FTIR characterization of Candida species: a study on some reference strains and pathogenic C. albicans isolates from HIV+ patients

ATR–FTIR has been used together with principal components analysis to study some reference strains belonging to five different Candida species and 20 Candida albicans isolates from three HIV⁺ patients under different fluconazole regimens. The five reference strains were easily differentiated by using first derivative spectra of the carbohydrate region comprising the spectral range 900–1200 cm⁻¹. For the C. albicans clinical isolates, classification obtained by Fourier-transform infrared (FTIR) spectroscopy was compared with data from the genotypic method, PFGE. Results show a good correlation between the two methods for one patient who presented a partially conserved immune system with no previous fluconazole treatment, where all C. albicans strains isolated were susceptible to this antifungal agent. Divergences in the two methods appear for the two immuno-compromised patients receiving long-term fluconazole treatment. It is very probable that such iterative antifungal treatment can cause strong alterations in the yeast cell wall membrane and that the mismatch of the two techniques highlights the complex situation of strain-level identification and differentiation of clinical isolates originating from patients under prolonged treatment.     

Fourier transform infrared imaging and microscopy studies of Pinus radiata pulps regarding the simultaneous saccharification and fermentation process

The distribution and chemical patterns of lignocellulosic components at microscopic scale and their effect on the simultaneous saccharification and fermentation process (SSF) in the production of bioethanol from Pinus radiata pulps were analyzed by the application of diverse microscopical techniques, including scanning electronic microscopy (SEM), confocal laser scanning microscopy (CLSM) and attenuated total reflectance (ATR) – Fourier transform infrared microspectroscopy. This last technique was accompanied with multivariate methods, including principal component analysis (PCA) and multivariate curve resolution with alternating least squares (MCR-ALS) to evaluate the distribution patterns and to generate pure spectra of the lignocellulosic components of fibers. The results indicate that the information obtained by the techniques is complementary (ultrastructure, confocality and chemical characterization) and that the distribution of components affects the SSF yield, identifying lignin coalescence droplets as a characteristic factor to increase the SSF yield. Therefore, multivariate analysis of the infrared spectra enabled the in situ identification of the cellulose, lignin and lignin-carbohydrates arrangements. These techniques could be used to investigate the lignocellulosic components distribution and consequently their recalcitrance in many applications where minimal sample manipulation and microscale chemical information is required.     

Compositional changes between free coatings and surface coating layers during mechanochemical process: An IR-based chemometric analysis

By mid-IR analysis, mixtures of stearic acid, a paraffinic compound and a fluorinated compound were analyzed using attenuated total reflection (ATR) spectroscopy and reflection-absorption infrared spectroscopy (RAIRS). The precision of the partial least square (PLS) models created for each of these two techniques were compared in order to work out a quality control process of the mixtures which are involved in mechanochemical coating technology. We obtained a good relationship between spectral data and concentration with ATR spectroscopy. However, RAIRS spectroscopy allows to observe the affinity of long-chain organic acids for steel surfaces, and to determine the actual composition of coatings.     

Classification of Brazilian Coffee Using Near-Infrared Spectroscopy and Multivariate Calibration

This work describes the use of near infrared spectroscopy (NIRS) and chemometric techniques calibration for the classification of coffee samples from different lots and producers acquired in supermarkets and roasting industries in some Brazilian cities. Seventy-three samples of finely ground roasted coffee were acquired in the market and 91 samples of roasted ground Arabica beans were analyzed in the full NIR spectral range (800–2500 nm) using a diffuse reflectance accessory coupled to an MB160 Bomem spectrophotometer. Two classification models were constructed: Soft Independent Modeling Class Analogy (SIMCA) and PLS Discriminant Analysis (PLS-DA). All findings reveal that NIR spectroscopy, coupled with either SIMCA or PLS-DA multivariate models, can be a useful tool to differentiate roasted coffee grains and to replace sensory tests.     

Back‐propagation artificial neural network and attenuated total reflectance‐Fourier transform infrared spectroscopy for diagnosis of basal cell carcinoma by blood sample analysis

A diagnostic method for the cancer, based on investigation of infrared spectra of blood samples, has been developed. The two‐layer modified principal component feed forward back‐propagation artificial neural network (BP‐ANN) was used to classify the attenuated total reflectance‐Fourier transform infrared (ATR‐FTIR) spectra of blood samples obtained from healthy people and those with basal cell carcinoma (BCC). Results showed 98.33% of accuracy, in comparison with the current clinical methods. In the first step, 20 blood samples (10 normal and 10 cancer cases) were applied to construct the calibration model. Spectroscopic studies were performed in 900–1800 cm⁻¹ spectral region with 3.85 cm⁻¹ data space. In order to modify the capability of ANN in prediction of test samples, two different algorithms were applied. The obtained results confirmed the compatibility of the proposed network with the architecture of 20‐8‐2 (input‐hidden‐output) with the pattern model. It was concluded that analysis of blood samples by ATR‐FTIR spectroscopy and ANN chemometric technique would be a reliable approach for detection of BCC. Copyright © 2009 John Wiley & Sons, Ltd.     

Classification and quantification analysis of Radix scutellariae from different origins with near infrared diffuse reflection spectroscopy

A rapid near infrared spectroscopy analysis method was developed for the geographical origin discrimination and content determination of Radix scutellariae, a kind of Traditional Chinese Medicine (TCM). 81 R. scutellariae samples from six different origins were analyzed with HPLC-UV as reference method. The NIR spectra were collected in integrating-sphere diffused reflection mode and processed with different spectra pretreated methods. Discriminant analysis (DA) and discriminant partial least squares (DPLS) were applied to classify the geographical origins of those samples, and the latter had a better predictive ability with 100% accuracy after two exceptional samples eliminated from the calibration set. For the quantitative calibration, the samples were divided into calibration set and validation set by Kennard-Stone algorithm. The models of baicalin, wogonoside, baicalein, wogonin were established with partial least squares (PLS) algorithm and the optimal principal component (PC) numbers were selected with Leave-One-Out (LOO) cross-validation. The established models were evaluated with the root mean square error of prediction (RMSEP) and corresponding correlation coefficients. The correlation coefficients of all the four calibration models are above 0.920, and the RMSEPs of baicalin, wogonoside, baicalein and wogonin are 0.752%, 0.094%, 0.418% and 0.139%, respectively. This research indicated that the NIR diffuse reflection spectroscopy could be used for the rapid analysis of R. scutellariae, which is beneficial to the quality control of this raw material in TCM pharmaceutical factory, and will also help to solve analogous problems.     

Comparison of two vibrational procedures for the direct determination of mancozeb in agrochemicals

The direct determination of mancozeb in agrochemicals has been made by diamond attenuated total reflectance (ATR) Fourier transform infrared spectroscopy in the middle range (DATR-MIR) and diffuse reflectance infrared Fourier transform spectroscopy in the near range (DR-NIR) methods using in both cases a previous identification of the samples using a dendrographic classification and an appropriate partial least squares (PLS) calibration established from a set of nine external standards and optimized for each type of sample. It was analyzed a heterogeneous population of 11 samples obtained from the Spanish market, containing different co-formulated products, such as fosetyl-Al, copper oxychloride, metalaxyl or cymoxanil. High performance liquid chromatography (HPLC) was used as reference method for validation of both vibrational strategies. The close agreement between values found for both, DATR-MIR and DR-NIR methods, and reference HPLC values indicates the accuracy and reliability of the proposed techniques for the direct determination of mancozeb in commercially available formulations.     

Coffee varietal differentiation based on near infrared spectroscopy

Near infrared spectroscopy (NIRS) was used to discriminate between arabica and robusta pure coffee varieties and blends of varied varietal composition. Direct orthogonal signal correction (DOSC) pre-processing method was applied on a set of 191 roasted coffee NIR spectra from both pure varieties and blends varying the final robusta content from 0 to 60% (w/w) in order to remove information unrelated to the actual varietal composition of samples. The corrected NIR spectra, as well as raw NIR spectra, were used to develop separate classification models using the potential functions method as class-modelling technique, exploring several options more or less restrictive according to the final number of considered categories. All constructed classification models were compared to evaluate their respective qualities and to show the suitability of applying DOSC method as pre-processing step for developing improved classification models for coffee varietal identification purposes.     

A comparative study of diesel analysis by FTIR, FTNIR and FT-Raman spectroscopy using PLS and artificial neural network analysis

Diesel properties determined by ASTM reference methods as cetane index, density, viscosity, distillation temperatures at 50% (T50) and 85% (T85) recovery, and the total sulfur content (%, w/w) were modeled by FTIR-ATR, FTNIR, and FT-Raman spectroscopy using partial last square regression (PLS) and artificial neural network (ANN) spectral analysis. In the PLS models, 45 diesel samples were used in the training group and the other 45 samples were used in the validation. In the ANN analysis a modular feedforward network was used. Sixty diesel samples were used in the neural network training and other 30 samples were used in the validation. Two different ATR configurations were compared in the FTIR, a conventional (ATR1) and an immersion (ATR2) cell. The ATR1 cell presented the best results, with smaller prediction errors (root mean square error of prediction, RMSEP). The comparison of the three PLS models (FTIR-ATR1, FTNIR, and FT-Raman) shows that reasonable values of R² and RMSEP were obtained by the FTIR-ATR1 and FTNIR models in the evaluation of density, viscosity, and T50. The PLS/FT-Raman models presented reasonable results only for the T50 property. None of the techniques was able to generate suitable PLS calibration models for the determination of sulfur content. The ANN/FT-Raman models presented the best performances, with all models presenting R²-values above 85% some of them with RMSEP values significantly smaller than those obtained with FTIR-ATR and FTNIR. The ANN/FT-Raman and ANN/FTIR-ATR1 models were able to estimate the total sulfur content of diesel with 0.01% (w/w) accuracy.     

Application of ATR–FTIR spectroscopy to the analysis of tannins in historic leathers: The case study of the upholstery from the 19th century Portuguese Royal Train

Tanning materials of historic leather samples collected from the 19th century Portuguese Royal train were analyzed by attenuated total reflection–Fourier transform infrared (ATR–FTIR) spectroscopy. Studied leathers were visually identified as morocco leathers, one of the most valued types of vegetable tanned leathers. In technical and historic literature, morocco leathers are described as a distinctive type of vegetable tanned leather, with a typical grain surface pattern, made from goat skins and sumac (Rhus coriaria) leaves.ATR–FTIR spectra of the Royal train leathers were investigated and compared with 10 reference tanning materials obtained from different plants in use in the 19th century, here described. Two different types of vegetable tanned leathers were identified. The obtained spectra allowed to confirm the presence of morocco leathers as well as to detect a different type of vegetable tanned leather, probably applied as a restoration material in a past intervention. This study shows the usefulness of ATR–FTIR to distinguish different types of historic leathers based in the spectroscopic characteristic IR bands of vegetable tannins used for leather production, which can be of great assistance for conservation condition assessments.     

Adulteration of diesel/biodiesel blends by vegetable oil as determined by Fourier transform (FT) near infrared spectrometry and FT-Raman spectroscopy

In this work it has been shown that the routine ASTM methods (ASTM 4052, ASTM D 445, ASTM D 4737, ASTM D 93, and ASTM D 86) recommended by the ANP (the Brazilian National Agency for Petroleum, Natural Gas and Biofuels) to determine the quality of diesel/biodiesel blends are not suitable to prevent the adulteration of B2 or B5 blends with vegetable oils. Considering the previous and actual problems with fuel adulterations in Brazil, we have investigated the application of vibrational spectroscopy (Fourier transform (FT) near infrared spectrometry and FT-Raman) to identify adulterations of B2 and B5 blends with vegetable oils. Partial least square regression (PLS), principal component regression (PCR), and artificial neural network (ANN) calibration models were designed and their relative performances were evaluated by external validation using the F-test. The PCR, PLS, and ANN calibration models based on the Fourier transform (FT) near infrared spectrometry and FT-Raman spectroscopy were designed using 120 samples. Other 62 samples were used in the validation and external validation, for a total of 182 samples. The results have shown that among the designed calibration models, the ANN/FT-Raman presented the best accuracy (0.028%, w/w) for samples used in the external validation.     

Origin Identification of Hungarian Honey Using Melissopalynology,Physicochemical Analysis,and Near Infrared Spectroscopy

The objective of the study was to check the authenticity of Hungarian honey using physicochemical analysis, near infrared spectroscopy, and melissopalynology. In the study, 87 samples from different botanical origins such as acacia, bastard indigo, rape, sunflower, linden, honeydew, milkweed, and sweet chestnut were collected. The samples were analyzed by physicochemical methods (pH, electrical conductivity, and moisture), melissopalynology (300 pollen grains counted), and near infrared spectroscopy (NIRS:740–1700 nm). During the evaluation of the data PCA-LDA models were built for the classification of different botanical and geographical origins, using the methods separately, and in combination (low-level data fusion). PC number optimization and external validation were applied for all the models. Botanical origin classification models were >90% and >55% accurate in the case of the pollen and NIR methods. Improved results were obtained with the combination of the physicochemical, melissopalynology, and NIRS techniques, which provided >99% and >81% accuracy for botanical and geographical origin classification models, respectively. The combination of these methods could be a promising tool for origin identification of honey.     

Excitation-emission-lifetime analysis of multicomponent systems—I. Principal component factor analysis

This paper defines methods and notation for the application of principal component factor analysis (PCFA) to porphyrin excitation-emission-lifetime data obtained from luminescence spectroscopy. We are able to determine non-exponential decay kinetics and the spectra of individual chemical species in impure or mixed samples with this technique. A data array of intensities, I(excitation wavelengths) × J(emission wavelengths) × K(times), is reduced to a P × P × P array by projection onto the principal components. The spectra of the emitting species are obtained by iteration from an initial guess, which is made by reference to the projections of the observed spectra onto a plane in the three-dimensional principal component vector space. It is shown that weighting the data with the variances speeds convergence of the iteration process.     

Forensic classification of ballpoint pen inks using high performance liquid chromatography and infrared spectroscopy with principal components analysis and linear discriminant analysis

Several varieties of blue ballpoint pen inks were analyzed by high performance liquid chromatography (HPLC) and infrared spectroscopy (IR). The chromatographic data extracted at four wavelengths (254, 279, 370 and 400 nm) was analyzed individually and at a combination of these wavelengths by the soft independent modeling of class analogies (SIMCA) technique using principal components analysis (PCA) to estimate the separation between the pen samples. Linear discriminant analysis (LDA) measured the probability with which an observation could be assigned to a pen class. The best resolution was obtained by HPLC using data from all four wavelengths together, differentiating 96.4% pen pairs successfully using PCA and 97.9% pen samples by LDA. PCA separated 60.7% of the pen pairs and LDA provided a correct classification of 62.5% of the pens analyzed by IR. The results of this study indicate that HPLC coupled with chemometrics provided a better discrimination of ballpoint pen inks compared to IR. The need to develop a suitable IR method for analysing blue ballpoint pen inks has been emphasized and it is hoped that the development of such a method would indeed provide a valuable tool for the non-destructive analysis of blue ballpoint pen ink samples for forensic purposes.     

Discrimination of industrial products by on-line near infrared spectroscopy with an improved dendrogram

Near infrared(NIR) spectroscopy technique has shown great power and gained wide acceptance for analyzing complicated samples.The present work is to distinguish different brands of tobacco products by using on-line NIR spectroscopy and pattern recognition techniques.Moreover,since each brand contains a large number of samples,an improved dendrogram was proposed to show the classification of different brands.The results suggest that NIR spectroscopy combined with principal component analysis (PCA) and hierarchical cluster analysis(HCA) performs well in discrimination of the different brands,and the improved dendrogram could provide more information about the difference of the brands.     

Varietal discrimination of Australian wines by means of mid-infrared spectroscopy and multivariate analysis

This study outlines the use of mid-infrared (MIR) spectroscopy combined with principal component analysis (PCA) and linear discriminant analysis (LDA) for the varietal classification of commercial red and white table wines. Three red varieties (Cabernet Sauvignon, Shiraz and Merlot) and four white varieties (Chardonnay, Riesling, Sauvignon Blanc and Viognier) were sourced from different wine regions in Australia. Wine samples were scanned in transmission on a FOSS WineScan FT 120 from wave numbers 926 to 5012 cm⁻¹. All samples were sourced from the 2006 vintage and had not been blended with any other variety or wine from other regions. Spectral data were reduced to a small number of principal components (PCs) and LDA was then performed to successfully separate the wines into the different varieties. To test the robustness of the LDA models developed for the red wines, a set of red wines scanned in 2005 were used. Correct classification of over 95% was achieved for the validation set.     

Analysis of berberine and total alkaloid content in cortex phellodendri by near infrared spectroscopy (NIRS) compared with high-performance liquid chromatography coupled with ultra-visible spectrometric detection

This paper developed a rapid method using near infrared spectroscopy (NIRS) to differentiate two species of Cortex Phellodendri (CP), Cortex Phellodendri Chinensis (PCS) and Cortex Phellodendri Amurensis (PAR), and to predict quantitatively the content of berberine and total alkaloid content in all Cortex Phellodendri samples. Three alkaloids, berberine, jatrorrhizine and palmatine were analyzed simultaneously with a Thermo ODS Hypersil column by gradient elution with a new mobile phase under high-performance liquid chromatography-diode array detection (HPLC-DAD). Berberine content determined by HPLC-DAD was exploited as a critical parameter for successful discrimination between them. Multiplicative scatter correction (MSC), second derivative and Savitsky-Golay (S.G.) were utilized together to correct the scattering effect and eliminate the baseline shift in all near infrared diffuse reflectance spectra as well as to enhance spectral features in order to give a better correlation with the results obtained by HPLC-DAD. With the use of principal component analysis (PCA), samples datasets were separated successfully into two different clusters corresponding to two species. Furthermore, a partial least squares (PLS) regression method was built on the correlation model. The results showed that the correlation coefficients of the prediction models were R = 0.996 for the berberine and R = 0.994 for total alkaloid content. The influences of water absorption bands present in the NIR spectra on the models were also investigated in order to explore the practicability of NIRS in routine use. The outcome showed that NIRS possibly acts as routine screening in the quality control of Chinese herbal medicine.     

Surface Modification of Polypropylene Non-Woven Fabrics by Atmospheric-Pressure Plasma Activation Followed by Acrylic Acid Grafting

Polypropylene (PP) non-woven fabrics have been activated by an atmospheric-pressure plasma treatment using surface dielectric barrier discharge in N₂ and ambient air. Subsequently, the plasma activated samples were grafted using catalyst-free water solution of acrylic acid. Surface properties of the activated and polyacrylic acid post-plasma grafted non-woven were characterised by scanning electron microscopy, Fourier transform infrared spectroscopy, electron spin resonance spectroscopy, surface energy and dyeability measurements. The grafted non-woven exhibit improved water transport and dyeing properties. The plasma activation in nitrogen plasma gas was more efficient than in air.     

ATR and transmission analysis of pigments by means of far infrared spectroscopy

In the field of FTIR spectroscopy, the far infrared (FIR) spectral region has been so far less investigated than the mid-infrared (MIR), even though it presents great advantages in the characterization of those inorganic compounds, which are inactive in the MIR, such as some art pigments, corrosion products, etc. Furthermore, FIR spectroscopy is complementary to Raman spectroscopy if the fluorescence effects caused by the latter analytical technique are considered. In this paper, ATR in the FIR region is proposed as an alternative method to transmission for the analyses of pigments. This methodology was selected in order to reduce the sample amount needed for analysis, which is a must when examining cultural heritage materials. A selection of pigments have been analyzed in both ATR and transmission mode, and the resulting spectra were compared with each other. To better perform this comparison, an evaluation of the possible effect induced by the thermal treatment needed for the preparation of the polyethylene pellets on the transmission spectra of the samples has been carried out. Therefore, pigments have been analyzed in ATR mode before and after heating them at the same temperature employed for the polyethylene pellet preparation. The results showed that while the heating treatment causes only small changes in the intensity of some bands, the ATR spectra were characterized by differences in both intensity and band shifts towards lower frequencies if compared with those recorded in transmission mode. All pigments' transmission and ATR spectra are presented and discussed, and the ATR method was validated on a real case study. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Discrimination and characterization of the volatile organic compounds of Acori tatarinowii rhizoma based on headspace-gas chromatography-ion mobility spectrometry and headspace solid phase microextraction-gas chromatography-mass spectrometry

Acori tatarinowii rhizome (ATR) is a Traditional Chinese Medicine (TCM), which has multiple effects, such as neuroprotective activity, antidepressant and other activity. However, the widespread cultivation of ATR has led to it varying quality. Therefore, it is important to find a method to quickly identify the components of ATR. Headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) and headspace solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC–MS) were applied to analyze and characterize the volatile organic compounds (VOCs) of ATR. 33 VOCs were identified by HS-GC-IMS and 95 VOCs were identified by HS-SPME-GC–MS from 15 batches of ATR. Then, quantification of estragole, methyleugenol, γ-asarone, β-asarone and asarone by gas chromatography-mass spectrometry (GC–MS). The fingerprint of HS-GC-IMS and the heatmap of HS-SPME-GC–MS were established. Which compared differential components of ATR. In addition, principal component analysis (PCA) was performed on the results of both instruments. The VOCs in the ATR were significantly correlated with β-asarone and asarone by PatternHunter analysis. It assisted HS-GC-IMS determine ATR quality. It is the first report regarding the method development of HS-GC-IMS and HS-SPME-GC–MS that targets the VOCs characterization of ATR, and the findings obtained would benefit the quality control and distinguish the complex analytical objects of ATR.