A multimodal spectrometer for Raman scattering and near-infrared absorption measurement

Raman and infrared (IR) spectroscopy are complementary spectroscopic techniques. However, measurement of Raman and IR spectra are commonly carried out on separate instruments. A dispersive system that enables both Raman spectroscopy and NIR spectroscopy was designed, built, and tested. The prototype system measures spectral ranges of 2600–300 cm⁻¹ and 752–987 nm for Raman and NIR channels, respectively. A wavelength accuracy better than 0.6 nm and spectral resolution better than 1 nm (14.4 cm⁻¹ for Raman channel) could be achieved with our configuration. The linearity of spectral response was better than 99.8%. The intensity stability of the instrument was found to be 0.7% and 0.4% for Raman and NIR channels, respectively. The performance of the instrument was evaluated using binary aqueous solutions of ethanol and ovalbumin. It was found that ethanol concentrations (2–10%) could be predicted with a root mean squared error of prediction (RMSEP) of 0.45% using Raman peak height at 882.2 cm⁻¹. Quantification of ovalbumin concentration (8–16 g/L) in aqueous solutions and in denatured states yielded RMSEP values of 1.05 g/L and 0.74 g/L, respectively. Using concentration as external perturbation in two-dimensional correlation spectroscopy (2DCOS), heterospectral correlation analysis revealed the relationship between NIR and Raman spectra.     

Classification of Greek Olive Oils from Different Regions by Machine Learning-Aided Laser-Induced Breakdown Spectroscopy and Absorption Spectroscopy

In the present work, the emission and the absorption spectra of numerous Greek olive oil samples and mixtures of them, obtained by two spectroscopic techniques, namely Laser-Induced Breakdown Spectroscopy (LIBS) and Absorption Spectroscopy, and aided by machine learning algorithms, were employed for the discrimination/classification of olive oils regarding their geographical origin. Both emission and absorption spectra were initially preprocessed by means of Principal Component Analysis (PCA) and were subsequently used for the construction of predictive models, employing Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). All data analysis methodologies were validated by both “k-fold” cross-validation and external validation methods. In all cases, very high classification accuracies were found, up to 100%. The present results demonstrate the advantages of machine learning implementation for improving the capabilities of these spectroscopic techniques as tools for efficient olive oil quality monitoring and control.     

Determination of edible oil parameters by near infrared spectrometry

A chemometric method has been developed for the determination of acidity and peroxide index in edible oils of different types and origins by using near infrared spectroscopy (NIR) measurements. Different methods for selecting the calibration set, after an hierarchical cluster analysis, were applied. After discrimination of olive oils from maize, seed and sunflower, the prediction capabilities of partial least squares (PLS) multivariate calibration of NIR data were evaluated. Several preprocessing alternatives (first derivative, multiplicative scatter correction, vector normalization, constant offset elimination, mean centering and standard normal variate) were investigated by using the root mean square error of validation (RMSEV) and prediction (RMSEP), as control parameters. Under the best conditions studied, the validation set provides RMSEP values of 0.034 and 0.037% (w/w) for acidity in (I) olive oil group and (II) sunflower, seed and maize oils group. RMSEP values for peroxide in both sample groups, expressed as mequiv. O₂ kg⁻¹, were, respectively 1.87 and 0.79. The limit of detection of the methodology developed was 0.03% for acidity in both groups of edible oils (I and II), and 0.9 and 0.8 mequiv. O₂ kg⁻¹ for peroxide in the olive oil and other edible oils groups, respectively. In fact, the methodology developed is proposed for direct acidity quantification and for the screening of peroxide index in edible oils, requiring less than 30 s per sample without any previous treatment.     

Comparison of near-infrared and Raman spectroscopy for on-line monitoring of etchant solutions directly through a Teflon tube

Both near-infrared (NIR) and Raman spectroscopy have been studied for the quantitative measurement of components (H(3)PO(4), HNO(3), and CH(3)COOH) in an etchant solution and the corresponding prediction robustness has been evaluated. Both measurements were accomplished by illuminating radiation directly through a Teflon tube. Raman spectral features of each component were much clearer and more selective than those observed in the NIR spectrum. Especially, NIR spectral variation pertinent to H(3)PO(4) and HNO(3) were mostly based on the displacement and perturbation of water bands rather than due solely to NIR absorption. Therefore, the resulting spectral variations were not highly specific. When the validation set contained minor spectral variations resulting from a slight instrumental change, NIR prediction performance for all three components degraded substantially by showing obvious prediction bias. However, the accuracies of Raman predictions were maintained. Since partial least squares (PLS) models for each component were built using NIR spectra of poor specificity with broadly overlapping features, even minor spectral differences introduced by instrumental variations sensitively influenced the prediction performance of the NIR models. Overall, the selectivity (specificity) of a targeting spectroscopic method should be considered critically to secure prediction robustness for monitoring components in an etchant solution.     

Development of a PLS based method for determination of the quality of beers by use of NIR: spectral ranges and sample-introduction considerations

Near infrared spectroscopy (NIR) has been used to determine important indicators of the quality of beers, for example original and real extract and alcohol content, using a partial least squares (PLS) calibration approach. A population of 43 samples, obtained commercially in Spain and including different types of beer, was used. Cluster hierarchical analysis was used to select calibration and validation data sets. Absorbance sample spectra, in transmission mode, were obtained in triplicate by using a 1-mm pathlength quartz flow cell and glass chromatography vials of 6.5 mm internal diameter. The two methods of sample introduction were compared critically, on the basis of spectral reproducibility for triplicate measurements and after careful selection of the best spectral pre-processing and the spectral range for building the PLS model, to obtain the best predictive capability. For each mode of sample introduction two calibration sets were assayed, one based on the use of 15 samples and a second extended based on use of 30 samples, thus leaving 28 and 13 samples, respectively, for validation. The best results were obtained for 1 mm flow cell measurements. For this method original zero-order spectra data in the ranges 2220–2221 and 2250–2350 nm were chosen. For the real extract, original extract, and alcohol d_x-y and s_x-y values of –0.04 and 0.07% w/w, –0.01 and 0.13% w/w, and –0.01 and 0.1% v/v, respectively, were obtained. The maximum errors in the prediction of any of these three indicators for a new sample were 2.2, 1.2, and 1.9%, respectively. This method compares favorably with the automatic reference method in terms of speed, reagent consumed, and waste generated.     

The detection and quantification of adulteration in olive oil by near-infrared spectroscopy and chemometrics.

A new procedure has been developed for the classification and quantification of the adulteration of pure olive oil by soya oil, sun flower oil, corn oil, walnut oil and hazelnut oil. The study was based on a chemometric analysis of the near-infrared (NIR) spectra of olive-oil mixtures containing different adulterants. The adulteration of olive oil was carefully carried out gravimetrically in a 4 mm quartz cuvette, starting with pure olive oil in the cuvette first. NIR spectra of the 525 adulterated mixtures were measured in the region of 12,000-4000 cm(-1). The spectra were subjected batch wise to multiplicative signal correction (MSC) before calculating the principal component (PCA) models. The MSC-corrected data were subjected to Savitzky-Golay smoothing and a mean normalization procedure before developing partial least-squares calibration (PLS) models. The results revealed that the models predicted the adulterants, corn oil, sun flower oil, soya oil, walnut oil and hazelnut oil involved in olive oil with error limits +/-0.57, +/-1.32, +/-0.96, +/-0.56 and +/-0.57% weight/weight, respectively. Furthermore, the PCA developed models were able to classify unknown adulterated olive oil mixtures with almost 100% certainty. Quantification of the adulterants was carried out using their respective PLS models within the same error limits as mentioned above.     

Combination of mid- and near-infrared spectroscopy for the determination of the quality properties of beers

The combination of infrared (MIR) and near-infrared (NIR) spectroscopy has been employed for the determination of important quality parameters of beers, such as original and real extract and alcohol content. A population of 43 samples obtained from the Spanish market and including different types of beer, was evaluated. For each technique, spectra were obtained in triplicate. In the case of NIR a 1 mm pathlength quartz flow cell was used, whereas attenuated total reflectance measurements were used in MIR. Cluster hierarchical analysis was employed to select calibration and validation data sets. The calibration set was composed of 15 samples, thus leaving 28 for validation. A critical evaluation of the prediction capability of multivariate methods established from the combination of NIR and MIR spectra was made. Partial least squares (PLS) and artificial neural networks (ANN) were evaluated for the treatment of data obtained in each individual technique and the combination of both. Different parameters of each methodology were optimized. A slightly better predictive performance was obtained for NIR-MIR combined spectra, and in all the cases ANN performs better than PLS, which may be interpreted from the existence of some non-linearity in the data. The root-mean-sqare-error of prediction (RMSEP) values obtained for the combined NIR-MIR spectra for the determination of real extract, original extract and ethanol were 0.076% w/w, 0.14% w/w and 0.091% v/v.     

Determination of Ni2+ using an equilibrium ion exchange technique: Important chemical factors and applicability to environmental samples

The main purpose of this study was to investigate the relationship between some coffee roasting variables (weight loss, density and moisture) with near infrared (NIR) spectra of original green (i.e. raw) and differently roasted coffee samples, in order to test the availability of non-destructive NIR technique to predict coffee roasting degree. Separate calibration and validation models, based on partial least square (PLS) regression, correlating NIR spectral data of 168 representatives and suitable green and roasted coffee samples with each roasting variable, were developed. Using PLS regression, a prediction of the three modelled roasting responses was performed. High accuracy results were obtained, whose root mean square errors of the residuals in prediction (RMSEP) ranged from 0.02 to 1.23%. Obtained data allowed to construct robust and reliable models for the prediction of roasting variables of unknown roasted coffee samples, considering that measured vs. predicted values showed high correlation coefficients (r from 0.92 to 0.98). Results provided by calibration models proposed were comparable in terms of accuracy to the conventional analyses, revealing a promising feasibility of NIR methodology for on-line or routine applications to predict and/or control coffee roasting degree via NIR spectra.     

Direct, reagent-free determination of free fatty acid content in olive oil and olives by Fourier transform Raman spectrometry

Fourier transform (FT) Raman spectrometry in combination with partial least squares (PLS) regression was used for direct, reagent-free determination of free fatty acid (FFA) content in olive oils and olives. Oils were directly investigated in a simple flow cell. Milled olives were measured in a dedicated sample cup, which was rotated eccentrically to the horizontal laser beam during spectrum acquisition in order to compensate sample heterogeneity. Both external and internal (leave-one-out) validation were used to assess the predictive ability of the PLS calibration models for FFA content (in terms of oleic acid) in oil and olives in the range 0.20-6.14 and 0.15-3.79%, respectively. The root mean square error of prediction (RMSEP) was 0.29% for oil and 0.28% for olives. The predicted FFA contents were used to classify oils and olives in different categories according to the European Union regulations. Ninety percent of the oil samples and 80% of the olives were correctly classified. These results demonstrate that the proposed procedures can be used for screening of good quality olives before processing, as well as, for the on-line control of the produced oil.     

Raman spectroscopy in combination with background near-infrared autofluorescence enhances the in vivo assessment of malignant tissues

The diagnostic ability of optical spectroscopy techniques, including near-infrared (NIR) Raman spectroscopy, NIR autofluorescence spectroscopy and the composite Raman and NIR autofluorescence spectroscopy, for in vivo detection of malignant tumors was evaluated in this study. A murine tumor model, in which BALB/c mice were implanted with Meth-A fibrosarcoma cells into the subcutaneous region of the lower back, was used for this purpose. A rapid-acquisition dispersive-type NIR Raman system was employed for tissue Raman and NIR autofluorescence spectroscopic measurements at 785-nm laser excitation. High-quality in vivo NIR Raman spectra associated with an autofluorescence background from mouse skin and tumor tissue were acquired in 5 s. Multivariate statistical techniques, including principal component analysis (PCA) and linear discriminant analysis (LDA), were used to develop diagnostic algorithms for differentiating tumors from normal tissue based on their spectral features. Spectral classification of tumor tissue was tested using a leave-one-out, cross-validation method, and the receiver operating characteristic (ROC) curves were used to further evaluate the performance of diagnostic algorithms derived. Thirty-two in vivo Raman, NIR fluorescence and composite Raman and NIR fluorescence spectra were analyzed (16 normal, 16 tumors). Classification results obtained from cross-validation of the LDA model based on the three spectral data sets showed diagnostic sensitivities of 81.3%, 93.8% and 93.8%; specificities of 100%, 87.5% and 100%; and overall diagnostic accuracies of 90.6%, 90.6% and 96.9% respectively, for tumor identification. ROC curves showed that the most effective diagnostic algorithms were from the composite Raman and NIR autofluorescence techniques.