Abstract: High Resolution NMR Spectroscopy offers unique screening capabilities for food quality and safety by combining non-targeted and targeted screening in one analysis.
The objective is to demonstrate, that due to its extreme reproducibility NMR can detect smallest changes in concentrations of many components in a mixture, which is best monitored by statistical evaluation however also delivers reliable quantification results.
The methodology typically uses a 400 MHz high resolution instrument under full automation after minimized sample preparation.
For example one fruit juice analysis in a push button operation takes at maximum 15 minutes and delivers a multitude of results, which are automatically summarized in a PDF report.
The method has been proven on fruit juices, where so far unknown frauds could be detected. In addition conventional targeted parameters are obtained in the same analysis. This technology has the advantage that NMR is completely quantitative and concentration calibration only has to be done once for all compounds. Since NMR is so reproducible, it is also transferable between different instruments (with same field strength) and laboratories. Based on strict SOP`s, statistical models developed once can be used on multiple instruments and strategies for compound identification and quantification are applicable as well across labs.
Abstract: Quantitative trait loci (QTL) experiments have yielded
important biological and biochemical information necessary for
understanding the relationship between genetic markers and
quantitative traits. For many years, most QTL algorithms only
allowed one observation per genotype. Recently, there has been an
increasing demand for QTL algorithms that can accommodate more
than one observation per genotypic distribution. The Bayesian
hierarchical model is very flexible and can easily incorporate this
information into the model. Herein a methodology is presented that
uses a Bayesian hierarchical model to capture the complexity of the
data. Furthermore, the Markov chain Monte Carlo model composition
(MC3) algorithm is used to search and identify important markers. An
extensive simulation study illustrates that the method captures the
true QTL, even under nonnormal noise and up to 6 QTL.