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.     

Prediction of sensory properties of espresso from roasted coffee samples by near-infrared spectroscopy

Thirty-five representative and suitably selected roasted coffee samples were characterised by near-infrared (NIR) spectroscopy and used to prepare the corresponding espresso samples to be subsequently subjected to sensory evaluation by trained panellists. The main purpose was to investigate the relationships between certain crucial sensory attributes of espresso coffees, including perceived acidity, mouthfeel, bitterness and aftertaste, and near-infrared spectra of original roasted coffee samples, in such a way that non-destructive near-infrared reflectance measurements would be used to predict all these sensory properties with a decisive influence from a quality assurance standpoint. Separate calibration models based on partial least squares regression (PLS), correlating NIR spectral data of roasted coffee samples with each sensory attribute of espresso samples studied, were developed. Wavelength selection was also performed applying iterative predictor weighting-PLS (IPW-PLS) in order to take into account only significant and characteristic spectral features, in an attempt to improve the quality of the final regression models constructed. Using IPW-PLS regression, prediction of the four sensory responses modelled was performed with high accuracy, with root mean square errors of the residuals in cross-validation (RMSECV) ranging from 4.7 to 7.0%. Thus, the results provided by the high-quality calibration models proposed in the present study, comparable in terms of accuracy to the evaluations provided by a trained sensory panel, are promising and prove the feasibility of using a similar methodology in on-line or routine applications to predict the sensory quality of unknown espresso coffee samples via their respective NIR roasted coffee spectra.     

Standardization of near infrared spectra measured on multi-instrument

Calibration model transfer is essential for practical applications of near infrared (NIR) spectroscopy because the measurements of the spectra may be performed on different instruments and the difference between the instruments must be corrected. An approach for calibration transfer based on alternating trilinear decomposition (ATLD) algorithm is proposed in this work. From the three-way spectral matrix measured on different instruments, the relative intensity of concentration, spectrum and instrument is obtained using trilinear decomposition. Because the relative intensity of instrument is a reflection of the spectral difference between instruments, the spectra measured on different instruments can be standardized by a correction of the coefficients in the relative intensity. Two NIR datasets of corn and tobacco leaf samples measured with three instruments are used to test the performance of the method. The results show that, for both the datasets, the spectra measured on one instrument can be correctly predicted using the partial least squares (PLS) models built with the spectra measured on the other instruments.     

Comparison and joint use of near infrared spectroscopy and Fourier transform mid infrared spectroscopy for the determination of wine parameters

A study of the statistic characteristics of the multidetermination of several enological parameters - namely, alcoholic degree, volumic mass, total acidity, glycerol, total polyphenol index, lactic acid and total sulphur dioxide - depending on the spectroscopic zone employed, was carried out. The two techniques used were near infrared spectroscopy (NIRS) and Fourier transform mid infrared spectroscopy (FT-MIRS). The combination of these two regions (sum of their spectra) was also studied. NIRS yielded better results, but the use of both zones improved the determination of glycerol and total sulphur dioxide. The training and validation sets used for developing general equations were built with samples from different apellation d’origine, different wine types, etc. Partial least squares regression was used for multivariate calibration, using systematic cross validation in the calibration stage and external validation in the testing stage. Sample preparation was not required.     

Multivariate calibration of near infrared spectra by orthogonal WAVElet correction using a genetic algorithm

Orthogonal WAVElet correction (OWAVEC) is a pre-processing method aimed at simultaneously accomplishing two essential needs in multivariate calibration, signal correction and data compression, by combining the application of an orthogonal signal correction algorithm to remove information unrelated to a certain response with the great potential that wavelet analysis has shown for signal processing. In the previous version of the OWAVEC method, once the wavelet coefficients matrix had been computed from NIR spectra and deflated from irrelevant information in the orthogonalization step, effective data compression was achieved by selecting those largest correlation/variance wavelet coefficients serving as the basis for the development of a reliable regression model. This paper presents an evolution of the OWAVEC method, maintaining the first two stages in its application procedure (wavelet signal decomposition and direct orthogonalization) intact but incorporating genetic algorithms as a wavelet coefficients selection method to perform data compression and to improve the quality of the regression models developed later. Several specific applications dealing with diverse NIR regression problems are analyzed to evaluate the actual performance of the new OWAVEC method. Results provided by OWAVEC are also compared with those obtained with original data and with other orthogonal signal correction methods.     

OWAVEC: a combination of wavelet analysis and an orthogonalization algorithm as a pre-processing step in multivariate calibration

In an spectroscopic context, when a calibration model based on partial least squares is developed to predict a response, it is often the case that a high percentage of variation in the data explained by the first latent variable is not accompanied by an equally high percentage of variation in the studied response. The addition of more components can slowly improve the calibration model, but with negative effects on the robustness and interpretability of the final model. To solve this problem, several pre-processing methods have been proposed to remove only a portion unrelated to the studied response from the spectral matrix.Moreover, the need for efficient compression methods is increasingly important due to the large size of the data currently collected. In this sense, discrete wavelet transform has proven that it can achieve good compression without losing relevant information when used on individual signals.This paper introduces a new pre-processing method, orthogonal wavelet correction (OWAVEC) that tries to lump together two important needs in multivariate calibration: signal correction and compression. The new method has been tested on a set of diesel fuels using viscosity as variable response, and its results have been compared not only with those obtained from original data but also with those provided by other correction methods. The first practical results are encouraging, as the method generates considerably better calibration models compared to the model developed from raw data and provides results as least so good as other orthogonal correction methods.     

Complete quality analysis of commercial surface-active products by Fourier-transform near infrared spectroscopy

Using proper calibration data Fourier-transform near infrared spectroscopy is used for developing multivariate calibrations for different analytical determinations routinely used in the surfactants industry. Four products were studied: oleyl-cetyl alcohol polyethoxylated, cocamidopropyl betaine (CAPB), sodium lauryl sulfate (SLS) and nonylphenol polyethoxylated (NPEO). Calibrations for major as well as very low concentrated compounds were achieved and every model was validated through linearity, bias, accuracy and precision tests, showing good results and the viability of NIR spectroscopy as a full quality control method for this products. Duplicate and complete analysis on a single sample takes at most 3 min, requiring neither sample preparation nor the use of reagents. The analytical reference procedures involved in this work represent the typical ones used in the industry and the NIR method shows good results in the analysis of components with weight concentrations less than 1%.     

Feasibility study on qualitative and quantitative analysis in tea by near infrared spectroscopy with multivariate calibration

This study attempted the feasibility to use near infrared (NIR) spectroscopy as a rapid analysis method to qualitative and quantitative assessment of the tea quality. NIR spectroscopy with soft independent modeling of class analogy (SIMCA) method was proposed to identify rapidly tea varieties in this paper. In the experiment, four tea varieties from Longjing, Biluochun, Qihong and Tieguanyin were studied. The better results were achieved following as: the identification rate equals to 90% only for Longjing in training set; 80% only for Biluochun in test set; while, the remaining equal to 100%. A partial least squares (PLS) algorithm is used to predict the content of caffeine and total polyphenols in tea. The models are calibrated by cross-validation and the best number of PLS factors was achieved according to the lowest root mean square error of cross-validation (RMSECV). The correlation coefficients and the root mean square error of prediction (RMSEP) in the test set were used as the evaluation parameters for the models as follows: R = 0.9688, RMSEP = 0.0836% for the caffeine; R = 0.9299, RMSEP = 1.1138% for total polyphenols. The overall results demonstrate that NIR spectroscopy with multivariate calibration could be successfully applied as a rapid method not only to identify the tea varieties but also to determine simultaneously some chemical compositions contents in tea.     

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.     

A new formulation to estimate the variance of model prediction. Application to near infrared spectroscopy calibration

Evaluation of uncertainty affecting predictions is a major trend in analytical chemistry and chemometrics. Several approximate expressions and resampling methods have been proposed for the estimation of prediction uncertainty when using multivariate calibration. This article proposes a new expression for the variance of prediction, adapted to near infrared spectroscopy specificities and particularly to the spectral error structure, induced by the high colinearity of the variables. The proposed analytical expression enables a detailed evaluation of the different contributions and components of uncertainty affecting the model. An application to real data of feedstuff near infrared spectra related to protein content has shown its advantages.     

Quantitative analysis of volatiles from solid matrices of vegetable origin by high concentration capacity headspace techniques: determination of furan in roasted coffee

The study compares standard addition (SA), stable isotope dilution assay (SIDA) and multiple headspace extraction (MHE) as methods to quantify furan and 2-methyl-furan in roasted coffee with HS-SPME-GC-MS, using CAR-PDMS as fibre coating, d(4)-furan as internal standard and in-fibre internal standardization with n-undecane to check the fibre reliability. The results on about 150 samples calculated with the three quantitation approaches were all very satisfactory, with coefficient of variation (CV) versus the U.S. Food and Drug Administration (FDA) method, taken as reference, almost always below the arbitrarily-fixed limit of 15%. Furan was detected in the 1-5 ppm range, 2-methyl-furan in the 4-20 ppm range. Moreover, experimental exponential slopes (Q) and linearity (r) of both furan and 2-methyl-furan MHE regression equation on 50 samples were very similar thus making possible to use the same average Q value for all samples of the investigated set and their quantitation with a single determination. This makes this approach very rapid and competitive in-time with SA and SIDA. A non-separative method (HS-SPME-MS) was also developed in view of possible application on-line monitoring of furan and 2-methyl-furan in a pilot-plant with the aim of optimizing the roasting process to reduce these compounds to a minimum. Sampling times of 20 and 5 min were tested, the latter enabling total analysis time to be reduced to about 9 min. The results on 105 samples with both SIDA and MHE approaches were again highly satisfactory most of the samples giving a CV% versus the conventional methods below 20%. In this case too average Q values for both furan and 2-methyl-furan were used for MHE. The separative method presented very good repeatability (RSD% always below 10%) and intermediate precision over three months (RSD% always below 15%); performance were similar for the non-separative method, with repeatability (RSD%) always below 12% and intermediate precision over three months (RSD%) always below 15%. The sensitivity of both separative and non-separative methods was also very good, LOD and LOQ being in the ppb range for both furan and 2-methyl-furan, i.e. well below the amounts present in the roasted coffee samples.     

Chemometric models for the quantitative descriptive sensory analysis of Arabica coffee beverages using near infrared spectroscopy

Mathematical models based on chemometric analyses of the coffee beverage sensory data and NIR spectra of 51 Arabica roasted coffee samples were generated aiming to predict the scores of acidity, bitterness, flavour, cleanliness, body and overall quality of coffee beverage. Partial least squares (PLS) were used to construct the models. The ordered predictor selection (OPS) algorithm was applied to select the wavelengths for the regression model of each sensory attribute in order to take only significant regions into account. The regions of the spectrum defined as important for sensory quality were closely related to the NIR spectra of pure caffeine, trigonelline, 5-caffeoylquinic acid, cellulose, coffee lipids, sucrose and casein. The NIR analyses sustained that the relationship between the sensory characteristics of the beverage and the chemical composition of the roasted grain were as listed below: 1 - the lipids and proteins were closely related to the attribute body; 2 - the caffeine and chlorogenic acids were related to bitterness; 3 - the chlorogenic acids were related to acidity and flavour; 4 - the cleanliness and overall quality were related to caffeine, trigonelline, chlorogenic acid, polysaccharides, sucrose and protein.     

Application of artificial neural network to determination of active principle ingredient in pharmaceutical quality control based on near infrared spectroscopy

In the construction of a neural network, most attentions have been paid to the selection of the architecture, the selection of the learning parameters and the network validation while the selection of input variables shared little. This study focused on the selection of input variables by various data pre-treatment for constructing ANN models. The results showed that the validation results differed from each other when different data-pretreatment methods combined with near-infrared spectroscopy (NIRS) to build a model using artificial neural network (ANN) for quality control of paracetamol in coldrex. And wavelet coefficients after orthogonal signal correction (OSC) in the ANN models reduced RMSEP by up to 77% compared to ANN models using derivatives combined with PCA pretreatment. The selection of input variables has potent to improve the calibration ability of ANN, and the model can be used for pressure reduction of quality control in the pharmaceutical industry.     

Rapid determination of sucrose in chocolate mass using near infrared spectroscopy

This paper reports the results of a rapid method to determine sucrose in chocolate mass using near infrared spectroscopy (NIRS). We applied a broad-based calibration approach, which consists in putting together in one single calibration samples of various types of chocolate mass. This approach increases the concentration range for one or more compositional parameters, improves the model performance and requires just one calibration model for several recipes. The data were modelled using partial least squares (PLS) and multiple linear regression (MLR). The MLR models were developed using a variable selection based on the coefficient regression of PLS and genetic algorithm (GA). High correlation coefficients (0.998, 0.997, 0.998 for PLS, MLR and GA-MLR, respectively) and low prediction errors confirms the good predictability of the models. The results show that NIR can be used as rapid method to determine sucrose in chocolate mass in chocolate factories.     

Classification and quantitation of finishing oils by near infrared spectroscopy

One of the steps in the manufacturing of synthetic fibres involves using finishing oils to ensure proper lubricity and adherence between fibres, and also the absence of static electricity. Choosing an appropriate oil and dosage are essential with a view to ensuring effective subsequent processing and use. The aim of this work was to develop a fast method for determining the different finishing oil content in acrylic fibres by use of near infrared spectroscopy (NIRS) in conjunction with partial least-squares regression (PLSR). The high similarity between the NIR spectra of finishing oils led us to assume that a single calibration model might allow determine the oil content. However, the inability to quantify accurately different finishing oils by using a sole calibration model, constrain to the prior classification of the fibres coated with the different finishing oils. Two different pattern recognition methods were used: supervised independent modeling of class analogy (SIMCA) and artificial neural networks (ANNs). However, the low contribution of the finishing oil to the NIR spectrum for the fibre sample, the high similarity between the NIR spectra for the different oils and the substantial contribution of the linear density of the acrylic fibre to the spectrum precluded correct classification by SIMCA; on the other hand, ANNs provided good results. By constructing appropriate PLSR models for the different types of finishing oils, these can be accurately determined in acrylic fibres.     

傅里叶变换近红外光谱法快速检测人血清生化成分

应用傅里叶变换近红外光谱透射技术结合偏最小二乘法(PLS)建立了人血清中7种生化成分的定标模型,利用内部交叉验证和自动优化功能对定标模型进行了优化,确定了最优建模参数。模型对人血清中总胆固醇、甘油三酯、总蛋白、白蛋白、载脂蛋白B、低密度脂蛋白胆固醇、葡萄糖定标样品集的预测值与化学值的相关系数r分别为0.9011、0.9593、0.9249、0.761、0.8831、0.5191、0 9148,预测校正标准误差RMSECV分别为15mg/dL,21.6mg/dL,2 66g/L,3 96g/L,0.091g/L,16.2mg/dL,0.49mmol/L。     

Determination of biodiesel content when blended with mineral diesel fuel using infrared spectroscopy and multivariate calibration

In this work, multivariable calibration models based on middle- and near-infrared spectroscopy were developed in order to determine the content of biodiesel in diesel fuel blends, considering the presence of raw vegetable oil. Soybean, castor and used frying oils and their corresponding esters were used to prepare the blends with conventional diesel. Results indicated that partial least squares (PLS) models based on MID or NIR infrared spectra were proven suitable as practical analytical methods for predicting biodiesel content in conventional diesel blends in the volume fraction range from 0% to 5%. PLS models were validated by independent prediction set and the RMSEPs were estimated as 0.25 and 0.18 (%, v/v). Linear correlations were observed for predicted vs. observed values plots with correlation coefficient (R) of 0.986 and 0.994 for the MID and NIR models, respectively. Additionally, principal component analysis (PCA) in the MID region 1700 to 1800 cm^− 1 was suitable for identifying raw vegetable oil contaminations and illegal blends of petrodiesel containing the raw vegetable oil instead of ester.     

Active content determination of non-coated pharmaceutical pellets by near infrared spectroscopy: Method development, validation and reliability evaluation

A robust near infrared (NIR) method able to quantify the active content of pilot non-coated pharmaceutical pellets was developed. A protocol of calibration was followed, involving 2 operators, independent pilot batches of non-coated pharmaceutical pellets and two different NIR acquisition temperatures. Prediction models based on Partial Least Squares (PLS) regression were then carried out. Afterwards, the NIR method was fully validated for an active content ranging from 80 to 120% of the usual active content using new independent pilot batches to evaluate the adequacy of the method to its final purpose. Conventional criteria such as the R², the Root Mean Square Error of Calibration (RMSEC), the Root Mean Square Error of Prediction (RMSEP) and the number of PLS factors enabled the selection of models with good predictive potential. However, such criteria sometimes fail to choose the most fitted for purpose model. Therefore, a novel approach based on accuracy profiles of the validation results was used, providing a visual representation of the actual and future performances of the models. Following this approach, the prediction model using signal pre-treatment Multiplicative Scatter Correction (MSC) was chosen as it showed the best ability to quantify accurately the active content over the 80-120% active content range. The reliability of the NIR method was tested with new pilot batches of non-coated pharmaceutical pellets containing 90 and 110% of the usual active content, with blends of validation batches and industrial batches. All those batches were also analyzed by the HPLC reference method and relative errors were calculated: the results showed low relative errors in full accordance with the results obtained during the validation of the method, indicating the reliability of the NIR method and its interchangeability with the HPLC reference method.     

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.     

An approach by using near-infrared diffuse reflectance spectroscopy and resin adsorption for the determination of copper, cobalt and nickel ions in dilute solution

Near-infrared spectroscopy (NIRS) has been proved to be a powerful analytical tool and used in various fields, it is seldom, however, used in the analysis of metal ions in solutions. A method for quantitative determination of metal ions in solution is developed by using resin adsorption and near-infrared diffuse reflectance spectroscopy (NIRDRS). The method makes use of the resin adsorption for gathering the analytes from a dilute solution, and then NIRDRS of the adsorbate is measured. Because both the information of the metal ions and their interaction with the functional group of resin can be reflected in the spectrum, quantitative determination is achieved by using multivariate calibration technique. Taking copper (Cu²⁺), cobalt (Co²⁺) and nickel (Ni²⁺) as the analyzing targets and D401 resin as the adsorbent, partial least squares (PLS) model is built from the NIRDRS of the adsorbates. The results show that the concentrations that can be quantitatively detected are as low as 1.00, 1.98 and 1.00 mg L⁻¹ for Cu²⁺, Co²⁺ and Ni²⁺, respectively, and the coexistent ions do not influence the determination.