In‐process monitoring in industrial olive mill by means of FT‐NIR

A total of 287 olive lots and 161 olive oil samples were analyzed for fat content, moisture and free acidity, using a Fourier transform near‐infrared (FT‐NIR) instrument located in an industrial mill. Samples having a wide range of both reference values and olive lot sizes (from <0.5 to >4 t) were collected at three industrial mill plants, located in the same Italian region, which utilize different technological equipment for virgin olive oil production. Olive paste spectra were acquired in diffuse reflectance, while oil samples were measured in transmission. Calibration models for oil content and moisture of olives as well as free acidity of virgin olive oils were developed using partial least squares (PLS) regression, first derivative and straight line subtraction. Results of calibration and validation of the PLS models selected were good. The PLS results indicate good similarity between data obtained from FT‐NIR and reference laboratory methods, allowing a rapid and less expensive screening analysis. Unfortunately, the correlation between the oil yield values recorded for all olive lots at the industrial mills and the oil content predicted by FT‐NIR was not satisfactory (R² = 0.605).     

Simultaneous determination of molecular weight and crystallinity of recycled HDPE by infrared spectroscopy and multivariate calibration

An attempt of correlating molecular weight (M_n) of recycled high‐density polyethylene (HDPE) as measured by size‐exclusion chromatography (SEC) with diffuse reflectance near and mid‐infrared spectroscopy (NIR/MIR) was made by means of multivariate calibration. The spectral data obtained was also used to extract information about the degree of crystallinity of the recycled resin. Differential scanning calorimetry (DSC) was used as the reference method. Partial least‐squares (PLS) calibration was performed on the MIR and NIR spectral data for prediction of M_n. Four PC factors described fully the PLS models. The root‐mean‐square error of prediction (RMSEP) obtained with MIR data was 360, whereas a RMSEP of 470 was achieved when calibration was carried out on the diffuse reflectance NIR data. A PLS calibration for prediction of degree of crystallinity was performed on the NIR data in the 1100–1900‐nm region, but the ability of prediction of this model was poor. However a PLS calibration in the region 2000–2500 nm yield better results. Four PC factors explained the most of the variance in the spectra and the RMSEP was 0.4 wt %. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 85: 321–327, 2002     

Assessment of resin formulations and determination of the formaldehyde to urea molar ratio by near‐ and mid‐infrared spectroscopy and multivariate data analysis

The molar ratios of formaldehyde (F) to urea (U) of three resin formulations in the range from 0.90 to 1.49 have been analyzed by means of Attenuated Total Reflection‐Fourier Transform Infrared (ATR‐FTIR) spectroscopy and Fourier Transform‐Near‐Infrared (FT‐NIR) spectroscopy. Application of Principal Component Analysis (PCA) to the spectra (MIR and NIR) allowed to separate them according to the molar ratio and to distinguish between two groups of resins. Soft Independent Modeling of Class Analogy (SIMCA) allowed classification of new resin samples with high model distances between the classes. Partial Least Squares Regression (PLS‐R) models based on MIR (NIR) spectra resulted in high coefficients of determination (R²) values, low errors, and high residual prediction deviations (RPD). To confirm the reproducibility of the process and to carefully evaluate twice all multivariate analysis results obtained, different batches of resins have been prepared to have an additional independent sample set. The number of samples required for MIR and possible applications of MIR and NIR spectroscopy in this context including limitations have been discussed. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2013     

Determination of the Si?H content of hydrogen silicone oil by a combination of the fourier transform near infrared,attenuated total reflectance–fourier transform infrared,and partial least squares regression models

To verify the feasibility of the determination of the Si? H content (HC) of hydrogen silicone oil (HS‐oil) with Fourier transform near infrared (FT‐NIR) spectroscopy and attenuated total reflectance (ATR)–Fourier transform infrared (FTIR) spectroscopy combined with the partial least squares regression (PLS‐R) model, HS‐oil samples were synthesized from concentrated hydrosilicone oil (HC = 1.4 wt %), octamethylcyclotetrasiloxane, and hexamethyldisiloxane or prepared by the dilution of concentrated hydrosilicone oil with octamethylcyclotetrasiloxane. The FT‐NIR PLS‐R model (8695–4000 cm^?1, two principal components) was developed from the FT‐NIR spectral data, and the coefficient of determination for cross‐validation (R²) and the coefficient of determination for external validation (r²) were 0.992 and 0.995, respectively. The ATR–FTIR PLS‐R model (2302–2040 cm^?1, one principal component) was developed from the ATR–FTIR spectral data; it produced an R² of 0.995 and an r² of 0.996. This study demonstrated that the combination of FT‐NIR and ATR–FTIR spectroscopy with the PLS‐R model were successfully used to determine the HC of the HS‐oil. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131, 40694.     

Determination of the SiH content of hydrogen silicone oil by a combination of the fourier transform near infrared,attenuated total reflectance–fourier transform infrared,and partial least squares regression models

To verify the feasibility of the determination of the Si?H content (HC) of hydrogen silicone oil (HS‐oil) with Fourier transform near infrared (FT‐NIR) spectroscopy and attenuated total reflectance (ATR)–Fourier transform infrared (FTIR) spectroscopy combined with the partial least squares regression (PLS‐R) model, HS‐oil samples were synthesized from concentrated hydrosilicone oil (HC = 1.4 wt %), octamethylcyclotetrasiloxane, and hexamethyldisiloxane or prepared by the dilution of concentrated hydrosilicone oil with octamethylcyclotetrasiloxane. The FT‐NIR PLS‐R model (8695–4000 cm^?1, two principal components) was developed from the FT‐NIR spectral data, and the coefficient of determination for cross‐validation (R²) and the coefficient of determination for external validation (r²) were 0.992 and 0.995, respectively. The ATR–FTIR PLS‐R model (2302–2040 cm^?1, one principal component) was developed from the ATR–FTIR spectral data; it produced an R² of 0.995 and an r² of 0.996. This study demonstrated that the combination of FT‐NIR and ATR–FTIR spectroscopy with the PLS‐R model were successfully used to determine the HC of the HS‐oil. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2014 , 131, 40694.     

Comparison of fatty acid profiles and mid‐infrared spectral data for classification of olive oils

The composition of olive oils may vary depending on environmental and technological factors. Fatty acid profiles and Fourier‐transform infrared (FT‐IR) spectroscopy data in combination with chemometric methods were used to classify extra‐virgin olive oils according to geographical origin and harvest year. Oils were obtained from 30 different areas of northern and southern parts of the Aegean Region of Turkey for two consecutive harvest years. Fatty acid composition data analyzed with principal component analysis was more successful in distinguishing northern olive oil samples from southern samples compared to spectral data. Both methods have the ability to differentiate olive oil samples with respect to harvest year. Partial least squares (PLS) analysis was also applied to detect a correlation between fatty acid profile and spectral data. Correlation coefficients (R²) of a calibration set for stearic, oleic, linoleic, arachidic and linolenic acids were determined as 0.83, 0.97, 0.97, 0.83 and 0.69, respectively. Fatty acid profiles were very effective in classification of oils with respect to geographic origin and harvest year. On the other hand, FT‐IR spectra in combination with PLS could be a useful and rapid tool for the determination of some of the fatty acids of olive oils.     

Chemometric Studies on zNose™ and Machine Vision Technologies for Discrimination of Commercial Extra Virgin Olive Oils

The aim of this study was to classify Turkish commercial extra virgin olive oil (EVOO) samples according to geographical origins by using surface acoustic wave sensing electronic nose (zNose?) and machine vision system (MVS) analyses in combination with chemometric approaches. EVOO samples obtained from north and south Aegean region were used in the study. The data analyses were performed with principal component analysis class models, partial least squares‐discriminant analysis (PLS‐DA) and hierarchical cluster analysis (HCA). Based on the zNose? analysis, it was found that EVOO aroma profiles could be discriminated successfully according to geographical origin of the samples with the aid of the PLS‐DA method. Color analysis was conducted as an additional sensory quality parameter that is preferred by the consumers. The results of HCA and PLS‐DA methods demonstrated that color measurement alone was not an effective discriminative factor for classification of EVOO. However, PLS‐DA and HCA methods provided clear differentiation among the EVOO samples in terms of electronic nose and color measurements. This study is significant from the point of evaluating the potential of zNose? in combination with MVS as a rapid method for the classification of geographically different EVOO produced in industry.     

A Systematic Chemometric Approach to Identify the Geographical Origin of Olive Oils

The verification of the geographical origin of olive oils by analytical techniques is still a challenge. The goal of this work is to explore the application and accuracy of different chemometric tools combined with near infrared spectroscopy (NIR) based analytical methods in the field of geographical authenticity of olive oils. As olive oils associated with different geographical origins are mainly characterized by different fatty acid (FA) and triacylglycerol (TAG) compositions, NIR methods for the fast and reliable determination of these parameters are developed. Next, these NIR methods are used to characterize a comprehensive set of olive oils (n > 5000) derived from 19 different countries. This set of data is used to build a statistical workflow, which allows the determination of the geographical origin of unknown olive oil samples. First of all, the untreated data set is pretreated by k‐means clustering and the selection of the relevant analytical variables by principal component analysis (PCA) and linear discriminant analysis (LDA) and min/max normalization of all parameters. Subsequently, classification is performed with a reduced sample set of the 200 most similar samples identified by k‐nearest neighbor tool (kNN). For classification purpose kNN, LDA, naïve Bayes classifier, and logit regression are applied. Practical Applications: The established statistical workflow can be used to verify the geographical origin of olive oils. The application and usage of up to four different statistical models for classification purpose results in a superior probability of the predicted origin in comparison to the application of only one single statistical classification test. As standardized methods are used as reference methods for building the NIR methods, the FA and TAG composition and the iodine value can be either determined by the standard methods or by the described NIR method. The presented statistical approach will help to build up a system for the verification of the geographical origin of olive oils.     

Prediction of quality of intact olives by near infrared spectroscopy

In this work we investigated the intact olive fruit quality prediction parameters measured directly by visible and near infrared spectroscopy (Vis/NIRS); the usefulness of a portable spectrometer is also assessed. The analysed parameters of the olive fruits were moisture, dry matter, oil content, oil free acidity and fruit maturity index. It was also studied whether NIR prediction of dry matter on olives may be more useful than NIR moisture measurement. Likewise, the results from the NIR prediction of olive oil contents related to dry matter as well as to fresh weight, were compared. Models for oil content were developed using Soxhlet extraction from dried olive paste as the reference analysis. Results indicate a good prediction potential of the models for the olive quality parameters analysed, with RPD ratios from 2.51 to 3.18. The successful NIR predictions of these quality parameters are reported for the first time. Practical applications: The technique presented here can expedite the milling procedure by allowing early detection of the olive quality and a quick calculation of the economic returns to the producers becomes possible. Furthermore, since oil quality depends largely on the optimal harvesting date when the olives should be taken to the mill, techniques that enable the monitoring of the oil content in olive fruit at different stages of maturity, even while still on the tree, become a useful and practical tool. This technique could allow monitoring the quality attributes of large amounts of the olive fruit entering the mill without the need of laboratory analysis that can only be conducted on a small number of olive fruit samples.     

On‐line prediction of crystallinity spatial distribution across polymer films using NIR spectral imaging and chemometrics methods

A rapid and non‐intrusive on‐line NIR imaging sensor is developed for monitoring spatio‐temporal crystallinity variations across the surface of polymer films. A multivariate image analysis and regression (MIA/MIR) approach is proposed and compared with standard NIR calibration techniques using averaged spectra or second order derivatives combined with PLS regression. Predictions of both the local and global crystallinity variations of HDPE, LDPE, and PP polymer samples were obtained with each approach. Our results show that small variations in crystallinity introduced by changes in cooling rates can be predicted within experimental errors. Crystallinity spatial distributions were also validated and found consistent with processing conditions.     

Authentication of Commercial Extra Virgin Olive Oils

The goal of the current contribution is to determine the geographical origins of olive oil samples obtained from eight different countries during the harvest seasons of 2013 and 2014. First, the contents of olive oil samples were quantified by integrating the peaks of ¹H‐NMR spectra and using linear mathematical equations. The results were analyzed by analysis of variance (ANOVA), a statistical method examining whether there is a significant difference between the groups mean. The origins of the majority of the olive oil samples were discriminated by ANOVA and the minor constituents of the olive oils. Tocopherol and cycloartenol were the most discriminative minor constituents of the olive oil samples.     

Developing FT-NIR and PLS1 Methodology for Predicting Adulteration in Representative Varieties/Blends of Extra Virgin Olive Oils

It was previously demonstrated that Fourier transform near infrared (FT‐NIR) spectroscopy and partial least squares (PLS1) were successfully used to assess whether an olive oil was extra virgin, and if adulterated, with which type of vegetable oil and by how much using previously developed PLS1 calibration models. This last prediction required an initial set of four PLS1 calibration models that were based on gravimetrically prepared mixtures of a specific variety of extra virgin olive oil (EVOO) spiked with adulterants. The current study was undertaken after obtaining a range of EVOO varieties grown in different countries. It was found that all the different types of EVOO varieties investigated belonged to four distinct groups, and each required the development of additional sets of specific PLS1 calibration models to ensure that they can be used to predict low concentrations of vegetable oils high in linoleic, oleic, or palmitic acid, and/or refined olive oil. These four distinct sets of PLS1 calibration models were required to cover the range of EVOO varieties with a linoleic acid content from 1.3 to 15.5 % of total fatty acids. An FT‐NIR library was established with 66 EVOO products obtained from California and Europe. The quality and/or purity of EVOO were assessed by determining the FT‐NIR Index, a measure of the volatile content of EVOO. The use of these PLS1 calibration models made it possible to predict the authenticity of EVOO and the identity and quantity of potential adulterant oils in minutes.     

Differentiation of mixtures of monovarietal olive oils by mid‐infrared spectroscopy and chemometrics

Fourier transform infrared (FT‐IR) spectroscopy in combination with chemometric techniques has become a useful tool for authenticity determination of extra‐virgin olive oils. Spectroscopic analysis of monovarietal extra‐virgin olive oils obtained from three different olive cultivars (Erkence, Ayvalik and Nizip) and mixtures (Erkence‐Nizip and Ayvalik‐Nizip) of monovarietal olive oils was performed with an FT‐IR spectrometer equipped with a ZnSe attenuated total reflection sample accessory and a deuterated tri‐glycine sulfate detector. Using spectral data, principal component analysis successfully classified each cultivar and differentiated the mixtures from pure monovarietal oils. Quantification of two different monovarietal oil mixtures (2–20%) is achieved using partial least square (PLS) regression models. Correlation coefficients (R²) of the proposed PLS regression models are 0.94 and 0.96 for the Erkence‐Nizip and Ayvalik‐Nizip mixtures, respectively. Cross‐validation was applied to check the goodness of fit for the PLS regression models, and R² of the cross‐validation was determined as 0.84 and 0.91, respectively, for the two mixtures.     

Novel,Rapid Identification,and Quantification of Adulterants in Extra Virgin Olive Oil Using Near‐Infrared Spectroscopy and Chemometrics

A new, rapid Fourier transform near infrared (FT‐NIR) spectroscopic procedure is described to screen for the authenticity of extra virgin olive oils (EVOO) and to determine the kind and amount of an adulterant in EVOO. To screen EVOO, a partial least squares (PLS1) calibration model was developed to estimate a newly created FT‐NIR index based mainly on the relative intensities of two unique carbonyl overtone absorptions in the FT‐NIR spectra of EVOO and other mixtures attributed to volatile (5280 cm^?1) and non‐volatile (5180 cm^?1) components. Spectra were also used to predict the fatty acid (FA) composition of EVOO or samples spiked with an adulterant using previously developed PLS1 calibration models. Some adulterated mixtures could be identified provided the FA profile was sufficiently different from those of EVOO. To identify the type and determine the quantity of an adulterant, gravimetric mixtures were prepared by spiking EVOO with different concentrations of each adulterant. Based on FT‐NIR spectra, four PLS1 calibration models were developed for four specific groups of adulterants, each with a characteristic FA composition. Using these different PLS1 calibration models for prediction, plots of predicted vs. gravimetric concentrations of an adulterant in EVOO yielded linear regression functions with four unique sets of slopes, one for each group of adulterants. Four corresponding slope rules were defined that allowed for the determination of the nature and concentration of an adulterant in EVOO products by applying these four calibration models. The standard addition technique was used for confirmation.     

The Use of FTIR Spectroscopy and Chemometrics for Rapid Authentication of Extra Virgin Olive Oil

The authenticity of high value edible fats and oils including extra virgin olive oil (EVOO) is an emerging issue, currently. The potential employment of Fourier transform infrared (FTIR) spectroscopy in combination with chemometrics of multivariate calibration and discriminant analysis has been exploited for rapid authentication of EVOO from canola oil (Ca‐O). The optimization of two calibration models of partial least square (PLS) and principle component regression was performed in order to quantify the level of Ca‐O in EVOO. The chemometrics of discriminant analysis (DA) was used for making the classification between pure EVOO and EVOO adulterated with Ca‐O. The individual oils and their blends were scanned on good contact with ZnSe crystals in horizontal attenuated total reflectance, as a sampling technique. The wavenumbers of 3,028–2,985 and 1,200–987 cm^?1 were used for quantification and classification of EVOO adulterated with Ca‐O. The results showed that PLS with normal FTIR spectra was well suited for quantitative analysis of Ca‐O with a value of the coefficient of determination (R²) > 0.99. The error, expressed as root mean square error of calibration obtained was relatively low, i.e. 0.108 % (v/v). DA can make the classification between pure EVOO and that adulterated with Ca‐O with one misclassified reported.     

Determination of the Fatty Acid Composition in Tree Peony Seeds Using Near-Infrared Spectroscopy

Tree peonies (Paeonia Sect Moutan DC) are an emerging oil crop because of their high oil and α‐linolenic acid (ALA) content. This research was to investigate the potential use of near infrared reflectance spectroscopy (NIRS) for estimating the major fatty acids contents, such as palmitic acid (C16:0), oleic acid (C18:1), linoleic acid (C18:2) and linolenic acid (C18:3) in tree peonies. A total of 115 small seed samples and 447 single seeds were selected to calibrate the predictive models. NIRS absorbance spectra were collected using a Fourier transform near infrared (FT‐NIR) spectrometer for the small seed samples, and acousto‐optic tunable filter‐near infrared (AOTF‐NIR) for the single seed samples. Statistical analysis was performed with partial least squares (PLS). For the husked samples, C18:3, C18:1 and C18:2 showed the highest correlation coefficient of validation (R_v; = 0.9756, 0.9467 and 0.8485, respectively) and the ratio of performance to deviation (RPD; = 3.58, 1.91 and 2.17, respectively); however, C16:0 did not reach expectations (R_v = 0.7783, RPD = 1.32). For intact samples, C18:3 showed the best prediction (R_v = 0.9096, RPD = 3.14), followed by C18:2 (R_v = 0.8479, RPD = 1.96). The results for C18:1 were poor (R_v = 0.7237, RPD = 1.70). For single seeds, only the results for C18:3 (R = 0.9150, RPD = 1.73) were good in the husked seed samples. It was concluded that NIRS can be used to rapidly assess the content of the major fatty acids in small samples.     

Near‐infrared spectroscopy for the determination of lipid oxidation in cereal food products

The present study was aimed at determining the ability of near‐infrared (NIR) spectroscopy to in situ describe fat oxidation kinetics in three different cereal‐based products: salted crackers (20% saturated palm oil and lauric oil, sprayed on surface); healthy crackers (10% unsaturated rapeseed oil, homogeneously distributed inside the product matrix); and moist pasteurised Asian noodles (1.5% unsaturated rapeseed oil, sprayed on surface). Products were stored under accelerated oxidation conditions at 40 °C. Lipid oxidation rates were determined from peroxide value (PV) measurements. We observed no significant changes in PV for the dry crackers (3 meq/kg after 60 days), a slight linear increase in PV for the healthy crackers (40 meq/kg after 60 days), and a rapid increase for the Asian noodles (80 meq/kg after 20 days). The NIR spectra were recorded between 1000 and 2500 nm by using a Fourier Transform NIR spectrometer, using an external probe. Measurements were done directly in situ on the product, on the ground samples, and on the extracted fat phase. The analysis of NIR spectral data by PLS statistical methods demonstrated some correlation trends (R² = 0.575–0.897; RMSEC = 17–55%) for the products having a significant increase in PV. It was not possible to propose predictive models to calculate the oxidation rate.     

Application of a spectroscopic method to estimate the olive oil oxidative status

A rapid Fourier transformed infrared (FTIR) attenuated total reflectance (ATR) spectroscopic method coupled with partial least squares (PLS), was developed to estimate the oxidation degree of extra virgin olive oil (EVOO). The reference values of EVOO oxidation for the FTIR calibration were obtained by the specific absorptions at 232 and 270 nm, due to the presence of conjugated diene (CD) and conjugated triene (CT) groups, as monitored by the UV spectrophotometric determination. Specific washing procedures were applied to the EVOO to obtain EVOOP and EVOOTP samples, without phenolic compounds and without tocopherols and phenols, respectively. To obtain different oxidation degrees covering wide CD and CT ranges, EVOO, EVOOP, and EVOOTP samples were subjected to a forced oxidation at 60°C for 20 days and aliquots of the oils were daily analyzed. Regression of the FTIR/PLS‐predicted CD and CT of individual oxidized oils EVOO, EVOOP, EVOOTP, and all combined oils (EVOOALL) against UV–Visible reference values demonstrated the good quality of the models in terms of R² and RMSECV values. The results of the study indicated that a strong correlation existed between FTIR and UV–Visible peak intensities. Practical applications: The FTIR‐ATR method coupled with PLS elaboration was developed and applied to predict the oxidation degree of EVOO samples with considerable advantages in terms of simplicity, analysis time, and solvent consumption as compared to the standard method. Moreover, suitable adjustments of the equipment could permit a rapid control at‐line in oil sector.     

Mid‐infrared spectroscopy as a tool for real‐time monitoring of ethanol absorption in glycols

Fast, simple, accurate, and inexpensive methods for obtaining analyte concentration data are desirable in the industrial sector. In the present study, the use of Fourier transform mid‐infrared (FT‐MIR) spectroscopy, combined with partial least squares (PLS) regression, was investigated as a tool for real‐time monitoring of processes of ethanol absorption in glycols. Calibration was performed using simple synthetic samples containing ethanol, water, and monoethylene glycol (MEG) or diethylene glycol (DEG). The PLS models presented excellent performance, with correlation coefficients (R²) close to unity and root‐mean‐square errors of cross‐validation (RMSECV) and prediction (RMSEP) lower than 2% of the calibration data ranges for both analytes (ethanol and water) in both absorbents (MEG and DEG). The monitoring technique developed has potential to be applied in absorption processes and could also be used in other large‐scale unit operations, providing information in real time and enhancing process control.