Homogeneity and stability assessment of a candidate to pumpkin seed flour reference material by means of computer vision based chemometrics assisted approach.

This study proposes a preliminary assessment of the homogeneity and stability through digital image acquisition of a candidate for mechanically processed pumpkin seed meal reference material, exploring the concepts of homogeneity curve and the analysis of texture characteristics by Continuous-Level Moving Block through Robust Principal Component Analysis. This innovative methodology allowed us to examine the percentage of homogeneity in a set of samples, revealing an average of 41% with only one outlier in relation to the entire sample, indicating low homogeneity. In the stability study carried out after storing samples for 12 months at different temperatures, 83% of the samples were considered regular and 17% were outlier, which means that most of them were considered stable. Therefore, this methodology is useful for screening samples for homogeneity, by textural analysis, and detected non-homogeneity can be corrected in advance for quantification by standard protocols.

Qualitative and quantitative analysis based on digital images to determine the adulteration of ketchup samples with Sudan I dye.

Sudan I is a synthetic-azo dye commonly used to adulterate foods to increase sensory appearance. However, it is banned due to its carcinogenic, mutagenic and genotoxic properties, which represent a serious risk to human health. Thus, this paper proposes a feasibility study to identify and quantify Sudan I dye in ketchup samples using colour histograms (obtained from digital images) and multivariate analysis. The successive projections algorithm coupled with linear discriminant analysis (SPA-LDA) classified correctly all samples, while the partial least squares coupled with SPA for interval selection (iSPA-PLS) quantified adequately the adulterant, attaining values of RMSEP of 11.64 mg kg-1, R2 of 0.96, RPD of 5.28, REP of 13.63% and LOD of 39.45 mg kg-1. Therefore, the proposed methodology provides a simple, fast, inexpensive, promising analytical tool for the screening of both the quality and safety of ketchup samples. As a consequence, it can help to protect the consumer's health.

Digital image-based tracing of geographic origin, winemaker, and grape type for red wine authentication.

This work proposes the development of a simple, fast, and inexpensive methodology based on color histograms (obtained from digital images), and supervised pattern recognition techniques to classify red wines produced in the São Francisco Valley (SFV) region to trace geographic origin, winemaker, and grape variety. PCA-LDA coupled with HSI histograms correctly differentiated all of the SFV samples from the other geographic regions in the test set; SPA-LDA selecting just 10 variables in the Grayscale + HSI histogram achieved 100% accuracy in the test set when classifying three different SFV winemakers. Regarding the three grape varieties, SPA-LDA selected 15 variables in the RGB histogram to obtain the best result, misclassifying only 2 samples in the test set. Pairwise grape variety classification was also performed with only 1 misclassification. Besides following the principles of Green Chemistry, the proposed methodology is a suitable analytical tool; for tracing origins, grape type, and even (SFV) winemakers.

Unfolded partial least squares/residual bilinearization combined with the Successive Projections Algorithm for interval selection: enhanced excitation-emission fluorescence data modeling in the presence of the inner filter effect.

The use of the successive projections algorithm (SPA) for elimination of uninformative variables in interval selection, and unfold partial least squares regression (U-PLS) modeling of excitation-emission matrices (EEM), when under the inner filter effect (IFE) is reported for first time. Post-calibration residual bilinearization (RBL) was employed against events of unknown components in the test samples. The inner filter effect can originate changes in both the shape and intensity of analyte spectra, leading to trilinearity losses in both modes, and thus invalidating most multiway calibration methods. The algorithm presented in this paper was named iSPA-U-PLS/RBL. Both simulated and experimental data sets were used to compare the prediction capability during: (1) simulated EEM; and (2) quantitation of phenylephrine (PHE) in the presence of paracetamol (PAR) (or acetaminophen) in water samples. Test sets containing unexpected components were built in both systems [a single interference was taken into account in the simulated data set, while water samples were added with varying amounts of ibuprofen (IBU), and acetyl salicylic acid (ASA)]. The prediction results and figures of merit obtained with the new algorithm were compared with those obtained with U-PLS/RBL (without intervals selection), and with the well-known parallel factors analysis (PARAFAC). In all cases, U-PLS/RBL displayed better EEM handling capability in the presence of the inner filter effect compared with PARAFAC. In addition, iSPA-U-PLS/RBL improved the results obtained with the full U-PLS/RBL model, in this case demonstrating the potential of variable selection.

The Successive Projections Algorithm for interval selection in trilinear partial least-squares with residual bilinearization.

In this work the Successive Projection Algorithm is presented for intervals selection in N-PLS for three-way data modeling. The proposed algorithm combines noise-reduction properties of PLS with the possibility of discarding uninformative variables in SPA. In addition, second-order advantage can be achieved by the residual bilinearization (RBL) procedure when an unexpected constituent is present in a test sample. For this purpose, SPA was modified in order to select intervals for use in trilinear PLS. The ability of the proposed algorithm, namely iSPA-N-PLS, was evaluated on one simulated and two experimental data sets, comparing the results to those obtained by N-PLS. In the simulated system, two analytes were quantitated in two test sets, with and without unexpected constituent. In the first experimental system, the determination of the four fluorophores (l-phenylalanine; l-3,4-dihydroxyphenylalanine; 1,4-dihydroxybenzene and l-tryptophan) was conducted with excitation-emission data matrices. In the second experimental system, quantitation of ofloxacin was performed in water samples containing two other uncalibrated quinolones (ciprofloxacin and danofloxacin) by high performance liquid chromatography with UV-vis diode array detector. For comparison purpose, a GA algorithm coupled with N-PLS/RBL was also used in this work. In most of the studied cases iSPA-N-PLS proved to be a promising tool for selection of variables in second-order calibration, generating models with smaller RMSEP, when compared to both the global model using all of the sensors in two dimensions and GA-NPLS/RBL.

A flow-batch analyzer using a low cost aquarium pump for classification of citrus juice with respect to brand.

This paper proposes a novel flow-batch analyzer (FBA), which employs a compact, low-cost aquarium air pump as an alternative to a peristaltic pump. The feasibility of using this simple propulsion device is demonstrated in a case study involving the classification of citrus juice samples with respect to brand. For this purpose, UV-vis spectra and SIMCA (soft independent modelling of class analogies), PLS-DA (Partial Least Squares for Discriminant Analysis) and SPA-LDA (Linear Discriminant Analysis with variables selected by the Successive Projections Algorithm) are employed. Good classification results were obtained, thus indicating that the proposed FBA system is a viable alternative to the use of more costly peristaltic pumps. In addition, the smaller size and weight of the aquarium pump are useful features for the construction of portable FBAs to be deployed in field applications.

Classification of biodiesel using NIR spectrometry and multivariate techniques.

This article describes the classification of biodiesel samples using NIR spectroscopy and chemometric techniques. A total of 108 spectra of biodiesel samples were taken (being three samples each of four types of oil, cottonseed, sunflower, soybean and canola), from nine manufacturers. The measurements for each of the three samples were in the spectral region between 12,500 and 4000 cm(-1). The data were preprocessed by selecting a spectral range of 5000-4500 cm(-1), and then a Savitzky-Golay second-order polynomial was used with 21 data points to obtain second derivative spectra. Characterization of the biodiesel was done using chemometric models based on hierarchical cluster analysis (HCA), principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) elaborated for each group of biodiesel samples (cotton, sunflower, soybean and canola). For the HCA and PCA, the formation of clusters for each group of biodiesel was observed, and SIMCA models were built using 18 spectral measurements for each type of biodiesel (training set), and nine spectral measurements to construct a classification set (except for the canola oil which used eight spectra). The SIMCA classifications obtained 100% accurate identifications. Using this strategy, it was feasible to classify biodiesel quickly and nondestructively without the need for various analytical determinations.