Kristian Hovde Liland

Optimal Choice of Baseline Correction for Multivariate Calibration of Spectra

Baselines are often chosen by visual inspection of their effect on selected spectra. A more objective procedure for choosing baseline correction algorithms and their parameter values for use in statistical analysis is presented. When the goal of the baseline correction is spectra with a pleasing appearance, visual inspection can be a satisfactory approach. If the spectra are to be used in a statistical analysis, objectivity and reproducibility are essential for good prediction. Variations in baselines from dataset to dataset means we have no guarantee that the best-performing algorithm from one analysis will be the best when applied to a new dataset. This paper focuses on choosing baseline correction algorithms and optimizing their parameter values based on the performance of the quality measure from the given analysis. Results presented in this paper illustrate the potential benefits of the optimization and points out some of the possible pitfalls of baseline correction.

Feasibility of NIR interactance hyperspectral imaging for on-line measurement of crude composition in vacuum packed dry-cured ham slices

There is a growing market for packaged slices of dry-cured ham. The heterogeneity of the composition of slices between packages is an important drawback when aiming to offer consumers a product with a known and constant composition which fits individual consumer expectations. The aim of this work was to test the feasibility of NIR interactance imaging for on-line analysis of water, fat and salt and their spatial distribution in dry-cured ham slices. PLSR models for predicting water, fat and salt contents with NIR spectra were developed with a calibration set of samples (n=82). The models were validated with an external validation set (n=42). The predictive models were accurate enough for screening purposes. The errors of prediction were 1.34%, 1.36% and 0.71% for water, fat and salt, respectively. The spatial distribution of these components within the slice was also obtained.

micropan: an R-package for microbial pan-genomics

BackgroundA pan-genome is defined as the set of all unique gene families found in one or more strains of a prokaryotic species. Due to the extensive within-species diversity in the microbial world, the pan-genome is often many times larger than a single genome. Studies of pan-genomes have become popular due to the easy access to whole-genome sequence data for prokaryotes. A pan-genome study reveals species diversity and gene families that may be of special interest, e.g because of their role in bacterial survival or their ability to discriminate strains.ResultsWe present an R package for the study of prokaryotic pan-genomes. The R computing environment harbors endless possibilities with respect to statistical analyses and graphics. External free software is used for the heavy computations involved, and the R package provides functions for building a computational pipeline.ConclusionsWe demonstrate parts of the package on a data set for the gram positive bacterium Enterococcus faecalis. The package is free to download and install from The Comprehensive R Archive Network.

A systematic search for discriminating sites in the 16S ribosomal RNA gene

BackgroundThe 16S rRNA is by far the most common genomic marker used for prokaryotic classification, and has been used extensively in metagenomic studies over recent years. Along the 16S gene there are regions with more or less variation across the kingdom of bacteria. Nine variable regions have been identified, flanked by more conserved parts of the sequence. It has been stated that the discriminatory power of the 16S marker lies in these variable regions. In the present study we wanted to examine this more closely, and used a supervised learning method to search systematically for sites that contribute to correct classification at either the phylum or genus level.ResultsWhen classifying phyla the site selection algorithm located 50 discriminative sites. These were scattered over most of the alignments and only around half of them were located in the variable regions. The selected sites did, however, have an entropy significantly larger than expected, meaning they are sites of large variation. We found that the discriminative sites typically have a large entropy compared to their closest neighbours along the alignments. When classifying genera the site selection algorithm needed around 80% of the sites in the 16S gene before the classification error reached a minimum. This means that all variation, in both variable and conserved regions, is needed in order to separate genera.ConclusionsOur findings does not support the statement that the discriminative power of the 16S gene is located only in the variable regions. Variable regions are important, but just as many discriminative sites are found in the more conserved parts. The discriminative power is typically found in sites of large variation located inside shorter regions of higher conservation.

Comparing K-mer based methods for improved classification of 16S sequences

BackgroundThe need for precise and stable taxonomic classification is highly relevant in modern microbiology. Parallel to the explosion in the amount of sequence data accessible, there has also been a shift in focus for classification methods. Previously, alignment-based methods were the most applicable tools. Now, methods based on counting K-mers by sliding windows are the most interesting classification approach with respect to both speed and accuracy. Here, we present a systematic comparison on five different K-mer based classification methods for the 16S rRNA gene. The methods differ from each other both in data usage and modelling strategies. We have based our study on the commonly known and well-used naïve Bayes classifier from the RDP project, and four other methods were implemented and tested on two different data sets, on full-length sequences as well as fragments of typical read-length.ResultsThe difference in classification error obtained by the methods seemed to be small, but they were stable and for both data sets tested. The Preprocessed nearest-neighbour (PLSNN) method performed best for full-length 16S rRNA sequences, significantly better than the naïve Bayes RDP method. On fragmented sequences the naïve Bayes Multinomial method performed best, significantly better than all other methods. For both data sets explored, and on both full-length and fragmented sequences, all the five methods reached an error-plateau.ConclusionsWe conclude that no K-mer based method is universally best for classifying both full-length sequences and fragments (reads). All methods approach an error plateau indicating improved training data is needed to improve classification from here. Classification errors occur most frequent for genera with few sequences present. For improving the taxonomy and testing new classification methods, the need for a better and more universal and robust training data set is crucial.

Mitochondrial oxygen consumption in permeabilized fibers and its link to colour changes in bovine M. semimembranosus muscle

Animal and muscle characteristics were recorded for 41 cattle. The oxygen consumption rate (OCR) of M. semimembranosus was measured between 3.0-6.4h post mortem (PM3-6) and after 3 weeks in a vacuum pack at 4°C. Colour change measurements were performed following the 3 weeks using reflectance spectra (400-1,100 nm) and the colour coordinates L, a and b, with the samples being packaged in oxygen permeable film and stored at 4°C for 167 h. Significant individual animal differences in OCR at PM3-6 were found for mitochondrial complexes I and II. OCR of complex I declined with increased temperature and time PM, while residual oxygen-consuming side-reactions (ROX) did not. OCR of stored muscles was dominated by complex II respiration. A three-way regression between samples, colour variables collected upon air exposure and OCR of 3 weeks old fibres revealed a positive relationship between OCR and complex II activity and also between OCR and OCR(ROX). The presence of complex I and β-oxidation activities increased metmyoglobin formation.

Towards on-line prediction of dry matter content in whole unpeeled potatoes using near-infrared spectroscopy

Prediction of dry matter content in whole potatoes is a desired capability in the processing industry. Accurate prediction of dry matter content may greatly reduce waste quantities and improve utilization of the raw material through sorting, hence also reducing the processing cost. The following study demonstrates the use of a low resolution, high speed NIR interactance instrument combined with partial least square regression for prediction of dry matter content in whole unpeeled potatoes. Three different measuring configurations were investigated: (1) off-line measurements with contact between the potato and the light collection tube; (2) off-line measurements without contact between the potato and the light collection tube; and (3) on-line measurements of the potatoes. The offline contact measurements gave a prediction performance of R(2)=0.89 and RMSECV=1.19. Similar prediction performance were obtained from the off-line non-contact measurements (R(2)=0.89, RMSECV=1.23). Significantly better (p=0.038) prediction performance (R(2)=0.92, RMSECV=1.06) was obtained with the on-line measuring configuration, thus showing the possibilities of using the instrument for on-line measurements. In addition it was shown that the dry matter distribution across the individual tuber could be predicted by the model obtained.

Relating fatty acid composition in human fingertip blood to age, gender, nationality and n-3 supplementation in the Scandinavian population

This study investigated data obtained from whole blood fatty acid (FA) composition of 3476 Norwegian and Swedish individuals, which provided background information including age, gender, nationality and self-motivated n-3 supplement consumption. The aim of this paper was to statistically relate this background information on the subjects to their whole blood FA profile, focusing mainly on the n-3 polyunsaturated FA (PUFA). Results showed that age had significant effects on the content of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) in blood lipids for the Norwegian individuals, while n-3 PUFA supplementation had a positive effect on EPA and DHA content in whole blood for the investigated population. Gender differences were also found for individual FA. A correlation also exists with previous studies on the FA profiling of blood lipids, further validating the test procedure.

microclass: An R-package for 16S taxonomy classification

BackgroundTaxonomic classification based on the 16S rRNA gene sequence is important for the profiling of microbial communities. In addition to giving the best possible accuracy, it is also important to quantify uncertainties in the classifications.ResultsWe present an R package with tools for making such classifications, where the heavy computations are implemented in C++ but operated through the standard R interface. The user may train classifiers based on specialized data sets, but we also supply a ready-to-use function trained on a comprehensive training data set designed specifically for this purpose. This tool also includes some novel ways to quantify uncertainties in the classifications.ConclusionsBased on input sequences of varying length and quality, we demonstrate how the output from the classifications can be used to obtain high quality taxonomic assignments from 16S sequences within the R computing environment. The package is publicly available at the Comprehensive R Archive Network.

Using GEMANOVA to explore the pattern generating properties of the Delta-Notch model†

In the area of systems biology, increasingly complex models are developed to approximate biological processes. The complexity makes it difficult to derive the properties of such models analytically. An alternative to analytical considerations is to use multivariate statistical methods to reveal essential properties of the models. In this paper it is shown how the properties of a relatively complex mathematical model for describing cell‐pattern development, the Delta‐Notch model, can be explored by means of statistical analyses of data generated from the model. ANOVA is a well‐known and one of the most commonly used methods for analyzing data from designed experiments, but it turns out that it is not always appropriate for finding and exploring higher‐order interactions. For this purpose a multiplicative alternative—GEMANOVA—was used in the present paper for studying the Delta‐Notch model, for which the properties depend on higher order interactions between the model parameters. It is shown here how a forward selection strategy combined with bootstrapping can be used to identify GEMANOVA models with reasonable fit to the data, and it is demonstrated how new insight about the Delta‐Notch model can be gained from interpreting the GEMANOVA output. Copyright