Discrimination of sugarcane according to cultivar by ¹H NMR and chemometric analyses

Abstract

Várias tecnologias para o desenvolvimento de novas cultivares de cana-de-açúcar têm focado, principalmente, no aumento da produtividade e maior resistência à doença. Cultivares de cana-deaçúcar são, geralmente, identificadas pela organografia das folhas e caule, análise de peroxidase e atividade da isoenzima esterase, proteínas solúveis totais e teor de sólidos solúveis. A ressonância magnética nuclear (RMN) associada às análises quimiométricas provou ser uma técnica valiosa para avaliação de plantas. Assim, este trabalho descreve o potencial das análises quimiométricas aplicadas a RMN de 1 H de alta resolução com giro no ângulo mágico (HRMAS) e em solução para investigação de cultivares de cana-de-açúcar. Para esta proposta, folhas de oito diferentes cultivares de cana-de-açúcar foram analisadas por espectroscopia de RMN de 1 H aliada à quimiometria. As técnicas empregadas apresentaram-se como ferramentas úteis para a distinção e classificação das diferentes cultivares, bem como para acessar as diferenças na composição química das cultivares.Several technologies for the development of new sugarcane cultivars have mainly focused on the increase in productivity and greater disease resistance. Sugarcane cultivars are usually identified by the organography of the leaves and stems, the analysis of peroxidase and esterase isoenzyme activities and the total soluble protein as well as soluble solid content. Nuclear magnetic resonance (NMR) associated with chemometric analysis has proven to be a valuable tool for cultivar assessment. Thus, this article describes the potential of chemometric analysis applied to 1 H high resolution magic angle spinning (HRMAS) and NMR in solution for the investigation of sugarcane cultivars. For this purpose, leaves from eight different cultivars of sugarcane were investigated by 1 H NMR spectroscopy in combination with chemometric analysis. The approach shows to be a useful tool for the distinction and classification of different sugarcane cultivars as well as to access the differences on its chemical composition.

1 H NMR spectra dataset and solid-state NMR data of cowpea ( Vigna unguiculata )

“…Two matrices were used for chemometric evaluation: Table 3 for PCA (Principal Component Analysis); Table 4 for clustering analysis. For the construction of the Table 3 , all the 1 H NMR data were converted to American Standard Code for Information Interchange (ASCII) files and imported to Microsoft Excel software (Elenilson G. [2] ). For the construction of the Table 4 , each spectrum was divided into 0.04 ppm wide buckets, using simple rectangular bucket, sum of intensities in integration mode and scaled to total intensity in scaling process (Elenilson G. [1] ) .…”

Metabolite Profiling of Sugarcane Genotypes and Identification of Flavonoid Glycosides and Phenolic Acids

“…17−20 Despite the key role of sugarcane in the economy of Brazil, there have been few comprehensive studies of the metabolite and genetic diversity of the genotypes developed by Brazilian breeding programs (IAC-IACSP, SP, and RB). The genetic variability of modern varieties has been investigated by target region amplification polymorphism (TRAP), 21 whereas tentative metabotype discrimination among RB varieties has been proposed by 1 H NMR in solution and in solid matrices by high-resolution magic angle spinning (HR-MAS), 22 but this study focused on the content of primary metabolites, mainly sucrose.…”

HR‐MAS NMR metabolomics of ‘Swingle’ citrumelo rootstock genetically modified to overproduce proline

“…A crude sample can be analyzed using the HR‐MAS technique because dipolar interactions, chemical shift anisotropy, and the heterogeneity of the sample, which are responsible for increasing the spectral linewidth, are minimized because of spinning the sample at the magic angle (54.7°), resulting in spectra with good resolution and sensitivity similar to the NMR spectra of liquid samples . Thus, HR‐MAS NMR technique is ideal for the metabolomic analyses with applications in plant science . Chemometric methods have been used to process and extract information from NMR data, thus yielding the most relevant spectral information.…”