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Spices have an important impact on daily aliment. Changes of their quality resulting in far reaching consequences in different foodstuffs and imply financial losing and even health hazard. In this study it is demonstrated that application of two different portable multi gas sensors (electronic nose and ion mobility spectrometer) supported by multivariate data analysis can contribute to ensure quality control of spice mixtures and to find out product adulteration. Headspace above spice mixtures for sausages and saveloy and product counterfeitings was investigated by a metal oxide based electronic nose (e-nose of KAMINA-type). Linear discriminant analysis (LDA) of sensor resistivity data was performed for differentiation. Simultaneously an ion mobility spectrometer (IMS) was coupled to the emission chamber for detection of gaseous components above spice mixtures. The measured spectra show differences between the two spice mixtures and were discussed using a principal component analysis (PCA). The two multigas sensors permit discrimination between the types of spice mixtures and can indicate product adulteration. Additionally, a headspace gas analysis by gas chromatography was performed to identify the main volatile components and to prove the chemical basis for the observed differences of the multi gas sensors.
The demands for the assessment of water quality are increasing steadily, making it necessary to routinely monitor multiple contaminants in water samples. For this application a hapten microarray was developed. In order to reach the required low detection limits a design of experiments (DoE) approach was used to optimize the assay performance. Here we show that a Box-Behnken design plan is an adequate choice for the straightforward exploration of hapten microarray assay parameters. For both read-out systems studied (fluorophore-labelled detection antibodies or enzymatic signal development followed by reflectometric scan, respectively), it was possible to significantly extend the measurement ranges. Furthermore, it could be shown that multivariate data analysis, here partial least squares regression (PLS), can improve the prediction accuracy of 'unknown' samples when used as calibration model, compared to classical, univariate data evaluation methods.
The transformation of a base-catalyzed, mechano-assisted Knoevenagel condensation of mono-fluorinated benzaldehyde derivatives (p-, m-, o-benzaldehyde) with malonodinitrile was investigated in situ and in real time. Upon milling, the para-substituted product was found to crystallize initially into two different polymorphic forms, depending on the quantity of catalyst used. For low catalyst concentrations, a mechanically metastable phase (monoclinic) was initially formed, converting to the mechanically stable phase (triclinic) upon further grinding. Instead, higher catalyst concentrations crystallize directly as the triclinic product. Inclusion of catalyst in the final product, as evidenced by mass spectrometric analysis, suggests this complex polymorphic pathway may be due to seeding effects. Multivariate analysis for the in situ Raman spectra supports this complex formation pathway, and offers a new approach to monitoring multi-phase reactions during ball milling.
The application of Raman spectroscopy as a monitoring technique for bioprocesses is severely limited by a large background signal originating from fluorescing compounds in the culture media. Here, we compare time-gated Raman (TG-Raman)-, continuous wave NIRprocess Raman (NIR-Raman), and continuous wave micro-Raman (micro-Raman) approaches in combination with surface enhanced Raman spectroscopy (SERS) for their potential to overcome this limit. For that purpose, we monitored metabolite concentrations of Escherichia coli bioreactor cultivations in cell-free supernatant samples. We investigated concentration transients of glucose, acetate, AMP, and cAMP at alternating substrate availability, from deficiency to excess. Raman and SERS signals were compared to off-line metabolite analysis of carbohydrates, carboxylic acids, and nucleotides. Results demonstrate that SERS, in almost all cases, led to a higher number of identifiable signals and better resolved spectra. Spectra derived from the TG-Raman were comparable to those of micro-Raman resulting in well-discernable Raman peaks, which allowed for the identification of a higher number of compounds. In contrast, NIR-Raman provided a superior performance for the quantitative evaluation of analytes, both with and without SERS nanoparticles when using multivariate data analysis.
Parchment has been in use for thousands of years
and has been used as the writing or drawing support for
many important historic works. A variety of analytical
techniques is currently used for routine assessment of the
degree of denaturation of historic parchment; however,
because parchment has a heterogeneous nature, analytical
methods with high spatial resolution are desirable. In this
work, the use of small-angle X-ray scattering (SAXS) and
synchrotron-IR (SR-IR) was examined in conjunction with
multivariate data analysis to study degradation of an
extended set of historic parchment samples, and particularly
to investigate the effect of lipids and the presence of iron
gall ink on the degradation processes. In the data analysis,
shrinkage temperature, lipid content, sample age, presence
of ink and accelerated degradation were included. The
analysis of loading factors in partial least-squares regression
and principal component analyses based on SAXS, SR-IR
and other analytical and descriptive data reveals the effect
of lipid removal on diffraction patterns, and lipids are found
to cause the degradation process in parchment to accelerate.
The effect of iron gall ink is also evident, although the
mechanism of ageing is different to that of natural ageing in
the absence of ink. In addition, a historic parchment score
from ca. 1750 is examined, demonstrating the significant
effect of iron gall ink, and lipids and inorganic soiling on its
increased degradation.
The quantification of the elemental content in soils with laser-induced breakdown spectroscopy (LIBS) is challenging because of matrix effects strongly influencing the plasma formation and LIBS signal. Furthermore, soil heterogeneity at the micrometre scale can affect the accuracy of analytical results. In this paper, the impact of univariate and multivariate data evaluation approaches on the quantification of nutrients in soil is discussed. Exemplarily, results for calcium are shown, which reflect trends also observed for other elements like magnesium, silicon and iron. For the calibration models, 16 certified reference soils were used. With univariate and multivariate approaches, the calcium mass fractions in 60 soils from different testing grounds in Germany were calculated. The latter approach consisted of a principal component analysis (PCA) of adequately pre-treated data for classification and identification of outliers, followed by partial least squares regression (PLSR) for quantification. For validation, the soils were also characterised with inductively coupled plasma optical emission spectroscopy (ICP OES) and X-ray fluorescence (XRF) analysis. Deviations between the LIBS quantification results and the reference analytical results are discussed.