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. r. marie-galante and Gossypium barbadense L. and to use molecular data to assessing genetic diversity and molecular discrimination of these species. METHODS AND RESULTS: The sets contained 12 G. barbadense genotypes and 16 G. hirsutum genotypes from a Brazilian collection. The 11 ISSR primers were used for genotyping yielded 101 bands (polymorphism = 47.5%) and were classified as moderately informative (PIC = 0.304). The ISSR markers exposed a greater diversity in G. hirsutum (P = 24.72%; HE =0.071 and I = 0.111) as compared to G. barbadense (P = 17.98%, HE = 0.043 and I = 0.070). The AMOVA analysis showed that 89.47% of the genetic variation was partitioned within species which is supported by Nei's genetic differentiation (Gst = 0.598) and gene flow (Nm = 0.338), suggesting that strong reproductive barriers between species. The UPGMA Cluster Analysis, Principal Coordinate Analysis and Bayesian Model-Based Structural Analysis divided the 28 genotypes into two main clades consistent with the taxonomical delimitation. CONCLUSION: The ISSR marker system offers a new approach to determining molecular differences between two cotton species (G. hirsutum L. r. marie-galante and G. barbadense L.). This study can expand the molecular marker resources for the identification and improvement of our knowledge about the genetic diversity and relationships between perennial cotton genotypes.

Distinguishing cotton seed genotypes by means of vibrational spectroscopic methods (NIR and Raman) and chemometrics.

The use of vibrational spectroscopy, such as near infrared (NIR) and Raman, combined with multivariate analysis methods to analyze agricultural products are promising for investigating genetically modified organisms (GMO). In Brazil, cotton is grown under humid tropical conditions and is highly affected by pests and diseases, requiring the use of large amounts of phytosanitary chemicals. To avoid the use of those pesticides, genetic improvement can be carried out to produce species tolerant to herbicides, resistant to fungi and insects, or even to provide greater productivity and better quality. Even with these advantages, it is necessary to manage and limit the contact of transgenic species with native ones, avoiding possible contamination or even extinction of conventional species. The identification of the presence of GMOs is based on complex DNA-based analysis, which is usually laborious, expensive, time-consuming, destructive, and generally unavailable. In the present study, a new methodology to identify GMOs using partial least squares discriminant analysis (PLS-DA) on NIR and Raman data is proposed to distinguish conventional and transgenic cotton seed genotypes, providing classification errors for prediction set of 2.23% for NIR and 0.0% for Raman.

Chemometric strategies for near infrared hyperspectral imaging analysis: classification of cotton seed genotypes.

Hyperspectral images have been increasingly employed in the agricultural sector for seed classification for different purposes. In the present paper we propose a new methodology based on HSI in the near infrared range (HSI-NIR) to distinguish conventional from transgenic cotton seeds. Three different chemometric approaches, one pixel-based and two object-based, using partial least squares discriminant analysis (PLS-DA) were built and their performances were compared considering the pros and cons of each approach. Specificity and sensitivity values ranged from 0.78-0.92 and 0.62-0.93, respectively, for the different approaches.

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. Thus, the search for rapid detection and/or identification methods for mycotoxins and mycotoxin-producing fungi for application in food safety remain important. In this work, we implemented use of near infrared hyperspectral images (HSI-NIR) combined with pattern recognition analysis, partial-least-squares discriminant analysis (PLS-DA) of images, to develop a rapid method for identification of Fusarium verticillioides and F. graminearum. Validation of the HSI-NIR method and subsequent analysis was realized using 15 Fusarium spp. isolates. The method was efficient as a rapid, non-invasive, and non-destructive assessment was achieved with 100% accuracy, sensitivity, and specificity for both fungi.

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. Two different parts of each species, stalk and cotton shell, were analyzed by bulk density, proximate analysis, higher heating value, cellulose, hemicellulose, protein, fat and lignin content, thermogravimetric analysis, and briquette mechanical strength. The results of the energetic characterization indicated a higher energetic potential of the colored species when compared with the white cotton, especially because of the volatile matter content, fixed carbon, and higher heating value. The briquette mechanical strength was higher in the samples formulated by a mixture of stalk and shell. Finally, it was concluded that the waste from colored cotton cultivars, Jade and Topazio, is capable to generate briquettes with good mechanical and physico-chemical characteristics, especially those formed by the mixture of stalk and shell.

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.

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.