Thin Layer Chromatography is a favorable method for the analysis of plant materials, as it is very tolerant towards samples with high matrix content. Moreover, the developed chromatograms can usually be examined in their entirety, which is very opportune for intuitive pattern recognition and comparison of samples.<br/>Surprisingly, it turned out to be difficult to translate this effortless visual classification into an automated statistical analysis. Apart from the obstacles common to chromatographic techniques – baseline correction, denoising, peak detection – thin layer chromatography features the intricacy of an uncontrolled flow of the mobile phase. The local inconstancies of the capillary force induced flow result in a variability of migration distances that obscure the actual differences in the samples. Before a meaningful statistical analysis can be conducted, these irregularities need to be adjusted.<br/>We analyzed the anthocyanin patterns in progeny from crosses between red-, purple- and blue-grained wheat varieties by High Performance Thin Layer Chromatography (HPTLC) and evaluated the chromatograms by clustering analysis. The obtained clusters were correlated to the total anthocyanin content and the genetic background. This approach for anthocyanin analysis of HPTLC analysis and data processing is compared to the previously employed method based on High Performance Liquid Chromatography (HPLC).<br/>Sample preparation consisted of a simple acidic extraction of the whole meal followed by centrifugation and filtration. In our previous work, HPLC had been used to analyze the anthocyanin samples. The high matrix load of the samples led to pronounced deterioration of the employed column, rendering it unusable after circa 100 injections. For HPTLC, deterioration of the stationary phase was unproblematic, as the plate is not reused. The required sample volume was smaller in HPTLC than in HPLC. In HPTLC, the lower resolution, the higher instability of the retention values and the additional variability introduced during plate imaging (contrast, brightness and saturation of the recorded image) were major obstacles that had to be overcome to obtain meaningful results.<br/>In summary, HPTLC was used to classify colored wheat varieties by cluster analysis, using a dedicated data processing tool. Compared to the commonly employed method of visual inspection and differentiation between samples by marker peaks, this is a considerable advancement.

HPTLC and clustering analysis for the classification of colored wheat varieties by anthocyanin patterns

249th National Meeting (2015)

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