Genetic polymorphism detection in brazilian perennial cottons (Gossypium spp.) using an ISSR marker system and its application for molecular interspecific differentiation.

Background: The semi-domesticated Brazilian perennial cotton (Gossypium spp.) germplasm is considered a source of variability for creating modern upland cotton varieties. Here we used Inter-simple Sequence Repeat (ISSR) markers to detect intra and interspecific genetic polymorphism in Gossypium hirsutum L.

Application of near-infrared hyperspectral (NIR) images combined with multivariate image analysis in the differentiation of two mycotoxicogenic Fusarium species associated with maize.

Maize (Zea mays L.) is one of the most versatile crops worldwide with high socioeconomic relevance. However, mycotoxins produced by pathogenic fungi are of constant concern in maize production, as they pose serious risks to human and animal health.

Energetic characterization and evaluation of briquettes produced from naturally colored cotton waste.

Cotton crops generate millions of tons of lignocellulosic waste in Brazil that could be used in energy generation; however, the main destination of this raw material is soil incorporation. The aim of this work was to perform an energetic characterization and evaluation of briquettes produced from different agricultural waste of naturally colored cotton for power generation. The cultivars Brasil Sementes (BRS) Jade and Topazio were studied, with white cotton (BRS 286) as standard for comparison purposes.

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).

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).