A comparative study of calibration transfer methods for determination of gasoline quality parameters in three different near infrared spectrometers

This work presents a comparative study of calibration transfer among three near infrared spectrometers for determination of naphthenes and RON (Research Octane Number) in gasoline. Seven transfer methods are compared: direct standardization (DS), piecewise direct standardization (PDS), orthogonal signal correction (OSC), reverse standardization (RS), piecewise reverse standardization (PRS), slope and bias correction (SBC) and model updating (MU). Two pre-treatment procedures, namely standard normal variate (SNV) and multiplicative scatter correction (MSC), are also investigated. The choice of an appropriate number of transfer samples for each technique, as well as the effect of window size in PDS/PRS and OSC components, are discussed. A broad set of gasoline samples representative of the Northeastern states of Brazil is employed in the investigation. The results show that the use of calibration transfer yields prediction errors comparable to those obtained with complete recalibration of the secondary instrument. Overall, the results point to RS as the best method for the analytical problem under consideration. When storage and/or physical transportation of transfer samples are impractical, MU is more appropriate. The comprehensive investigation carried out in the present work will be of value for practitioners involved in networks of fuel monitoring.     

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.     

Flow injection determination of peroxide value in edible oils using triiodide detector

A flow injection analysis (FIA) method for the determination of peroxide value (PV) in edible oils is described. Oil sample (undiluted) and KI reagent were aspirated into a homemade reaction chamber where the redox reaction between iodide in the aqueous phase and hydroperoxides in the oil was effected by applying a short (typically 30 s) vortex action. After allowing for the emulsified oil phase to be separated from the aqueous phase (bottom layer), an aliquot of the aqueous phase containing triiodide was next aspirated to the surface of a triiodide-selective membrane for detection. The optimized FIA procedure is linear over 0.35-28.0 PV (mequiv. O₂/kg) with a detection limit of 0.32 PV. Exhibiting good reproducibility (R.S.D. of 2.7% (n = 8) for the determination of 1.1 PV) and sampling rate of 80 samples h⁻¹, the proposed method, unlike previous FIA procedures, completely eliminated the use of organic solvents (except the use of 2-propanol for cleaning of reaction chamber). Excellent correlation (R² = 0.9949) between the proposed method and the manual official AOCS method was found when applied to the determination of PV in diverse type of edible oils (n = 20).     

A novel storage method for near infrared spectroscopy chemometric models

Chemometric Modeling Markup Language (CMML) is developed by us for containing chemometrics models within one document through converting binary data into strings by base64 encode/decode algorithms to solve the interoperability issue in sharing chemometrics models. It provides a base functionality for storage of sampling, variable selection, pretreating, outlier and modeling parameters and data. With the help of base64 algorithm, the usability of CMML is in equilibrium with size by transforming the binary data into base64 encoded string. Due to the advantages of Extensible Markup Language (XML), models stored in CMML can be easily reused in various other software and programming languages as long as the programming language has XML parsing library. One can also use the XML Path Language (XPath) query language to select desired data from the CMML file effectively. The application of this language in near infrared spectroscopy model storage is implemented as a class in C++ language and available as open source software (http://code.google.com/p/cmml), and the implementations in other languages, such as MATLAB and R are in progress.     

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.     

Fundamentals and applications of near infrared spectroscopy in spectroelectrochemistry

Near infrared spectroscopy as a tool for in situ spectroelectrochemical investigations of electrochemical systems is reviewed with particular attention to experimental approaches and typical results from all parts of chemistry and applied chemistry     

Optimization of transmission near infrared spectrometry procedures for quality control of pesticide formulations

The use of different response functions to be optimized in the frame of the use of near infrared spectrometry for quality control of active principles in agrochemical formulations has been evaluated. Both, simple functions, based on parameters like sensitivity, repeatability, accuracy, signal to noise ratio, limit of detection or sample throughput, and a complex function, considering all the aforementioned aspects, were employed in the development of a new method for Iprodione determination in agrochemicals. Optimization strategies were based on the previous screening of the most important instrumental factors like number of cumulated scans, nominal resolution, mirror velocity and zero filling factor, based on a two-level full factorial design and on the search for the optimum conditions using central composite designs. Data found evidenced the influence of the response function on the optimum values of experimental conditions and could be employed as a general guide to evaluate the experimental factors in routine use of near infrared spectrometry. Finally the optimized method for Iprodione has been applied to the determination of Diuron and results found compared with those obtained by a conventional approach.     

Speciation analysis of arsenic compounds in edible oil by ion chromatography-inductively coupled plasma mass spectrometry

An inductively coupled plasma mass spectrometer (ICP-MS) was used as an ion chromatographic (IC) detector for the speciation analysis of arsenic in edible oil. The arsenic species studied include arsenite, arsenate, monomethylarsonic acid, dimethylarsinic acid, arsenobetaine and arsenocholine. Gradient elution using (NH(4))(2)CO(3) and methanol at pH 8.5 allowed the chromatographic separation of all species in less than 8 min. Effluents from the IC column were delivered to the nebulizer of ICP-MS for the determination of arsenic. The concentrations of arsenic species have been determined in several used and fresh vegetable oil samples. In this study, a microwave-assisted extraction method was used for the extraction of arsenic species from oil samples. The extraction efficiency was better than 92% and the recoveries from spiked samples were in the range of 90-105%. The precision between sample replicates was better than 8% for all determinations. The limits of detection were in the range of 0.008-0.024 ng mL(-1) for various arsenic species based on peak height, which corresponded to 0.08-0.24 ng g(-1) in the original oil sample. The major arsenic species in the used oil samples varied based on the food items cooked.     

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.     

Maintenance of a calibration model for near infrared spectrometry by a combined principal component analysis-partial least squares approach

A novel strategy for building and maintaining calibration models has been developed for use when the future boundaries of the sample set are unknown or likely to change. Such a strategy could have an impact on the economics and time required to obtain and maintain a calibration model for routine analysis. The strategy is based on both principal component analysis (PCA) and partial least squares (PLS) multivariate techniques. The principal action of the strategy is to define how “similar” a new sample is to the samples currently defining the calibration dataset. This step is performed by residuals analysis, following PCA. If the new sample is considered to have a spectrum “similar” to previously available spectra, then the model is assumed able to predict the analyte concentration. Conversely, if the new sample is considered “dissimilar”, then there is new information in this sample, which is unknown to the calibration model and the new sample is added automatically to the calibration set in order to improve the model. The strategy has been applied to a real industrial dataset provided by BP Amoco Chemicals. The data consists of spectra of 102 sequential samples of a raw material. The strategy produced an accurate calibration model for both target components starting with only the first four samples, and required a further 17 reference measurements to maintain the model for the whole sampling sequence, which was over a 1-year period.     

近红外光谱法结合化学计量学方法用于茶油真伪鉴别分析

采用近红外光谱透反射模式结合化学计量学方法对纯茶油进行真伪鉴别.收集并扫描了163个样品(合格97个,不合格66个),对样本进行光谱数据预处理优化及有效波段筛选.在5750～6000 cm-1波段处,光谱经过平滑,一阶导数以及自归一化后,采用无监督学习算法即主成分分析法(Principal component anal...     

Determination of detergent and dispensant additives in gasoline by ring-oven and near infrared hypespectral imaging

A method using the ring-oven technique for pre-concentration in filter paper discs and near infrared hyperspectral imaging is proposed to identify four detergent and dispersant additives, and to determine their concentration in gasoline. Different approaches were used to select the best image data processing in order to gather the relevant spectral information. This was attained by selecting the pixels of the region of interest (ROI), using a pre-calculated threshold value of the PCA scores arranged as histograms, to select the spectra set; summing up the selected spectra to achieve representativeness; and compensating for the superimposed filter paper spectral information, also supported by scores histograms for each individual sample. The best classification model was achieved using linear discriminant analysis and genetic algorithm (LDA/GA), whose correct classification rate in the external validation set was 92%. Previous classification of the type of additive present in the gasoline is necessary to define the PLS model required for its quantitative determination. Considering that two of the additives studied present high spectral similarity, a PLS regression model was constructed to predict their content in gasoline, while two additional models were used for the remaining additives. The results for the external validation of these regression models showed a mean percentage error of prediction varying from 5 to 15%.     

Flow-injection methylene blue-based spectrophotometric method for the determination of peroxide values in edible oils

A flow-injection method for measuring the peroxide value (PV, mequiv. O₂ kg⁻¹) in edible oils is described. The technique is based on spectrophotometric monitoring at 660 nm of methylene blue (MB), generated from leucomethylene blue (LMB) oxidation with peroxides present in oil samples. After being optimized, the method was validated in terms of linearity, precision sensitivity and recovery.Linear calibration graph was obtained in the range 0.1-5 mequiv. O₂ kg⁻¹, with a detection limit (S/N  =  3) of 0.014 mequiv. O₂ kg⁻¹. The precision of the method (R.S.D., n = 9) for within and between-days is better than 1.5% and 2.2%, respectively at 0.4 mequiv. O₂ kg⁻¹. The method was applied successfully to the determination of PV in six edible oil samples, and compared to the classical official method. Using the linear regression test, Student's t-test and variance ratio F-test, there was no significant difference between the compared methods. The proposed method is accurate, simple, cheap and could be used to control edible oil rancidity with a high sample throughputs (30 samples h⁻¹).     

Building the quality into pellet manufacturing environment--feasibility study and validation of an in-line quantitative near infrared (NIR) method

The present study focuses on the implementation of an in-line quantitative near infrared (NIR) spectroscopic method for determining the active content of pharmaceutical pellets. The first aim was to non-invasively interface a dispersive NIR spectrometer with four realistic particle streams existing in the pellets manufacturing environment. Regardless of the particle stream characteristics investigated, NIR together with Principal Component Analysis (PCA) was able to classify the samples according to their active content. Further, one of these particle stream interfaces was non-invasively investigated with a FT-NIR spectrometer. A predictive model based on Partial Least Squares (PLS) regression was able to determine the active content of pharmaceutical pellets. The NIR method was finally validated with an external validation set for an API concentration range from 80 to 120% of the targeted active content. The prediction error of 0.9% (root mean standard error of prediction, RMSEP) was low, indicating the accuracy of the NIR method. The accuracy profile on the validation results, an innovative approach based on tolerance intervals, demonstrated the actual and future performance of the in-line NIR method. Accordingly, the present approach paves the way for real-time release-based quality system.     

Characterization of estuarine sediments by near infrared diffuse reflectance spectroscopy

It has been developed a partial least squares near infrared (PLS-NIR) method for the determination of estuarine sediment physicochemical parameters. The method was based on the chemometric treatment of first order derivative reflectance spectra obtained from samples previously lyophilized and sieved through a lower than 63 μm grid. Spectra were scanned from 833 to 2976 nm, averaging 36 scans per spectrum at a resolution of 8 cm⁻¹, using chromatographic glass vials of 9.5 mm internal diameter as measurement cells. Models were built using reference data of 31 samples selected through the use of a hierarchical cluster analysis of NIR spectra of sediments obtained from the Ria de Arousa estuary and prediction parameters were established from a validation set of 50 samples of the same area. pH, redox potential (Eh), carbon (C), nitrogen (N) and hydrogen (H) content together with Sn, Pb, Cd, As, Sb and total Cr and also acid soluble, reducible and oxidable Cr fractions were employed as characteristic parameters of the studied sediments. Standard error of prediction values for C and N content were of the order of 4 and 1.3 mg g⁻¹ for H. Prediction errors for pH and Eh were 0.15 units and 37 mV, respectively, thus indicating the good prediction capabilities of the method. Regarding trace metal concentrations PLS-NIR provided prediction error levels for unknown samples around 20% for Sn, Pb, As and Sb and root mean square errors of prediction around 40% for concentration levels of 400 ng g⁻¹ Cd and 100 μg g⁻¹ Cr. For the different extractable fractions of Cr the residual prediction deviation varied from 1.3 to 1.7 but relative errors found for samples of the validation set were only useful for screening purposes.     

Determination of oil and water content in olive pomace using near infrared and Raman spectrometry. A comparative study

Near infrared (NIR) reflectance and Raman spectrometry were compared for determination of the oil and water content of olive pomace, a by-product in olive oil production. To enable comparison of the spectral techniques the same sample sets were used for calibration (1.74–3.93% oil, 48.3–67.0% water) and for validation (1.77–3.74% oil, 50.0–64.5% water). Several partial least squares (PLS) regression models were optimized by cross-validation with cancellation groups, including different spectral pretreatments for each technique. Best models were achieved with first-derivative spectra for both oil and water content. Prediction results for an independent validation set were similar for both techniques. The values of root mean square error of prediction (RMSEP) were 0.19 and 0.20–0.21 for oil content and 2.0 and 1.8 for water content, using Raman and NIR, respectively. The possibility of improving these results by combining the information of both techniques was also tested. The best models constructed using the appended spectra resulted in slightly better performance for oil content (RMSEP 0.17) but no improvement for water content.     

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.     

Pattern recognition of Chinese flue-cured tobaccos by an improved and simplified K-nearest neighbors classification algorithm on near infrared spectra

In tobacco industry of China, tobacco leaves are classified and managed in terms of their cultivation areas and plant parts of tobacco-stalks. However, sometimes intentionally or involuntary mislabeling cultivation areas, blending tobacco plant parts would occur into tobacco market. The error will affect the style and quality of cigarettes. In the present work, more than 1000 Chinese flue-cured tobacco leaf samples, which have 12 genotypes and cultivated from 5 to 10 regions of China in 2003 and 2004, have been discriminated by means of an improved and simplified KNN classification algorithm (IS-KNN) based on near infrared (NIR) spectra. An original method of optimizing number of significant principal components (PCs) based on analysis of error and cross-validation was advanced. Compared with conventional pattern recognition methods KNN, NN, LDA and PLS-DA, IS-KNN exhibits good adaptability in discrimination of complicated Chinese flue-cured tobaccos. The practice in this work shows that optimized number of PCs and performance of classification models are closely relative to complicated extent of samples but not to number of categories or samples. The results demonstrated the usefulness of NIR spectra combined with chemometrics as an objective and rapid method for the authentication and identification of tobacco leaves or other kinds of powder samples.     

近红外光谱法分析土壤中的有机质和氮素

应用近红外光谱技术测定土壤中的全氮、有机质、碱解氮，分别测定了2mm、0.15mm粒度的风干土在4000cm^-1～12000cm^-1波数范围的近红外光谱，用偏最小二乘法建立数学模型来进行含量预测，结果表明近红外光谱与土壤有机质、全氮、碱解氮具有良好的相关性，2mm风干土碱解氮建模的决定系数R^2为92.39，相对标准偏差为7.5%；2mm风干土全氮建模的决定系数R^2为88，相对标准偏差为8.2%;0.15mm的全氮建模的决定系数R2为89.86，相对标准偏差为7.2%;0.15mm风干土有机质建模的决定系数R^2为96.41，相对标准偏差为8.3%。因此，用近红外光普法测定土壤有机质、全氮、碱解氮的含量是可行的。