A prototype gas chromatography-ion mobility spectrometry (GC-IMS) system, hyphenating temperature-ramped headspace GC to a modified drift time IMS cell, was evaluated and compared to a conventional, isothermal capillary column (CC)-IMS system on the example of the geographical differentiation of extra virgin olive oils (EVOO) from Spain and Italy. It allows orthogonal, 2D separation of complex samples and individual detection of compounds in robust and compact benchtop systems. The information from the high-resolution 3D fingerprints of volatile organic compound (VOC) fractions of EVOO samples were extracted by specifically developed chemometric MATLAB® routines to differentiate between the different olive oil provenances. A combination of unsupervised principal component analysis (PCA) with two supervised procedures, linear discriminant analysis (LDA) and k-nearest neighbors (kNN), was applied to the experimental data. The results showed very good discrimination between oils of different geographical origins, featuring 98 and 92% overall correct classification rate for PCA-LDA and kNN classifier, respectively. Furthermore, the results showed that the higher resolved 3D fingerprints obtained from the GC-IMS system provide superior resolving power for non-targeted profiling of VOC fractions from highly complex samples such as olive oil. Graphical abstract Principle of the determination of geographic origins of olive oils by chemometric analysis of three-dimensional HS-GC-IMS fingerprints.

This work describes a simple approach for the untargeted profiling of volatile compounds for the authentication of the botanical origins of honey based on resolution-optimized HS-GC-IMS combined with optimized chemometric techniques, namely PCA, LDA, and kNN. A direct comparison of the PCA-LDA models between the HS-GC-IMS and H NMR data demonstrated that HS-GC-IMS profiling could be used as a complementary tool to NMR-based profiling of honey samples. Whereas NMR profiling still requires comparatively precise sample preparation, pH adjustment in particular, HS-GC-IMS fingerprinting may be considered an alternative approach for a truly fully automatable, cost-efficient, and in particular highly sensitive method. It was demonstrated that all tested honey samples could be distinguished on the basis of their botanical origins. Loading plots revealed the volatile compounds responsible for the differences among the monofloral honeys. The HS-GC-IMS-based PCA-LDA model was composed of two linear functions of discrimination and 10 selected PCs that discriminated canola, acacia, and honeydew honeys with a predictive accuracy of 98.6%. Application of the LDA model to an external test set of 10 authentic honeys clearly proved the high predictive ability of the model by correctly classifying them into three variety groups with 100% correct classifications. The constructed model presents a simple and efficient method of analysis and may serve as a basis for the authentication of other food types.

The globalization of trade of foods with its overwhelming variety has led to an increased demand for authentic products by all parts of the food value chain. In particular high-priced products are commonly adulterated, mislabeled or completely substituted. Honey is a typical example for such foods, which show an increasing number of adulterations, mostly related to the declaration of the botanical origin. A non-targeted 1 H-NMR-based screening, combined with multivariate statistical analysis was applied as a fast and simple comprehensive approach to verify the botanical origin of honey samples. The NMR fingerprints of honey sample were processed by taylor-made chemometric tools, based on principal component analysis (PCA) and linear discriminant analysis (LDA) in custom MATLAB routines. The results obtained by PCA-LDA showed very good discrimination between the different honey types with 98.9 % correct overall classification rate of the samples. Hence, this NMR based screening approach could be an effective alternative to traditional, laborious methods.

To study the network dynamics of the riboflavin biosynthesis pathway and to identify potential bottlenecks in the system, an ordinary differential equation-based model was constructed using available literature data for production strains. The results confirmed that the RibA protein is rate limiting in the pathway. Under the conditions investigated, we determined a potential limiting order of the remaining enzymes under increased RibA concentration (>0.102 mM) and therefore higher riboflavin production (>0.045 mmol g(CDW)(-1) h(-1) and 0.0035 mM s(-1), respectively). The reductase activity of RibG and lumazine synthase (RibH) might be the next most limiting steps. The computational minimization of the enzyme concentrations of the pathway suggested the need for a greater RibH concentration (0.251 mM) compared with the other enzymes (RibG: 0.188 mM, RibB: 0.023 mM).

In this study, we applied a coupled in silico thermodynamic and probabilistic metabolic control analysis methodology to investigate the control mechanisms of the commercially relevant riboflavin biosynthetic pathway in bacteria. Under the investigated steady-state conditions, we found that several enzyme reactions of the pathway operate far from thermodynamic equilibrium (transformed Gibbs energies of reaction below about -17 kJ mol(-1)). Using the obtained thermodynamic information and applying enzyme elasticity sampling, we calculated the distributions of the scaled concentration control coefficients (CCCs) and scaled flux control coefficients (FCCs). From the statistical analysis of the calculated distributions, we inferred that the control over the riboflavin producing flux is shared among several enzyme activities and mostly resides in the initial reactions of the pathway. More precisely, the guanosine triphosphate (GTP) cyclohydrolase II activity, and therefore the bifunctional RibA protein of Bacillus subtilis because it catalyzes this activity, appears to mainly control the riboflavin producing flux (mean FCCs = 0.45 and 0.55, respectively). The GTP cyclohydrolase II activity and RibA also exert a high positive control over the riboflavin concentration (mean CCCs = 2.43 and 2.91, respectively). This prediction is consistent with previous findings for microbial riboflavin overproducing strains.