Preprocessing Strategies for Sparse Infrared Spectroscopy: A Case Study on Cartilage Diagnostics

The aim of the study was to optimize preprocessing of sparse infrared spectral data. The sparse data were obtained by reducing broadband Fourier transform infrared attenuated total reflectance spectra of bovine and human cartilage, as well as of simulated spectral data, comprising several thousand spectral variables into datasets comprising only seven spectral variables. Different preprocessing approaches were compared, including simple baseline correction and normalization procedures, and model-based preprocessing, such as multiplicative signal correction (MSC). The optimal preprocessing was selected based on the quality of classification models established by partial least squares discriminant analysis for discriminating healthy and damaged cartilage samples. The best results for the sparse data were obtained by preprocessing using a baseline offset correction at 1800 cm−1, followed by peak normalization at 850 cm−1 and preprocessing by MSC.publishedVersio

Description and composition iron-manganese concretions from the Pacific

One the most interesting features of ocean sedimentation is the manganese formations on the surface of the ocean floor in some areas. These are especially widespread in the Pacific Ocean as concretions, grains, and crusts on rock fragments and bedrock outcrops. Iron-manganese concretions are the most abundant as they completely cover about 10% of the bottom of the Pacific Ocean where there are ore concentrations. The concretions occupy from 20-50% of the bottom and up to 80-90% on separate submarine rises. Such concretions are found in different types of bottom deposits, from abyssal red clays to terrigenous muds, but they occur most widely in red clays and quite often in carbonate muds. Their shape and their dimensions are very diverse and change from place to place, from station to station, varying from 0.5-20 cm. They may be oval, globular, reniform, or slaggy and often they are fiat or isometric concretions of an indefinite shape. The concretions generally have nuclei of pumice, basalt fragments, clayey and tuffaceous material, sharks' teeth, whale ossicles, and fossil sponges. Most concretions have concentric layers, combined with dendritic ramifications of iron and manganese oxides

Prediction of deoxynivalenol contamination in wheat via infrared attenuated total reflection spectroscopy and multivariate data analysiss

The climate crisis further exacerbates the challenges for food production. For instance, the increasingly unpredictable growth of fungal species in the field can lead to an unprecedented high prevalence of several mycotoxins, including the most important toxic secondary metabolite produced by Fusarium spp., i.e., deoxynivalenol (DON). The presence of DON in crops may cause health problems in the population and livestock. Hence, there is a demand for advanced strategies facilitating the detection of DON contamination in cereal-based products. To address this need, we introduce infrared attenuated total reflection (IR-ATR) spectroscopy combined with advanced data modeling routines and optimized sample preparation protocols. In this study, we address the limited exploration of wheat commodities to date via IR-ATR spectroscopy. The focus of this study was optimizing the extraction protocol for wheat by testing various solvents aligned with a greener and more sustainable analytical approach. The employed chemometric method, i.e., sparse partial least-squares discriminant analysis, not only facilitated establishing robust classification models capable of discriminating between high vs low DON-contaminated samples adhering to the EU regulatory limit of 1250 μg/kg but also provided valuable insights into the relevant parameters shaping these models

Preclassification of Broadband and Sparse Infrared Data by Multiplicative Signal Correction Approach

Preclassification of raw infrared spectra has often been neglected in scientific literature. Separating spectra of low spectral quality, due to low signal-to-noise ratio, presence of artifacts, and low analyte presence, is crucial for accurate model development. Furthermore, it is very important for sparse data, where it becomes challenging to visually inspect spectra of different natures. Hence, a preclassification approach to separate infrared spectra for sparse data is needed. In this study, we propose a preclassification approach based on Multiplicative Signal Correction (MSC). The MSC approach was applied on human and the bovine knee cartilage broadband Fourier Transform Infrared (FTIR) spectra and on a sparse data subset comprising of only seven wavelengths. The goal of the preclassification was to separate spectra with analyte-rich signals (i.e., cartilage) from spectra with analyte-poor (and high-matrix) signals (i.e., water). The human datasets 1 and 2 contained 814 and 815 spectra, while the bovine dataset contained 396 spectra. A pure water spectrum was used as a reference spectrum in the MSC approach. A threshold for the root mean square error (RMSE) was used to separate cartilage from water spectra for broadband and the sparse spectral data. Additionally, standard noise-to-ratio and principle component analysis were applied on broadband spectra. The fully automated MSC preclassification approach, using water as reference spectrum, performed as well as the manual visual inspection. Moreover, it enabled not only separation of cartilage from water spectra in broadband spectral datasets, but also in sparse datasets where manual visual inspection cannot be applied

Molecular Allergen-Specific IgE Recognition Profiles and Cumulative Specific IgE Levels Associated with Phenotypes of Cat Allergy

Cat allergy is a major trigger factor for respiratory reactions (asthma and rhinitis) in patients with immunoglobulin E (IgE) sensitization. In this study, we used a comprehensive panel of purified cat allergen molecules (rFel d 1, nFel d 2, rFel d 3, rFel d 4, rFel d 7, and rFel d 8) that were obtained by recombinant expression in Escherichia coli or by purification as natural proteins to study possible associations with different phenotypes of cat allergy (i.e., rhinitis, conjunctivitis, asthma, and dermatitis) by analyzing molecular IgE recognition profiles in a representative cohort of clinically well-characterized adult cat allergic subjects (n = 84). IgE levels specific to each of the allergen molecules and to natural cat allergen extract were quantified by ImmunoCAP measurements. Cumulative IgE levels specific to the cat allergen molecules correlated significantly with IgE levels specific to the cat allergen extract, indicating that the panel of allergen molecules resembled IgE epitopes of the natural allergen source. rFel d 1 represented the major cat allergen, which was recognized by 97.2% of cat allergic patients; however, rFel d 3, rFel d 4, and rFel d 7 each showed IgE reactivity in more than 50% of cat allergic patients, indicating the importance of additional allergens in cat allergy. Patients with cat-related skin symptoms showed a trend toward higher IgE levels and/or frequencies of sensitization to each of the tested allergen molecules compared with patients suffering only from rhinitis or asthma, while there were no such differences between patients with rhinitis and asthma. The IgE levels specific to allergen molecules, the IgE levels specific to cat allergen extract, and the IgE levels specific to rFel d 1 were significantly higher in patients with four different symptoms compared with patients with 1–2 symptoms. This difference was more pronounced for the sum of IgE levels specific to the allergen molecules and to cat extract than for IgE levels specific for rFel d 1 alone. Our study indicates that, in addition to rFel d 1, rFel d 3, rFel d 4, and rFel d 7 must be considered as important cat allergens. Furthermore, the cumulative sum of IgE levels specific to cat allergen molecules seems to be a biomarker for identifying patients with complex phenotypes of cat allergy. These findings are important for the diagnosis of IgE sensitization to cats and for the design of allergen-specific immunotherapies for the treatment and prevention of cat allergy