Diagnosing the cage of covariance to increase understanding and robustness of spectroscopic calibration models.

When vibrational spectroscopy is used for quantification purposes, multivariate analysis is often used to extract information from covariances between the spectra and any given reference values. In complex samples, there is a high risk that the constituents covary with each other. In such scenarios many methods may confuse the analytes and use signal from several analytes, rather than just the analyte of interest. While this allows the method to use more signal, and thus have a better effective signal-to-noise ratio, it also makes them less robust to changes to the chemical composition in the samples. This effect has been termed the cage of covariance. In order to avoid cage of covariance to affect predictive performances, it is highly important to have simple diagnostic tools to analyze and review this effect. Therefore, in the present paper, a systematic overview of tools for diagnosing and quantifying the cage of covariance in spectroscopic calibration models is provided. A collection of previously published methods with some expansions is provided, as well as two completely new tools: covariance ratio and virtual spiking. Practical applications of the tools on three different datasets are also shown.

Raman and near Infrared Spectroscopy for Quantification of Fatty Acids in Muscle Tissue-A Salmon Case Study.

The aim of the present study was to critically evaluate the potential of using NIR and Raman spectroscopy for prediction of fatty acid features and single fatty acids in salmon muscle. The study was based on 618 homogenized salmon muscle samples acquired from Atlantic salmon representing a one year-class nucleus, fed the same high fish oil feed. NIR and Raman spectra were used to make regression models for fatty acid features and single fatty acids measured by gas chromatography. The predictive performance of both NIR and Raman was good for most fatty acids, with R2 above 0.6. Overall, Raman performed marginally better than NIR, and since the Raman models generally required fewer components than respective NIR models to reach high and optimal performance, Raman is likely more robust for measuring fatty acids compared to NIR. The fatty acids of the salmon samples co-varied to a large extent, a feature that was exacerbated by the overlapping peaks in NIR and Raman spectra. Thus, the fatty acid related variation of the spectroscopic data of the present study can be explained by only a few independent principal components. For the Raman spectra, this variation was dominated by functional groups originating from long-chain polyunsaturated FAs like EPA and DHA. By exploring the independent EPA and DHA Raman models, spectral signatures similar to the respective pure fatty acids could be seen. This proves the potential of Raman spectroscopy for single fatty acid prediction in muscle tissue.

N-Glycan profile analysis of transferrin using a microfluidic compact disc and MALDI-MS.

It has been known for a long time that diseases can be associated with changes to the glycosylation of specific proteins. This has been shown for cancer, immunological disorders, and neurodegenerative diseases. The possibility of using the glycosylation patterns of proteins as biomarkers for disease would be a great asset for clinical research or diagnosis. There is at present a lack of rapid, automated, and cost-efficient analytical techniques for the determination of the glycosylation of specific serum proteins. We have developed a method for determining the glycosylation pattern of proteins based on the affinity capture of a specific serum protein, the enzymatic release of the N-linked glycans, and the analysis of the glycan pattern using MALDI-MS. All sample preparation is performed in a disposable centrifugal microfluidic disc. The sample preparation is miniaturized, requiring only 1 µL of sample per determination, and automated with the possibility of processing 54 samples in parallel in 3.5 h. We have developed a method for the glycosylation pattern analysis of transferrin. The method has been tested on serum samples from chronic alcohol abusers and a control group. Also, a SIMCA model was created and evaluated to discriminate between the two groups.

Non-target time trend screening: a data reduction strategy for detecting emerging contaminants in biological samples.

Non-targeted mass spectrometry-based approaches for detecting novel xenobiotics in biological samples are hampered by the occurrence of naturally fluctuating endogenous substances, which are difficult to distinguish from environmental contaminants. Here, we investigate a data reduction strategy for datasets derived from a biological time series. The objective is to flag reoccurring peaks in the time series based on increasing peak intensities, thereby reducing peak lists to only those which may be associated with emerging bioaccumulative contaminants. As a result, compounds with increasing concentrations are flagged while compounds displaying random, decreasing, or steady-state time trends are removed. As an initial proof of concept, we created artificial time trends by fortifying human whole blood samples with isotopically labelled standards. Different scenarios were investigated: eight model compounds had a continuously increasing trend in the last two to nine time points, and four model compounds had a trend that reached steady state after an initial increase. Each time series was investigated at three fortification levels and one unfortified series. Following extraction, analysis by ultra performance liquid chromatography high-resolution mass spectrometry, and data processing, a total of 21,700 aligned peaks were obtained. Peaks displaying an increasing trend were filtered from randomly fluctuating peaks using time trend ratios and Spearman's rank correlation coefficients. The first approach was successful in flagging model compounds spiked at only two to three time points, while the latter approach resulted in all model compounds ranking in the top 11 % of the peak lists. Compared to initial peak lists, a combination of both approaches reduced the size of datasets by 80-85 %. Overall, non-target time trend screening represents a promising data reduction strategy for identifying emerging bioaccumulative contaminants in biological samples. Graphical abstract Using time trends to filter out emerging contaminants from large peak lists.

TracMass 2--a modular suite of tools for processing chromatography-full scan mass spectrometry data.

In untargeted proteomics and metabolomics, raw data obtained with an LC/MS instrument are processed into a format that can be used for statistical analysis. Full scan MS data from chromatographic separation of biological samples are complex and analyte concentrations need to be extracted and aligned so that they can be compared across the samples. Several computer programs and methods have been developed for this purpose. There is still a need to improve the ease of use and feedback to the user because of the advanced multiparametric algorithms used. Here, we present and make publicly available, TracMass 2, a suite of computer programs that gives immediate graphical feedback to the data analyst on parameter settings and processing results, as well as producing state-of-the-art results. The main advantage of TracMass 2 is that the feedback and transparency of the processing steps generate confidence in the end result, which is a table of peak intensities. The data analyst can easily validate every step of the processing pipeline. Because the user receives feedback on how all parameter values affect the result before starting a lengthy computation, the user's learning curve is enhanced and the total time used for data processing can be reduced. TracMass 2 has been released as open source and is included in the Supporting Information . We anticipate that TracMass 2 will set a new standard for how chemometrical algorithms are implemented in computer programs.

A concept study on non-targeted screening for chemical contaminants in food using liquid chromatography-mass spectrometry in combination with a metabolomics approach.

A generic method to screen for new or unexpected contaminants at ppm levels in food has been developed. The method comprises an acidic acetonitrile extraction, detection with ultra-high-pressure liquid chromatography coupled to electrospray ionisation time-of-flight mass spectrometry and statistical evaluation using a metabolomics approach comparing suspected contaminated food with uncontaminated foods. The method was tested for 26 model contaminants from 100 µg/g down to 0.4 µg/g in three brands of fresh orange juice. Blinded statistical evaluation revealed signals from all added contaminants detectable by liquid chromatography-electrospray ionisation using positive ionisation mode, while only two false-positive signals were reported. The method is primarily intended to be used for investigation of food samples suspected to be contaminated with unknown substances. Additionally it could be used to continuously monitor for appearance of new food contaminants as a complement to the specific targeted analysis that is today's foundation of food safety analysis.

Discrimination between glycosylation patterns of therapeutic antibodies using a microfluidic platform, MALDI-MS and multivariate statistics.

Optimal glycosylation with respect to the efficacy, serum half-life time, and immunogenic properties is essential in the generation of therapeutic antibodies. The glycosylation pattern can be affected by several different parameters during the manufacture of antibodies and may change significantly over cultivation time. Fast and robust methods for determination of the glycosylation patterns of therapeutic antibodies are therefore needed. We have recently presented an efficient method for the determination of glycans on therapeutic antibodies using a microfluidic CD platform for sample preparation prior to matrix-assisted laser-desorption mass spectrometry analysis. In the present work, this method is applied to analyse the glycosylation patterns of three commercially available therapeutic antibodies and one intended for therapeutic use. Two of the antibodies produced in mouse myeloma cell line (SP2/0) and one produced in Chinese hamster ovary (CHO) cells exhibited similar glycosylation patterns but could still be readily differentiated from each other using multivariate statistical methods. The two antibodies with most similar glycosylation patterns were also studied in an assessment of the method's applicability for quality control of therapeutic antibodies. The method presented in this paper is highly automated and rapid. It can therefore efficiently generate data that helps to keep a production process within the desired design space or assess that an identical product is being produced after changes to the process.