Determination of the conjugated linoleic acids in cow's milk fat by Fourier transform Raman spectroscopy

The collective term "conjugated linoleic acid" or "CLA" generally refers to a mixture of conjugated positional and geometric isomers of linoleic (cis-9,cis-12-octodecadienoic) acid. In nature, these isomers are mainly formed in the rumen by biohydrogenation of polyunsaturated fatty acids. This study concerns a first trial of CLA determination in cow's milk fat by Raman spectroscopy. The spectra of pure cis-9-oleic, cis-9,cis-12-linoleic, cis-9,trans-11-linoleic, and trans-10,cis-12-linoleic acids have been examined in comparison with the spectra of selected milk-fat samples containing between 0 and 3% of CLA. The trial of CLA determination by Raman spectroscopy on cow milk fat has reached its objective with the two following results. First, the examination of the Raman spectra allows to identify three specific Raman signals of the chemical bonds associated to the cis,trans conjugated C=C in the rumenic and trans-10,cis-12-octodecadienoic acids at 1652, 1438, and 3006 cm(-1). Second, the calibration of Raman spectrometer for the CLA determination has indicated that these three specific signals suit very well for the accurate and reliable measurement of CLA concentration in milk fat. To our knowledge, the present study is the first successful attempt to determine the CLA content of milk fat by a spectrophotometric method.     

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.     

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.     

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.     

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.     

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.     

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.     

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.     

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.     

SO2 detection by dual beam fourier transform spectroscopy

Bar-Lev (1967) described an instrument incorporating a dual detector, dual beam Michelson interferometer, and presented experimental results on the detection of gases in air. A 20 pass, 10 m cell was used, and IR spectra in the 8 to 12 μ region of 70 ppm by weight of Freon-12 and 10 ppm by weight of methanol were obtained. Single 1 s scans were used and emphasis was apparently placed on speed. It seemed to us that such instrumentation should be capable of detecting gas at concentrations lower than those reported, and we consequently carried out the measurements described below.     

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.     

Performance comparison of UV and FT-Raman spectroscopy in the determination of conjugated linoleic acids in cow milk fat

The determination of conjugated linoleic acids (CLA) in cow milk fat was studied by using UV (210-250 nm) and Fourier transform (FT)-Raman (900-3400 cm (-1)) spectroscopy in order to determine the best spectrophotometric technique for routine analysis of milk fat. A collection of 57 milk fat samples was randomly divided into two sets, a calibration set and a validation set, representing two-thirds and one-third of the samples, respectively. All calculations were performed on the calibration set and then applied to the validation set. The CLA content ranged from 0.56 to 4.70%. A comparison of various spectral pretreatments and different multivariate calibration techniques, such as partial least-squares (PLS) and multiple linear regression (MLR), was done. This paper shows that UV spectroscopy is as reliable as FT-Raman spectroscopy to monitor CLA in cow milk fat. The best calibration for FT-Raman was given by a PLS model of seven factors with a standard error of prediction (SEP) of 0.246. For UV spectroscopy, PLS models were also better than MLR models. The most robust PLS model was constructed with only one factor and with SEP=0.288.     

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.     

Discrimination of olives according to fruit quality using Fourier transform Raman spectroscopy and pattern recognition techniques

Fourier transform Raman spectroscopy combined with pattern recognition has been used to discriminate olives of different qualities. They included samples of sound olives, olives with frostbite, olives that have been collected from the ground, fermented olives, and olive samples with diseases. Milled olives were measured in a dedicated sample cup, which was rotated during spectrum acquisition. A preliminary study of the data set structure was performed using hierarchical cluster analysis and principal component analysis. Two supervised pattern recognition techniques, K-nearest neighbors and soft independent modeling of class analogy (SIMCA), were tested using a "leave-a-fourth-out" cross-validation procedure. SIMCA provided the best results, with prediction abilities of 95% for sound, 93% for frostbite, 96% for ground, and 92% for fermented olives. The olive samples with diseases (too few to define a class) were included in the validation and recognized as not belonging to any class. None of the damaged olive samples was wrongly predicted to the class of sound olives. With this approach a selection of sound olives for the production of high-quality virgin olive oil can be achieved.     

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.     

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.     

Quantitative determination of short-chain free fatty acids in milk using solid-phase microextraction and gas chromatography

The objective was to establish a rapid, precise, and accurate methodology for the quantification of short-chain free fatty acids (FFA) (C(4)-C(12)) in milk by solid-phase microextraction and gas chromatography. Sampling conditions such as fiber type, pH, salt addition, temperature, volume, and time were investigated. FFA extraction consisted of placing 40 mL of milk containing 28% NaCl at pH 1.5 in a sealed vial and equilibrating for 30 min at 70 degrees C. A polyacrylate fiber was exposed to the sample headspace for 60 min and desorbed for 5 min into the gas chromatograph. Calibration curves for FFA followed linear relationships with highly significant (p < 0.001) correlation coefficients (R(2) = 0.99). Coefficients of variation of less than 7.7% for FFA concentrations indicated that the technique was reproducible. The limits of quantification for C(4)-C(10) were in the low parts per million level, which were below the concentration range found in fresh pasteurized milk (0.48-2.52 ppm) or rancid milk (4.73-32.31 ppm).     

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%.     

Chemical discrimination of arabica and robusta coffees by Fourier transform Raman spectroscopy

This article deals with the potential of Fourier transform (FT) Raman spectroscopy in discrimination of botanical species of green and roasted coffees. There are two species of commercial importance: Coffea arabica (arabica) and Coffea canephora (robusta). It is recognized that they differ in their lipid fraction, especially in the content of the diterpene kahweol, which is present at 0.1-0.3% dry matter basis in arabica beans and only in traces (<0.01%) in robusta. The visual examination of the Raman spectra of the lipid fraction extracted from arabica, robusta and liberica samples shows differences in the mid-wavenumbers region: arabica spectra have two characteristic scattering bands at 1567 and 1478 cm(-1). The spectrum of the pure kahweol shows the same bands. Principal component analysis is applied to the spectra and reveals clustering according to the coffee species. The first principal component (PC1) explains 93% of the spectral variation and corresponds to the kahweol concentration. Using the PC1 score plot, two groups of arabica can be distinguished as follows: one group with high kahweol content and another group with low kahweol content. The first group includes samples coming from Kenya and Jamaica; the second group includes samples from Australia. The main difference between these coffees is that those from Kenya and Jamaica are well-known for growing at a high altitude whereas those ones from Australia are grown at a low altitude. To our knowledge, the application of Raman spectroscopy has never been used in coffee analysis.     

Predicting the age and type of tuocha tea by fourier transform infrared spectroscopy and chemometric data analysis

Fourier transform infrared (FTIR) spectroscopy combined with chemometric multivariate methods was proposed to discriminate the type (unfermented and fermented) and predict the age of tuocha tea. Transmittance FTIR spectra ranging from 400 to 4000 cm(-1) of 80 fermented and 98 unfermented tea samples from Yunnan province of China were measured. Sample preparation involved finely grinding tea samples and formation of thin KBr disks (under 120 kg/cm(2) for 5 min). For data analysis, partial least-squares (PLS) discriminant analysis (PLSDA) was applied to discriminate unfermented and fermented teas. The sensitivity and specificity of PLSDA with first-derivative spectra were 93 and 96%, respectively. Multivariate calibration models were developed to predict the age of fermented and unfermented teas. Different options of data preprocessing and calibration models were investigated. Whereas linear PLS based on standard normal variate (SNV) spectra was adequate for modeling the age of unfermented tea samples (RMSEP = 1.47 months), a nonlinear back-propagation-artificial neutral network was required for calibrating the age of fermented tea (RMSEP = 1.67 months with second-derivative spectra). For type discrimination and calibration of tea age, SNV and derivative preprocessing played an important role in reducing the spectral variations caused by scattering effects and baseline shifts.