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.     

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.     

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.     

Investigation of the potential utility of single-bounce attenuated total reflectance Fourier transform infrared spectroscopy in the analysis of distilled liquors and wines

A new Fourier transform infrared (FTIR) spectroscopic method based on single-bounce attenuated total reflectance (SB-ATR) spectroscopy was developed for the analysis of distilled liquors and wines. For distilled liquors, a partial least-squares (PLS) calibration was developed for alcohol determination based on the SB-ATR/FTIR spectra of mixtures of ethanol and distilled water. An independent set of 12 different distilled liquor samples was predicted from the PLS calibration, and a standard deviation of the differences for accuracy (SDD(a)) between actual and predicted values of 0.142% (v/v) was obtained. The potential utility of SB-ATR/FTIR spectroscopy for the analysis of wines was initially evaluated based on a comparison with Fourier transform near-infrared (FT-NIR) spectroscopy and FTIR spectroscopy using a flow-through transmission cell. PLS calibrations for alcohol, total reducing sugars, total acidity and pH were developed using pre-analyzed wine samples (n = 28), and for SB-ATR/FTIR spectroscopy, the SDD(a) for the leave-one-out cross-validation statistics were of the order of 0.100% (v/v), 0.707 g L(-1), 0.189 g L(-1) (H2SO4), and 0.230, respectively. Overall, the SB-ATR/FTIR results were better than those obtained using FT-NIR spectroscopy and comparable to those obtained with transmission FTIR spectroscopy. A PLS calibration based on preanalyzed wine samples (n = 72) for the prediction of 11 different components and parameters in wines by SB-ATR/FTIR spectroscopy was subsequently developed and validated using an independent sample set (n = 77). Good coefficients of correlation between the reference and predicted values for the validation set were obtained for most of the components and parameters except citric acid, volatile acids, and total SO2. The results of this study demonstrate the suitability of SB-ATR/FTIR spectroscopy for the routine analysis of distilled liquors and wines.     

Development and use of an attenuated total reflectance/fourier transform infrared (ATR/FT-IR) spectral database to identify foreign matter in cotton

The presence of foreign matter in cotton seriously affects the cotton grade and thus the price per bale paid by the spinner to the grower, the efficiency of the spinning and ginning operations, and the quality of the final woven product. Rapid identification of the nature of the extraneous matter in cotton at each stage of cleaning and processing is necessary to permit actions to eliminate or reduce its presence and improve efficiency and quality. Although several instruments are being successfully employed for the measurement of contamination in cotton fibers based on particle size/weight, no commercial instrument is capable of accurate qualitative identification of contaminants. To this end, ATR/FT-IR spectra of retrieved foreign matter were collected and subsequently rapidly matched to an authentic spectrum in a spectral database. The database includes contaminants typically classified as "trash", cotton plant parts (hull, shale, seed-coat fragments, bract, cacyx, leaf, bark, sticks, and stems) and grass plant parts (leaf and stem); "foreign objects and materials", synthetic materials (plastic bags, film, rubber, bale wrapping and strapping); organic materials (other fibers, yarns, paper, feathers, and leather); plus entomological and physiological sugars and inorganic materials (sand and rust). The spectral matching resulted in consistently high-score identification of the foreign matter based on chemical composition, irrespective of its particle size. The method is envisioned to be employed with stand-alone rugged infrared instrumentation to provide specific identification of extraneous materials in cotton as opposed to only general classification of the type by particle size or shape.     

Characterization of commercial carrageenans by Fourier transform infrared spectroscopy using single-reflection attenuated total reflection

The purity and composition of commercial carrageenans vary widely and, therefore, have to be checked prior to their use in the food industry. Infrared spectroscopy is an alternative method to the expensive and time-consuming wet chemical and NMR methods to characterize carrageenan samples. The use of an attenuated total reflection accessory coupled to a Fourier transform infrared spectrophotometer allows a direct analysis of the sample without any preparation step, which is an additional benefit for the rapid identification check of raw material at reception in an industrial environment. Using a set of calibration samples, three multivariate calibrations were developed to predict the total carrageenan content as well as the molar ratio of kappa- and iota-carrageenans. A validation with an independent set of samples confirmed the robustness of the calibrations and the accuracy of the predictions. The accuracies of the calibrations given by their respective standard errors of prediction are 5.6 g/100 g, and 6.1 mol %, and 6.6 mol %, respectively, for the total carrageenan content and the molar ratios of kappa- and iota-carrageenans. The total preparation and analysis time is <5 min per sample.     

Improved method for determination of pectin degree of esterification by diffuse reflectance Fourier transform infrared spectroscopy

An improved method for the determination of pectin degree of esterification (DE) by diffuse reflectance Fourier transform infrared spectroscopy (DRIFTS) was developed. Pectin samples with a range of DE as determined by gas chromatography were used for developing a calibration curve by DRIFTS. A linear relationship between the DE of pectin standards and FTIR peak ratio for ester carboxyl peak area to total carboxyl peak area was found (R(2) = 0.97). Pectin DE of various samples was calculated from the linear fit equation developed by DRIFTS. Accuracy of the DRIFTS method was determined by comparing the DE values of four commercial pectins obtained by DRIFTS methods to the values obtained by the gas chromatography method. Greater precision was obtained for the FTIR measurement of test pectin samples when the ester peak ratio was used relative to the ester peak area.     

基于红外衰减全反射光谱的温室土壤盐分特征研究

与传统的露地耕作土壤不同,温室土壤在耕作过程中受较多的人为调控,但在调控的过程中产生了多种问题,如大量施肥导致的土壤盐渍化[1]。因此,研究如何表征温室土壤的离子特征并预测温室土壤的发展是当前设施农业发展所面临的重要问题。常规农化分析只从不同的角度分析温室土壤的特征,很难实现整体上的综合表征。红外光谱能够综合反应土壤的理化性质,在温室土壤研究中有具有明显的特点。常规的透射光谱在研究土壤时存在制样时间长和难以定量的缺点,而衰减全反射红外光谱(ATR-FTIR)则可克服这一缺点,结合化学计量学的方法,能够实现土壤的定性与定量分析[2,3]。红外光谱包含大量化学键信息,在分析时需进行合理的降维,抽取     

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.     

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.     

Determination of secondary structural changes in gluten proteins during mixing using Fourier transform horizontal attenuated total reflectance spectroscopy

Fourier transform horizontal attenuated total reflectance (FT-HATR) was used to examine changes in the secondary structure of gluten proteins in a flour-water dough system during mixing. Midinfrared spectra of mixed dough revealed changes in four bands in the amide III region associated with secondary structure in proteins: 1317 (alpha-helix), 1285 (beta-turn), 1265 (random coil), and 1242 cm (-1) (beta-sheet). The largest band, which also showed the greatest change in second derivative band area (SDBA) during mixing, was located at 1242 cm (-1). The bands at 1317 and 1285 cm (-1) also showed an increase in SDBA over time. Conversely, the band at 1265 cm (-1) showed a corresponding decrease over time as the doughs were mixed. All bands reached an optimum corresponding to the minimum mobility of the dough as determined by the mixograph. Increases in alpha-helix, beta-turn, and beta-sheet secondary structures during mixing suggest that the dough proteins assume a more ordered conformation. These results demonstrate that it is possible, using infrared spectroscopic techniques, to relate the rheological behavior of developing dough in a mixograph directly to changes in the structure of the gluten protein system.     

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.     

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.     

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.     

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.     

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)).     

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.     

Nondestructive determination of lignans and lignan glycosides in sesame seeds by near infrared reflectance spectroscopy

Sesame (Sesamum indicum L.) contains abundant lignans including lipid-soluble lignans (sesamin and sesamolin) and water-soluble lignan glycosides (sesaminol triglucoside and sesaminol diglucoside) related to antioxidative activity. In this study, near infrared reflectance spectroscopy (NIRS) was used to develop a rapid and nondestructive method for the determination of lignan contents on intact sesame seeds. Ninety-three intact seeds were scanned in the reflectance mode of a scanning monochromator. This scanning procedure did not require the pulverization of samples, allowing each analysis to be completed within minutes. Reference values for lignan contents were obtained by high-performance liquid chromatography analysis. Calibration equations for lignans (sesamin and sesamolin) and lignan glycosides (sesaminol triglucoside and sesaminol diglucoside) contents were developed using modified partial least squares regression with internal cross-validation (n = 63). The equations obtained had low standard errors of cross-validation and moderate R2 (coefficient of determination in calibration). The prediction of an external validation set (n = 30) showed significant correlation between reference values and NIRS predicted values based on the SEP (standard error of prediction), bias, and r2 (coefficient of determination in prediction). The models developed in this study had relatively higher values (more than 2.0) of SD/SEP(C) for all lignans and lignan glycosides except for sesaminol diglucoside, which had a minor amount, indicating good correlation between the reference and the NIRS estimate. The results showed that NIRS, a nondestructive screening method, could be used to rapidly determine lignan and lignan glycoside contents in the breeding programs for high quality sesame.