An evaluation of orthogonal signal correction methods for the characterisation of arabica and robusta coffee varieties by NIRS

Two orthogonal signal correction methods (OSC and DOSC) were applied on a set of 83 roasted coffee NIR spectra from varied origins and varieties in order to remove information unrelated to a specific chemical response (caffeine), which was selected due to its high discriminant ability to differentiate between arabica and robusta coffee varieties. These corrected NIR spectra, as well as raw NIR spectra and three chemical quantities (caffeine, chlorogenic acids and total acidity), were used to develop separate classification models accordingly using the potential functions method as a class-modelling technique in order to evaluate their respective capacities to discriminate between coffee varieties and the influence of these pre-processing methods on the classification of the coffee samples into their corresponding variety class. The transformation of roasted coffee NIR spectra by means of an orthogonal signal correction method, taking into account in this correction a chemical response closely related to the sample origin, prompted a notable improvement in the specificity of the constructed classification models.     

Application of near-infrared spectroscopy in the sugar industry for the detection of betaine

One problem in industrial molasses desugarization is the lack of a fast analytical method for process control. At the moment, control of the chromatographic production process is achieved by detecting refractive index and conductivity. However, since elution of some components takes place only in a narrowly defined time frame, the data gained are insufficient for effective online product quantification. Near-infrared (NIR) spectroscopy was applied to this process by development of a simple method for detection of betaine. Compared to chemometric models currently used, the developed method demonstrates the advantage of requiring only a small calibration set. Additionally, it can easily be transferred to other processes without further re-calibration. Based on the NIR spectrum of betaine, a characteristic peak in the spectrum could be assigned to the molasses compound betaine. A calibration was developed by using dissolved betaine in pure water. Afterwards, the calibration was tested for samples from a molasses desugarization process. The method was than successfully transferred to a complete chromatographic cycle of the industrial molasses desugarization process.     

Selective removal of interference signals for near-infrared spectra of biomedical samples by using region orthogonal signal correction

New approach for chemometrics algorithm named region orthogonal signal correction (ROSC) has been introduced to improve the predictive ability of PLS models for biomedical components in blood serum developed from their NIR spectra in the 1280-1849 nm region. Firstly, a moving window partial least squares regression (MWPLSR) method was employed to locate the region due to water as a region of interference signals and to find the informative regions of glucose, albumin, cholesterol and triglyceride from NIR spectra of bovine serum samples. Next, a novel chemometrics method named searching combination moving window partial least squares (SCMWPLS) was used to optimize those informative regions. Then, the specific regions that contained the information of water, glucose, albumin, cholesterol and triglyceride were obtained. When an interested component in the bovine serum solution, such as glucose, albumin, cholesterol or triglyceride is being an analyte, the other three interests and water are considered as the interference factors. Thus, new approach for ROSC has employed for each specific region of interference signal to calculate the orthogonal components to the concentrations of analyte that were removed specifically from the NIR spectra of bovine serum in the region of 1280-1849 nm and the highest interference signal for model of analyte will be revealed. The comparison of PLS results for glucose, albumin, cholesterol and triglyceride built by using the whole region of original spectra and those developed by using the optimized regions suggested by SCMWPLS of original spectra, spectra treated OSC for orthogonal components of 1-3 and spectra treated ROSC using selected removing the highest interference signals from the spectra for orthogonal components of 1-3 are reported. It has been found that new approach of ROSC to remove the highest interference signal located by SCMWPLS improves of the performance of PLS modeling, yielding the lower RMSECV and smaller number of PLS factors.     

Direct orthogonal signal correction as data pretreatment in the classification of clinical lots of creams from near infrared spectroscopy data

Direct orthogonal signal correction (DOSC) is applied to correct for major variance sources such as temperature effects, time influences and instrumental differences in near infrared (NIR) data. The samples analysed are creams containing different concentrations of an active drug. The final aim is to classify the samples according to their concentration of active compound. Having performed DOSC on the data, it is not necessary anymore to apply sophisticated chemometric techniques to correct for temperature or time effects and to attribute the samples to their respective concentration classes. Moreover, the application of DOSC on the NIR spectra recorded on two different instruments shows that this method can be considered as a valuable alternative for the standardisation in classification applications. Since the applied algorithm tends to overfit, in a second part of this paper, a comparison is made with an algorithm designed by Westerhuis, which should overcome this problem. Although the calibration set results show that the overfitting has been partially corrected for by the latter algorithm, the test set results did not improve significantly.     

Determination of low analyte concentrations by near-infrared spectroscopy: effect of spectral pretreatments and estimation of multivariate detection limits

Near infrared spectroscopy (NIRS) was used in combination with partial least squares (PLS) calibration to determine low concentrated analytes. The effect of the orthogonal signal correction (OSC) and net analyte signal (NAS) pretreatments on the models obtained at concentrations of analyte near its detection limit was studied. Both pretreatments were found to accurately resolve the analyte signal and allow the construction of PLS models from a reduced number of factors; however, they provided no substantial advantage in terms of %RSE for the prediction samples. Multiple methodologies for the estimation of detection limits could be found in the bibliography. Nevertheless, detection limits were determined by a multivariate method based on the sample-specific standard error for PLS regression, and compared with the univariate method endorsed by ISO 11483. The two methods gave similar results, both being effective for the intended purpose of estimating detection limits for PLS models. Although OSC and NAS allow isolating the analyte signal from the matrix signal, they provide no substantial improvement in terms of detection limits. The proposed method was used to the determine 2-ethylhexanol at concentrations from 20 to 1600 ppm in an industrial ester. The detection limit obtained, round 100 ppm, testifies to the ability of NIR spectroscopy to detect low concentrated analytes.     

Monitoring of substrate and product concentrations in acetic fermentation processes for onion vinegar production by NIR spectroscopy: value addition to worthless onions

Wastes and by-products of the onion-processing industry pose an increasing disposal and environmental problem and represent a loss of valuable sources of nutrients. The present study focused on the production of vinegar from worthless onions as a potential valorisation route which could provide a viable solution to multiple disposal and environmental problems, simultaneously offering the possibility of converting waste materials into a useful food-grade product and of exploiting the unique properties and health benefits of onions. This study deals specifically with the second and definitive step of the onion vinegar production process: the efficient production of vinegar from onion waste by transforming onion ethanol, previously produced by alcoholic fermentation, into acetic acid via acetic fermentation. Near-infrared spectroscopy (NIRS), coupled with multivariate calibration methods, has been used to monitor the concentrations of both substrates and products in acetic fermentation. Separate partial least squares (PLS) regression models, correlating NIR spectral data of fermentation samples with each kinetic parameter studied, were developed. Wavelength selection was also performed applying the iterative predictor weighting–PLS (IPW-PLS) method in order to only consider significant spectral features in each model development to improve the quality of the final models constructed. Biomass, substrate (ethanol) and product (acetic acid) concentration were predicted in the acetic fermentation of onion alcohol with high accuracy using IPW-PLS models with a root-mean-square error of the residuals in external prediction (RMSEP) lower than 2.5% for both ethanol and acetic acid, and an RMSEP of 6.1% for total biomass concentration (a very satisfactory result considering the relatively low precision and accuracy associated with the reference method used for determining the latter). Thus, the simple and reliable calibration models proposed in this study suggest that they could be implemented in routine applications to monitor and predict the key species involved in the acetic fermentation of onion alcohol, allowing the onion vinegar production process to be controlled in real time.     

Determination of alcohol content in beverages using short-wave near-infrared spectroscopy and temperature correction by transfer calibration procedures

This paper reports the utilization of short-wave near-infrared (SW-NIR) transmission spectroscopy for rapid and conclusive analysis of alcoholic content (% v/v) in beverages. This spectral region is interesting because common visible diode array spectrometers can be utilized, reducing time and costs in comparison with traditional near-infrared or mid-infrared instruments. A correction of external temperature influence is necessary, and for this purposes two calibration transfer procedures were compared: piecewise direct standardization (PDS) and orthogonal signal correction (OSC). The RMSEP found for the alcoholic content model at 20 °C was 0.13% v/v and, after application of transfer calibration procedures at other temperatures (15, 25, 30 and 35 °C) using the model built at 20 °C, errors of the same order of magnitude were obtained.     

Feasibility study for the use of near infrared spectroscopy in the qualitative and quantitative analysis of green tea, Camellia sinensis (L.)

This paper indicates the possibility to use near infrared spectroscopy (NIR) combined with PLS as a rapid method to estimate the quality of green tea. NIR is used to build calibration models to predict the content of caffeine, epigallocatechin gallate (EGCG) and epicatechin (EC) and for the prediction of the total antioxidant capacity of green tea. For the determination of the total antioxidant capacity, the trolox equivalent antioxidant capacity (TEAC) method is used. Until now, the prediction of the antioxidant capacity as such by use of NIR has not been reported. For caffeine and TEAC, models are build for the whole green tea leaves and also for the ground leaves. For the polyphenols (EGCG and EC), only models for the whole leaves are investigated. 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 with the lowest root mean square error of cross-validation (RMSECV) for the training set is chosen. The correlation coefficient (r) between the predicted and the reference results for the test set is used as an evaluation parameter for the models: for the TEAC results r=0.90 for the model with the whole leaves, r=0.86 for the model with the powdered leaves are obtained. The caffeine prediction model has a correlation coefficient r=0.96 for the whole leaves and r=0.93 for the ground leaves. The correlation coefficient for the EGCG and the EC content models are, respectively 0.83 and 0.44.     

The spatial effect of near-infrared spectroscopy and its application to the study of supramolecular chemistry

An unlooked-for experimental observation that in near-infrared spectroscopy (NIR) the absorption peak of the second overtone of aniline adsorbed by 13X molecular sieve nearly disappeared led us investigate a fundamental question: the behavior of NIR when the outside space surrounding a molecule is too small to allow the molecule to vibrate freely. Through NIR of various organic compounds adsorbed by different porous inorganic materials like 13X molecular sieve, silica gel and active aluminium oxide, and NIR of supramolecular cyanuric acid-melamine, we can reasonably confirm a theoretical inference that in the micro-environment above, all intensities of NIR absorbance decrease, and the second overtone decreases more than the first overtone does. Furthermore, one distinct feature of NIR, higher sensitivity to the size of micro-environmental space as compared with mid-infrared (MIR), and its potential application to the study of supramolecular structure are outlined by our experiments.     

Influence of data pre-processing on the quantitative determination of the ash content and lipids in roasted coffee by near infrared spectroscopy

In near-infrared (NIR) measurements, some physical features of the sample can be responsible for effects like light scattering, which lead to systematic variations unrelated to the studied responses. These errors can disturb the robustness and reliability of multivariate calibration models. Several mathematical treatments are usually applied to remove systematic noise in data, being the most common derivation, standard normal variate (SNV) and multiplicative scatter correction (MSC). New mathematical treatments, such as orthogonal signal correction (OSC) and direct orthogonal signal correction (DOSC), have been developed to minimize the variability unrelated to the response in spectral data. In this work, these two new pre-processing methods were applied to a set of roasted coffee NIR spectra. A separate calibration model was developed to quantify the ash content and lipids in roasted coffee samples by PLS regression. The results provided by these correction methods were compared to those obtained with the original data and the data corrected by derivation, SNV and MSC. For both responses, OSC and DOSC treatments gave PLS calibration models with improved prediction abilities (4.9 and 3.3% RMSEP with corrected data versus 7.1 and 8.3% RMSEP with original data, respectively).     

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.     

Application of UV–Vis,near-infrared and mid-infrared spectroscopy to the study of Mn-bearing humites

The combination of electronic and vibrational spectra has been applied to correlate the spectral properties, with composition, structure and cation substitutions such as Mn, Fe, Ca and Zn for Mg in humites: norbergite, alleghanyite, leucophoenicite and sonolite with increasing number of silicate layers, 1, 2, 3 and 4. The observation of two broad bands in the visible range, near 550 and 450 nm (18 180 and 22 220 cm⁻¹) and one sharp band around 410 nm (24 390 cm⁻¹) is characteristic of Mn²⁺ in alleghanyite and leucophoenicite. The study of UV–Vis (electronic) spectral features confirms Mn as a major substituent in these two samples. Cation impurities like Zn and Ca as revealed from EDX analysis might be the cause for the absence of Mn-type spectrum in sonolite. The first observation is the near-infrared spectra of all four minerals in the first fundamental overtone OH-stretching mode are different and each mineral is characterized by its NIR spectrum. The feature in the range 7180–6600 cm⁻¹ [1393–1515 nm or 1.39–1.52 μm] corresponds to the overtones of OH stretching vibrational modes of the humite groups observed in their IR spectra over the range, 3680–3320 cm⁻¹. The infrared spectra of the hydrous components of OH and SiO₄ groups in the mineral structure act as an aid to distinguish the minerals of the humite mineral group. A band at 541 cm⁻¹ is assigned to MnO stretching mode.     

HPLC‐DAD methodology for the quantification of organic acids,furans and polyphenols by direct injection of wine samples

This article proposes a simple and sensitive HPLC method with photo‐diode array detection for the analysis of organic acids, monomeric polyphenols and furanic compounds in wine samples by direct injection. The chromatographic separation of 8 organic acids, 2 furans and 22 phenolic compounds was carried out with a buffered solution (pH 2.70) and acetonitrile as mobile phases and a difunctionally bonded C18 stationary phase, Atlantis dC18 (250×4.6 mm, 5 μm) column. The elution was performed in 12 min for the organic acids and in 60 min for the phenolic compounds, including phenolic acids, stilbenes and flavonoids. Target compounds were detected at 210 nm (organic acids, flavan‐3‐ols and benzoic acids), 254 nm (ellagic acid), 280 nm (furans and cinnamic acid), 315 nm (hydroxycinnamic acids and trans‐resveratrol) and 360 nm (flavonoids). The RSD for the repeatability test (n=5) of peak area and retention times were below 3.1 and 0.3%, respectively, for phenolics and below 1.0 and 0.2% for organic acids. The RSDs expressing the reproducibility of the method were higher than for the repeatability results but all below 9.0%. Method accuracy was evaluated by the recovery results, with averaged values between 80 and 104% for polyphenols and 97–105% for organic acids. The calibration curves, obtained by triplicate injection of standard solutions, showed good linearity with regression coefficients higher than 0.9982 for polyphenols and 0.9997 for organic acids. The LOD was in the range of 0.07–0.49 mg/L for polyphenols (cinnamic and gallic acids, respectively) and 0.001–0.046 g/L for organic acids (oxalic and lactic acids, respectively). The method was successfully used to measure and assess the polyphenolic fingerprint and organic acids profile of red, white, rosé and fortified wines.     

Highly selective artificial gel antibodies for detection and quantification of biomarkers in clinical samples. I. Spectrophotometric approach to design the calibration curve for the quantification

High selectivity of a biomarker is a basic requirement when it is used for diagnosis, prognosis and treatment of a disease. The artificial gel antibodies, which we synthesise by a molecular imprinting method, have this property not only for proteins, but also for bioparticles, such as viruses and bacteria. However, diagnosis of a disease requires not only that the biomarker can be “fished out” from a body fluid with high selectivity, but also that its concentration in the sample can rapidly be determined and preferably by a simple technique. This paper deals primarily with the development of a spectrophotometric method, which is so simple and fast that it can be used with advantage in a Doctor's Office. The development of this method was not straight‐forward. However, by modifications of the performance of these measurements we can now design standard curves in the form of a straight line, when we plot the true (not the recorded “apparent” absorption) against known protein concentrations. In an additional publication (see the following paper in this issue of JSS) we show an application of such a plot: determination of the concentration of albumin in serum and cerebrospinal fluid from patients with neurological disorders to investigate whether albumin is a biomarker for these diseases.     

Multivariate curve resolution applied to the simultaneous analysis of electrochemical and spectroscopic data: study of the Cd(II)/glutathione-fragment system by voltammetry and circular dichroism spectroscopy

Multivariate curve resolution with alternating least squares (MCR-ALS) is applied for the first time to the simultaneous analysis of electrochemical and spectroscopic data. Then, a data analysis is done with augmented matrices constituted by Differential Pulse Polarography and Circular Dichroism data submatrices. The use of proper, and different for each submatrix, constrains in the iterative ALS optimization allows to obtain chemically meaningful results constituted by a common matrix containing the concentration profiles, and two matrices with the pure electrochemical and spectroscopic signals. MCR-ALS is applied to the study of the complexation of Cd by Cys-Gly, a glutathione-fragment of great interest for understanding metal-phytochelatins complexation.     

Activated aluminum oxide selectively retaining long chain n-alkanes: Part II. Integration into an on-line high performance liquid chromatography-liquid chromatography-gas chromatography-flame ionization detection method to remove plant paraffins for the determination of mineral paraffins in foods and environmental samples

Aluminum oxide activated by heating to 300-400 °C retains n-alkanes with more than about 20 carbon atoms, whereas iso-alkanes largely pass non-retained (with characteristics described in more detail in Part I). This property is useful for the analysis of mineral oil contamination of foods and other matrices: it enables the removal of plant n-alkanes, typically ranging from C₂₃ to C₃₃, when they disturb the analysis of mineral paraffins (usually almost exclusively consisting of iso-alkanes). An on-line HPLC-LC-GC-FID method is proposed in which a first silica gel HPLC column isolates the paraffins from the bulk of edible oils or extracts and is backflushed with dichloromethane. In a second separation step, a 10 cm × 2 mm i.d. column packed with activated aluminum oxide separates the long chain n-alkanes from the fraction of the iso-alkanes which is transferred to GC-FID by the on-column interface and the retention gap technique. The retained n-alkanes are removed by flushing with iso-octane.