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The interference of nontarget adulterant on FT-IR-based target adulterant quantitative analysis was explored and a sequential strategy was proposed to improve the prediction accuracy of the quantitative analysis model. Based on the FT-IR data of fish oil adulterated with terrestrial animal lipid, PLS and PLS-DA results show that quantitative analysis modeled by multiple and single adulteration data do not apply to each other; quantitative models based on the fusion of single and multiple adulteration data were established and showed a low quantitative analysis precision (higher RSD); and the sensitivity and specificity of discrimination analysis for multiply and singly adulterated fish oils both all exceed 0.910. To enhance the detection accuracy, a sequential strategy was proposed; identifying singly or multiply adulterated fish oil and then quantifying the content of adulterant was considered an efficient approach., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Attenuated total reflectance Fourier transform spectroscopy (ATR-FTIR) was applied on fresh (NF), freeze-dried (FD) and cell wall materials (AIS) of raw and processed apples. These samples prepared from 36 apple sets and the corresponding 72 purees, issued from different varieties, agricultural practices, storage periods and processing conditions, were used to build models including exploratory analysis, supervised classification and multivariate calibration. Fresh and freeze-dried samples presented similar fingerprint spectral variations due to processing. ATR-FTIR directly on fresh purees satisfactorily predicted textural properties such as particle average size and volume (RPD > 3.0), while freeze-drying improved assessment of chemical (RPD > 3.2) and rheological (RPD > 3.1) parameters using partial least-squares regression. The assessment of texture and macrocomponents of purees can be obtained with a limited sample preparation. For research applications because of a need of sample preparation, changes of cell wall composition during fruit processing could be assessed in relationship with pectin degradation., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

The identification of grapevine (Vitis vinifera L.) genotypes is conventionally a laborious activity that must be carried out by specialized staff. In this work a novel and simple method for differentiation of grapevine genotypes is presented. Direct measurements of leaves by attenuated total reflectance Fourier-transform infrared spectroscopy (ATR-FTIR) combined with chemometric methods were used for classification of six genotypes (five varieties and a pair of clones), viz. Cinsault, Gewurztraminer (clone 643), Moscatel de Alejandría, País, Pinot Noir (French clone 777), Pinot Noir (local clone 'Valdivieso'). These were successfully classified and identified through supervised pattern recognition methods such as soft independent modeling of class analogy (SIMCA) and partial least square discriminant analysis (PLS-DA). The error rate for spectra classification of test sets by both models was 0.08. The results demonstrate the advantages of using ATR-FTIR as a rapid and non-destructive tool that achieves accurate grapevine genotype differentiation., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Examining diagenetic parameters such as the organic carbonate contents and the crystallinity of bone apatite quantify the post-mortem alteration of bone. Burial conditions are one of the factors that can influence the diagenesis process. We studied the changes to the organic and mineral components and crystallinity of human bone remains from five Medieval sites in Turkey: Hakemi Use, Komana, İznik, Oluz Höyük and Tasmasor using Attenuated Total Reflectance Fourier Transform Infrared spectroscopy (ATR-FTIR) and principal component analysis (PCA). Analysis of spectral band ratios related to organic and mineral components of bone demonstrated differences in the molecular content in the skeletal remains from the five sites. In order to examine the degree of carbonation of a phosphate matrix, curve-fitting procedures were applied to the carbonate band. We found that the infrared crystallinity index appears to not be sensitive to carbonate content at room temperature for the bone remains studied here. The recrystallization process in bone remains behaved differently among the archaeological sites. The results demonstrate that the burial environments differently affect the organic and mineral components of archaeological bone remains., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Physiological imbalance is an abnormal physiological condition that cannot be directly observed but is assumed to precede subclinical and clinical diseases in the beginning of lactation. Alert systems to detect the physiological imbalance in a cow using Fourier transform mid-infrared spectroscopy in milk have been developed. The objective of this study was to estimate the value of information provided from such system with different indicator accuracies, herd prevalence and prices. A decision tree was created to model the probabilities of detection and associated costs of test outcome, intervention and occurrence of disease. We assumed that the negative effect of physiological imbalance was the development of subclinical ketosis and that this negative effect was prevented by drenching the cows with propylene glycol for 5 days. We simulated the economic impact of subclinical ketosis mediated through physiological imbalance to be $194 per case. The results showed that if the alert system was highly accurate (Se = 0.99/Sp = 0.99), and the prevalence of physiological imbalance was 30 %, the value of information provided from the system is $19 per cow-year. In case the prevalence is 5 % or 50 %, the value of information is $3 and $13, respectively. These estimates for the value do not cover the capital costs and operational costs of the alert system. This study furthermore clearly demonstrated that in order to estimate the value of information correctly, it is important to consider that drenching all cows and not drenching any of the cows are the two relevant alternative options in the absence of the alert system. In conclusion, the decision tree and sensitivity analysis developed in this study show that final economic results are highly variable to the prevalence of physiological imbalance and highest at an intermediate prevalence. Other relevant factors are the costs associated with drenching and the cost associated with treating false positives and not treating false negatives. In addition, this study highlights the benefits of simulation to pinpoint where additional information is needed to further quantify the economic value and required accuracy of an indication-based intervention system., (Copyright © 2020. Published by Elsevier B.V.)

Biomaterials' surface properties elicit diverse cellular responses in biomedical and biotechnological applications. Predicting the cell behavior on a polymeric surface is an ongoing challenge due to its complexity. This work proposes a novel modeling methodology based on attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy. Spectra were collected on wetted polymeric surfaces to incorporate both surface chemistry and information on water-polymer interactions. Results showed that predictive models built with spectra from wetted surfaces ("wet spectra") performed much better than models built using spectra acquired from dry surfaces ("dry spectra"), suggesting that the water-polymer interaction is critically important to the prediction of subsequent cell behavior. The best model was seen to predict total area of focal adhesions with coefficient of determination for prediction ( R 2 P ) of 0.94 and root-mean-square errors of prediction (RMSEP) of 4.03 μm 2 when tested on an independent experimental set. This work offers new insights into our understanding of cell-biomaterial interactions. The presence of carboxyl groups in polymers promoted larger adhesion areas, yet the formation of carbonyl-to-water interaction decreased adhesion areas. Surface wettability, which was related to the water-polymer interaction, was proven to highly influence cell adhesion. The good predictive ability opens new possibilities for high throughput monitoring of cell attachment on polymeric substrates.

Model-based algorithms have recently attracted much attention for data pre-processing in tissue mapping and imaging by Fourier transform infrared micro-spectroscopy (FTIR). Their versatility, robustness and computational performance enabled the improvement of spectral quality by mitigating the impact of scattering and fringing in FTIR spectra of chemically homogeneous biological systems. However, to date, no comprehensive algorithm has been optimized and automated for large-area FTIR imaging of histologically complex tissue samples. Herein, for the first time, we propose a unique, integrated and fully-automated Multiple Linear Regression Multi-Reference (MLR-MR) method for correcting linear baseline effects due to diffuse scattering, for compensating substrate thickness inhomogeneity and accounting for sample chemical heterogeneity in FTIR images. In particular, the algorithm uses multiple-reference spectra for histologically heterogeneous biological samples. The performance of the procedure was demonstrated for FTIR imaging of chemically complex rat brain frontal cortex tissue samples, mounted onto Ultralene® films. The proposed MLR-MR correction algorithm allows the efficient retrieval of "pure" absorbance spectra and greatly improves the histological fidelity of FTIR imaging data, as compared with the one-reference approach. In addition, the MLR-MR algorithm here presented opens up the possibility for extracting information on substrate thickness variability, thus enabling the indirect evaluation of its topography. As a whole, the MLR-MR procedure can be easily extended to more complex systems for which Mie scattering effects must also be eliminated., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Evaluating the possibility of extending shelf life of rice germ (a by-product of rice milling process) by reducing water activity in combination with storage atmosphere packaging, without any heat treatment, is the aim of the present study. Samples at different water activities (0.55, 0.45 and 0.36) were packed in air, argon or under vacuum, and stored at 27 °C for 150 days. To the aim, a non-targeted approach was applied by means of an FT-NIR spectrometer in reflectance with a rotating sample holder and a portable electronic nose, equipped with 10 non-specific sensors. For understanding the impact of the factors under study on the rice germ shelf life, a modified mid-level data fusion approach was applied to enhance the information most correlated with time. Moreover, Principal Component Analysis was applied on fused data to follow samples evolution during storage and identify different clusters according to the storage conditions. The rice germ case study allowed to better understand the information captured by the non-specific sensors: a 2D correlation map was developed combining the e-nose data with the NIR spectral information, highlighting relationships among NIR absorption bands and classes of chemical compounds inducing e-nose responses. A data fusion approach highlighted the importance of water activity on rice germ storage, while no interesting differences were ascribable to storage atmosphere packaging systems. In terms of correlation, the sensors could be divided in two groups, negatively inter-correlated: sensors ascribable to aromatic compounds (WC) and correlated with the NIR band around 4800-4900 cm -1 (N-H bending of primary amides, typical for peptides coming from protein hydrolysis); broad-range response sensors (WS), linked with the NIR band at 5128 cm -1 (second overtone of CO stretching of esters)., (Copyright © 2019 Elsevier B.V. All rights reserved.)

In the presented study, Fourier-transform infrared (FTIR) spectroscopy is used to predict the average molecular weight of protein hydrolysates produced from protein-rich by-products from food industry using commercial enzymes. Enzymatic protein hydrolysis is a well-established method for production of protein-rich formulations, recognized for its potential to valorize food-processing by-products. The monitoring of such processes is still a significant challenge as the existing classical analytical methods are not easily applicable to industrial setups. In this study, we are reporting a generic FTIR-based approach for monitoring the average molecular weights of proteins during enzymatic hydrolysis of by-products from the food industry. A total of 885 hydrolysate samples from enzymatic protein hydrolysis reactions of poultry and fish by-products using different enzymes were studied. FTIR spectra acquired from dry-films of the hydrolysates were used to build partial least squares regression (PLSR) models. The most accurate predictions were obtained using a hierarchical PLSR approach involving supervised classification of the FTIR spectra according to raw material quality and enzyme used in the hydrolysis process, and subsequent local regression models tuned to specific enzyme-raw material combinations. The results clearly underline the potential of using FTIR for monitoring protein sizes during enzymatic protein hydrolysis in industrial settings, while also paving the way for measurements of protein sizes in other applications., (Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.)

Objectives: Drug checking is a harm reduction intervention that allows for identification of drug composition. The objective of the study was to assess drug market components and concordance between expected substance reported by clients and results from point-of-care drug checking at music festivals and events in British Columbia., Methods: From July to September 2018, we provided drug checking services at four events using combination Fourier Transform Infrared (FTIR) spectroscopy and fentanyl immunoassay strips. We measured concordance between expected substance as reported by clients to the results from the FTIR/fentanyl immunoassay strip and tracked unexpected adulterants., Results: In total, 336 checks were completed. Most samples were expected by clients to be psychedelics (69.3%) or stimulants (19.6%). Of the 233 psychedelic samples, 169 (72.5%) contained the expected, unadulterated substance, and 27 (11.6%) contained additional contaminants. Of 66 stimulant samples, 41 (62.1%) contained expected substance, while 24 (36.4%) contained additional contaminants. Unexpected adulterants such as fentanyl, levamisole, and phenacetin were also found, in addition to several novel psychoactive substances., Discussion: We found a large proportion of substances that contained unexpected adulterants. Our findings highlight the value of continued drug checking and will be helpful in designing future harm reduction interventions in similar contexts., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Indirect measurements of taste-related compounds are required when a high number of samples has to be analyzed in a short period of time, with a minimum cost. For this purpose, FT-MIR partial least square (PLS) regression models for the prediction of total soluble solids, sugars and organic acids have been developed using three sample sets including breeding lines and commercial varieties of watermelon. Specific models with excellent performance were obtained only for sugars. Nevertheless, a general model supposed a compromise between the best and worse models and offered %RMSEP values of 11.3%, 11.1% and 11.7% for fructose, glucose and sucrose respectively. The model was applied to the selection of high content samples (selection pressure 20% and 30%) obtaining good sensitivity levels and mean percentile of selected samples close to the expected values (100% sensitivity). The robustness of FT-MIR models was assessed with predictions of external assays, obtaining reasonable performances., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Over a third of brain tumour patients visit their general practitioner more than five times prior to diagnosis in the UK, leading to 62% of patients being diagnosed as emergency presentations. Unfortunately, symptoms are non-specific to brain tumours, and the majority of these patients complain of headaches on multiple occasions before being referred to a neurologist. As there are currently no methods in place for the early detection of brain cancer, the affected patients' average life expectancy is reduced by 20 years. These statistics indicate that the current pathway is ineffective, and there is a vast need for a rapid diagnostic test. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy is sensitive to the hallmarks of cancer, as it analyses the full range of macromolecular classes. The combination of serum spectroscopy and advanced data analysis has previously been shown to rapidly and objectively distinguish brain tumour severity. Recently, a novel high-throughput ATR accessory has been developed, which could be cost-effective to the National Health Service in the UK, and valuable for clinical translation. In this study, 765 blood serum samples have been collected from healthy controls and patients diagnosed with various types of brain cancer, contributing to one of the largest spectroscopic studies to date. Three robust machine learning techniques - random forest, partial least squares-discriminant analysis and support vector machine - have all provided promising results. The novel high-throughput technology has been validated by separating brain cancer and non-cancer with balanced accuracies of 90% which is comparable to the traditional fixed diamond crystal methodology. Furthermore, the differentiation of brain tumour type could be useful for neurologists, as some are difficult to distinguish through medical imaging alone. For example, the highly aggressive glioblastoma multiforme and primary cerebral lymphoma can appear similar on magnetic resonance imaging (MRI) scans, thus are often misdiagnosed. Here, we report the ability of infrared spectroscopy to distinguish between glioblastoma and lymphoma patients, at a sensitivity and specificity of 90.1% and 86.3%, respectively. A reliable serum diagnostic test could avoid the need for surgery and speed up time to definitive chemotherapy and radiotherapy.

The aim of this study was to evaluate the efficacy of a multi-analytical approach for origin authentication of cocoa bean shells (CBS). The overall chemical profiles of CBS from different origins were characterized using diffuse reflectance near-infrared spectroscopy (NIRS) and attenuated total reflectance mid-infrared spectroscopy (ATR-FT-IR) for molecular composition identification, as well as inductively coupled plasma-optical emission spectroscopy (ICP-OES) for elemental composition identification. Exploratory chemometric techniques based on Principal Component Analysis (PCA) were applied to each single technique for the identification of systematic patterns related to the geographical origin of samples. A combination of the three techniques proved to be the most promising approach to establish classification models. Partial Least Squares-Discriminant Analysis modelling of fused PCA scores of three independent models was used and compared with single technique models. Improved classification of CBS samples was obtained using the fused model. Satisfactory classification rates were obtained for Central African samples with an accuracy of 0.84., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

In the present study, Fourier-transform infrared spectroscopy (FTIR) is investigated as a method to measure connective tissue components that are important for the quality of Atlantic cod filets (Gadus morhua L.). The Atlantic cod used in this study originated from a feeding trial, which found that fish fed a high starch diet contained relative more collagen type I, while fish fed a low starch (LS) diet contained relative more glycosaminoglycans (GAGs) in the connective tissue. FTIR spectra of pure commercial collagen type I and GAGs were acquired to identify spectral markers and compare them with FTIR spectra and images from connective tissue. Using principal component analysis, high and LS diets were separated due to collagen type I in the spectral region 1800 to 800 cm -1 . The spatial distribution of collagen type I and GAGs were further investigated by FTIR imaging in combination with immunohistochemistry. Pixel-wise correlation images were calculated between preprocessed connective tissue images and preprocessed pure components spectra of collagen type I and GAGs, respectively. For collagen, the FTIR images reveal a collagen distribution that closely resembles the collagen distribution as imaged by immunohistochemistry. For GAGs, the concentration is very low. Still, the FTIR images detect the most GAGs rich regions., (© 2019 The Authors. Journal of Biophotonics published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Adulteration is one of the major concerns among all the quality problems of milk powder. Soybean flour and rice flour are harmless adulterations in the milk powder. In this study, mid-infrared spectroscopy was used to detect the milk powder adulterated with rice flour or soybean flour and simultaneously determine the adulterations content. Partial least squares (PLS), support vector machine (SVM) and extreme learning machine (ELM) were used to establish classification and regression models using full spectra and optimal wavenumbers. ELM models using the optimal wavenumbers selected by principal component analysis (PCA) loadings obtained good results with all the sensitivity and specificity over 90%. Regression models using the full spectra and the optimal wavenumbers selected by successive projections algorithm (SPA) obtained good results, with coefficient of determination (R 2 ) of calibration and prediction all over 0.9 and the predictive residual deviation (RPD) over 3. The classification results of ELM models and the determination results of adulterations content indicated that the mid-infrared spectroscopy was an effective technique to detect the rice flour and soybean flour adulteration in the milk powder. This study would help to apply mid-infrared spectroscopy to the detection of adulterations such as rice flour and soybean flour in real-world conditions.

Grape, orange, peach and passion fruit nectars were formulated and adulterated by dilution with syrup, apple and cashew juices at 10 levels for each adulterant. Attenuated total reflectance Fourier transform mid infrared (ATR-FTIR) spectra were obtained. Partial least squares (PLS) multivariate calibration models allied to different variable selection methods, such as interval partial least squares (iPLS), ordered predictors selection (OPS) and genetic algorithm (GA), were used to quantify the main fruits. PLS improved by iPLS-OPS variable selection showed the highest predictive capacity to quantify the main fruit contents. The selected variables in the final models varied from 72 to 100; the root mean square errors of prediction were estimated from 0.5 to 2.6%; the correlation coefficients of prediction ranged from 0.948 to 0.990; and, the mean relative errors of prediction varied from 3.0 to 6.7%. All of the developed models were validated., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Boletus griseus and Boletus edulis are two well-known wild-grown edible mushrooms which have high nutrition, delicious flavor and high economic value distributing in Yunnan Province. In this study, a rapid method using Fourier transform infrared (FT-IR) and ultraviolet (UV) spectroscopies coupled with data fusion was established for the discrimination of Boletus mushrooms from seven different geographical origins with pattern recognition method. Initially, the spectra of 332 mushroom samples obtained from the two spectroscopic techniques were analyzed individually and then the classification performance based on data fusion strategy was investigated. Meanwhile, the latent variables (LVs) of FT-IR and UV spectra were extracted by partial least square discriminant analysis (PLS-DA) and two datasets were concatenated into a new matrix for data fusion. Then, the fusion matrix was further analyzed by support vector machine (SVM). Compared with single spectroscopic technique, data fusion strategy can improve the classification performance effectively. In particular, the accuracy of correct classification of SVM model in training and test sets were 99.10% and 100.00%, respectively. The results demonstrated that data fusion of FT-IR and UV spectra can provide higher synergic effect for the discrimination of different geographical origins of Boletus mushrooms, which may be benefit for further authentication and quality assessment of edible mushrooms., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Nucleic acid & serology based methods have revolutionized plant disease detection, however, they are not very reliable at asymptomatic stage, especially in case of pathogen with systemic infection, in addition, they need at least 1-2days for sample harvesting, processing, and analysis. In this study, two reflectance spectroscopies i.e. Near Infrared reflectance spectroscopy (NIR) and Fourier-Transform-Infrared spectroscopy with Attenuated Total Reflection (FT-IR, ATR) coupled with multivariate exploratory methods like Principle Component Analysis (PCA) and Partial least square discriminant analysis (PLS-DA) have been deployed to detect begomovirus infection in papaya leaves. The application of those techniques demonstrates that they are very useful for robust in vivo detection of plant begomovirus infection. These methods are simple, sensitive, reproducible, precise, and do not require any lengthy samples preparation procedures., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Colorectal cancer constitutes 33% of all cancer morbidity, so the research of the new methods for colorectal cancer diagnosis and chemotherapy monitoring is gaining its momentum. Diagnostic instruments are being sought, which enable the detection of single malignant cells based on the analysis of tissue material potentially reusable at further stages of diagnostic management. The most common approach to tissue specimen processing is paraffin-embedding. Yet, paraffin may cause background noise in spectroscopic measurements with the wavenumber ranging between 900cm -1 and 3500cm -1 . However, the study by Depciuch et al. (2016) proved that appropriate specimen processing and paraffin-embedding technique as well as a strict measurement methodology may eliminate paraffin vibrations. As a result, spectroscopic measurements may become a reliable and precise method for the diagnosis and treatment monitoring in patients with colorectal cancer as long as the high standards of specimen processing are maintained. Chemotherapy is the main medical treatment in colorectal cancer. Unfortunately, the absence of tools which enable monitoring its efficacy leads to the partial response or non-response frequently seen in affected patients. Hence, diagnostic instruments are also being sought capable of monitoring treatment efficacy so as to enable early changes of chemotherapy regimen thus increasing the chance of cure. The paper aims at comparing the results of FTIR (Fourier Transform Infrared) spectroscopy in several types of colon tissue: healthy colon, cancerous colon, post-chemotherapy colon and healthy surgical margin of colon cancer sample. The obtained FTIR spectra along with the Principal Component Analysis-Linear Discriminant Analysis (PCA-LDC) as well as bandwidth analysis of the primary amide region revealed some differences between the spectra of healthy tissues as compared to cancerous tissues (pre- or post-chemotherapy). Apart from confirming that FTIR spectroscopy is a good source of information on the composition of analysed samples, this fact supports its application as a tool to facilitate understanding the pathophysiology of various conditions and to monitor efficacy of chemotherapy in cancer patients., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Infrared spectroscopy is a powerful tool in protein science due to its sensitivity to changes in secondary structure or conformation. In order to take advantage of the full power of infrared spectroscopy in structural studies of proteins, complex band contours, such as the amide I band, have to be decomposed into their main component bands, a process referred to as curve fitting. In this paper, we report on an improved curve fitting approach in which absorption spectra and second derivative spectra are fitted simultaneously. Our approach, which we name co-fitting, leads to a more reliable modelling of the experimental data because it uses more spectral information than the standard approach of fitting only the absorption spectrum. It also avoids that the fitting routine becomes trapped in local minima. We have tested the proposed approach using infrared absorption spectra of three mixed α/β proteins with different degrees of spectral overlap in the amide I region: ribonuclease A, pyruvate kinase, and aconitase.

Absolute concentrations of total macromolecules (triglycerides, proteins and carbohydrates) in microorganisms can be rapidly measured by FTIR spectroscopy, but caution is needed to avoid non-specific experimental bias. Here, we assess the limits within which this approach can be used on model solutions of macromolecules of interest. We used the Bruker HTSXT-FTIR system. Our results show that the solid deposits obtained after the sampling procedure present physical and chemical properties that influence the quality of the absolute concentration prediction models (univariate and multivariate). The accuracy of the models was degraded by a factor of 2 or 3 outside the recommended concentration interval of 0.5-35 µg spot(-1). Change occurred notably in the sample hydrogen bond network, which could, however, be controlled using an internal probe (pseudohalide anion). We also demonstrate that for aqueous solutions, accurate prediction of total carbohydrate quantities (in glucose equivalent) could not be made unless a constant amount of protein was added to the model solution (BSA). The results of the prediction model for more complex solutions, here with two components: glucose and BSA, were very encouraging, suggesting that this FTIR approach could be used as a rapid quantification method for mixtures of molecules of interest, provided the limits of use of the HTSXT-FTIR method are precisely known and respected. This last finding opens the way to direct quantification of total molecules of interest in more complex matrices.

Over the past couple of decades there have been major advances in the field of nanoscience and nanotechnology. Many applications have sprouted from these fields of research. It is essential, given the scale of the materials, to attain accurate, valid and reproducible measurements. Material properties have shown to be a function of their size and composition. Physiochemical properties of the nanomaterials can significantly alter material behavior compared to bulk counterparts. For example, metal oxide nanoparticles have found broad applications ranging from photo-catalysis to antibacterial agents. In our study, we synthesized CuO nanoparticles using well established sol-gel based methods with varying levels of Ni doping. However, upon analysis of measured infrared data, we discovered the presence of quasi-periodic (QP) processes. Such processes have previously been reported to be tightly associated with measurement memory effects. We were able to detect the desired QP process in these measurements from three highly accurate repetitive experiments performed on each Ni (1-7%) doped CuO sample. In other words, successive measurements performed in a rather short period of time remember each other at least inside a group of neighboring measurements.

Optical spectroscopic tools are used to monitor protein folding/unfolding dynamics after a fast triggering such as the laser induced temperature jump. These techniques provide new opportunities for comparison between theory and simulations and atom-level understanding protein folding mechanism. However, the direct comparison still face two main challenges: a gap between folding relevant timescales (microseconds or above) and length of molecular dynamics simulations (typically tens to hundreds of nanoseconds), and difficulty in directly calculating spectroscopic observables from simulation configurations. Markov State Model (MSM) approach is one of the most powerful means which can increase simulations timescale up to microsecond or even millisecond. We address progress on modeling infrared and fluorescence spectroscopic signals of temperature jump induced unfolding dynamics for a few small proteins. The harmoniousness between experiment and theoretical can both improve our understanding of protein folding mechanisms and provide direct validation of those theoretical models.

Tartaric instability of wines still represents a serious problem in terms of the commercial value of bottled wines, particularly whites, leading consumers to be suspicious as regards the effective healthiness or wholesomeness of products. The study, carried out on 536 Italian wines, investigated the potential of using Fourier Transform Infrared Spectroscopy, distinguishing between white and red/rosé wines, to create models predicting the instability of wines, assessed in comparison to two of the most widespread methods of reference: the "mini-contact test" (10 min, 0 °C, KHT) and the "cooling test" (5 days, -4 °C). The models proposed, constructed using 80% of the samples and based on Partial Least Squares-Regression and Artificial Neural Networks, were shown to work well in terms of correct classification (from 89% to 97%) of the external validation subset (20%). As regards the more problematical question of technical management of wines before bottling, in the worst cases only 4-6% of unstable samples were erroneously classified as stable., (© 2013 Elsevier B.V. All rights reserved.)

The current study presents an application of Diffuse Reflectance Infrared Fourier Transform Spectroscopy for detection and quantification of fraudulent addition of commonly employed adulterants (spent coffee grounds, coffee husks, roasted corn and roasted barley) to roasted and ground coffee. Roasted coffee samples were intentionally blended with the adulterants (pure and mixed), with total adulteration levels ranging from 1% to 66% w/w. Partial Least Squares Regression (PLS) was used to relate the processed spectra to the mass fraction of adulterants and the model obtained provided reliable predictions of adulterations at levels as low as 1% w/w. A robust methodology was implemented that included the detection of outliers. High correlation coefficients (0.99 for calibration; 0.98 for validation) coupled with low degrees of error (1.23% for calibration; 2.67% for validation) confirmed that DRIFTS can be a valuable analytical tool for detection and quantification of adulteration in ground, roasted coffee., (Copyright © 2013 Elsevier B.V. All rights reserved.)

Phase correction of Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry data allows the spectra to be presented in absorption mode. Absorption mode spectra offer superior mass resolving power (up to a factor of 2), mass accuracy, and sensitivity over the conventional magnitude mode. Hitherto, the use of absorption mode in FT-ICR mass spectrometry has required either specially adapted instrumentation or a manually intensive process of phase correction or has ignored the potentially significant effects of image charge and the associated frequency shifts. Here we present an algorithm that allows spectra recorded on unadapted FT-ICR mass spectrometers to be phase corrected, their baseline deviations removed, and then an absorption mode spectrum presented in an automated manner that requires little user interaction.

Innovative diagnostic methods are the need of the hour that could complement conventional histopathology for cancer diagnosis. In this perspective, we propose a new concept based on spectral histopathology, using IR spectral micro-imaging, directly applied to paraffinized colon tissue array stabilized in an agarose matrix without any chemical pre-treatment. In order to correct spectral interferences from paraffin and agarose, a mathematical procedure is implemented. The corrected spectral images are then processed by a multivariate clustering method to automatically recover, on the basis of their intrinsic molecular composition, the main histological classes of the normal and the tumoral colon tissue. The spectral signatures from different histological classes of the colonic tissues are analyzed using statistical methods (Kruskal-Wallis test and principal component analysis) to identify the most discriminant IR features. These features allow characterizing some of the biomolecular alterations associated with malignancy. Thus, via a single analysis, in a label-free and nondestructive manner, main changes associated with nucleotide, carbohydrates, and collagen features can be identified simultaneously between the compared normal and the cancerous tissues. The present study demonstrates the potential of IR spectral imaging as a complementary modern tool, to conventional histopathology, for an objective cancer diagnosis directly from paraffin-embedded tissue arrays.

Background: To visualise and characterise skin architecture, the tissue usually has to be destroyed and labelled., Objectives: The use of Fourier transform infrared (FTIR) spectroscopy as a label-free, minimally sample destructive method to define hair follicular structure has been explored and demonstrated in this paper., Methods: Human scalp skin cryosections were imaged using FTIR microscopy and the data was subsequently analysed with N-FINDR spectral unmixing algorithm., Results: This resulted in an excellent distinction of known hair follicle tissue layers, which could be discerned based on their molecular structure., Conclusion: The development of a minimally sample-destructive, label-free spectroscopy based technique that can differentiate layers of cells in the dermal papilla and connective tissue sheath in the mesenchyme of the hair follicle paves the way forward to identifying spectral markers important in wound healing and stem cell therapies., (Copyright © 2011 Japanese Society for Investigative Dermatology. Published by Elsevier Ireland Ltd. All rights reserved.)

Conventional Fourier-transform infrared (FTIR) microspectroscopic systems are limited by an inevitable trade-off between spatial resolution, acquisition time, signal-to-noise ratio (SNR) and sample coverage. We present an FTIR imaging approach that substantially extends current capabilities by combining multiple synchrotron beams with wide-field detection. This advance allows truly diffraction-limited high-resolution imaging over the entire mid-infrared spectrum with high chemical sensitivity and fast acquisition speed while maintaining high-quality SNR.

Fourier-transform mid-infrared (FT-MIR) spectroscopy, combined with partial least-squares (PLS) regression and IPW as feature selection method, was used to develop reduced-spectrum calibration models based on a few IR bands to provide near-real-time predictions of two key parameters for the characterization of finished red wines, which are essential from a quality assurance standpoint: total and volatile acidity. Separate PLS calibration models, correlating IR data (only considering those regions showing a high signal to noise ratio) with each response studied, were developed. Wavenumber selection was also performed applying IPW-PLS to take into account only significant predictors, in an attempt to improve the quality of the final models constructed. Using both PLS and IPW-PLS regression, prediction of the two responses modelled was performed with very high reliability, with RMSECV and RMSEP values on the order of 1% (comparable in terms of accuracy to the results provided by the respective reference analysis methods). An important advantage derived from the application of the IPW-PLS method had to do with the low number of original variables needed for modelling both total acidity (22 significant wavenumbers) and volatile acidity (only 11 selected predictor variables), in such a way that variable selection contributed to enhance the stability and parsimony properties of the final calibration models. The high quality of the calibration models proposed encourages the feasibility of implementing them as a fast and reliable tool in routine analysis for the determination of critical parameters for wine quality.

Numerous methods like distillation followed by iodometric titrations, gas chromatograph (GC)-flame ionization detector, gas chromatograph-mass spectrophotometer, GC-Headspace, Breath analyzer, and biosensors including alcohol dehydrogenase (enzymatic) have been used to determine blood alcohol concentration (BAC). In the present study, horizontal attenuated total reflectance-Fourier transform infrared spectroscopy had been used to determine BAC in whole blood. The asymmetric stretching frequency of C-C-O group of ethanol in water (1,045 cm(-1)) had been used to calculate BAC using Beer's Law. A seven-point calibration curve of ethanol was drawn in the concentration range 24-790 mg dL(-1). The curve showed good linearity over the concentration range used (r(2)=0.999, standard deviation=0.0023). The method is accurate, reproducible, rapid, simple, and nondestructive in nature., (2010 Elsevier Inc. All rights reserved.)

In the present paper, the authors extracted active components of herba epimedii and their important features using Fourier transform infrared spectroscopy (FTIR), correlation coefficient comparison, and multilevel wavelet analysis. The extracted features were then used to classify herba epimedii via radial basis function (RBF) neural network. There were altogether 250 samples of the medicine with various different types, including epimedium brevicornu Maxim., E. sagittatum (Sieb. et Zucc. ) Maxim, E. pubescens Maxim., E. koreanum Nakai and E. wushanense T. S. Ying. An important component of herba epimedii, herba epimedii icariin, has a special peak at 1 259 cm(-1) on the FTIR spectra obtained from the methanol extraction, which is consistent with the result obtained by traditional HPLC qualitative analysis. Therefore, this special peak can be used to determine if herba epimedii contains herba epimedii icariin. Furthermore, large variations in the spectrum caused by low content of icariin, weak absorption peaks and noise were successfully removed by applying correlation coefficient comparison and multilevel wavelet analysis, which significantly increased the quality of classification of RBF neural network. This paper creates a framework of fast identification of herba epimedii icariin in raw herba epimedii by FTIR spectra via wavelet analysis and RBF neural network.

Infrared spectra of Papaver somniferum L. and Papaver rhoeas were obtained directly, quickly and accurately by Fourier transform infrared spectroscopy (FTIR)with OMNI sampler. Discrete stationary wavelet transform was used to extrude local region of infrared spectra of Papaver somniferum L. and Papaver rhoeas. The difference of infrared spectra between Papaver somniferum L. and Papaver rhoeas was extruded. Accurate identification rate is improved greatly. One dimensional discrete stationary wavelet transform was implemented to the infrared spectra of Papaver somniferum L. and Papaver rhoeas. The difference between Papaver somniferum L. and Papaver rhoeas was observed at all scales in wavelet domain. Two scales, at which the difference between Papaver somniferum L and Papaver rhoeas is the most obvious, were selected to extract the features of Papaver somniferum L. and Papaver rhoeas. A feature vector including eight feature parameters was constructed. The feature vector was input to RBFNN for training in order to accurately identify Papaver somniferum L. and Papaver rhoeas. In experiment, the authors used one hundred and twenty-eight couples of data of Papaver somniferum L. and Papaver rhoeas (including seventy-eight couples of training samples and fifty couples of testing samples). The experimental results show that it is effective to apply discrete stationary wavelet transform on the basis of FTIR to identify the Papaver somniferum L. and Papaver rhoeas. The accurate identification rate of Papaver somniferum L and Papaver rhoeas is 99.8% and 99.9% respectively.

An improved genetic algorithm was used to implement an automated wavelength selection procedure for use in building multivariate calibration models based on partial least squares regression (PLS). The region selecting by genetic algorithms (R-SGA) was applied in building calibration model of soluble solid content (SSC) of Western pear, and the numbers of latent variables used to build calibration model were further reduced. The Fourier transform near infrared reflectance (FT-NIR) spectra were processed by GA after MSC or SNV, and four PLS calibration models were built by using the optimal combinations of these sub-regions. Meanwhile, the full region selecting PLS (Fr-PLS) models were developed. The R-SGA models variables were 434, 496, 310 and 496, for Early Red Comice, Wujiuxiang, Cascade and Kang Buddha, respectively. Despite the complexity of the spectral data, the R-SGA procedure was found to perform well (RMSEP = 0.428, 0.567 for Early Red Comice and Kang Buddha, respectively), leading to calibration models that significantly outperform those based on full-spectrum analyses (RM-SEP = 0.518, 0.633). The prediction precision of GA-PLS models was similar to that of Fr-PLS for Wujiuxiang and Cascade, with RMSEP of 0.696/0.694 and 0.425/0.421 respectively. This work proved that the R-SGA could find optimal values for several disparate variables associated with the calibration model and that the PLS procedure could be integrated into the objective function driving the optimization.

Infrared (IR) and Raman spectroscopy are emerging biophotonic tools to recognize various diseases. The current review gives an overview of the experimental techniques, data-classification algorithms and applications to assess soft tissues, hard tissues and body fluids. The methodology section presents the principles to combine vibrational spectroscopy with microscopy, lateral information and fiber-optic probes. A crucial step is the classification of spectral data by a variety of algorithms. We discuss unsupervised algorithms such as cluster analysis or principal component analysis and supervised algorithms such as linear discriminant analysis, soft independent modeling of class analogies, artificial neural networks support vector machines, Bayesian classification, partial least-squares regression and ensemble methods. The selected topics include tumors of epithelial tissue, brain tumors, prion diseases, bone diseases, atherosclerosis, kidney stones and gallstones, skin tumors, diabetes and osteoarthritis., (((c) 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim).)

The effect of pH on the structure of methanol was investigated by FT-NIR (Fourier transform near-infrared) spectroscopy and generalized two-dimensional (2D) correlation spectroscopy. pH perturbed 2D correlation spectra are calculated for the spectra in the 5,500-4,000, 7,500-5,500 cm(-1) regions at different pH values. We observed that the stretching of CH(3) was shifted because of the direct interaction of the CH(3) group of methanol with the OH group of water, the change of free OH is more sensitive to pH than the cyclic dimmer and CH.O.

A control sample background-subtraction algorithm was developed for thorough subtraction of background and matrix-related signals in high-resolution, accurate mass liquid chromatography/mass spectrometry (LC/MS) data to reveal ions of interest in an analyte sample. This algorithm checked all ions in the control scans within a specified time window around the analyte scan for potential subtraction of ions found in that analyte scan. Applying this method, chromatographic fluctuations between runs were dealt with and background and matrix-related signals in the sample could be thoroughly subtracted. The effectiveness of this algorithm was demonstrated using four test compounds, clozapine, diclofenac, imipramine, and tacrine, to reveal glutathione (GSH)-trapped reactive metabolites after incubation with human liver microsomes supplemented with GSH (30 microM compound, 45-min incubation). Using this algorithm with a+/-1.0 min control scan time window, a+/-5 ppm mass error tolerance, and appropriate control samples, the GSH-trapped metabolites were revealed as the major peaks in the processed LC/MS profiles. Such profiles allowed for comprehensive and reliable identification of these metabolites without the need for any presumptions regarding their behavior or properties with respect to mass spectrometric detection. The algorithm was shown to provide superior results when compared to several commercially available background-subtraction algorithms. Many of the metabolites detected were doubly charged species which would be difficult to detect with traditional GSH adduct screening techniques, and thus, some of the adducts have not previously been reported in the literature.

Humic acids are part of the stable organic matter fraction in soils and composts. Due to their favorable properties for soils and plants, and their role in carbon sequestration, they are considered a quality criterion of composts. Time-consuming chemical extraction of humic acids and the inherent source of errors require alternative approaches for humic acids quantification. Different measurement techniques in the mid-infrared (MIR: KBr pellet technique) and near-infrared (NIR: fiber probe as well as an integrating sphere with a sample rotator) regions were applied. Partial least squares regression (PLSR) models based on infrared spectra were developed to determine humic acids contents in composts. As the wavenumber regions used (NIR: 6105-5380 cm(-1) and 4360-4220 cm(-1), MIR: 1745-1685 cm(-1) and 1610-1567 cm(-1)) represent different molecular vibrations, the importance of the methylene-group-derived vibrations for the NIR models is discussed. The correlation coefficients obtained for the KBr pellet technique, the NIR fiber probe technique, and the NIR integrating sphere (r = 0.94, 0.93, and 0.94) and the root mean square errors of cross-validation (RMSECV = 2.2% organic dry matter (ODM), 2.5% ODM, and 2.2% ODM) make the models appropriate for application in composting practice.

Application of multivariate curve resolution alternating least squares (MCR-ALS), for the resolution and quantification of different analytes in different type of pharmaceutical and agricultural samples is shown. In particular, MCR-ALS is applied first to the UV spectrophotometric quantitative analysis of mixtures of commercial steroid drugs, and second to the near-infrared (NIR) spectrophotometric quantitative analysis of humidity and protein contents in forage cereal samples. Quantitative results obtained by MCR-ALS are compared to those obtained using the well established partial least squares regression (PLSR) multivariate calibration method.

Modifications of phenylalanine amino acid after its exposure to near-infrared (NIR) radiation have been investigated using ATRFTIR (Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy). The process of amino acid aggregation after its exposure to NIR has been observed. A possible mechanism of amino acid dimer formation has been proposed with the help of theoretical calculations of quantum mechanics (MP2 and B3LYP/6-31 G* level) using the GAUSSIAN 03 package. The usefulness of spectroscopy for biomedical engineering is discussed. ATR-FTIR appears to be a powerful tool for measuring tissue damage in aqueous environments.

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.

Spectroscopic characterization of poplar wood samples with different crystallinity indices, lignin contents, and acetyl contents was performed to determine changes in the biomass spectra and the effects of these changes on the hydrolysis yield. The spectroscopic methods used were X-ray diffraction for determining cellulose crystallinity (CrI), diffuse reflectance infrared (DRIFT) for changes in C-C and C-O bonds, and fluorescence to determine lignin content. Raman spectroscopy was also used to determine its effectiveness in the determination of crystallinity and C-C and C-O bond changes in the biomass as a complement to better-known methods. Changes in spectral characteristics and crystallinity were statistically correlated with enzymatic hydrolysis results to identify and better understand the fundamental features of biomass that influence enzymatic conversion to monomeric sugars. In addition, the different spectroscopic methods were evaluated separately to determine the minimum amount of spectroscopic data needed to obtain accurate predictions. The principal component regression (PCR) model with only the DRIFT data gives the best correlation and prediction for both initial rate of hydrolysis and also the 72-h hydrolysis yield. The factor that most affects both the initial rate and the 72-h conversion is the O-H bond content of the sample, which directly relates to the breakage of structural carbohydrates into smaller molecules.

Fourier transform infrared (FT-IR) spectroscopy was compared directly to independent standard analytical techniques for the routine measurement of carbon monoxide (CO) and nitric oxide (NO) yields from cigarette sidestream smoke. The FT-IR instrument was configured in-line with a nondispersive infrared (NDIR) analyzer for CO analysis and a chemiluminescence (CL) analyzer for NO analysis to monitor the sidestream smoke from a single port of a linear smoking machine. A cold trap was inserted prior to the FT-IR to minimize the levels of vapor phase interferents, such as water. Univariate and multivariate regression analysis were evaluated for the prediction of cigarette yield from time-resolved spectral data at 1, 2, 4 and 8 cm-1 spectral resolution. Regressions were developed using three different spectral ranges including unique rotation-vibration lines, the R-branch, and the entire absorption band. As per standard methods, yields were calculated from the concentration traces generated during the smoke runs for five different cigarettes spanning the expected range of mainstream total particulate matter deliveries. The FT-IR traces for the smoke runs revealed improved temporal resolution yielding analytical information from smoke generated in between puffs. The performance between the validation methods and the FT-IR calibrations was statistically compared. In general, for the determination of CO, the FT-IR calibrations underestimated the yield measured by NDIR by less than 10%. For the NO measurement, the univariate FT-IR calibrations overestimated the NO yield measured by the CL analyzer, whereas the partial least squares (PLS) calibrations showed good agreement. PLS calibrations were developed for both analytes providing no significant difference when compared to the respective standard analytical techniques. Results for sidestream CO and NO yields for Kentucky reference cigarette 1R4F utilizing 8 cm-1 calibrations compared favorably to values reported elsewhere in the literature. Hence, calibration of the FT-IR system at 8 cm-1 spectral resolution clearly revealed the potential of this method, providing enhanced temporal resolution, simultaneous determination of several smoke components, and reduced complexity of the experimental setup in contrast to the standard techniques.

Two-dimensional (2D) correlation spectroscopy has been extensively applied to analyze various vibrational spectroscopic data, especially infrared and Raman. However, when it is applied to real-world experimental data, which often contains various imperfections (such as noise interference, baseline fluctuations, and band-shifting) and highly overlapping bands, many artifacts and misleading features in synchronous and asynchronous maps will emerge, and this will lead to difficulties with interpretation. Therefore, an approach that counters many artifacts and therefore leads to simplified interpretation of 2D correlation analysis is certainly useful. In the present contribution, band-target entropy minimization (BTEM) is employed as a spectral pretreatment to handle many of the artifact problems before the application of 2D correlation analysis. BTEM is employed to elucidate the pure component spectra of mixtures and their corresponding concentration profiles. Two alternate forms of analysis result. In the first, the normally vxv problem is converted to an equivalent nvxnv problem, where n represents the number of species present. In the second, the pure component spectra are transformed into simple distributions, and an equivalent and less computationally intensive nv'xnv' problem results (v'

Infrared spectroscopy is commonly applied to the analysis of small gas-phase molecules. One of the limitations of using Fourier transform infrared (FT-IR) spectroscopy for these applications is the time response of long path length gas cells. Hollow waveguides (HW) that transmit in the mid-infrared spectral range have higher optical efficiencies compared to long path length cells due to smaller cell volumes. This study characterizes a silver coated, 2 mm inner diameter HW for the analysis of carbon monoxide (CO) and nitric oxide (NO) and compares the performance to a 3 m gas cell and traditional gas analyzers. The HW was found to have a CO response time less than the NDIR analyzer and approximately one-tenth of the response time on the FT-IR system equipped with a 3 m gas cell. The utility of the increased response time was demonstrated by measuring CO concentrations in sidestream cigarette smoke at the same temporal resolution as an NDIR analyzer. A 10 to 60% increase in sensitivity using various frequencies for both CO and NO was observed using the HW compared to the 3 m multipass gas cell. However, cost savings for gas-sensing applications can be achieved on a per analyte basis by using FT-IR spectroscopy, especially in combination with a HW gas-sensing module, which is significantly less expensive than a multipass gas cell.

Near-infrared (NIR) spectroscopy is being applied to the solution of problems in many areas of biomedical and pharmaceutical research. In this paper we investigate the use of NIR spectroscopy as an analytical tool to quantify concentrations of urea, creatinine, glucose and oxyhemoglobin (HbO2). Measurements have been made in vitro with a portable spectrometer developed in our labs that consists of a two beam interferometer operating in the range of 800-2300 nm. For the data analysis a pattern recognition philosophy was used with a preprocessing stage and a multi-layer perceptron (MLP) neural network for the measurement stage. Results show that the interferogram signatures of the above compounds are sufficiently strong in that spectral range. Measurements of three different concentrations were possible with mean squared error (MSE) of the order of 10(-6).

Chlamydomonas reinhardtii centrin is a 169-amino acid residue calcium binding protein belonging to the EF-hand protein superfamily. Centrin is associated with the microtubule organizing center (MTOC) in all eukaryotes, and in Chlamydomonas, centrin is a component of the flagellar basal body apparatus. Recombinant full-length centrin, calmodulin, and terminal domain fragments [Ccen-N (residues 1-94) and Ccen-C (residues 99-169)] were used to examine hydrogen-deuterium (H --> D) exchange dynamics using combined attenuated total reflectance (ATR) Fourier transform-infrared (FT-IR) spectroscopy, curve fit, and two-dimensional correlation analysis. Analysis of the Ccen-N and Ccen-C fragments allowed separation of domain specific solvent exchange events and together with analysis of the full-length proteins provides novel insight into domain accessibility to the aqueous environment and the internal dynamics of the protein.

pH dependence of the efficiencies of the flash-induced S-state transitions in the oxygen-evolving center (OEC) was studied by means of Fourier transform infrared (FTIR) difference spectroscopy using photosystem II (PSII) core complexes from the thermophilic cyanobacterium Thermosynechoccocus elongatus. The PSII core complexes dark-adapted at different pHs in the presence of ferricyanide as an electron acceptor were excited by four consecutive saturating laser flashes, and FTIR difference spectra induced by each flash were recorded in the region of 1800-1200 cm(-1). Each difference spectrum was fitted with a linear combination of standard spectra measured at pH 6.0, which represent the spectra upon individual S-state transitions, and the transition efficiencies were estimated from the fitting parameters. It was found that the S1 --> S2 transition probability is independent of pH throughout the pH region of 3.5-9.5, while the S2 --> S3, S3 --> S0, and S0 --> S1 transition probabilities decrease at acidic pH with pK values of 3.6 +/- 0.2, 4.2 +/- 0.3, and 4.7 +/- 0.5, respectively. These findings, i.e., the pH-independent S1 --> S2 transition probability and the pK values for the inhibition in the acidic range of the other three transitions, were in good agreement with recent results obtained by electron paramagnetic resonance measurements for PSII-enriched membranes of spinach [Bernát, G., Morvaridi, F., Feyziyev, Y., and Styring, S. (2002) Biochemistry 41, 5830-5843]. On the basis of this correspondence for quite different types of PSII preparations exhibiting marked difference in the pH dependence of the apparent proton release pattern, it is concluded that the inhibition of the S2 --> S3, S3 --> S0, and S0 --> S1 transitions in the acidic region is an inherent property of the OEC. This feature probably reflects proton release from substrate water in these three transitions. On the other hand, all of the S-state transitions remained generally efficient up to pH 9.5 in the alkaline region, except for a slight decrease of the S3 --> S0 transition probability above pH 8 (pK approximately 10). This observation partly differs from the tendency reported for spinach preparations, suggesting that a mechanism different from that in the acidic region is responsible for the transition efficiencies in the alkaline region.

Time-resolved FTIR difference spectroscopy is a valuable tool to monitor the dynamics of protein-ligand interactions, which selects out of the background absorbance of the whole sample the absorbance bands of the protein groups and of the ligands, which are involved in the protein reaction. The absorbance changes can be monitored with time-resolutions down to nanoseconds and followed then over nine orders of time up to seconds even in membrane proteins with the size of 100,000 Dalton. Here, we will discuss the various experimental setups. We will show new developments for sample cells and how to trigger a reaction within these cells. The kinetic analysis of the data will be discussed. A crucial step in the data analysis is the clear-cut band assignment to chemical groups of the protein and the ligand. This is done either by site directed mutagenesis or by isotopically labeling. Examples for band assignments will be presented in this chapter.

Accurate quantitation has been demonstrated on many different types of mass spectrometer. However, quantitative applications of Fourier transform ion cyclotron resonance mass spectrometry (FTICRMS) have been limited. In this study, the quantitative potential of FTICRMS has been investigated using an exact matching isotope dilution method for the determination of creatinine in serum. Creatinine is an important clinical biomarker and its measurement is used as an assessment of renal function. The quantitation of creatinine was selected because a high-accuracy high-performance liquid chromatography/mass spectrometry (HPLC/MS) determination using a triple quadrupole mass spectrometer has already been successfully developed in-house. Therefore, a direct comparison of the quantitative capability of FTICRMS could be made against an established method. The accuracy of the quantitation of creatinine was found to be equivalent to that obtained using LC/MS. However, the expanded measurement uncertainty (k = 2) was larger, at 6%, when using FTICRMS compared with 1% when using HPLC/MS with the triple quadrupole mass spectrometer., (Copyright (c) 2005 John Wiley & Sons, Ltd.)