Monitoring lipase-catalyzed butterfat interesterification with rapeseed oil by Fourier transform near-infrared spectroscopy

This work demonstrates the application of FT-NIR spectroscopy in order to monitor the enzymatic interesterification process for butterfat modification. The reactions were catalyzed by Lipozyme TL IM at 70 °C for the blend of butterfat/rapeseed oil (70/30, w/w) in a packed-bed reactor. The blend and interesterified fat samples were measured in liquid form at 70 °C by transmission mode-based FT-NIR over the spectral region 12000–4000 cm⁻¹. The calibration of FT-NIR for conversion degree (evaluated by the triglyceride profile, which was represented by the triglyceride peak ratio) and solid fat content (SFC) of the interesterified products was carried out using partial least squares (PLS) regression. Good correlations were observed between the NIR spectra and ln (peak ratio), and between the NIR spectra and the SFC at 5 °C over the spectral range 5269–4513 cm⁻¹. Overall, transmission-mode FT-NIR spectroscopy performed at 70 °C yielded conditions close to those used during the interesterification process, implying that this method could be used to control the enzymatic interesterification process online.     

Potentiality of partial least-squares multivariate calibration in the spectrophotometric analysis of binary mixtures of purine bases

Abstract  

A partial least-squares (PLS) modeling was developed for the simultaneous spectrophotometric determination of adenine (AD) and guanine (GU). The determination of these analytes is pharmacologically necessary. Multivariate calibration is used because of spectral overlapping. The calibration set contained AD and GU in the concentration range of 1.4–20.3 and 1.5–25.7 μg cm⁻³, respectively. The absorption spectra were recorded from 200 to 300 nm. The predicted residual error sum-of-squares for AD and GU was 0.0500 and 0.4000 for number of principal components 3 and 2, respectively. The root mean square error of prediction for AD and GU was 0.0913 and 0.2582, respectively. The limits of detection were 0.02 and 0.03 μg cm⁻³ for AD and GU, respectively. The proposed method allows the simultaneous determination of AD and GU in spiked real matrixes of human urine, serum, and plasma.     

Simultaneous measurement of glucose and glutamine in aqueous solutions by near infrared spectroscopy

A method is described for measuring the concentrations of both glucose and glutamine in binary mixtures from near infrared (NIR) absorption spectra. Spectra are collected over the range from 5000–4000/cm (2.0–2.5μm) with a 1-mm optical path length. Glucose absorbance features at 4710, 4400, and 4300/cm and glutamine features at 4700, 4580, and 4390/cm provide the analytical information required for the measurement. Multivariate calibration models are generated by using partial least squares (PLS) regression alone and PLS regression combined with a preprocessing digital Fourier filtering step. The ideal number of PLS factors and spectral range are identified separately for each analyte. In addition, the optimum Fourier filter parameters are established for both compounds. The best overall analytical performance is obtained by combining Fourier filtering and PLS regression. Glucose measurements are established over the concentration range from 1.66–59.91 mM, with a standard error of prediction (SEP) of 0.32 mM and a mean percent error of 1.84%. Glutamine can be measured over the concentration range from 1.10–30.65 mM with a SEP of 0.75 mM and a mean percent error of 6.67%. These results demonstrate the analytical utility of NIR spectroscopy for monitoring glucose and glutamine levels in mammalian and insect cell cultures.     

Near infrared spectroscopy as a rapid tool to measure volatile aroma compounds in Riesling wine: possibilities and limits

Volatile chemical compounds responsible for the aroma of wine are derived from a number of different biochemical and chemical pathways. These chemical compounds are formed during grape berry metabolism, crushing of the berries, fermentation processes (i.e. yeast and malolactic bacteria) and also from the ageing and storage of wine. Not surprisingly, there are a large number of chemical classes of compounds found in wine which are present at varying concentrations (ng L⁻¹ to mg L⁻¹), exhibit differing potencies, and have a broad range of volatilities and boiling points. The aim of this work was to investigate the potential use of near infrared (NIR) spectroscopy combined with chemometrics as a rapid and low-cost technique to measure volatile compounds in Riesling wines. Samples of commercial Riesling wine were analyzed using an NIR instrument and volatile compounds by gas chromatography (GC) coupled with selected ion monitoring mass spectrometry. Correlation between the NIR and GC data were developed using partial least-squares (PLS) regression with full cross validation (leave one out). Coefficients of determination in cross validation (R ²) and the standard error in cross validation (SECV) were 0.74 (SECV: 313.6 μg L⁻¹) for esters, 0.90 (SECV: 20.9 μg L⁻¹) for monoterpenes and 0.80 (SECV: 1658 μg L⁻¹) for short-chain fatty acids. This study has shown that volatile chemical compounds present in wine can be measured by NIR spectroscopy. Further development with larger data sets will be required to test the predictive ability of the NIR calibration models developed.     

A new direct Fourier transform infrared analysis of free fatty acids in edible oils using spectral reconstitution

A new transmission-based Fourier transform infrared (FTIR) spectroscopic method for the direct determination of free fatty acids (FFA) in edible oils has been developed using the developed spectral reconstitution (SR) technique. Conventional neat-oil and SR calibrations were devised by spiking hexanoic acid into FFA-free canola oil and measuring the response to added FFA at 1,712 cm⁻¹ referenced to a baseline at 1,600 cm⁻¹(1,712 cm⁻¹/1,600 cm⁻¹). To compensate for the known oil dependency of such calibration equations resulting from variation of the triacylglycerol ester (C═O) absorption with differences in oil saponification number (SN), a correction equation was devised by recording the spectra of blends of two FFA-free oils (canola and coconut) differing substantially in SN and correlating the intensity of the ester (C═O) absorption at the FFA measurement location with the intensity of the first overtone of this vibration, measured at 3,471 cm⁻¹/3,427 cm⁻¹. Further examination of the spectra of the oil blends by generalized 2D correlation spectroscopy revealed an additional strong correlation with an absorption in the near-infrared (NIR) combination band region, which led to the development of a second correction equation based on the absorbance at 4,258 cm⁻¹/4,235 cm⁻¹. The NIR-based correction equation yielded superior results and was shown to completely eliminate biases due to variations in oil SN, thereby making a single FFA calibration generally applicable to oils, regardless of SN. FTIR methodology incorporating this correction equation and employing the SR technique has been automated.     

白酒基酒中酮类物质的近红外光谱检测方法

应用近红外光谱技术建立了白酒基酒中2,3-丁二酮和3-羟基-2-丁酮的快速检测模型。从洛阳杜康酒厂选取182个白酒基酒样品为材料,运用气相色谱法测得两种物质的化学值,同时采集其在12 000~4 000 cm-1范围内的光谱数据,采用偏最小二乘法(PLS)结合内部交叉验证建立校正模型。通过对比不同光谱预处理下PLS模型效果对其进行优化,确定2,3-丁二酮和3-羟基-2 丁酮的最佳预处理方法分别为一阶导数+多元散射校正和二阶导数,最佳光谱区间分别为9 403.2~7 497.9 cm-1和9 403.2~7 497.9 cm-1+6 101.7~5 449.8 cm-1。优化后2,3-丁二酮和3 羟基-2-丁酮校正集样品的化学值和近红外预测值的决定系数(R2)分别为0.960 2和0.963 2,交叉验证均方根误差(RMSECV)分别为0.39、0.22 mg/100 mL;通过外部检验,验证集样品的R2分别为0.957 6和0.957 8,预测均方根误差(RMSEP)分别为0.40、0.24 mg/100 mL。结果表明,应用近红外光谱技术结合化学计量学方法所建立的模型有较高的准确度,能够满足白酒生产中酮类物质的快速检测需要。     

A feasibility study on the use of visible and short wavelengths in the near-infrared region for the non-destructive measurement of wine composition

The aim of this study was to explore the capability of spectroscopy in the visible (Vis) and short wavelength near-infrared (NIR) regions for the non-destructive measurement of wine composition in intact bottles. In this study we analysed a wide range of commercial wines obtained in Australia in different types of bottles (e.g. colours, diameters and heights), including different wine styles and varieties. Wine bottles were scanned in the Vis-NIR region (600–1,100 nm) in a monochromator instrument in transflectance mode. Principal component analysis (PCA) and partial least-squares (PLS) regression were used to interpret the spectra and develop calibrations for wine composition. Due to the relatively small number of samples available full cross-validation (leave-one-out) was used as validation. The coefficient of correlation in calibration and the standard error of cross-validation (SECV) were 0.67 (SECV: 0.48%), 0.83 (SECV: 4.01 mg L⁻¹), 0.70 (SECV: 28.6 mg L⁻¹) and 0.50 (SECV: 0.15) for alcohol content, total SO₂, free SO₂ and pH, respectively, in the set of wine samples analysed. These preliminary results showed that the assessment of wine composition by Vis and short wavelengths in the NIR is possible for either qualitative analysis (e.g. low-, medium- and high-quality grading), or for screening of composition during bottling and storage. Although low accuracy and precision were obtained for the chemical parameters routinely analysed in wine, calibration models for the chemical parameters were considered acceptable for screening purposes in terms of the standard errors obtained.     

Screening of humic and fulvic acids in estuarine sediments by near-infrared spectrometry

Diffuse reflectance near-infrared spectroscopy (NIR) combined with partial least squares (PLS) data treatment has been employed for the rapid and nondestructive determination of sedimentary humic substances. Forty one samples of surface estuarine sediments, taken during distinct seasonal periods from different locations across Ria de Arousa (northwest of Spain), were scanned at wavelengths from 833 to 2,976 nm (12,000 to 3,360 cm⁻¹). Twenty four samples were randomly selected, from previous hierarchical cluster analysis of their NIR spectra, for the calibration set, and the 17 remaining samples were assigned to the validation set. NIR spectra of calibration samples were correlated to measured values of humic acids (HAs) and fulvic acids (FAs), which ranged from 1.53 to 28.17 mg/g and from 0.37 to 2.45 mg/g, respectively, using PLS regression and multiplicative scattering correction on the raw and first-derivative NIR spectra, respectively. Low root mean square error of prediction values of 4.3 mg HA/g sediment and 0.25 mg FA/g sediment were obtained. Good residual prediction deviation values of 1.16 and 1.2 were obtained for HA and FA, respectively, allowing the PLS models built to be considered as appropriate tools for screening purposes. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Determination of chlorinated hydrocarbons in water by fiber-optic evanescent wave spectroscopy and partial least-squares regression

An application of the multivariate calibration technique of partial least-squares (PLS) regression to near-infrared spectra of a fiber-optic sensor based on the evanescent wave principle is presented. The sensing element consists of a quartz glass fiber with a silicone cladding which enriches nonpolar water contaminants. Due to the interaction of the extracted molecules with the part of the light which is transmitted in the evanescent wave zone of the cladding, absorbance spectra of the contaminants can be collected. In view of a sensor application for in-situ environmental analysis, aqueous solutions of chlorinated hydrocarbon solvents (CHS), which often can be found as major water contaminants, have been measured. PLS regression was applied to three sets of CHS samples, representing typical features of NIR evanescent wave spectral data. These are, e.g., strong overlapping of the absorption bands of different CHS components, peak distortions due to temperature variations between reference and sample measurement and noisy data at analyte concentrations near to the limit of detection, respectively. For trichloroethene and 1,1-dichloroethene, where the calibration model was built for samples within a small concentration range of 1–9 mg l^–1, satisfactory prediction results could be obtained with a relatively small root-mean-square error of 0.3 mg l^–1 compared to analytical reference measurements. In contrast to this, for a three component system of dichloromethane, trichloromethane and trichloroethene with strongly overlapping absorption bands, where samples over a very broad concentration range from 3–4940 mg l^–1 were included in the PLS model, the prediction accuracy decreased enormously and for some samples strong deviations between real and predicted data occurred. Nevertheless, applying multivariate calibration to this difficult system with similar spectral features and huge differences in the concentration of the species allowed an acceptable spectral distinction and at least a semi-quantitative determination of the CHS species.     

Selectivity for glucose,glucose-6-phosphate,and pyruvate in ternary mixtures from the multivariate analysis of near-infrared spectra

Near-infrared spectroscopy offers the potential for direct in situ analysis in complex biological systems. Chemical selectivity is a critical issue for such measurements given the extent of spectral overlap of overtone and combination spectra. In this work, the chemical basis of selectivity is investigated for a set of multivariate calibration models designed to quantify glucose, glucose-6-phosphate, and pyruvate independently in ternary mixtures. Near-infrared spectra are collected over the combination region (4,000–5,000 cm⁻¹) for a set of 60 standard solutions maintained at 37 °C. These standard solutions are composed of randomized concentrations (0.5–30 mM) of glucose, glucose-6-phosphate, and pyruvate. Individual calibration models are constructed for each solute by using the partial least-squares (PLS) algorithm with optimized spectral range and number of latent variables. The resulting standard errors are 0.90, 0.72, and 0.32 mM for glucose, glucose-6-phosphate, and pyruvate, respectively. A pure component selectivity analysis (PCSA) demonstrates selectivity for each solute in these ternary samples. The concentration of each solute is also predicted for each sample by using a set of net analyte signal (NAS) calibration models. A comparison of the PLS and NAS calibration vectors demonstrates the chemical basis of selectivity for these multivariate methods. Selectivity of each PLS and NAS calibration model originates from the unique spectral features associated with the targeted analyte. Overall, selectivity is demonstrated for each solute with an order of sensitivity of pyruvate > glucose-6-phosphate > glucose. Figure Combination near-infrared spectroscopy allows selective analytical measurements for glucose, glucose-6-phosphate, and pyruvate in ternary mixtures owing to the uniqueness of the individual absorption spectra for each solute     

Simultaneous determination of total polyphenols and caffeine contents of green tea by near-infrared reflectance spectroscopy

This paper indicates the possibility to use near infrared (NIR) spectroscopy as a rapid method to predict quantitatively the content of caffeine and total polyphenols in green tea. A partial least squares (PLS) algorithm is used to perform the calibration. To decide upon the number of PLS factors included in the PLS model, the model is chosen according to the lowest root mean square error of cross-validation (RMSECV) in training. The correlation coefficient R between the NIR predicted and the reference results for the test set is used as an evaluation parameter for the models. The result showed that the correlation coefficients of the prediction models were R = 0.9688 for the caffeine and R = 0.9299 for total polyphenols. The study demonstrates that NIR spectroscopy technology with multivariate calibration analysis can be successfully applied as a rapid method to determine the valid ingredients of tea to control industrial processes.     

Improving prediction selectivity for on-line near-infrared monitoring of components in etchant solution by spectral range optimization

The components (H₃PO₄, HNO₃, CH₃COOH and water) in an etchant solution have been accurately measured in an on-line manner using near-infrared (NIR) spectroscopy by directly illuminating NIR radiation through a Teflon line. In particular, the spectral features according to the change of H₃PO₄ or HNO₃ concentrations were not mainly from NIR absorption themselves, but from the perturbation (or displacement) of water bands; therefore, the resulting spectral variations were quite similar to each other. Consequently partial least squares (PLS) prediction selectivity among the components should be the most critical issue for continuous on-line compositional monitoring by NIR spectroscopy. To improve selectivity of the calibration model, we have optimized the calibration models by finding selective spectral ranges with the use of moving window PLS. Using the optimized PLS models for each component, the resulting prediction accuracies were substantially improved. Furthermore, on-line prediction selectivity was evaluated by spiking individual pure components step by step and examining the resulting prediction trends. When optimized PLS models were used, each concentration was selectively and sensitively varied at each spike; meanwhile, when whole or non-optimized ranges were used for PLS, the prediction selectivity was greatly degraded. This study verifies that the selection of an optimal spectral range for PLS is the most important factor to make Teflon-based NIR measurements successful for on-line and real-time monitoring of etching solutions.     

Determination of biogenic amines in apples and wine with 8-phenyl-(4-oxy-acetic acid N-hydroxysuccinimide ester)-4, 4-difluoro-1,3,5,7-tetramethyl-4-bora-3a,4a-diaza-s-indacene by high performance liquid chromatography

A solvent free, fast and environmentally friendly photoacoustic-infrared-based methodology (PAS-FTIR) was developed for the determination of Mancozeb in agrochemicals. This methodology was based on the direct measurement of the transmittance spectra of solid samples and a multivariate calibration model to determine the active ingredient concentration. The proposed partial least squares (PLS) model was made using nine standards prepared by mixing different amounts of kaolin and Mancozeb, with concentrations between 5.43 and 88.10% (w/w).A hierarchical cluster analysis was made in order to classify the samples in terms of similarity in the PAS-FTIR spectra. From their spectra different commercially available fungicide samples were classified in four groups, attending to the presence of other active ingredients co-formulated with Mancozeb. Different PLS models were applied for the analysis of each group of samples.So, for samples containing copper oxychloride (group 1), the information in the spectral range from 1543 to 1474 and 1390 to 1269 cm⁻¹ was employed. For samples co-formulated with Fosetyl-Al (group 2) the range between 3334 and 3211 cm⁻¹, corrected with a single point baseline located at 3055 cm⁻¹, was used. For samples containing Metalaxyl (group 3) it was used the information in the spectral range from 1543 to 1474 cm⁻¹ was used to determine Mancozeb. Finally, the range between 1456 and 1306 cm⁻¹ was used for Mancozeb determination in samples containing Cymoxanil (group 4).The PLS factors used for Mancozeb determination depends on the PLS model employed. 3, 2, 2 and 3 factors were used for Mancozeb determination in commercially available pesticides for groups 1, 2, 3 and 4, respectively. The mean accuracy errors found were 3.1, 2.1, 2.5 and 3.0% for groups 1, 2, 3 and 4, respectively. The developed PAS-FTIR methodology does not consume any solvent, as no sample preparation is necessary it improves the laboratory efficiency without sacrifice the accuracy and avoids the contact of the operator with toxic substances.     

Quantitative determination of acyclovir in plasma by near infrared spectroscopy

The aim of this study was to assess the feasibility of near infrared spectroscopy (NIRS) for analysis of acyclovir in plasma. This methodology was based on the direct measurement of the transmission spectra of liquid samples and a multivariate calibration model (partial least squares, PLS) to determine the acyclovir concentration in plasma sample. The PLS calibration set was built on using the spiked samples by mixing different amounts of acyclovir. Concentration of acyclovir in the plasma samples was calculated employing a 6-factors PLS calibration using the spectral information in the range of 6102-5450 cm^− 1. The root mean square errors of prediction (RMSEP) found was 1.21 for acyclovir. The developed PLS-NIRS procedure allows the determination of 120 samples/h does not require any sample pretreatment and avoids waste generation.     

Multivariate calibration of glucose in blood by PLS using spectral and fourier-domain data

Infrared attenuated total reflection spectra of 133 whole EDTA blood samples, from patients of a general hospital population, in the range from 1500 to 750 cm^–1 were used for the calibration of glucose. Reference concentration values were provided by the enzymatic glucose dehydrogenase method. The partial-least squares (PLS) algorithm was used to solve the inverse regression problem. The prediction results from, calculations using spectral and Fourier-transformed data were compared, and in the latter case, the data reduction yielded no advantage. The spectral range optimization for calibration can be carried out more flexibly in the spectral domain which is more readily interpreted by the spectroscopist.     

Rapid and Simple Method for Simultaneous Determination of Escin and Diethylamine Salicylate in Pharmaceutical Preparations by Partial Least-Squares Multivariate Calibration

Summary. A partial least-squares calibration (PLS) method has been developed for simultaneous quantitative determination of escin (ES) and diethylamine salicylate (DAS) in pharmaceutical preparations. The resolution of these mixtures has been accomplished without prior separation or derivatisation, by using partial least-squares (PLS-2) regression analysis of electronic absorption spectral data. The experimental calibration matrix was constructed with 9 samples. The concentration ranges considered were 10, 20, 30 (ES) and 40, 50, 60 (DAS) μg cm⁻³. The absorbances were recorded between 200 and 325 nm every 5 nm. Proposed method was compared with conventional spectrophotometric method. The results show that PLS-2 is a simple, rapid, and accurate method applied to the determination of these compounds in pharmaceuticals.     

Rapid and Simple Method for Simultaneous Determination of Escin and Diethylamine Salicylate in Pharmaceutical Preparations by Partial Least-Squares Multivariate Calibration

A partial least-squares calibration (PLS) method has been developed for simultaneous quantitative determination of escin (ES) and diethylamine salicylate (DAS) in pharmaceutical preparations. The resolution of these mixtures has been accomplished without prior separation or derivatisation, by using partial least-squares (PLS-2) regression analysis of electronic absorption spectral data. The experimental calibration matrix was constructed with 9 samples. The concentration ranges considered were 10, 20, 30 (ES) and 40, 50, 60 (DAS) μg cm⁻³. The absorbances were recorded between 200 and 325 nm every 5 nm. Proposed method was compared with conventional spectrophotometric method. The results show that PLS-2 is a simple, rapid, and accurate method applied to the determination of these compounds in pharmaceuticals.     

Infrared spectroscopy and outer product analysis for quantification of fat, nitrogen, and moisture of cocoa powder

The combination of the near infrared (NIR) and Fourier-transform infrared (FTIR) absorbance spectra (1100-2500 nm and 4000-600 cm⁻¹) of 100 cocoa powder samples was used to build calibration models for the determination of the content of fat, nitrogen, and moisture. The samples that comprised the dataset had an average composition of 13.51% of fat, 3.77% nitrogen, and 3.98% moisture. The fat content ranged from 2.42 to 22.00%, the nitrogen from 0.88 to 4.48%, and moisture from 1.60 to 7.80%. For NIR, the relative root mean square error of cross-validation (RMSECV) was 7.0% (R² = 0.96) for fat, 1.7% (R² = 0.98) for nitrogen, and 5.2% (R² = 0.94) for moisture. For FTIR, the relative RMSECV was 10.4% (R² = 0.94) for fat and 3.9% (R² = 0.95) for nitrogen. However, for moisture, it was not possible to build a calibration model with suitable predictability. The combination of the NIR and FTIR domains (data fusion) by outer product analysis PLS1 allowed to predict these parameters and to characterise frequencies in one domain based on the information of the other domain. This work allows to conclude that the second derivative of NIR is the recommended procedure to quantify fat, nitrogen, and moisture content in cocoa powders by infrared spectroscopy.     

Comparison of performance of partial least squares regression,secured principal component regression,and modified secured principal component regression for determination of human serum albumin, γ-globulin and glucose in buffer solutions and in vivo blood glucose quantification by near-infrared spectroscopy

The performances of three multivariate analysis methods—partial least squares (PLS) regression, secured principal component regression (sPCR) and modified secured principal component regression (msPCR)—are compared and tested for the determination of human serum albumin (HSA), γ-globulin, and glucose in phosphate buffer solutions and blood glucose quantification by near-infrared (NIR) spectroscopy. Results from the application of PLS, sPCR and msPCR are presented, showing that the three methods can determine the concentrations of HSA, γ-globulin and glucose in phosphate buffer solutions almost equally well provided that the prediction samples contain the same spectral information as the calibration samples. On the other hand, when some potential spectral features appear in new measurements, sPCR and msPCR outperform PLS significantly. The reason for this is that such spectral features are not included during calibration, which leads to a degradation in PLS prediction performance, while sPCR and msPCR can improve their predictions for the concentrations of the analytes by removing the uncalibrated features from the original spectra. This point is demonstrated by successfully applying sPCR and msPCR to in vivo blood glucose measurements. This work therefore shows that sPCR and msPCR may provide possible alternatives to PLS in cases where some uncalibrated spectral features are present in measurements used for concentration prediction.     

Robust calibrations on reduced sample sets for API content prediction in tablets: Definition of a cost-effective NIR model development strategy

Owing to spectral variations from other sources than the component of interest, large investments in the NIR model development may be required to obtain satisfactory and robust prediction performance. To make the NIR model development for routine active pharmaceutical ingredient (API) prediction in tablets more cost-effective, alternative modelling strategies were proposed. They used a massive amount of prior spectral information on intra- and inter-batch variation and the pure component spectra to define a clutter, i.e., the detrimental spectral information. This was subsequently used for artificial data augmentation and/or orthogonal projections. The model performance improved statistically significantly, with a 34–40% reduction in RMSEP while needing fewer model latent variables, by applying the following procedure before PLS regression: (1) augmentation of the calibration spectra with the spectral shapes from the clutter, and (2) net analyte pre-processing (NAP). The improved prediction performance was not compromised when reducing the variability in the calibration set, making exhaustive calibration unnecessary. Strong water content variations in the tablets caused frequency shifts of the API absorption signals that could not be included in the clutter. Updating the model for this kind of variation demonstrated that the completeness of the clutter is critical for the performance of these models and that the model will only be more robust for spectral variation that is not co-linear with the one from the property of interest.