Monitoring and modifying brain oxygenation in patients at risk of hypoxic ischaemic brain injury after cardiac arrest.

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2021. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2021 . Further information about the Annual Update in Intensive Care and Emergency Medicine is available from https://link.springer.com/bookseries/8901 .

Applications of two-dimensional infrared spectroscopy.

Two-dimensional infrared (2D IR) spectroscopy has recently emerged as a powerful tool with applications in many areas of scientific research. The inherent high time resolution coupled with bond-specific spatial resolution of IR spectroscopy enable direct characterization of rapidly interconverting species and fast processes, even in complex systems found in chemistry and biology. In this minireview, we briefly outline the fundamental principles and experimental procedures of 2D IR spectroscopy. Using illustrative example studies, we explain the important features of 2D IR spectra and their capability to elucidate molecular structure and dynamics. Primarily, this minireview aims to convey the scope and potential of 2D IR spectroscopy by highlighting select examples of recent applications including the use of innate or introduced vibrational probes for the study of nucleic acids, peptides/proteins, and materials.

Application of image spectrometer to in situ infrared broadband optical monitoring for thin film deposition.

A path-folded infrared image spectrometer with five sub-gratings and five linear-array detectors was applied to a broadband optical monitoring (BOM) system for thin film deposition. Through in situ BOM, we can simultaneously acquire the thickness and refractive index of each layer in real time by fitting the measured spectra, and modify the deposition parameters during deposition process according to the fitting results. An effective data processing method was proposed and applied in the BOM process, and it shortened the data processing time and improved the monitoring efficiency greatly. For demonstration, a narrow band-pass filter (NBF) at 1540 nm with ~10 nm full width at half-maximum (FWHM) had been manufactured using the developed BOM system, and the results showed that this BOM method was satisfying for monitoring deposition of thin film devices.

A novel method based on infrared spectroscopic inception-resnet networks for the detection of the major fish allergen parvalbumin.

We have developed a novel approach that involves inception-resnet network (IRN) modeling based on infrared spectroscopy (IR) for rapid and specific detection of the fish allergen parvalbumin. SDS-PAGE and ELISA were used to validate the new method. Through training and learning with parvalbumin IR spectra from 16 fish species, IRN, support vector machine (SVM), and random forest (RF) models were successfully established and compared. The IRN model extracted highly representative features from the IR spectra, leading to high accuracy in recognizing parvalbumin (up to 97.3%) in a variety of seafood matrices. The proposed infrared spectroscopic IRN (IR-IRN) method was rapid (~20 min, cf. ELISA ~4 h) and required minimal expert knowledge for application. Thus, it could be extended for large-scale field screening and identification of parvalbumin or other potential allergens in complex food matrices.

Can infrared spectroscopy provide information on protein-protein interactions?

For most biophysical techniques, characterization of protein-protein interactions is challenging; this is especially true with methods that rely on a physical phenomenon that is common to both of the interacting proteins. Thus, for example, in IR spectroscopy, the carbonyl vibration (1600-1700 cm(-1)) associated with the amide bonds from both of the interacting proteins will overlap extensively, making the interpretation of spectral changes very complicated. Isotope-edited infrared spectroscopy, where one of the interacting proteins is uniformly labelled with (13)C or (13)C,(15)N has been introduced as a solution to this problem, enabling the study of protein-protein interactions using IR spectroscopy. The large shift of the amide I band (approx. 45 cm(-1) towards lower frequency) upon (13)C labelling of one of the proteins reveals the amide I band of the unlabelled protein, enabling it to be used as a probe for monitoring conformational changes. With site-specific isotopic labelling, structural resolution at the level of individual amino acid residues can be achieved. Furthermore, the ability to record IR spectra of proteins in diverse environments means that isotope-edited IR spectroscopy can be used to structurally characterize difficult systems such as protein-protein complexes bound to membranes or large insoluble peptide/protein aggregates. In the present article, examples of application of isotope-edited IR spectroscopy for studying protein-protein interactions are provided.

Infrared differential-absorption Mueller matrix spectroscopy and neural network-based data fusion for biological aerosol standoff detection.

An active spectrophotopolarimeter sensor and support system were developed for a military/civilian defense feasibility study concerning the identification and standoff detection of biological aerosols. Plumes of warfare agent surrogates gamma-irradiated Bacillus subtilis and chicken egg white albumen (analytes), Arizona road dust (terrestrial interferent), water mist (atmospheric interferent), and talcum powders (experiment controls) were dispersed inside windowless chambers and interrogated by multiple CO(2) laser beams spanning 9.1-12.0 microm wavelengths (lambda). Molecular vibration and vibration-rotation activities by the subject analyte are fundamentally strong within this "fingerprint" middle infrared spectral region. Distinct polarization-modulations of incident irradiance and backscatter radiance of tuned beams generate the Mueller matrix (M) of subject aerosol. Strings of all 15 normalized elements {M(ij)(lambda)/M(11)(lambda)}, which completely describe physical and geometric attributes of the aerosol particles, are input fields for training hybrid Kohonen self-organizing map feed-forward artificial neural networks (ANNs). The properly trained and validated ANN model performs pattern recognition and type-classification tasks via internal mappings. A typical ANN that mathematically clusters analyte, interferent, and control aerosols with nil overlap of species is illustrated, including sensitivity analysis of performance.

ANOVA-Simultaneous Component analysis modelling of low-level-fused spectroscopic data: A food chemistry case-study.

Ripening is a crucial step to guarantee the high commercial value of cheddar cheese, one of the dairy products the European Union exports the most. Although several methods have lately been proposed to assess its ageing process from a chemical point of view, the majority of them is not particularly time-efficient and implies destructive analytical tests, thus, exhibiting limitations for, e.g., industrial applications. Here, a fast approach based on combining Raman and Mid-InfraRed (MIR) spectroscopy with ANOVA-Simultaneous Component Analysis (ASCA) is proposed in a low-level data fusion framework. This approach allowed to evaluate how storage temperature and time (as well as their interaction) influence cheddar ripening in a relatively cheap, rapid and green fashion.

[Isomap-PLS nonlinear modeling method for near infrared spectroscopy].

For modeling the nonlinear relationship existing between samples' near infrared (NIR) spectra and their chemical or physical properties, a novel modeling method was put forward in the present paper, which builds model by combining Isomap and partial least squares (PLS). Isomap is a newly proposed nonlinear dimension reduction algorithm, and belongs to the algorithm family of manifold learning, which is a new branch of machine learning. Isomap is based on multidimensional scaling (MDS) algorithm; however, it replaces the Euclidean distance in MDS with an approximated geodesic distance, so it can effectively find out the intrinsic low dimensional structure from high dimensional data. By combining Isomap and PLS, refered to as Isomap-PLS, a novel nonlinear modeling method for NIR spectra analysis was proposed. In this method, Isomap was used to extract nonlinear information from high dimensional NIR spectra while keeping the invariance of geometric property, and then PLS was adopted to remove linear information redundancy and build a calibration model. The parameters of the Isomap, i.e. the number of the nearest neighbor k and output dimension d, can affect the performance of the method. In this paper, a grid search approach was used for parameter optimization. The Isomap-PLS modeling method was applied to two public benchmark NIR datasets, and the modeling results were compared with that of PLS. The results demonstrated that in both datasets, each model built with Isomap-PLS had a smaller rooted mean square error of cross-validation (RMSECV) than the corresponding model built with PLS. Moreover, for some properties, the RMSECV of Isomap-PLS was significantly reduced by a factor of 2-5 compared with that of PLS. It can be concluded that by taking the virtue that Isomap can reflect the intrinsic nonlinear structure of NIR spectra, Isomap-PLS can effectively model the nonlinear correlations between spectra and physicochemical properties of the samples, and so it gains more power in calibration and prediction than PLS.

[Classification of pharmaceutical tablet with canonical variates analysis method in spectra analysis].

A novel classifier was constructed in the present paper by combination of an improved canonical variates analysis (ICVA) with Fish linear discriminant analysis (LDA). The resulting discrimination model based on this proposed approach (ICVA-LDA) was divided into two parts: the inner part that estimated the robust weight vector of canonical variates by linear partial least square algorithm and the outer part that built the LDA discrimination model by making use of the extracted canonical variates. The method utilized partial least squares regression as an engine for solving an eigenvector problem involving singular covariance matrices and the canonical variates were more relevant for discriminative purposes. Thus, the weight vectors found in the modified CVA method not only possessed the same properties as weight vectors of the standard CVA method, but also forced the discriminative information into the first fewer of canonical variates. The improved discrimination model was more concise and efficient in dealing with the problem of the effect sensitivity and numerous predictor variables with serious multicollinearity in the spectra data. Furthermore, in ICVA-LDA the interpretation could be performed with respect to the original high-dimensional data space. Finally, application to a four-group problem with near-infrared transmittance spectroscopy data consisting of 310 samples and 404 variables of the proposed ICVA-LDA approach was presented with comparison to the LDA combined with principal component analysis (PCA-LDA) and standard CVA-LDA methods. All the three discrimination models were validated using fivefold segmented cross-validation. The result demonstrates that the limitations of LDA were overcome with PLS algorithm and then the classification performance of LDA was improved by ICVA. This proposed approach can also be widely used in other fields for classification and discrimination of small samples and collinear data.

[Nondestructive identification of different oil content maize kernels by near-infrared spectra].

Using 220 maize single kernels, containing 75 common maize single kernels, 72 high-oil maize single kernels and 73 super high-oil maize single kernels as study materials, BPANN identification model was set up for maize single kernel with different oil content based on principal components of near infrared (NIR) spectra. Four fifths of the samples were randomly selected as training set and the other samples as prediction set. Fourteen principal components from the second to the fifteenth were selected as nets input and -1, 0, 1 as nets output. Ten models were set up like this and the accurate identification rate of all the training sets can reach 100%. For prediction sets, fifteen common corn grain samples had an average accurate identification rate of 99.33%, fourteen high-oil corn grain samples had an average accurate identification rate of 97.88%, fourteen super high-oil corn grain samples had an average accurate identification rate of 91.43%, and total maize grains in prediction set had an average accurate identification rate of over 95%. Results showed that NIR spectroscopy combined with BP-ANN technology could identify maize kernels fast and nondestructively according to oil content, which offered a very useful classification method for maize seed breeding. The effect of different principal component on BPANN models was also studied. Results told us that the first principal component with over 99% of variance contribution had negative effect on the identification model. The predictive ability of identification models set up by different principal component was discriminatory, although the learning accurate identification rates were all 100%. So it is necessary to choose correlative principal component to set up identification model.

[Study of quantitative analysis of protein in barley using OSC-PLS algorithm].

Spectra of barley containing vast information were obtained with the dispersion spectrograph. The contents of protein in barley were determined by dispersive near infrared (NIR) spectroscopy. Pretreatment method of orthogonal signal correction (OSC) was used to reject uncorrelated variables in the original spectra before building the partial least squares NIR method (OSC-PLS). The results were compared with the regular PLS model. With the OSC-PLS method, the determination coefficient R2 was 0.901. The correlation coefficient of validation set was 0.971 7. The standard deviation (SD) and relative standard deviation (RSD)were 0.545 0 and 4.2% respectively. Applying OSC-PLS resulted in removal of non-correlated variation in spectra and reduced model's complexity with preserved ability and improved interpretative ability of variation in spectra. It means that the OSC-PLS is a fungible model to predict the contents of protein in barley veraciously to meet the demand of fast analysis of agricultural products.

[Application of multispectral image texture to discriminating tea categories based on DCT and LS-SVM].

Based on multispectral digital image texture feature, a new rapid and nondestructive method for discriminating tea categories was put forward. The new method combines the advantages of DCT (discrete cosine transform) and least squares-support vector machine (LS-SVM). In the present study, the images for each sample were captured using a red (R) waveband, near infrared (NIR) waveband and green (G) waveband multispectral digital imager. The three wavebands of image can be combined into one image, which contains more information than images captured by ordinary digital cameras, and the NIR image can catch more information than visible spectrum. Three images for one sample can be obtained simultaneously. Eighty filters were designed based on DCT. One hundred twenty images (twenty for each category) were used for calibration set and one hundred twenty mages (twenty for each category) were used as the prediction. Finally, tea category was classified by LS-SVM. The classification rate using Sd of NIR image was only 73.33%, while it reached 100% using 8 filtered images. The overall results show that the technique combining DCT and SVM can be efficiently utilized for texture recognition of multispectral image, and it also is an effective and simple discrimination way for the tea categories. The whole process is simple and easy to operate, and can be transferred to the industrial world for on-line application.

[Local straight line screening method for the detection of Chinese proprietary medicines containing undeclared prescription drugs].

The present paper constructs a new approach named local straight-line screening (LSLS) to detect Chinese proprietary medicines (CPM) containing undeclared prescription drugs (UPD). Different from traditional methods used in analysis of multi-component spectrum, LSLS is proposed according to the characteristics of original infrared spectra of the UPD and suspected CPM, without any pattern recognition or concentration model establishment. Spectrum-subtraction leads to the variance in local straight line, which serves as a key in discrimination of whether suspected CPD is adulterated or not. Sibutramine hydrochloride, fenfluramine hydrochloride, sildenafil citrate and lovastatin were used as reference substances of UPD to analyze 16 suspected CPM samples. The results show that LSLS can obtain an accurate quantitative and qualitative analysis of suspected CPM. It is possible for the method to be potentially used in the preliminary screening of CPM containing possible UPD.

Potentiality of front-face fluorescence and mid-infrared spectroscopies coupled with partial least square regression to predict lipid oxidation in pound cakes during storage.

The potentialities of front-face fluorescence (FFF) and mid-infrared (MIR) spectroscopies coupled with partial least square regression (PLSR) were compared to predict the lipid oxidation of pound cakes. The level of lipid oxidation in pound cakes determined using classical methods showed some changes. Similarly, the fluorescence emission (305-490 nm) and excitation (252-390 nm) spectra and MIR spectra scanned in the 4000-700 cm-1 region showed some changes in pound cakes as a function of both storage time and the type of oil used in the formulation. The application of PLSR to the MIR spectra, provided excellent predictive results for free fatty acid (R2 = 0.97) and peroxide values (R2 = 0.87). Similar results were obtained from both tryptophan and MIR spectra for the prediction of TOTOX (R2 > 0.86) demonstrating the efficiency of the MIR and FFF spectroscopies to qualify and quantify the level of lipid oxidation in pound cakes.

MCR-ALS analysis of IR spectroscopy and XRD for the investigation of ibuprofen - nicotinamide cocrystal formation.

To improve aqueous solubility, a poorly water-soluble active ingredient is classically combined with a conformer to form cocrystals. Hot melt extrusion is one preparation method for the formation of cocrystal solids. The aim of our study was to determine the optimal temperature conditions for the formation of ibuprofen and nicotinamide cocrystals using real-time infrared (IR) and X-ray diffraction (XRD) measurements. IR spectra and XRD patterns were subjected to multivariate curve resolution alternating least squares (MCR-ALS) analysis and decomposed into several components. Each component was descriptive of a specific step in the formation of the cocrystal. Cocrystal formation was followed by a separation phase between amorphous ibuprofen and crystalline nicotinamide. Our results suggest that, when using the hot melt exclusion method, careful consideration should be made towards optimizing processing temperatures in order to prevent amorphization and promote control over the process of cocrystal formation.

Chemical changes that occur in Jerusalem artichoke silage.

The effects of 10 and 20 days of fermentation followed by freeze-drying on the vitamin C and fatty acids contents, chemical conversions and overall chemical composition of Jerusalem artichoke were studied. Fermentation between the 10th and 20th days increased content of all saturated fatty acids and two of the four unsaturated fatty acids. The only fatty acid content that decreased was that of C18:1 cis 9 acid, which was suggested to be converted to other fatty acids. The experimental data, which were supported by energetical feasibility, suggested the reaction pathways of the mutual conversions of fatty acids and confirmed the decreased vitamin C content during fermentation. Discriminant modelling of the spectral data successfully distinguished the fresh, 10 days and 20 days fermented samples. The correlation of the spectral and reference data allowed to construct reference models for predicting the content of vitamin C and C18:1 cis 9 fatty acid.

Phase angle description of perturbation correlation analysis and its application to time-resolved infrared spectra.

A method of spectral analysis, phase angle description of perturbation correlation analysis, is proposed. This method is based on global phase angle description of generalized two-dimensional (2D) correlation spectroscopy, proposed by Shin-ichi Morita et al., and perturbation-correlation moving-window 2D (PCMW2D) correlation spectroscopy, proposed by Shigeaki Morita et al. For a spectral data set collected under an external perturbation, such as time-resolved infrared spectra, this method provides only one phase angle spectrum. A phase angle of the Fourier frequency domain correlation between a spectral intensity (e.g., absorbance) variation and a perturbation variation (e.g., scores of the first principle component) as a function of spectral variable (e.g., wavenumber) is plotted. Therefore, a degree of time lag of each band variation with respect to the perturbation variation is directly visualized in the phase angle spectrum. This method is applied to time-resolved infrared spectra in the O-H stretching region of the water sorption process into a poly(2-methoxyethyl acrylate) (PMEA) film. The time-resolved infrared (IR) spectra show three broad and overlapping bands in the region. Each band increases toward saturated water sorption with different relaxation times. In comparison to conventional methods of generalized 2D correlation spectroscopy and global phase angle mapping, the method proposed in the present study enables the easier visualization of the sequence as a degree of phase angle in the spectrum.

Direct determination of lycopene content in tomatoes (Lycopersicon esculentum) by attenuated total reflectance infrared spectroscopy and multivariate analysis.

Lycopene is a potent antioxidant that has been shown to play critical roles in disease prevention. Efficient assays for detection and quantification of lycopene are desirable as alternatives to time- and labor-intensive methods. Attenuated total reflectance infrared (ATR-IR) spectroscopy was used for quantification of lycopene in tomato varieties. Calibration models were developed by partial least-squares regression (PLSR) using quantitative measures of lycopene concentration from liquid chromatography as reference method. IR spectra showed a distinct marker band at 957 cm(-1) for trans Carbon-Hydrogen (CH) deformation vibration of lycopene. PLSR models predicted the lycopene content accurately and reproducibly with a correlation coefficient (sigma) of 0.96 and standard error of cross-validation <0.80 mg/100 g. ATR-IR spectroscopy allowed for rapid, simple, and accurate determination of lycopene in tomatoes with minimal sample preparation. Results suggest that the ATR-IR method is applicable for high-throughput quantitative analysis and screening for lycopene in tomatoes.

Current practice and knowledge of nurses regarding patient temperature measurement.

BACKGROUND: The measurement of patient temperature by nursing staff is a common activity. In recent years this area of practice has been largely under-investigated despite the introduction of new technologies into clinical practice, such as infrared tympanic thermometry (IRTT). STUDY AIM: To investigate current practice in clinical temperature measurement in general, and the use of IRTT in particular by nursing staff. SAMPLE AND METHODS: 139 nursing staff from a general hospital in the UK were surveyed via a self-administered questionnaire. MAIN FINDINGS AND CONCLUSIONS: The group most involved in temperature measurement was characterized as nursing auxiliary grades with the fewest years of experience. IRTT was the most frequently-used method for measuring patient temperatures, with high perceived accuracy, reliability, ease of use and acceptability to patients. A poor level of understanding and training in the use of IRTT was revealed across all clinical grades. Recommendations for future investigations are made.