Everaldo Medeiros

Classification of edible vegetable oils using square wave voltammetry with multivariate data analysis.

This paper proposes a simple and non-expensive electroanalytical methodology for classification of edible vegetable oils with respect to type (canola, sunflower, corn and soybean) and conservation state (expired and non-expired shelf life). The proposed methodology employs an alcoholic extraction procedure followed by square wave voltammetry (SWV). Two chemometric methods were compared for classification of the resulting voltammograms, namely Soft Independent Modelling of Class Analogy (SIMCA) and Linear Discriminant Analysis (LDA) with variable selection by the Successive Projections Algorithm (SPA). The results were evaluated in terms of errors in a set of samples not included in the modelling process. The best results were obtained with the SPA-LDA method, which correctly classified all samples in terms of type and conservation state.

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.

Changes in chemical attributes of a Fluvent cultivated with castor bean and irrigated with wastewater

Agricultural use of wastewater is an alternative to increase water availability, especially in semiarid regions. However, it may cause undesirable chemical changes in the soil. The aim of this study was to evaluate the influence of wastewater irrigation and castor bean (Ricinus communis L.) cultivation on the chemical attributes of a Fluvic Neosol. The experimental design was in a randomized block, in split-plot scheme, where the main plots were represented by the treatments of irrigation water and castor bean cultivation, and the subplots were the soil layers, with three replications. The treatments were T1 - wastewater irrigation + castor bean cultivation ; T2 - mixture of supply water and wastewater (1:1 ratio) + castor bean cultivation; T3 - supply water irrigation + castor bean cultivation ; and T4 - wastewater application, without castor bean cultivation. The depths of soil layers were 0-10, 10-20, 20-30, 30-40, and 40-50 cm. At the end of study, the content of phosphorus, calcium, potassium, and organic matter increased mainly in the upper layers, and sodium increased in the deeper layers in the wastewater treatments, in comparison to the supply water irrigation. In T4, the disposal of wastewater increased the concentration of magnesium. The pH values, iron and zinc concentration did not statistically differ in the treatments.

Biamperometric Determination of Tetracycline in Pharmaceuticals

Abstract A fast, reliable, and low cost biamperometric flow‐injection method, with an error of 1.3% and an analytical throughput of 55 samples h−1, for determination of tetracycline hydrochloride in pharmaceuticals capsules is proposed. The analytical curve was linear (r=0.998) in the range 10 to 50 mg l−1 using Fe(CN)6 3− and NaOH solutions as reagent and carrier stream/supporting electrolyte, respectively. A relative standard deviation of 1.6% (10 sequential injections of 30.0 mg l−1) was verified with detection and quantification limits of 0.6 and 3.4 mg l−1, respectively.

Classification of Individual Castor Seeds Using Digital Imaging and Multivariate Analysis

This paper presents a method based on digital imaging and multivariate analysis for the classification of castor seeds with respect to the cultivar type. For this purpose, two seed groups most commonly employed on Brazilian plantations were evaluated: BRS Nordestina and BRS Paraguacu cultivars (group I) and BRS Energia cultivar and CNPA 2009-7 genotype (group II). Images of these two different seed groups were recorded from a webcam and the frequency distribution of color indexes in the red-green-blue (RGB), hue (H), saturation (S), intensity (I), and grayscale channels were obtained. Pattern recognition methods based on partial least squares-discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) were applied separately to each seed group. The best results were obtained by using the PLS-DA model, which correctly classified 97.5% and 98.8% of the prediction samples for groups I and II, respectively. The proposed method is simple, fast, non-destructive and non-expensive.

Flow Injection Determination of Metronidazole through Spectrophotometric Measurement of the Nitrite Ion Produced upon Alkaline Hydrolysis

A new method for metronidazole determination, based on spectrometric monitoring of a diazonium salt produced in-line by alkaline hydrolysis released nitrite ions, was developed and successfully applied to pharmaceutical tablets (r = 0.9993, 2.0-20.0 mg L-1, DL = 0.7 mg L-1) with no interference from common ingredients accompanying the drug.

A novel strategy for the classification of naturally colored cotton fibers based on digital imaging and pattern recognition techniques

This study proposes the use of digital images and multivariate analysis as an alternative methodology for the classification of naturally colored cotton fiber, according to cultivar type. For this purpose, two groups were evaluated: the first comprised of BRS 200 Marrom and BRS Topazio cultivars, while the second contained BRS Rubi and BRS Safira cultivars. Cotton fiber sample images were obtained using a webcam and the frequency distribution of color indexes on the grayscale, red–green–blue (RGB), hue (H), saturation (S), value (V), and grayscale channels was obtained. Classification models based on linear discriminant analysis (LDA) with prior variable selection by successive projection algorithm (SPA) and stepwise (SW) were used. For the purpose of comparison, partial least squares discriminant analysis (PLS-DA) applied to the full-histogram was also used. For both groups, the best results were achieved using the LDA/SW model, with a correct classification rate (CCR) of 96% for the prediction set using the HSV combination. The proposed method is simple, low cost, does not use a reagent, does not destroy the sample and provides results in a short period of time.

Non-destructive NIR spectrometric cultivar discrimination of castor seeds resulting from breeding programs

This article proposes a near-infrared (NIR) diffuse reflectance spectrometric method for non-destructive discrimination of castor seeds from the two cultivars most commonly employed in Brazilian plantations (BRS Nordestina and BRS Paraguacu). For this purpose, two classification techniques are compared, namely SIMCA (soft independent modelling of class analogies) and PLS-DA (partial least squares discriminant analysis). By applying the SIMCA classifier to a test set comprising 150 seeds, the BRS Nordestina and BRS Paraguacu class models yielded sensitivity/specificity values of 0.91/0.99 and 0.71/1.00, respectively. Better results were obtained by using PLS-DA, which correctly classified all test samples, i.e., yielded sensitivity and specificity values of 1.00. These findings suggest that the proposed method is a promising approach to identify castor seed genotypes, either in seed lots or for breeding purposes, prior to being planted.

Classification of individual cotton seeds with respect to variety using near-infrared hyperspectral imaging

This paper proposes the use of Near Infrared Hyperspectral Imaging (NIR-HSI) as a new strategy for fast and non-destructive classification of cotton seeds with respect to variety. A total of 807 seeds of four different cotton varieties are employed in this study. For classification purposes, each seed is represented by an average spectrum obtained by coaveraging the pixel spectra of the NIR-HSI image. Conventional NIR and VIS-NIR spectra are also employed for comparison. By using Partial-Least-Squares Discriminant Analysis (PLS-DA), correct classification rates of 98.0%, 89.7% and 91.7% were achieved in the NIR-HSI, conventional NIR and conventional VIS-NIR datasets. The superiority of the NIR-HSI system can be ascribed to a more comprehensive scan of the seed area, as compared to the conventional VIS-NIR spectrometer.

Um fotômetro multi-led microcontrolado, portátil e de baixo custo

A microcontrolled, portable and inexpensive photometer is described. It uses six light-emitting diodes (LEDs) as radiation sources and a phototransistor as detector, as well as a microcontroller (PIC - Programmable Controller of Interruption). This device provided total autonomy to the proposed photometer, which was successfully applied to determination of Fe2+ in ferrous syrups and of seven clinical biochemical parameters. As the components are cheap (~U