Quantitative analysis of red wine tannins using Fourier-transform mid-infrared spectrometry

Tannin content and composition are critical quality components of red wines. No spectroscopic method assessing these phenols in wine has been described so far. We report here a new method using Fourier transform mid-infrared (FT-MIR) spectroscopy and chemometric techniques for the quantitative analysis of red wine tannins. Calibration models were developed using protein precipitation and phloroglucinolysis as analytical reference methods. After spectra preprocessing, six different predictive partial least-squares (PLS) models were evaluated, including the use of interval selection procedures such as iPLS and CSMWPLS. PLS regression with full-range (650-4000 cm(-1)), second derivative of the spectra and phloroglucinolysis as the reference method gave the most accurate determination for tannin concentration (RMSEC = 2.6%, RMSEP = 9.4%, r = 0.995). The prediction of the mean degree of polymerization (mDP) of the tannins also gave a reasonable prediction (RMSEC = 6.7%, RMSEP = 10.3%, r = 0.958). These results represent the first step in the development of a spectroscopic methodology for the quantification of several phenolic compounds that are critical for wine quality.     

Rapid determination of lignin content of straw using fourier transform mid-infrared spectroscopy

To determine lignin content in triticale and wheat straws, calibration models were built using Fourier transform mid-infrared spectroscopy combined with partial least-squares regression. The best model for triticale and wheat straws was built using averaged spectra with raw spectrum in spectrum format and constant in path length as spectral pretreatments. The values of r(2), root-mean-square error of prediction (RMSEP), and residual predictive deviation (RPD) for the triticale straw model were 0.935, 0.305, and 3.89, respectively. The r(2), RMSEP, and RPD values for the wheat straw model were 0.985, 0.163, and 8.50, respectively. Both models showed good predictive ability. A model built using both triticale and wheat straws indicated that the values of r(2), RMSEP, and RPD were 0.952, 0.27, and 4.63, respectively. This model also showed good predictive ability and could predict lignin contents in triticale and wheat straws with the same high accuracy.     

Rapid method for the discrimination of red wine cultivars based on mid-infrared spectroscopy of phenolic wine extracts

Mid-infrared spectroscopy and UV-vis spectroscopy combined with multivariate data analysis have been applied for the discrimination of Austrian red wines, including the cultivars Cabernet Sauvignon, Merlot, Pinot Noir, Blaufr?nkisch (Lemberger), St. Laurent, and Zweigelt. Both authentic wines and their phenolic extracts were investigated by attenuated total reflectance (ATR)-mid-infrared spectroscopy. Phenolic extracts were also investigated by UV-vis spectroscopy. The wine extracts were obtained by solid-phase extraction with C-18 columns and elution by methanol containing 0.01% hydrochloric acid. Hierarchical cluster analysis was performed with mid-infrared spectra of both wines and extracts, as well as with UV-vis spectra of the phenolic extracts. Data processing involved vector normalization and derivation of the spectra. Due to varying concentrations of main components including sugar and organic acids, satisfactory classification of untreated wines was not achieved. However, when using mid-infrared spectra of the phenolic extracts, almost complete discrimination of all cultivars investigated was achieved. The use of UV-vis spectroscopy for cultivar discrimination was found to be limited to the authentication of the Burgundy species Pinot Noir. In addition, soft independent modeling of class analogy was applied to the mid-infrared spectra of the extracts. It was possible to establish class models for five different wine cultivars and to classify test samples correctly.     

Prediction of relative tissue proportions in wheat mill streams by fourier transform mid-infrared spectroscopy

Fourier-transform mid-infrared (FTIR) spectroscopy was investigated as a method to quantify the relative wheat grain tissue proportion in milling fractions. Spectra were acquired with a FTIR spectrometer equipped with an attenuated total reflectance device on ground samples, and the relative tissue proportion was determined according to the biochemical marker methodology as the reference method. Partial least-squares models were developed independently to predict the amount of outer pericarp, aleurone layer, starchy endosperm, and an intermediate layer (made up of inner pericarp plus seed coat plus nucellar epidermis). Good quality of prediction was obtained regardless of the target tissue. The standard errors of prediction obtained for the outer pericarp, intermediate layer, aleurone layer, and starchy endosperm quantification were, respectively, 3.4, 1.3, 3.4, and 4.6%.     

Comparison of diffuse reflectance fourier transform mid-infrared and near-infrared spectroscopy with grating-based near-infrared for the determination of fatty acids in forages

Diffuse reflectance Fourier transform mid infrared (FTMIR) and near-infrared spectroscopy (FTNIR) were compared to scanning monochromator-grating-based near-infrared spectroscopy (SMNIR), for their ability to quantify fatty acids (FA) in forages. A total of 182 samples from thirteen different forage cultivars and three different harvest times were analyzed. Three calibration analyses were conducted for lauric (C12:0), myristic (C14:0), palmitic (C16:0), stearic (C18:0), palmitoleic (C16:1), oleic (C18:1), linoleic (C18:2), and alpha-linolenic (C18:3) acids. When all samples were used in a one-out partial least squares (PLS) calibration, the average R (2) were FTNIR (0.95) > SMNIR (0.94) > FTMIR (0.91). Constituents C18:2 and C16:0 had among the highest R (2) regardless of the spectroscopic method used. The FTNIR did better for C12:0, C14:0, and C18:3. The SMNIR did better for C16:0, C16:1, C18:0, C18:1, and C18:2. A second set of calibrations developed with half of the samples as the calibration set and the rest as the validation set showed that all the methods produce acceptable calibrations, with calibration R (2) above 0.9 for most constituents. However, the SMNIR had a better average calibration relative error than the FTNIR, which was slightly better than the FTMIR. A third set of calibration equations developed using 100 random PLS runs with the 182 samples split randomly also shows that the three spectral methods are satisfactory for predicting FA. It is not clear whether any of the spectral methods is distinctly better than another. Calibration R (2) and validation R (2) were higher for most FA with the SMNIR than the FTMIR and FTNIR.     

Evaluation of the quality of frozen minced red hake: use of fourier transform near-infrared spectroscopy.

The quality of frozen minced red hake was assessed by FTNIR spectroscopy in transmission mode. The region 1530-1866 nm was best correlated to dimethylamine (DMA) concentration, which is an accepted quality index for frozen gadoid fish. Partial least-squares (PLS) analysis showed that this technique predicts the DMA content sufficiently well for quality assurance (SD/SEP > 3). The PLS calibration was equally successful when five narrow spectral regions were chosen instead of the continuous spectrum, indicating that a cheap filter instrument may be developed for industrial use.     

Rapid determination of carbohydrates, ash, and extractives contents of straw using attenuated total reflectance fourier transform mid-infrared spectroscopy

Analysis of the chemical components of lignocellulosic biomass is essential to understanding its potential for utilization. Mid-infrared spectroscopy and partial least-squares regression were used for rapid measurement of the carbohydrate (total glycans; glucan; xylan; galactan; arabinan; mannan), ash, and extractives content of triticale and wheat straws. Calibration models for total glycans, glucan, and extractives showed good and excellent predictive performance on the basis of slope, r2, RPD, and R/SEP criteria. The xylan model showed good and acceptable predictive performance. However, the ash model was evaluated as providing only approximate quantification and screening. The models for galactan, arabinan, and mannan indicated poor and insufficient prediction for application. Most models could predict both triticale and wheat straw samples with the same degree of accuracy. Mid-infrared spectroscopic techniques coupled with partial least-squares regression can be used for rapid prediction of total glycans, glucan, xylan, and extractives in triticale and wheat straw samples.     

Use of fourier transform near-infrared reflectance spectroscopy for rapid quantification of castor bean meal in a selection of flour-based products

Methodology was developed and evaluated for the rapid detection of castor bean meal (CBM) containing the toxic protein ricin by using Fourier transform near-infrared (FT-NIR) spectroscopy and multivariate techniques. The method is intended to be a prototype to develop a more general approach to detect food tampering. Measurements were made on an FT-NIR system using a diffuse reflection-integrating sphere. Flours spiked with caffeine, crystalline sugar, and corn meal, 1-20% w/w, were used as test articles to evaluate the methodologies. Food matrices (bleached flour, wheat flour, and blueberry pancake mix) spiked with CBM (0.5-8% w/w) were analyzed. Multiplicative scatter correction transformed partial least-squares regression models, using a specific NIR spectral region, predicted CBM contamination in foods with a standard error of cross-validation of <0.6% and a coefficient of determination (R(2)) of >94%. Models discriminated between flour samples contaminated with CBM and other protein sources (egg white, soybean meal, tofu, and infant formula). CBM had loading spectra with bands characteristic of amide groups (4880 and 4555 cm(-1)) and lipids (5800, 5685, 4340, and 4261 cm(-1)).     

Development of a rapid "fingerprinting" system for wine authenticity by mid-infrared spectroscopy

This paper reports on the development of a rapid and simple method for red wine authenticity confirmation during transport and processing; namely, a wine "fingerprinting" system. When wine is transported between two sites, a sample is taken and a mid-infrared (MIR) spectrum is obtained. One hundred sixty-one (n = 161) samples of three main red wine varieties grown in Australia, Shiraz, Cabernet Sauvignon, and Merlot, were collected from six commercial wineries across Australia and scanned in transmission on two MIR spectrophotometers located at The Hardy Wine Company's main site at Reynella, South Australia (Foss WineScan FT 120) (926-5012 cm-1). A similarity index (SI) method was used as a tool to classify wine samples on the basis of their spectral data. The results showed that high rates of classification were obtained when wine samples scanned in different instruments were analyzed. The SI has been proven to provide an acceptable measurement for authentication of red wine integrity during transportation. In five of the six winery data sets, the SI correctly classified 98% of the wines. It was also observed that less than 1% of wines were misclassified between the different wineries investigated. Further studies are needed in order to test the applicability of the SI in a commercial situation and to evaluate its potential as a rapid quality control tool for routine use to authenticate wine samples during transport.     

Identification of agricultural Mediterranean soils using mid-infrared photoacoustic spectroscopy

This study investigated the use of Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS) for rapid identification of agricultural soil samples. The PAS spectra of 166 air-dried samples belonging to five Mediterranean soil types most common in Israeli agriculture were recorded. The various soil types exhibited distinctive mid-IR bands, especially around the 2900–3700 cm^? 1, 2500–2550 cm^? 1, 1800–2050 cm^? 1 and 900–1600 cm^? 1 regions. Following smoothing and normalization of the spectra, principal component analysis (PCA) was used to reduce the dimensionality of the data and the PCA scores were used in classifiers based either on linear discriminant analysis or on probabilistic neural networks. The two classifiers based on four PCA scores yielded very similar results and correctly identified over 96% of the 77 validation samples. Comparison with the attenuated total reflectance (ATR) spectra of similar soils used in a previous study showed that the PAS spectra contained more information than the ATR ones, both in terms of the number of soil-specific bands and in terms of the bands' distinctiveness. The results clearly show that FTIR-PAS can be used for rapid soil identification and the abundance of information in the PAS spectra indicates that this technique could be further developed to assess important soil features.     

Characterization of essential oils from lamiaceae species by fourier transform Raman spectroscopy

The Fourier transform Raman (FT-Raman) spectra of pure terpenes and essential oils obtained by hydrodistillation of some Lamiaceae species, are presented. This study shows that principal components of an essential oil can be recognized by FT-Raman. Components predicted by FT-Raman spectrum of an essential oil correlate well with those found as major constituents by GC-MS. In this way the basic chemical character of an essential oil can be recognized. The results demonstrate that certain Raman intensities can be correlated to specific terpenes and therefore FT-Raman can discriminate between the essential oils of which main components belong to different classes of compounds.     

Prediction of Japanese green tea ranking by fourier transform near-infrared reflectance spectroscopy

A rapid and easy determination method of green tea's quality was developed by using Fourier transform near-infrared (FT-NIR) reflectance spectroscopy and metabolomics techniques. The method is applied to an online measurement and an online prediction of green tea's quality. FT-NIR was employed to measure green tea metabolites' alteration affected by green tea varieties and manufacturing processes. A set of ranked green tea samples from a Japanese commercial tea contest was analyzed to create a reliable quality-prediction model. As multivariate analyses, principal component analysis (PCA) and partial least-squares projections to latent structures (PLS) were used. It was indicated that the wavenumber region from 5500 to 5200 cm(-1) had high correlation with the quality of the tea. In this study, a reliable quality-prediction model of green tea has been achieved.     

Classification of modified starches by fourier transform infrared spectroscopy using support vector machines

The aim of this study was to compare the performance of different supervised discrimination methods based on IR data for the classification of starches according to the type of chemical modification undergone. The goal of the supervised classification methods is to develop classification rules. Representative samples of each group (known beforehand) were available, from which the relevant characteristics (chemical modification) were known. On the basis of a training data set, classification rules are determined, which can then be applied to classify new (unknown) samples.     

Differentiation of carbohydrate gums and mixtures using fourier transform infrared spectroscopy and chemometrics

Guar gum, a nonionic galactomannan, is used as an economical thickener and stabilizer in the food industry and is often combined with xanthan, locust bean gum (LBG), or carboxymethylcellulose (CMC) to promote synergistic changes in viscosity or gelling behavior via intermolecular interactions; however, the adulteration of LBG with guar gum is a well-known industrial problem. The ability to identify the purity of gums and concentrations of individual gums in mixtures would be advantageous for quality control in the food industry. Fourier transform infrared spectroscopy (FTIR) methods are rapid and require minimum sample preparation. The objectives of this study were to evaluate the ability of FTIR techniques to (1) differentiate LBG with a variety of mannose/galactose (M/G) ratios, (2) differentiate guar, LBG, tara, and fenugreek gums, (3) differentiate pure guar gum from guar gum mixed with LBG, xanthan gum, or CMC, (4) quantify LBG, xanthan gum, and CMC in guar gum, and (5) quantify guar gum in LBG. Two FTIR methods were used: diffuse reflectance (DRIFT) on powdered gum samples added to KBr at 5%, w/w, and attenuated total reflectance (ATR) on 1%, w/w, gum solutions. Spectra were collected and then analyzed by multivariate statistical procedures (chemometrics). The DRIFT method provided better discrimination and quantitative results than the ATR method. Canonical variate analysis (CVA) of DRIFT spectra (1200-700 cm(-1)) was able to classify LBG with various M/G ratios, pure galactomannans, and pure versus mixtures of gums with 100% accuracy. Quantification of an individual gum in gum mixtures (0.5-15%, w/w) was possible using partial least-squares (PLS) analysis of DRIFT spectra with R2 > 0.93 and using this approach for quantifying guar gum added to LBG resulted in an R2 > 0.99, RMSEC = 0.29, and RMSEP = 3.31. Therefore, the DRIFT FTIR method could be a useful analytical tool for quality control of select gums and gum mixtures used in the food industry.     

Detection of sugar adulterants in apple juice using fourier transform infrared spectroscopy and chemometrics

Fourier transform infrared spectroscopy and attenuated total reflection sampling have been used to detect adulteration of single strength apple juice samples. The sample set comprised 224 authentic apple juices and 480 adulterated samples. Adulterants used included partially inverted cane syrup (PICS), beet sucrose (BS), high fructose corn syrup (HFCS), and a synthetic solution of fructose, glucose, and sucrose (FGS). Adulteration was carried out on individual apple juice samples at levels of 10, 20, 30, and 40% w/w. Spectral data were compressed by principal component analysis and analyzed using k-nearest neighbors and partial least squares regression techniques. Prediction results for the best classification models achieved an overall (authentic plus adulterated) correct classification rate of 96.5, 93.9, 92.2, and 82.4% for PICS, BS, HFCS, and FGS adulterants, respectively. This method shows promise as a rapid screening technique for the detection of a broad range of potential adulterants in apple juice.     

ATR-Fourier transform mid-infrared spectroscopy for determination of trans fatty acids in ground cereal products without oil extraction

Fourier transform mid-infrared (FT-IR) spectroscopy was investigated as a method of analysis for trans fatty acid content of cereal products without the need for prior oil extraction. Spectra were obtained, with an FT-IR spectrometer equipped with an attenuated total reflectance (ATR) device, of ground samples pressed onto the diamond ATR surface, and trans fatty acids were measured by a modification of AOAC Method 996.01. Partial least-squares (PLS) models were developed for the prediction of trans fatty acids in ground samples using several wavenumber selections on the basis of bands related to lipids. The models (n = 79) predicted trans fatty acids in ground samples with standard error of cross-validation (SECV) of 1.10-1.25 (range 0-12.4) % and R2 of 0.85-0.88 and in validation samples (n = 26) with standard error of performance (SEP) of 0.96-1.12 (range 0-12.2) % and r2 of 0.89-0.92, indicating sufficient accuracy for screening. Sample trans fatty acid % was predicted as accurately with the fingerprint region (1500-900 cm(-1)) as with the entire range (4000-650 cm(-1)) indicating, in concert with the regression coefficients, the importance of the isolated trans double bonds at 966 cm(-1) in development of the model. Data is also presented on prediction of trans fatty acids using the spectra of residual oil films on the ATR surface after removing the solid portion of the sample.     

Compositional and conformational analysis of yam proteins by near infrared fourier transform Raman spectroscopy

Fourier transform (FT)-Raman spectroscopy was employed to study the molecular structure of yam proteins isolated from three commonly consumed yam species including Dioscorea alata L., D. alata L. var. purpurea, and Dioscorea japonica. Although D. alata L. and D. alata L. var. purpurea consisted of similar amino acid residues, they still exhibited significant differences in conformational arrangement. The secondary structure of D. alata L. was mainly an alpha-helix, while D. alata L. var. purpurea was mostly in antiparallel beta-sheets. In contrast, D. japonica, which belongs to a different species, exhibited explicit differences in amino acid compositions and molecular structures of which the conformation was a mixed form of alpha-helices and antiparallel beta-sheets. FT-Raman directly proved the existence of S-S in yam proteins, implying that oligomer formation in yam proteins might be due to disulfide linking of dioscorin (32 kDa). The microenvironment of aromatic amino acids and the state of S-S in yam proteins were also discussed.     

Fourier transform infrared spectroscopy and chemometric analysis of white wine polysaccharide extracts

Monovarietal white wines from Maria Gomes and Bical Portuguese Bairrada varieties were prepared according to different maceration and pectic enzyme clarification procedures. The polysaccharide-rich extracts, obtained by wine concentration, dialysis, and lyophilization, were fractionated by graded ethanol precipitation. A wide range of fractions rich in polysaccharides were obtained. Using the spectral region between 1200 and 800 cm(-)(1) of the FTIR spectra of the wine polysaccharide dry extracts, using PCA and CCA chemometric methods, it was possible to discriminate the extracts on the basis of their polysaccharide composition. Moreover, it was possible to identify the wine-making processes involved and their influence on the wine polysaccharides. Furthermore, a calibration model using a PLS1 was proposed for the quantification of mannose in the samples obtained by precipitation with 60% ethanol aqueous solutions. This information will allow an expeditious assessment and monitoring of the polysaccharide composition and modifications that occur during the wine-making processing and evolution.     

Effect of peat type and ph on breakdown of peat using fourier transform infrared spectroscopy

Abstract

Information on breakdown of peats as evidenced by shrinkage during cropping is generally lacking. The objective of this investigation was to study the breakdown of peat of various degrees of decomposition, effect of pH on breakdown and to relate the compositional changes during breakdown using Fourier Transform Infrared Spectroscopy (FTIR). Incubation studies were used in this investigation. Peat with a higher level of decomposition was less susceptible to breakdown. The pH had a major effect on breakdown with high pH leading to increased rate of breakdown. Lignin content of the peats was somewhat related to breakdown of the peats. The breakdown was also strongly correlated to the changes in the ratio of FTIR spectra of the start and to the end of the incubation particularly the 1600/1060 ratio. The 1600 spectra in mostly lignin 1060 spectra are mostly carbohydrate. There was relative enrichment of 1600 spectra in relation 1060 spectra. Other FTIR spectra ratio changes were also significantly correlated with breakdown. The FTIR technique has the potential to predict breakdown of peats.     

Quantification of saccharides in multiple floral honeys using fourier transform infrared microattenuated total reflectance spectroscopy

Fourier transform infrared (FTIR) spectroscopy with microattenuated total reflectance (mATR) sampling accessory and chemometrics (partial least squares and principal component regression) was used for the simultaneous determination of saccharides such as fructose, glucose, sucrose, and maltose in honey. Two calibration models were developed. The first model used a set of 42 standard mixtures of fructose, glucose, sucrose, and maltose prepared over the range of concentrations normally present in honey, whereas the second model used a set of 45 honey samples from various floral and regional sources. The developed models were validated with different data sets and verified by high-performance liquid chromatography (HPLC) measurements. The R (2) values between the FTIR-mATR predicted and HPLC results of the different sugars were between 0.971 and 0.993, demonstrating the predictive ability and accuracy of the procedure.