In Vivo Analysis of a Biodegradable Magnesium Alloy Implant in an Animal Model Using Near-Infrared Spectroscopy.

Biodegradable magnesium-based implants offer mechanical properties similar to natural bone, making them advantageous over nonbiodegradable metallic implants. However, monitoring the interaction between magnesium and tissue over time without interference is difficult. A noninvasive method, optical near-infrared spectroscopy, can be used to monitor tissue's functional and structural properties. In this paper, we collected optical data from an in vitro cell culture medium and in vivo studies using a specialized optical probe. Spectroscopic data were acquired over two weeks to study the combined effect of biodegradable Mg-based implant disks on the cell culture medium in vivo. Principal component analysis (PCA) was used for data analysis. In the in vivo study, we evaluated the feasibility of using the near-infrared (NIR) spectra to understand physiological events in response to magnesium alloy implantation at specific time points (Day 0, 3, 7, and 14) after surgery. Our results show that the optical probe can detect variations in vivo from biological tissues of rats with biodegradable magnesium alloy "WE43" implants, and the analysis identified a trend in the optical data over two weeks. The primary challenge of in vivo data analysis is the complexity of the implant interaction near the interface with the biological medium.

A retrospective look at cross model validation and its applicability in vibrational spectroscopy.

In this paper, it is presented how Cross Model Validation (CMV), also known as double cross validation, efficiently can be applied for variable selection in spectroscopic applications. The chosen applications are FT-IR spectroscopic measurements of mixtures of marzipan and NIR spectra of diesel fuels. Standard Normal Variate (SNV) is applied as a spectral pre-treatment to reduce baseline effects in the spectra for the FT-IR data whereas 2nd derivative was applied for the diesel fuels. Variable selection based on jack-knifing and frequency of significance from Cross Model Validation is employed for identifying non-relevant spectral regions as well as providing a relevant subset for model optimization. The results show a high degree of correspondence between the objectively found wavelength bands and the reported chemical interpretation found in the literature. In addition, the stability of the models due to conservative validation with respect to predictive performance is exemplified. Finally, an example of how the use of downweighing variables ensures optimal prediction ability and detailed model interpretation is shown.

Probeless non-invasive near-infrared spectroscopic bioprocess monitoring using microspectrometer technology.

Real-time measurements and adjustments of critical process parameters are essential for the precise control of fermentation processes and thus for increasing both quality and yield of the desired product. However, the measurement of some crucial process parameters such as biomass, product, and product precursor concentrations usually requires time-consuming offline laboratory analysis. In this work, we demonstrate the in-line monitoring of biomass, penicillin (PEN), and phenoxyacetic acid (POX) in a Penicilliumchrysogenum fed-batch fermentation process using low-cost microspectrometer technology operating in the near-infrared (NIR). In particular, NIR reflection spectra were taken directly through the glass wall of the bioreactor, which eliminates the need for an expensive NIR immersion probe. Furthermore, the risk of contaminations in the reactor is significantly reduced, as no direct contact with the investigated medium is required. NIR spectra were acquired using two sensor modules covering the spectral ranges 1350-1650 nm and 1550-1950 nm. Based on offline reference analytics, partial least squares (PLS) regression models were established for biomass, PEN, and POX either using data from both sensors separately or jointly. The established PLS models were tested on an independent validation fed-batch experiment. Root mean squared errors of prediction (RMSEP) were 1.61 g/L, 1.66 g/L, and 0.67 g/L for biomass, PEN, and POX, respectively, which can be considered an acceptable accuracy comparable with previously published results using standard process spectrometers with immersion probes. Altogether, the presented results underpin the potential of low-cost microspectrometer technology in real-time bioprocess monitoring applications. Graphical abstract.

Automated multivariate analysis of multi-sensor data submitted online: Real-time environmental monitoring.

A pilot study demonstrating real-time environmental monitoring with automated multivariate analysis of multi-sensor data submitted online has been performed at the cabled LoVe Ocean Observatory located at 258 m depth 20 km off the coast of Lofoten-Vesterålen, Norway. The major purpose was efficient monitoring of many variables simultaneously and early detection of changes and time-trends in the overall response pattern before changes were evident in individual variables. The pilot study was performed with 12 sensors from May 16 to August 31, 2015. The sensors provided data for chlorophyll, turbidity, conductivity, temperature (three sensors), salinity (calculated from temperature and conductivity), biomass at three different depth intervals (5-50, 50-120, 120-250 m), and current speed measured in two directions (east and north) using two sensors covering different depths with overlap. A total of 88 variables were monitored, 78 from the two current speed sensors. The time-resolution varied, thus the data had to be aligned to a common time resolution. After alignment, the data were interpreted using principal component analysis (PCA). Initially, a calibration model was established using data from May 16 to July 31. The data on current speed from two sensors were subject to two separate PCA models and the score vectors from these two models were combined with the other 10 variables in a multi-block PCA model. The observations from August were projected on the calibration model consecutively one at a time and the result was visualized in a score plot. Automated PCA of multi-sensor data submitted online is illustrated with an attached time-lapse video covering the relative short time period used in the pilot study. Methods for statistical validation, and warning and alarm limits are described. Redundant sensors enable sensor diagnostics and quality assurance. In a future perspective, the concept may be used in integrated environmental monitoring.

Integrated environmental monitoring and multivariate data analysis-A case study.

The present article describes integration of environmental monitoring and discharge data and interpretation using multivariate statistics, principal component analysis (PCA), and partial least squares (PLS) regression. The monitoring was carried out at the Peregrino oil field off the coast of Brazil. One sensor platform and 3 sediment traps were placed on the seabed. The sensors measured current speed and direction, turbidity, temperature, and conductivity. The sediment trap samples were used to determine suspended particulate matter that was characterized with respect to a number of chemical parameters (26 alkanes, 16 PAHs, N, C, calcium carbonate, and Ba). Data on discharges of drill cuttings and water-based drilling fluid were provided on a daily basis. The monitoring was carried out during 7 campaigns from June 2010 to October 2012, each lasting 2 to 3 months due to the capacity of the sediment traps. The data from the campaigns were preprocessed, combined, and interpreted using multivariate statistics. No systematic difference could be observed between campaigns or traps despite the fact that the first campaign was carried out before drilling, and 1 of 3 sediment traps was located in an area not expected to be influenced by the discharges. There was a strong covariation between suspended particulate matter and total N and organic C suggesting that the majority of the sediment samples had a natural and biogenic origin. Furthermore, the multivariate regression showed no correlation between discharges of drill cuttings and sediment trap or turbidity data taking current speed and direction into consideration. Because of this lack of correlation with discharges from the drilling location, a more detailed evaluation of chemical indicators providing information about origin was carried out in addition to numerical modeling of dispersion and deposition. The chemical indicators and the modeling of dispersion and deposition support the conclusions from the multivariate statistics. Integr Environ Assess Manag 2017;13:387-395. © 2016 SETAC.

Validation of chemometric models - a tutorial.

In this tutorial, we focus on validation both from a numerical and conceptual point of view. The often applied reported procedure in the literature of (repeatedly) dividing a dataset randomly into a calibration and test set must be applied with care. It can only be justified when there is no systematic stratification of the objects that will affect the validated estimates or figures of merits such as RMSE or R(2). The various levels of validation may, typically, be repeatability, reproducibility, and instrument and raw material variation. Examples of how one data set can be validated across this background information illustrate that it will affect the figures of merits as well as the dimensionality of the models. Even more important is the robustness of the models for predicting future samples. Another aspect that is brought to attention is validation in terms of the overall conclusions when observing a specific system. One example is to apply several methods for finding the significant variables and see if there is a consensus subset that also matches what is reported in the literature or based on the underlying chemistry.

Development of satiating and palatable high-protein meat products by using experimental design in food technology.

BACKGROUND AND OBJECTIVES: Foods high in protein are known to satiate more fully than foods high in other constituents. One challenge with these types of food is the degree of palatability. This study was aimed at developing the frankfurter style of sausages that would regulate food intake as well as being the preferred food choice of the consumer. DESIGN AND MEASURES: 16 sausage varieties with commercial (PE% 20) or higher amount of protein (PE% 40), being modified with vegetable fat (3% of rapeseed oil), and smoked or not, underwent a sensory descriptive analysis, in which the information was used to choose a subsample of four sausages for a satiety test. Twenty-seven subjects were recruited based on liking and frequency of sausage consumption. The participants ranged in age from 20 to 28, and in body mass index (BMI) between 19.6 and 30.9. The students were served a sausage meal for five consecutive days and then filled out a questionnaire to describe their feelings of hunger, satiety, fullness, desire to eat an their prospective consumption on a visual analogue scale (VAS) starting from right before, right after the meal, every half hour for 4 h until the next meal was served, and right after the second meal. RESULTS AND CONCLUSION: The higher protein sausages were less juicy, oily, fatty, adhesive, but harder and more granular than with lower amount of protein. The high-protein sausages were perceived as more satiating the first 90 min after the first meal. Some indication of satiety effect of added oil versus meat fat. No significant differences in liking among the four sausage varieties.

Pixel-based analysis of multiple images for the identification of changes: a novel approach applied to unravel proteome patterns [corrected] of 2-D electrophoresis gel images.

A novel approach for revealing patterns of proteome variation among series of 2-DE gel images is presented. The approach utilises image alignment to ensure that each pixel represents the same information across all gels. Gel images are normalised, and background corrected, followed by unfolding of the images to 1-D pixel vectors and analysing pixel vectors by multivariate data modelling. Information resulting from the data analysis is refolded back to the image domain for visualisation and interpretation. The method is rapid and suitable for automatic routines applied after the gel alignment. The approach is compared with spot volume analysis to illustrate how this approach can solve persistent problems like mismatch of protein spots, erroneous missing values and failure to detect variation in overlapping proteins. The method may also detect variation in the border area of saturated proteins. The approach is given the name pixel-based analysis of multiple images for the identification of changes (PMC). The method can be used for multiple images in general. Effects of pretreatment of the images are discussed.

Finding relevant spectral regions between spectroscopic techniques by use of cross model validation and partial least squares regression.

In this paper, we extend the concept of cross model validation (CMV) to multiple X and Y variables where different spectroscopic techniques serve as X and Y data in a regression context. For the first dataset on marzipan samples the main objective was to find significant regions in the spectral data, and to discuss the issue of false discovery, i.e. combinations of variables that erroneously are found to be significant. A permutation test within the framework of CMV showed that no regression coefficients in the partial least squares regression (PLSR) model between FT-IR and VIS/NIR spectra show significance at the 5% level. We believe the reason is that the CMV acts as strong filter towards spurious correlations. Corresponding CH- and OH-bands between FT-IR and NIR spectra gave significant regions. For the second dataset, the results from CMV are interpreted more in detail with chemical background knowledge in mind. Most of the significant regions found between the Raman and NIR spectra could be interpreted from the chemical composition of the oil mixtures. Some regions were more difficult to interpret, which could be due to systematic baseline effects in the NIR data.

Changes in enzymes associated with energy metabolism during the early post mortem period in longissimus thoracis bovine muscle analyzed by proteomics.

Changes in metabolic protein levels in biopsies during the early post mortem period in the bovine longissimus thoracis muscle were investigated by 2-DE based proteome analyses. Nine NRF (Norwegian Red) dual purpose bulls were included in the study. Twenty-four proteins underwent changes between the two sampling times and were classified into two major groups: metabolic proteins and heat shock proteins. Of the metabolic proteins, 5 enzymes involved in the glycolytic pathway and the tricarboxylic acid (TCA) cycle, increased in intensities during the post mortem period. In addition, the NADP-dependent enzyme 3-hydroxyisobutyrate dehydrogenase, associated with the TCA cycle in muscle, was increased. This documents that an increased aerobic energy metabolism occurs immediately after slaughter, with the aim to replenish the ATP levels in the muscle.

Analysis of genetic marker-phenotype relationships by jack-knifed partial least squares regression (PLSR).

The utility of a relatively new multivariate method, bi-linear modelling by cross-validated partial least squares regression (PLSR), was investigated in the analysis of QTL. The distinguishing feature of PLSR is to reveal reliable covariance structures in data of different types with regard to the same set objects. Two matrices X (here: genetic markers) and Y (here: phenotypes) are interactively decomposed into latent variables (PLS components, or PCs) in a way which facilitates statistically reliable and graphically interpretable model building. Natural collinearities between input variables are utilized actively to stabilise the modelling, instead of being treated as a statistical problem. The importance of cross-validation/jack-knifing as an intuitively appealing way to avoid overfitting, is emphasized. Two datasets from chromosomal mapping studies of different complexity were chosen for illustration (QTL for tomato yield and for oat heading date). Results from PLSR analysis were compared to published results and to results using the package PLABQTL in these data sets. In all cases PLSR gave at least similar explained validation variances as the reported studies. An attractive feature is that PLSR allows the analysis of several traits/replicates in one analysis, and the direct visual identification of individuals with desirable marker genotypes. It is suggested that PLSR may be useful in structural and functional genomics and in marker assisted selection, particularly in cases with limited number of objects.