality control in the food industry.

Evaluation of efficiency and selectivity in the sorption process assisted by chemometric approaches: Removal of emerging contaminants from water.

This paper describes, by the first time, a chemometric approach that combines a simple set of the UV-Vis spectra and partial least square regression (PLSR) for measuring the removal of five pharmaceuticals present in simulated hospital effluents by sorption using activated carbon. The use of multivariate calibration allowed the quantification of the remaining concentrations of the studied drugs present in a complex mixture with high accuracy, avoiding the need for the use of sophisticated methodologies based on chromatography. Isothermal sorption studies were performed on single-component solutions containing amoxicillin, paracetamol, propranolol, sodium diclofenac, or tetracycline as well as on a solution containing a mixture of all these 5 compounds. The isotherm data obtained were fitted to the Langmuir, Freundlich and Liu models. It was observed that for each pharmaceutical, the maximum sorption capacity of the activated carbon was higher for the single component than in the mixture. It was observed that the removal of paracetamol, propranolol, and tetracycline, the removal was complete (100%) and for amoxicillin and sodium diclofenac it was at least 92.71 ±â€¯3.15% and 91.82 ±â€¯0.95% respectively, indicating that the avocado seed activated carbon is an adsorbent with high sorption capacity that can remove five pharmaceuticals from simulated hospital effluents.

Speciation analysis based on digital image colorimetry: Iron (II/III) in white wine.

This work proposes an analytical strategy utilizing digital images (DI) for the iron inorganic speciation in white wine. The method was established by the reaction of iron(II) ions with 1,2 ortho-phenanthroline as a chromogenic reagent. Total iron was determined using the same reagent after the addition of hydroxyl ammonium chloride as a reducing agent. In both cases, digital images of the standards/chromogenic reagent and samples were acquired and stored in JPEG format. The region of interest (ROI) was determined with a constant square shape for all images. The ROI was submitted to decomposition in color values according to the RGB additive color model. However, the data obtained by the blue channel was the one used in the construction of the analytical curves because it presented the highest sensitivity. The optimization of the experimental conditions of the procedure was performed by employing multivariate techniques. The precision was evaluated using a wine sample with iron (II) and total iron contents of 0.41 and 0.69 mg L-1, respectively. The results expressed as relative standard deviations were 3.57% for iron (II) and 4.76% for total iron contents. A comparison between the results obtained for total iron by the DI method with the results found using flame atomic absorption spectrometry confirmed the method accuracy. The DI procedure was applied for speciation analysis in six white wine samples and the contents found varied from 0.41 to 1.67 mg L-1 for iron (II) and from 0.69 to 1.71 mg L-1 for total iron. These results are in agreement with those found for speciation analysis of iron in wine samples. Iron (III) contents can be found by the difference between the total iron and iron (II) contents.

Using near infrared spectroscopy to classify soybean oil according to expiration date.

A rapid and non-destructive methodology is proposed for the screening of edible vegetable oils according to conservation state expiration date employing near infrared (NIR) spectroscopy and chemometric tools. A total of fifty samples of soybean vegetable oil, of different brands andlots, were used in this study; these included thirty expired and twenty non-expired samples. The oil oxidation was measured by peroxide index. NIR spectra were employed in raw form and preprocessed by offset baseline correction and Savitzky-Golay derivative procedure, followed by PCA exploratory analysis, which showed that NIR spectra would be suitable for the classification task of soybean oil samples. The classification models were based in SPA-LDA (Linear Discriminant Analysis coupled with Successive Projection Algorithm) and PLS-DA (Discriminant Analysis by Partial Least Squares). The set of samples (50) was partitioned into two groups of training (35 samples: 15 non-expired and 20 expired) and test samples (15 samples 5 non-expired and 10 expired) using sample-selection approaches: (i) Kennard-Stone, (ii) Duplex, and (iii) Random, in order to evaluate the robustness of the models. The obtained results for the independent test set (in terms of correct classification rate) were 96% and 98% for SPA-LDA and PLS-DA, respectively, indicating that the NIR spectra can be used as an alternative to evaluate the degree of oxidation of soybean oil samples.

Screening analysis of biodiesel feedstock using UV-vis, NIR and synchronous fluorescence spectrometries and the successive projections algorithm.

This paper investigates the use of UV-vis, near infrared (NIR) and synchronous fluorescence (SF) spectrometries coupled with multivariate classification methods to discriminate biodiesel samples with respect to the base oil employed in their production. More specifically, the present work extends previous studies by investigating the discrimination of corn-based biodiesel from two other biodiesel types (sunflower and soybean). Two classification methods are compared, namely full-spectrum SIMCA (soft independent modelling of class analogies) and SPA-LDA (linear discriminant analysis with variables selected by the successive projections algorithm). Regardless of the spectrometric technique employed, full-spectrum SIMCA did not provide an appropriate discrimination of the three biodiesel types. In contrast, all samples were correctly classified on the basis of a reduced number of wavelengths selected by SPA-LDA. It can be concluded that UV-vis, NIR and SF spectrometries can be successfully employed to discriminate corn-based biodiesel from the two other biodiesel types, but wavelength selection by SPA-LDA is key to the proper separation of the classes.

Determination of biodiesel content in biodiesel/diesel blends using NIR and visible spectroscopy with variable selection.

This work is concerned of evaluate the use of visible and near-infrared (NIR) range, separately and combined, to determine the biodiesel content in biodiesel/diesel blends using Multiple Linear Regression (MLR) and variable selection by Successive Projections Algorithm (SPA). Full spectrum models employing Partial Least Squares (PLS) and variables selection by Stepwise (SW) regression coupled with Multiple Linear Regression (MLR) and PLS models also with variable selection by Jack-Knife (Jk) were compared the proposed methodology. Several preprocessing were evaluated, being chosen derivative Savitzky-Golay with second-order polynomial and 17-point window for NIR and visible-NIR range, with offset correction. A total of 100 blends with biodiesel content between 5 and 50% (v/v) prepared starting from ten sample of biodiesel. In the NIR and visible region the best model was the SPA-MLR using only two and eight wavelengths with RMSEP of 0.6439% (v/v) and 0.5741 respectively, while in the visible-NIR region the best model was the SW-MLR using five wavelengths and RMSEP of 0.9533% (v/v). Results indicate that both spectral ranges evaluated showed potential for developing a rapid and nondestructive method to quantify biodiesel in blends with mineral diesel. Finally, one can still mention that the improvement in terms of prediction error obtained with the procedure for variables selection was significant.