The aim of the present study was to differentiate sensory good and bad virgin rapeseed oils based on the composition of the volatile compounds by means of linear discriminant analysis (LDA). A total number of 64 compounds were detected by dynamic headspace GC‐MS and combined olfactometry with potential for sensory quality assessment either as aroma‐active compound by substance concentration or flavor property or as a non‐aroma‐active compound by correlation with the panel assessment. From this initial set of substances, 13 volatile compounds showed significant differences in the concentration between sensory good and bad oils, whereas their concentration was higher in sensory bad oils. No significant differences were found for compounds characterizing sensory good virgin rapeseed oils. Based on these 13 volatile compounds, the present work shows for the first time that LDA was able to classify a training data set of 43 samples satisfactorily according to the sensory quality. As a proof for suitability, a discriminant function was calculated and successfully used to classify samples from an additional validation sample set. Practical applications: Virgin rapeseed oils are high‐quality products on the market, which are defined above all by their sensory quality. The sensory evaluation of virgin rapeseed oils by a trained sensory panel group is time and labor consuming and sometimes the results are not reliable. Therefore, the development of an analytical method for the discrimination of sensory good and bad virgin rapeseed oils based on the determination and statistical evaluation of volatile, aroma‐active compounds is a helpful tool to support the sensory evaluation. The repeatability of the analytical method is better than the sensory evaluation, which also helps to support the work of a panel group. Additionally, the amount of work for a panel group can be reduced due to a pre‐selection by the analytical tool. The present work shows a chemometric approach to differentiate sensory good and bad quality virgin rapeseed oils on basis of 13 volatile compounds. For the first time volatile compounds have been identified that can be correlated with sensory bad virgin rapeseed oils.

Motivation: The research area metabolomics achieved tremendous popularity and development in the last couple of years. Owing to its unique interdisciplinarity, it requires to combine knowledge from various scientific disciplines. Advances in the high-throughput technology and the consequently growing quality and quantity of data put new demands on applied analytical and computational methods. Exploration of finally generated and analyzed datasets furthermore relies on powerful tools for data mining and visualization.Results: To cover and keep up with these requirements, we have created MeltDB 2.0, a next-generation web application addressing storage, sharing, standardization, integration and analysis of metabolomics experiments. New features improve both efficiency and effectivity of the entire processing pipeline of chromatographic raw data from pre-processing to the derivation of new biological knowledge. First, the generation of high-quality metabolic datasets has been vastly simplified. Second, the new statistics tool box allows to investigate these datasets according to a wide spectrum of scientific and explorative questions.Availability: The system is publicly available at https://meltdb.cebitec.uni-bielefeld.de. A login is required but freely available.Contact: nkessler@cebitec.uni-bielefeld.de

Based on individual cultivars, metabolite profiling showed promising results for the categorization of organic and conventional wheat. Further investigations are necessary with wheat from more growing seasons and locations before definite conclusions can be drawn concerning the feasibility to evolve a combined set of biomarkers for organically grown wheat using metabolite profiles.

Virgin rapeseed (Brassica napus) oil is a valuable niche product, if delivered with a high quality. In this study, the effects of moist storage of B. napus seeds for 1 to 4 days on the seed metabolome and the chemo-sensory properties of the produced oils were determined. The concentrations of several primary metabolites, including monosaccharides and amino acids, rapidly increased in the seeds, probably indicating the breakdown of storage compounds to support seed germination. Seed concentrations of indole glucosinolates increased with a slight time offset suggesting that amino acids may be used to modify secondary metabolism. The volatile profiles of the oils were pronouncedly influenced by moist seed storage, with the sensory quality of the oils decreasing. This study provides a direct time-resolved link between seed metabolism under moist conditions and the quality of the resulting oils, thereby emphasizing the crucial role of dry seed storage in ensuring high oil quality.

Abstract:The solubilisation of non-starch polysaccharides from cell wall materials (CWM) of palm-kernel and sunflower meals was studied using multi-component polysaccharidase preparations or fractions thereof. In sunflower CWM, the degradability of pectic compounds and mannosecontaining polysaccharides was highest, as estimated from 40 to 70% solubilisation of their constituent monosaccharides arabinose, galactose, galacturonic acid, rhamnose and mannose. Dimers and monomers were produced as major neutral and acidic end-products.Mannans in palm-kernel CWM were hydrolysed to about 20-50 YO, depending on enzyme composition, with the monomer and dimer of mannose as major endproducts. In both materials, xylans, or other xylose-containing polymers, were most resistant to enzymic hydrolysis. The results indicate the preferential solubilisation of parenchyma tissues from the seed in sunflower meal and the endosperm in palm-kernel meal, and the resistance of sunflower hull and palmkernel endocarp to enzymic attack. The concentrations of notably cellulolytic, mannanolytic and arabinolytic activities clearly determined the extent to which the corresponding constituents were solubilised.