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In identifying spectral outliers in near infrared calibration it is common to use a distance measure that is related to Mahalanobis distance. However, different software packages tend to use different variants, which lead to a translation problem if more than one package is used. Here the relationships between squared Mahalanobis distance D2, the GH distance of WinISI, and the T2 and leverage (L) statistics of The Unscrambler are established as D2 = T2 ≈ L × n ≈ GH × k, where n and k are the numbers of samples and variables, respectively, in the set of spectral data used to establish the distance measure. The implications for setting thresholds for outlier detection are discussed. On the way to this result the principal component scores from WinISI and The Unscrambler are compared. Both packages scale the scores for a component to have variances proportional to the contribution of that component to total variance, but the WinISI scores, unlike those from The Unscrambler, do not have mean zero.

This study evaluated odor generated during the first stage of the composting process in a dynamic respirometer using different raw materials such as the organic fraction of municipal solid waste (OFMSW), a mixture of this organic fraction with orange peel waste (OFMSW-OPW), sewage sludge with bulking agent (SL) and a mixture of strawberry extrudate, fish waste, sewage sludge and bulking agent (SFWSL). The combination of near infrared reflectance (NIR) spectroscopy and chemometric analysis is proposed to correlate the chemical composition and the operational variables of each raw material to odor generated during the composting process. The operational variables temperature, dynamic respirometric index (DRI), airflow (Q), odor concentration (OC) and odor emission rate (OER) were monitored. Adequate linear correlations were obtained between temperature and DRI for each compostable substrate within a confidence interval of 10% and 30%. Operational variables were statistically analyzed by principal component analysis with 87% of total variance explained and from which the substrates were clearly grouped. Near infrared reflectance spectroscopy provided the chemical composition of each raw material, and was found to be an advantageous technique to predict the relationship between odor emissions and absorption bands. Odor emissions were also predicted from the operational variables by multivariate regression, with temperature and DRI being the most influential variables.

Pinus pinea L. is one of the most important nut species in the world given the high nutritional and culinary value of its seed, the pine nuts, with increasing demand. The objective of this study was to assess the potential of visible and near infrared spectroscopy (VIS + NIRS) to analyse different sample presentations and to discriminate geographical origins of Mediterranean pine nut grown in Chile. Pine nuts were collected from 76 adult trees in three growth macrozones previously defined for stone pine in Chile. Original spectroscopic data were obtained by means of a Foss NIRSystems 6500 SYII spectrophotometer using a transport module. Reflectance was employed in the wavelength range of 400–2500 nm. The best means of sampling for the pine nuts used in this study were also studied. After analysing the spectroscopic data, discriminant models were obtained by means of discriminant partial least square (DPLS) with all samples. For the three macrozones previously identified, 87.8% of samples was correctly classified in the cross validation stage with the best model for pine nuts spectra analysis, obtained with shelled intact pine nuts. Results indicate that NIRS technology is capable of differentiating between pine nut samples of different geographical origins with errors ranging between 12.2 and 9.2%, and demonstrate the potential of VIS + NIRS technology as a rapid and accurate method for predicting the geographical origin of Mediterranean pine nuts.

The aim of this study was to determine the moisture content of cork granulate between maximum moisture content and equilibrium moisture content and evaluate the potential of Vis/NIR spectroscopy in predicting the moisture content in that range. The study was carried out in a set comprising 118 samples classified into nine granulometric classes (0.5–1, 1–1.5, 1–2, 1.5–2.5, 2–4, 2–5, 2–6, 3–7, and 4–8 mm). Spectra were collected by reflectance in the wavelength range of 400–2500 nm. Approximately 400 spectra were used to develop modified partial least squares calibration equations, while 180 spectra were used for the external validation. The mean maximum moisture content was 661.2 % (range 967.5–271.3 %) after 80 days of saturation, while the mean equilibrium moisture content was 6.2 % (range 5.8–7.5 %) after 58 days of drying depending on the granulometric class. The calibrations achieved excellent accuracy and precision with coefficients of determination in the cross-validation $$ (r_{\text{cv}}^{2} ) $$ of 0.99. The best equation showed a coefficient of determination of external validation $$ (r_{\text{EV}}^{2} ) $$ of 0.99, similar standard errors of cross-validation (24.22 %) and external validation (26.96 %), a ratio of performance to deviation of 11.7, and a range error ratio of 39.7 in the external validation, thus confirming that Vis/NIRS technology is suitable for quantifying the moisture content of cork granulate in routine analyses.

Biofiltration is the most widely used technique for eliminating odours in waste treatment plants. Volatile organic compounds (VOCs) are among the odorous compounds emitted by waste management plants, and serve as variables to measure odour emissions depending on the type of aeration process used. In this work, we assess the performance of an industrial-scale biofilter where composting is the main source of VOCs and odour emissions. Dynamic olfactometry is the sensorial technique used to determine odour concentration, while gas chromatography–time of flight-mass spectrometry (GC–TOFMS) is used to perform the chemical characterization. This work examines a total of 82 compounds belonging to 15 odorous families of VOCs, particularly mercaptans, sulphur-containing compounds, alcohols and terpenes, among others. Principal component analysis (PCA) is used to assess the influence of each of these families of VOCs on the total variance of the measure with regard to both the input and output flow of the biofilter. Finally, partial least-squares (PLS) regression is used to estimate the odour concentration in each of the samples taken at the inlet and outlet of the biofilter in each of the samples based on the chemical information provided by chromatographic analysis. The study shows that there is an adequate correlation (r = 0.9751) between real and estimated odour concentrations, both of which are expressed in European odour units per cubic metre (ou E ·m − 3 ).

The Dumas method (DM) of protein analysis in foods was standardized using two cereal references in order to identify the main sources of error during analysis; specifically, the study sought to establish which of the apparent critical factors (ACFs) were really significant influencing factors (IF) for the application of the DM to protein analysis in cereals. Using a non-geometric Plackett-Burman experimental design augmented to resolution IV via fold-over by increasing to twenty-four runs, the following eleven ACFs were investigated: sample weight ( W S ), capsule sealing process ( C W ), carrier gas flow ( P C ), purge gas flow (F He ), oxygen volume (O 2 ), oxygen pressure (ΔO 2 ), sample delay time ( S D ), sample run time ( S R ), front furnace temperature ( F FT ), rear furnace temperature ( R FT ), and GC oven temperatures ( G CT ). Two IFs were identified for each of the factors R FT and G CT , so an optimization process was carried out, based on a central composite design. Optimized conditions, ensuring enhanced accuracy and repeatability when determining protein content in cereals using the DM, are reported.

Low-pH cements are designed to be used in underground repositories for high level waste. When they are based on Ordinary Portland Cements (OPC), high mineral admixture contents must be used which significantly modify their microstructure properties and performance. This paper evaluates the microstructure evolution of low-pH cement pastes based on OPC plus silica fume and/or fly ashes, using Mid-Infrared and Near-Infrared spectroscopy to detect cement pastes mainly composed of high polymerized C-A-S-H gels with low C/S ratios. In addition, the lower pore solution pH of these special cementitious materials have been monitored with embedded metallic sensors. Besides, as the use of reinforced concrete can be required in underground repositories, the influence of low-pH cementitious materials on steel reinforcement corrosion was analysed. Due to their lower pore solution pH and their different pore solution chemical composition a clear influence on steel reinforcement corrosion was detected., Authors are grateful to EU Project ESDRED (FI6W-CT-2004-508851) and ENRESA for their support for this work. Authors also acknowledge to the Spanish Ministry of Economy and Competiveness for the economic support through project BIA 2011_22760. They are also grateful to “Cementos Portland Valderribas” for supply some of the raw materials used in this study. M. Sánchez also acknowledges her Juan de la Cierva Postdoctoral position (JCI-2011-09572)., We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI).

5 Figuras.-- 4 Tablas, Agricultural recovery is the primary option for the final destination of sewage sludge, which must be properly treated, stabilized and sanitized. However, these processes are occasionally eliminated or not carried out properly for economic reasons or due to the design and size of the treatment plant. In this study, 62 samples of sewage sludge (21 and 41 samples from Seville and from Huelva, respectively) were characterized. Variable annual sludge production and equivalent inhabitants are virtually proportional (related by a potential power function of 0.9134). The results were evaluated through multivariate statistical analysis in order to assess the operability status of the WWTPs and the final quality of the sludge generated in the processes. Principal components analysis provided information on two separate groups for the bulk of waste water treatment plant (WWTPs) analyzed: (1) a nickel group, where the samples showed outliers above 50 mg/kg with respect to other WWTPs; and (2) the group where total nitrogen, organic matter and total potassium (KO) showed abnormally high values. Linear discriminant analysis models allowed the detection of WWTPs that used inadequate treatments but where geographical area and number of equivalent inhabitants were not significant., The authors are very grateful to the Water Agency of Andalusia, now integrated into the Environment and Water Agency of the Regional Ministry of the Environment and Territorial Planning, UTE Ingeniería y Gestión del Sur-Biomasa Peninsular and the Government of Andalusia for funding this research in the framework of the “Estudio de alternativas para el tratamiento de lodos en las provincias de Sevilla y Huelva” (Study of alternatives for the treatment of sludge in the provinces of Seville and Huelva). We also wish to express our gratitude to the Spanish Ministry of Science and Innovation for funding this research through Project CTQ2014-60050-R.

In agreement with the principles of green chemistry, near infrared spectroscopy (NIRS) allows multi-component analysis in a fast and nondestructive way, without requiring complex pre-treatments, being a safe, clean and energy saving technique. In this work, a preliminary study to develop near infrared calibration models to predict methyl esters (ME) yield, monoglycerides (MG), diglycerides (DG), triglycerides (TG), free glycerol (FG) and total glycerol (TotalG) content in biodiesel has been carried out. These parameters are considered key factors to determine biofuel quality, its commercialization and to study and monitor the transesterification reaction. For this purpose, samples of biodiesel produced from three different vegetable oils (maize oil, sunflower oil and olive–pomace oil) were analyzed following the EN14103 and EN14105 European standards as reference methods. NIRS calibration equations were validated with a group of validation samples. The mean spectra showed that the main variability on biodiesel NIR spectra occurred around 1700 and 2300 nm. Moreover, the principal components analysis (PCA) applied to the spectra revealed the grouping of samples according to the type of oils used for biodiesel production. The standard deviation of the prediction (cross validation) errors (RMSEPCV) of the calibration models and the standard deviation error (RMSEP) of the validation set resulted similar to the measurement errors (intra lab SELr) and repeatability (inter lab SELR) of each analyte. Results confirm the accuracy of the developed NIRS models for determination of glycerides content and methyl esters yield in biodiesel.

We have studied in this work the natural variability in Holm oak (Quercus ilex subsp. ballota) by analyzing acorn morphometry and chemical composition in 13 populations from the Andalusia region (southern Spain). Acorn morphometry showed statistic significant differences between the populations with a higher acorn weight, length, and diameter in the northern provenances (TJA, GSE, CTH, CHU, APS, and PCO), while the Southern provenances (RMA, SAA, CCO, BCA, and SCA) showed lower value that even decreased with the altitude. Acorn chemical composition also showed statistic significant differences between populations. Morphometry and chemical composition data were subjected to clustering analysis, and two major clusters were detected. This analysis showed that northern populations (CTH, TJA, SCA, CHU, PCO, GSE, and APS) showed a tendency to present values highest in acorn weight (5.42 g vs. 3.21 g); acorn length (37.21 mm vs. 28.73 mm); acorn diameter (16.12 mm vs. 13.79 mm); ash (1.88% vs. 1.55%); protein (4.82% vs. 4.38%); fat (12.55% vs. 9.92%); and oleic acid (66.93 vs. 66.4%). On the contrary, Southern populations (SSA, VJA, CCO, BCA, RG, and RMA) showed a tendency to present values lowest in sugar (10.28% vs. 11.25%); energy (20,076.71 kJ/kg vs. 20,149.26 kJ/kg); palmitic (16.11% vs. 16.39%) and linoleic acids (13.07% vs. 13.11%). The populations studied here seemed to integrate acorn morphology and chemical composition related to environmental conditions of the original provenances. These data have provided biochemical markers that help establish phylogenic associations between populations and also reveal potentially account as an alternative source for dietary nutrition.

The monitoring of the C content of agricultural lands requires long-term experiments, the analysis of a large number of samples, various sampling depths and adequate analytical tools to perform the experiments. Near infrared (NIR) reflectance spectroscopy is a fast, reliable and low-cost analytical tool for the determination of soil C content. The objective of this study was to present NIR spectroscopy technology as a reliable method to monitor C concentration (total C, inorganic C and organic C) of soil samples from a long-term experiment established in 1986 in Cordoba (Spain). The soil is a rain-fed vertisol, characteristic of Mediterranean agricultural systems. The soil samples studied from four different years (1997, 2000, 2003 and 2006) included all of the variabilities existing in the long-term experiment: tillage system (conventional tillage vs no tillage), crop rotations [wheat ( Triticum aestivum L)-chickpea ( Cicer arietinum L), wheat-faba bean ( Vicia faba L), wheat-sunflower ( Helianthus annus L), wheat-fallow and wheat-wheat], N fertiliser rate (0 kg N ha−1, 50 kg N ha−1, 100 kg N ha−1 and 150 kg N ha−1), as well as different soil depths (0–15 cm, 15–30 cm, 30–60 cm and 60–90 cm). Calibration models were obtained with 492 samples from the aforementioned vertisol using visible and NIR spectroscopy (vis-NIR) and, when tested using a validation sample set, returned the values: total C ( r2 = 0.92, SEP = 1 g kg−1, RER = 26, RPD = 4.8); inorganic C ( r2 = 0.95, SEP = 1.5 g kg−1, RER = 26.6, RPD = 4.6) and organic C ( r2 = 0.76, SEP = 0.8 g kg−1 RER = 12.3, RPD = 2). The vis-NIR models developed reliably predicted the total, inorganic and organic C content at plot scale (homogeneous sample set). The advantage deriving from very specific vis-NIR calibrations for prediction of soil C is that one can rapidly and economically predict C in the study area, enabling monitoring to compare soil C evolution depending on time and variables such as tillage, crop rotation and fertilisation. To develop vis-NIR models at local and regional scales would require a more heterogeneous sample set and adequate calibration/validation strategies.

The aim of this study was to evaluate the potential of near infrared spectroscopy (NIRS) in predicting the coefficient of porosity of raw and boiled cork samples used mainly for producing cork stoppers for wine bottling. The classification of cork planks is currently performed visually in the last stage of preprocessing after boiling in water. In this study, calibration models have been developed on 279 cork samples before and after boiling. Three models were obtained one which predicts the coefficient of porosity of unprocessed samples, a second one that predicts porosity in boiled samples and finally a model that predicts the coefficient of porosity of boiled samples from spectra of raw samples. The models obtained showed a coefficient of determination of 0.58, 0.59 and 0.52, respectively. These results show that the modified partial least squares models based on NIRS technology can be used for screening cork porosity, not only at the two stages of processing, but also to predict boiled porosity. This suggests that NIRS technology could be used for determining the coefficient of porosity in boiled cork planks, and could also provide a useful tree selection tool. © 2016, Springer-Verlag Berlin Heidelberg.

The feasibility of developing a qualitative model based on cheap and simple instrumentation to differentiate and classify wines from the apellation d’origine “La Mancha” (Spain) has been studied. The criteria for discrimination were origin, grape variety and ageing process. Ultraviolet–visible spectroscopy was used for the development of a inexpensive and simple screening approach. Once spectra were collected, a data exploratory analysis was carried out in order to both show trends hidden in the data matrix from the sample spectra and study the characteristics of the models thus developed. Principal components analysis and soft independent modelling of class analogy were used for the exploratory analysis and the development of classification models, respectively. The ultraviolet region has for the first time been used for the discrimination of types of wines. This region is of great importance for differentiation of wines according to both origin within a same apellation d’origine and ageing process. The sum of false positives and false negatives – the criteria used for evaluating errors – were not higher than 25%, and the classification of wines according to the origin zone yielded the best values (10%). Sample preparation was not necessary.

A study of the feasibility of near infrared reflectance spectroscopy (NIRS) for analytical monitoring in wineries is presented, in which equations for the determination or screening of the commonest enological parameters are proposed. The training and validation sets to develop NIR general equations were built with samples (180) from different apellation d’origine, different wine types, etc. By the calibration step (partial least squares regression and cross-validation were used for multivariate calibration), major components such as ethanol, volumic mass, total acidity, pH, glycerol, colour, tonality and total polyphenol index are accurate determined by the proposed equations as compared with the reference data obtained by the official and standard methods—determination coefficients ( R 2 ) were higher than 0.800 (and higher than 0.900 most times) and standard error cross-validation (SECV) values were close to those of the reference methods. The proposed method also offers screening capability for components such as volatile acidity ( R 2 = 0.481), organic acids (R 2 = 0.432 for malic acid, R 2 = 0.544 for tartaric acid, R 2 = 0.541 for gluconic acid)—with the exception of the accurate determination of lactic acid (0.860 and 0.35 g l −1 for R 2 and SECV, respectively)—reducing sugars (R 2 = 0.705) and total sulphur dioxide (R 2 = 0.615). In equations validation, the correlation between the reference and NIRS methods was tested, and slope and bias values statistically not different from 1 and 0, respectively, were obtained for most parameters. © 2004 Elsevier B.V. All rights reserved.

Chemometric techniques have been used to compare two methods for fat extraction, namely focused microwave-assisted Soxhlet extraction (FMASE) and dynamic ultrasound-assisted extraction (DUAE), with the conventional Folch method, frequently used as reference. The data generated by a mid infrared spectrometer, after appropriate treatment, provide a simple and effective way for the detection of potential alterations of the fat obtained with the assistance of auxiliary energies, in this case, microwaves and ultrasounds. The results thus obtained are as compared with those from the Folch method, a mild extraction method with a view to finding faster alternatives for routine analysis. Moreover, classification of the samples between cookies and snacks based on extraction kinetics studies was possible, thus demonstrating the importance of these studies for the development of analytical methods. Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were used for both purposes, namely detection of alterations and classification of the samples as a function of their extraction kinetics, while K Nearest Neighbours (KNN) and Soft Independent Modelling of Class Analogy (SIMCA), based on PCA, models were generated in order both to predict the extraction kinetics of unknown samples, thus adjusting the extraction time as a function of the matrix, and find out explanation to the different extraction kinetics.

A collaborative study was carried out to determine the repeatability (error in each laboratory) and reproducibility (error between the different laboratories) of the fatty acid analysis of Iberian pig fat by gas chromatography. To do this, a comparison was made, in accordance with the ISO 5725-2 procedure, of the weight percentage (%) of the main fatty acids (C16:0, C18:0, C18:1 and C18:2) in 60 samples, determined by six laboratories. The results obtained for the relative standard deviation of the repeatability (0.56, 0.49, 0.29, and 0.69%) and for the relative standard deviation of the reproducibility (3.04, 3.54, 1.78 and 2.86%) show that the accuracy obtained is adequate for this analysis method, and in the same order or even higher than that obtained by other authors in similar samples. The differences between the results obtained on using two extraction methods of liquid fat from subcutaneous adipose tissue were recorded with no significant differences being observed between them.

The aim of this study was to evaluate the potential of visible and near infrared spectroscopy (Vis/NIRS) in predicting the chemical, physical and mechanical behavior of single-piece natural corks stoppers used for sealing wine bottles. Two training sets of 90 and 150 cork stoppers were used to obtain four spectra per sample in different positions: two of the stopper bases (transversal section) and two of the stopper sides (tangential section and radial section). The samples were scanned in the range of 400–2,500 nm using a Foss-NIRSystems 6500 SY II spectrophotometer equipped with a remote reflectance fiber-optic probe. On each training set, two-thirds of the samples were used to develop modified partial least square (MPLS) calibration equations, and the remaining one-third of the sample for the external validation of these MPLS equations. The best equations were obtained for the transversal section, which is the recommended one when applying Vis/NIRS technology to cork. The best results for the chemical composition were obtained for waxes and total polyphenols, showing coefficient of determination of the cross validation (r cv 2 ) values of 0.64 and 0.56 and coefficient of determination of the external validation (r EV 2 ) values of 0.53 and 0.55, respectively. The best equation for the physical and mechanical parameters was obtained for moisture content (r cv 2 = 0.86 and r EV 2 = 0.85), with somewhat lower results for density, compression force and extraction force (r cv 2 = 0.66, 0.72, 0.52 and r EV 2 = 0.52, 0.49, 0.51, respectively). The SECV (standard error of cross validation) and SEP (standard error of external validation) were similar for all the physical and mechanical parameters, thus confirming the robustness of the equations. MPLS model for moisture content fulfills the requirements for screening (RPD >2.5), but MPLS models obtained for waxes, total polyphenols, density, compression force and extraction force are not good enough for routine analysis or quality control. The results obtained from the MPLS models based on Vis/NIRS technology would permit the continuous quality control of humidity in the production line as well as obtaining information about certain chemical components (extractives contents) and some physical and mechanical parameters (density, extraction force and compression force).

La calidad del corcho es una de las cuestiones mas relevantes para el sector corchero. Es un proceso complejo que requiere evaluar multiples factores y manejar simultaneamente variables cuantitativas y cualitativas, en ocasiones, con un alto grado de subjetividad. Se presentan dos objetivos, en primer lugar establecer la relacion entre la calidad comercial del corcho y la porosidad medida mediante analisis de imagen en diferentes etapas de la transformacion industrial (planchas, laminas y tapones), y en segundo lugar, evaluar el potencial de la tecnologia NIRS como metodo de caracterizacion de la porosidad y, por tanto, de la calidad industrial. Para ello se ha utilizado un colectivo muestral formado por 479 planchas, 176 laminas y 90 tapones de corcho natural de una pieza, clasificados industrialmente en 4, 2 y 3 clases de calidad, respectivamente. El coeficiente de porosidad se evaluo aplicando tecnicas de analisis de imagen, en dos y tres clases de color. Los espectros NIRS se obtuvieron en la seccion transversal (planchas y tapones), en la seccion tangencial (laminas y tapones) y en la seccion radial (tapones), con un espectrofotometro Foss NIRSystems 6500 SY II, mediante la modalidad de reflectancia remota. El analisis estadistico de la relacion entre calidad y coeficiente de porosidad, medido mediante analisis de imagen, permite discriminar dos clases de calidad para planchas y laminas y tres clases para tapones. La clasificacion de la imagen en un mayor numero de cluster mejora los resultados. Las mejores calibraciones NIRS se obtuvieron para la porosidad medida en 3 clases de color, coincidiendo con los resultados obtenidos mediante analisis de imagen y permiten discriminar dos clases de calidad en planchas (refugo y corcho taponable), las dos clases en laminas (R2=0,83; r2 =0,78) y las tres clases en tapones (R2=0,67; r2=0,53). Debido a las dificultades de automatizacion del analisis de imagen en las primeras etapas de la transformacion industrial, la tecnologia NIRS podria ser una herramienta objetiva que permitiera evaluar la porosidad y diferenciar dos clases de calidad en planchas y laminas y tres clases en tapones. Abstract The quality of cork is one of the most important issues for the cork industry. It is a complex process that requires evaluating many factors and simultaneously handles continuous and discrete variables, sometimes with a high degree of subjectivity. There are two objectives, in first place to establish the relationship between the commercial quality of the cork and the porosity measured by image analysis at different stages of industrial processing (planks, sheets, and stoppers), and in second place, to evaluate the potential of NIRS technology as a method of assessing the porosity and, therefore, the industrial quality. For this, there has been used a sample group formed by 479 planks, 176 sheets and 90 one piece natural cork stoppers, classified industrially into 4, 2 and 3 quality classes, respectively. The coefficient of porosity was evaluated into two and three classes of color by using image analysis techniques. NIRS spectra were obtained in the transverse section (planks and stoppers), in the tangential section (sheets and stoppers) and in the radial section (stoppers), with a spectrophotometer Foss NIRSystems 6500 SY II, by remote reflectance mode. Statistical analysis of the relationship between quality and coefficient of porosity, measured by image analysis, discriminates two quality classes for planks and sheets, and three classes for stoppers. Classifying the image into a larger number of clusters improves the results. Best NIRS calibrations were obtained measuring the porosity into 3 classes of color, matching the results obtained by image analysis. This allow to discriminate two quality classes in planks (refuse and race), the two classes in sheets (R2=0.83; r2 =0.78) and the three classes for stoppers (R2=0.67; r2=0.53). Due to the difficulties of automated image analysis in the early stages of industrial processing, NIRS technology could be an objective tool to evaluate the porosity and to differentiate two classes of quality in planks and sheets, and three classes in stoppers.

Near-infrared spectroscopy has been used in nutritional metabolomics fingerprinting for the assessment of the intake of intervention breakfasts prepared with four different vegetable oils that were previously subjected to a deep frying process of 20 cycles for 5 min at 180°C. The target oils were an extra virgin olive oil and three varieties of refined sunflower oil. Of the three latter, one of them was used as such, other was spiked with a synthetic oxidation inhibitor (dimethylsiloxane) and, finally, the last one was enriched with an extract of phenolic compounds from olive pomace, the antioxidant properties of which are well known. Urine sampled from individuals before intake and 2 and 4 h after intake was directly analyzed by NIRS to obtain fingerprint characteristics of the metabolome composition. The resulting urinary patterns were combined for statistical analysis by unsupervised and supervised approaches. Partial least squares-class modeling enabled to develop class-models for each intervention breakfast, thus achieving discrimination of urinary fingerprints from individuals after breakfast intake. The models were statistically characterized by estimation of sensitivity and specificity parameters for the training and evaluation (validation) steps. The application of variable importance in projection algorithm enabled to detect the spectral regions with higher significance to explain the variability observed in the partial least squares class-models. Quantitative differences of variable importance in projection scores discriminated among the different classes under study. Copyright © 2013 John Wiley & Sons, Ltd.

The objective of this study was to assess the potential of visible and near infrared spectroscopy (VIS+NIRS) combined with multivariate analysis for identifying the geographical origin of cork. The study was carried out on cork planks and natural cork stoppers from the most representative cork-producing areas in the world. Two training sets of international and national cork planks were studied. The first set comprised a total of 479 samples from Morocco, Portugal, and Spain, while the second set comprised a total of 179 samples from the Spanish regions of Andalusia, Catalonia, and Extremadura. A training set of 90 cork stoppers from Andalusia and Catalonia was also studied. Original spectroscopic data were obtained for the transverse sections of the cork planks and for the body and top of the cork stoppers by means of a 6500 Foss-NIRSystems SY II spectrophotometer using a fiber optic probe. Remote reflectance was employed in the wavelength range of 400 to 2500 nm. After analyzing the spectroscopic data, discriminant models were obtained by means of partial least square (PLS) with 70% of the samples. The best models were then validated using 30% of the remaining samples. At least 98% of the international cork plank samples and 95% of the national samples were correctly classified in the calibration and validation stage. The best model for the cork stoppers was obtained for the top of the stoppers, with at least 90% of the samples being correctly classified. The results demonstrate the potential of VIS + NIRS technology as a rapid and accurate method for predicting the geographical origin of cork plank and stoppers. Copyright © Taylor & Francis Group, LLC.

Biodiesel quality control is of relevant importance as biodiesel properties influence diesel engine performance. In the present work, the benefits of the use of visible and near-infrared Spectroscopy (NIRS) as a technique for screening undesirable contaminants, i.e. methanol and glycerol content in biodiesel are presented. Excess of methanol decreases heating value and flash point and increases carbon deposits, while the presence of glycerol may cause injector tip coking and deposits in the combustion chamber. Biodiesel samples contaminated with different amounts of methanol and glycerol were scanned by NIRS. Their NIR spectra were acquired at 2-nm intervals over a wavelength range from 400 to 2500 nm (visible plus near-infrared regions). First derivative of the spectra were calculated and correlated to the raw optical data by means of modified partial least-squares (MPLS) regression. First derivative equation of the optical data, pretreated by standard normal variate (SNV) and De-trending (DT) transformations, showed a coefficient of determination r2 in the cross-validation step of 0.99 and 0.81, for the samples contaminated with methanol and glycerol, respectively. Also, the standard deviation to standard error of cross-validation ratio (RPD) was 10.0 and 2.5, respectively. These statistics are indicative of the high capacity of prediction of the equations for methanol content and acceptable for glycerol content. Visible spectra also showed differences related to the samples, thus indicating it could serve to determine the presence of these contaminants. The use of NIRS technology provides a trustworthy and low-cost method to determine the presence of undesirable amounts of methanol and glycerol. It also offers an important saving of time (each analysis requires less than two minutes). © 2011 Elsevier Ltd. All rights reserved., This work was funded by Junta de Andalucia, Spain (TEP 4994) and Ministry of Science and Education, Spain (ENE2007-65490/ALT).

The procedures used today to characterize cork plank for the manufacture of cork bottle stoppers continue to be based on a traditional, manual method that is highly subjective. Furthermore, there is no specific legislation regarding cork classification. The objective of this viability study is to assess the potential of near-infrared spectroscopy (NIRS) technology for characterizing cork plank according to the following variables aspect or visual quality, porosity, moisture and geographical origin. In order to calculate the porosity coefficient, an image analysis program was specifically developed in Visual Basic language for a desktop scanner. A set comprising 170 samples from two geographical areas of Andalusia (Spain) was classified into eight quality classes by visual inspection. Spectra were obtained in the transverse and tangential sections of the cork planks using an NIRSystems 6500 SY II reflectance spectrophotometer. The quantitative calibrations showed cross-validation coefficients of determination of 0.47 for visual quality, 0.69 for porosity and 0.66 for moisture. The results obtained using NIRS technology are promising considering the heterogeneity and variability of a natural product such as cork in spite of the fact that the standard error of cross validation (SECV) in the quantitative analysis is greater than the standard error of laboratory (SEL) for the three variables. The qualitative analysis regarding geographical origin achieved very satisfactory results. Applying these methods in industry will permit quality control procedures to be automated, as well as establishing correlations between the different classification systems currently used in the sector. These methods can be implemented in the cork chain of custody certification and will also provide a certainly more objective tool for assessing the economic value of the product. © 2010 IOP Publishing Ltd.

Calcium aluminate cement (CAC) is less commonly used as ordinary Portland cements (OPCs) for structural concrete, is relatively expensive, but may have certain advantages when used for solidification of wastes; it introduces rapid strength gain and has a higher resistance to chemical attack than OPC. However, the most widely identified degradation process suffered by CAC is the so-called conversion of hexagonal calcium aluminate hydrate to a cubic form. Mixes of CAC with silica fume (SF) or fly ash (FA) represent an interesting alternative for the stabilization of CAC hydrates, which might be attributed to a microstructure based mainly on aluminosilicates. This paper deals with the microstructure of cement pastes fabricated with binders of CAC–SF and CAC–FA, and their evolution over time. Mid infrared and near infrared spectroscopy have been used to assess the microstructure of these formulations. Microstructural characterization was completed by backscattering electron microscopy observation and microanalysis.

The potential of visible and near infrared spectroscopy to predict texture and colour of dry-cured ham samples was investigated. Sensory evaluation was performed on 117 boned and cross-sectioned dry-cured ham samples. Slices of approximate thickness 4 cm were cut, vacuum-packaged and kept under frozen storage until spectral analysis. Then, Biceps femoris muscle from the thawed slices was taken and scanned (400-2200 nm) using a fiber optic probe. The exploratory analysis using principal component analysis shows that there are two ham groups according to the appearance or not of defects. Then, a K nearest neighbours was used to classify dry-cured hams into defective or no defective classes. The overall accuracy of the classification as a function of pastiness was 88.5%; meanwhile, according to colour was 79.7%. Partial least squares regression was used to formulate prediction equations for pastiness and colour. The correlation coefficients of calibration and cross-validation were 0.97 and 0.86 for optimal equation predicting pastiness, and 0.82 and 0.69 for optimal equation predicting colour. The standard error of cross-validation for predicting pastiness and colour is between 1 and 2 times the standard deviation of the reference method (the error involved in the sensory evaluation by the experts). The magnitude of this error demonstrates the good precision of the methods for predicting pastiness and colour. Furthermore, the samples were classified into defective or no defective classes, with a correct classification of 94.2% according to pasty texture evaluation and 75.7% as regard to colour evaluation.

A database of 352 near infrared (NIR) spectral data from Iberian pig liquid fat was used for the prediction of palmitic, stearic, oleic and linoleic fatty acids content. The results showed the excellent accuracy of NIR equations with coefficients of determination ( r2) reaching values close to 1 and standard error of cross-validation ( SECV) values similar to the standard error in the reference method ( SEL). The possibility of transferring the calibrations developed in one instrument to another NIR instrument, following the Shenk and Westerhaus standardisation algorithm, was examined. Two monochromator instruments were optically matched using four different standardisation sets of unsealed samples (Set A: one sample of Iberian pig liquid fat with a similar fatty acids composition to the average of the calibration set; Set B: 20 samples of Iberian pig liquid fat covering the range in optical density and fatty acid composition of the calibration set; Set C: oleic acid; Set D: linoleic acid. The standardisation completed with Set A and Set B produced spectra on the satellite instrument which resembled spectra scanned on the master instrument. The standard error of the differences ( SED) between the values predicted by the master and the satellite after standardisation were similar to, or even lower than, the standard error of prediction ( SEP) values for the same samples scanned on the master instrument.

The present paper reports the results of a methodological work carried out to optimize interactance–reflectance NIR analysis of Iberian pig adipose tissue. The RMS statistic included in ISI software, which computes the agreement between spectra, was used for quantitation of errors due to operational factors (scan location and room light). The results obtained suggest that it would be advisable to take scans from at least two different locations and after setting an STD RMS limit of 45000 μlog (1/ R) at the Scan program. The room light has no effect on the spectra repeatability.