Search

First of all, I want to transmit my most humble thanks to all people who believe that I deserve the “2019 Thomas Hirschfeld” award (kindly supported by FOSS) for my work on near-infrared spectroscopy and, especially, applied on hyperspectral images. I must confess that this award caught me by surprise and that I felt a bit overwhelmed when I received it. It is an honour full of respect and responsibility. I have been given the opportunity of writing this article, and I will profit it to express different personal thoughts about general but relevant aspects of near infrared applied to hyperspectral imaging. Also, since I am more a practitioner in chemometrics (or machine learning or data mining, or …) than a developer, I will also include some insights about the beautiful combination of near-infrared hyperspectral image with chemometrics. This article is just a glimpse of constructive criticism with personal thoughts that comes from my little experience in this field. Therefore, and of course, all opinions here are open for constructive discussion with the only purpose of learning (like the machines do nowadays).

In recent years, three Spanish wine vinegars have obtained the indication of Protected Denomination of Origin (PDOs) due to their unique characteristics and traditional method of production: “Vinagre de Jerez”, “Vinagre de Condado de Huelva” and “Vinagre de Montilla-Moriles”. These vinegars are expensive due to their high quality, the long aging time and the high cost of production, reason why the adulteration and unfair competition in the vinegar industry are frequent practices. To avoid these frauds, several analytical techniques have been already studied for the characterization and authentication of these high quality vinegars. Nevertheless, ultraviolet–visible (UV–vis) spectroscopy, especially attractive for its simplicity and low cost, has not been previously used to assess PDO or other qualities as type of production or aging, in wine vinegars. For this reason, the potential of UV–vis spectroscopy was investigated for the first time as a rapid and inexpensive methodology for developing classification models to discriminate wine vinegars according to the production method, the PDO and the aging category. Spectra from 70 wine vinegars -including different categories within the 3 PDOs and also vinegars without PDO as known as rapid vinegars-have been analyzed and compared in the selected region of 280–600 nm. Principal components analysis (PCA) was used as exploratory method, while soft independent modelling-class (SIMCA) and partial least squares-discriminant analysis (PLS-DA) were employed for the development of a hierarchical classification model. Differences between categories and PDOs, as well as between PDO and Non-PDO wine vinegars, were observed according to the spectral regions around 300 nm and the visible regions around 500 nm. Furthermore, bootstrap resampling method was employed to generate distributions of classification results and to obtain confidence intervals in the classification. The hierarchical classification results open up the possibility of developing a tool that provides an easy and fast differentiation for the authentication of wine vinegars from different categories and denomination of origins. Fil: Ríos Reina, Rocío. Universidad de Sevilla; España Fil: Azcarate, Silvana Mariela. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias de la Tierra y Ambientales de La Pampa. Universidad Nacional de La Pampa. Facultad de Ciencias Exactas y Naturales. Instituto de Ciencias de la Tierra y Ambientales de La Pampa; Argentina Fil: Camiña, José Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias de la Tierra y Ambientales de La Pampa. Universidad Nacional de La Pampa. Facultad de Ciencias Exactas y Naturales. Instituto de Ciencias de la Tierra y Ambientales de La Pampa; Argentina Fil: Callejón, Raquel M.. Universidad de Sevilla; España Fil: Amigo, José Manuel. Universidad de Copenhagen; Dinamarca

[EN] Pasta is mostly composed by wheat flour and water. Nevertheless, flour can be partially replaced by fibers to provide extra nutrients in the diet. However, fiber can affect the technological quality of pasta if not properly distributed. Usually, determinations of parameters in pasta are destructive and time-consuming. The use of Near Infrared-Hyperspectral Imaging (NIR-HSI), together with machine learning methods, is valuable to improve the efficiency in the assessment of pasta quality. This work aimed to investigate the ability of NIR-HSI and augmented Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) for the evaluation, resolution and quantification of fiber distribution in enriched pasta. Results showed R2V between 0.28 and 0.89, %LOF < 6%, variance explained over 99%, and similarity between pure and recovered spectra over 96% and 98% in models using pure flour and control as initial estimates, respectively, demonstrating the applicability of NIR-HSI and MCR-ALS in the identification of fiber in pasta., This work was supported by the Coordenaçao de Aperfeicoamento de Pessoal de Nivel Superior -Brasil (CAPES) [Finance Code 001]; Sao Paulo Research Foundation (FAPESP) [grant numbers 2008/57808-1, 2014/50951-4, 2015/24351-2, 2017/17628-3, 2019/06842-0]; and by GVA-IVIA and FEDER funds through project IVIA-51918. The authors would like to thank Nutrassim Food Ingredients company for the donation of the fiber samples, the support provided by Mrs. Cristiane Vidal during NIR-HSI system operation and data processing and Dr. Celio Pasquini for promptly receiving us in the laboratory that he coordinates (Grupo de instrumentaçao e automaçao em quimica analitica, Instituto de quimica, Universidade Estadual de Campinas, Campinas-SP, Brazil) to data acquisition.

Microplastics are defined as microscopic plastic particles in the range from few micrometers and up to 5 mm. These small particles are classified as primary microplastics when they are manufactured in this size range, whereas secondary microplastics arise from the fragmentation of larger objects. Microplastics are widespread emerging pollutants, and investigations are underway to determine potential harmfulness to biota and human health. However, progress is hindered by the lack of suitable analytical methods for rapid, routine, and unbiased measurements. This work aims to develop an automated analytical method for the characterization of small microplastics (

This paper explores how the staling of white bread affects the behavior of the whole crumb surface and how that mechanism is interrupted/changed by the addition of maltogenic α-amylases. This is done using near infrared hyperspectral imaging, machine learning methodologies and the knowledge acquired in the previous two manuscripts. Methods like principal component analysis and multivariate curve resolution demonstrate how the constituents of the bread being stored (for 21 days) evolve differently depending on the presence/absence of maltogenic α-amylases and also which parts of the crumb are primarily exposed to changes. The spatial distribution of the hardness is calculated in the entire surface of the slice area during staling by using partial least square regression. This manuscript comprehends one of the largest studies made on white bread staling and proposes a complete methodology using near infrared hyperspectral imaging and machine learning.

Remote identification of illegal plantations of Cannabis sativa Linnaeus is an important task for the Brazilian Federal Police. The current analytical methodology is expensive and strongly dependent on the expertise of the forensic investigator. A faster and cheaper methodology based on automatic methods can be useful for the detection and identification of Cannabis sativa L. in a reliable and objective manner. In this work, the high potential of Near Infrared Hyperspectral Imaging (HSI-NIR) combined with machine learning is demonstrated for supervised detection and classification of Cannabis sativa L. This plant, together with other plants commonly found in the surroundings of illegal plantations and soil, were directly collected from an illegal plantation. Due to the high correlation of the NIR spectra, sparse Principal Component Analysis (sPCA) was implemented to select the most important wavelengths for identifying Cannabis sativa L. One class Soft Independent Class Analogy model (SIMCA) was built, considering just the spectral variables selected by sPCA. Sensitivity and specificity values of 89.45% and 97.60% were, respectively, obtained for an external validation set subjected to the s-SIMCA. The results proved the reliability of a methodology based on NIR hyperspectral cameras to detect and identify Cannabis sativa L., with only four spectral bands, showing the potential of this methodology to be implemented in low-cost airborne devices.

This paper demonstrates that the use of the homogeneity curve approach introduced in the first part of this work for assessing the unique homogeneity percentage (%H) can also be applied in single channel non-binary (SCNB) images (a.k.a grayscale images) analysis. In order to address a more general homogeneity index approach for SCNB images, more realistic assumptions such as the presence of divisible objects or the mixture of different objects in the same pixel, are considered. The proposed parameter, %H, is an absolute homogeneity percentage that uses only the self-contained information of one image, with no need for additional modelling steps, as described in most of the works addressing the homogeneity assessment. To demonstrate it, two cases have been evaluated. The first case validates the %H by assessing the homogeneity in images with different textures (distribution of objects). In the second case, the reliability of assessing the evolution of homogeneity and providing an end-point detection in blending process trials done at small scale has been evaluated. The results obtained in the current work show the high potential when using the %H for industrial applications where the distributional homogeneity represents a critical process parameter to ensure the quality of the final product.

3 Tablas.-- 3 Figuras.-- 2 Figuras suplementarias, High-quality wine vinegars protected by the indication “Protected Designation of Origin” (PDO) need efficient tools to protect their brands and prevent adulteration and unfair competition. In this sense, Near-Infrared spectroscopy (NIRs) combined with chemometrics has demonstrated its usefulness in food authentication. This work assessed NIRs and Chemometrics as a rapid and non-destructive methodology for this purpose. In this study, 83 high-quality wine vinegars of the Spanish PDOs “Vinagre de Jerez” “Vinagre de Condado de Huelva” and “Vinagre de Montilla-Moriles” of different categories, and 11 wine vinegars without PDO, were analyzed in the range 12000-4000 cm. Principal component analysis (PCA) was performed to explore the spectra and Partial Least Squares-Discriminant Analysis (PLS-DA) was used to build classification models. The high ability of prediction obtained (>90% correct classification) demonstrated the usefulness of this methodology for authentication of PDO wine vinegars and their categories., The authors would like to thank the Spanish Regulatory Councils of the wine vinegars PDOs for their invaluable help with the acquisition of the samples for the study. Authors also thank University of Copenhagen, specifically to Professor Franciscus Winfried J van der Berg and the department of food science for providing the equipment and their knowledge in the methodology. This work was supported by “Consejeria de Economía, Innovación y Ciencia” of “Junta de Andalucia” [P12-AGR-1601], and “Ministerio de Educación Cultura y Deporte” (MECD, Spanish Government) for the FPU pre-doctoral fellowship [grant number FPU2014/01247].

This study investigates the use of fractal algorithms to analyse MRI of meat products, specifically loin, in order to determine sensory parameters of loin. For that, the capability of different fractal algorithms was evaluated (Classical Fractal Algorithm, CFA; Fractal Texture Algorithm, FTA and One Point Fractal Texture Algorithm, OPFTA). Moreover, the influence of the acquisition sequence of MRI (Gradient echo, GE; Spin Echo, SE and Turbo 3D, T3D) and the predictive technique of data mining (Isotonic regression, IR and Multiple Linear regression, MLR) on the accuracy of the prediction was analysed. Results on this study firstly demonstrate the capability of fractal algorithms to analyse MRI from meat product. Different combinations of the analysed techniques can be applied for predicting most sensory attributes of loins adequately (R > 0.5). However, the combination of SE, OPFTA and MLR offered the most appropriate results. Thus, it could be proposed as an alternative to the traditional food technology methods.

[EN] Product inspection is essential to ensure good quality and to avoid fraud. New nectarine cultivars with similar external appearance but different physicochemical properties may be mixed in the market, causing confusion and rejection among consumers, and consequently affecting sales and prices. Hyperspectral reflectance imaging in the range of 450¿1040 nm was studied as a non-destructive method to differentiate two cultivars of nectarines with a very similar appearance but different taste. Partial least squares discriminant analysis (PLS-DA) was used to develop a prediction model to distinguish intact fruits of the cultivars using pixel-wise and mean spectrum approaches, and then the model was projected onto the complete surface of fruits allowing visual inspection. The results indicated that mean spectrum of the fruit was the most accurate method, a correct discrimination rate of 94% being achieved. Wavelength selection reduced the dimensionality of the hyperspectral images using the regression coefficients of the PLS-DA model. An accuracy of 96% was obtained by using 14 optimal wavelengths, whereas colour imaging and a trained inspection panel achieved a rate of correct classification of only 57% of the fruits., This work was partially funded by INIA and FEDER funds through project RTA2015-00078-00-00. Sandra Munera thanks INIA for the FPI-INIA grant num. 43 (CPR2014-0082), partially supported by European Union FSE funds. The authors wish to thank Fruits de Ponent (Lleida) for providing the fruit.

HYPER-Tools is a new graphical user-friendly interface (GUI) especially designed for the analysis of hyperspectral images. This easy-to-use interface works under Matlab environment and integrates fundamental types of spectral and spatial pre-processing methods as well as the main chemometric tools (exploratory data analysis, clustering, regression, and classification) for the comprehensive analysis of hyperspectral images. The main features of HYPER-Tools are the powerful visualization tools implemented, the availability of working with several images simultaneously and the easiness of the interaction of the user with the interface. Together with the GUI, several tutorials and videos are provided in the official website ( https://www.hypertools.org/ ) showing the working procedure of HYPER-Tools step by step in different situations.

Analysis of gunshot residues (GSR) is a vital point of interest in forensic chemistry. GSR analysis is very well established, but there is still a lack of methodology to identify GSR obtained from lead-free or ‘non-toxic ammunition’ (NTA). To identify GSR from NTA, we have proposed the incorporation of luminescent markers to provide an optical and chemical signature to GSR. Furthermore, the luminescent markers can open up prospectives for ammunition encoding by adjusting their composition. Herein, we report the distinction among GSR particles containing different MOF-based markers by analyzing their organic fractions using near-infrared hyperspectral imaging (NIR-HSI) together with multivariate analysis (MCR-ALS). The method has been proven to be effective, non-destructive and fast in distinguishing marked GSR. It is possible to detect and correctly identify 72.2% of the samples, and all cases of misclassification are related to slight amount of material collected (8.3%) or confusion caused by one of the markers (19.4%). The study is developed in a forensic context of GSR analysis; however, the methodology described here is excellent and can be extrapolated to other applications that require distinguishing MOFs with different ligands.

Multispectral images of clothing targets shot at seven different distances (from 10 to 220 cm) were recorded at 18 specific wavelengths in the 400&-1000 nm range to visualize the gunshot residue (GSR) pattern. Principal component analysis (PCA) showed that the use of violet-blue wavelengths (430, 450 and 470 nm) provided the largest contrast between the GSR particles and the white cotton fabric. Then, the correlation between the amount of GSR particles on clothing targets and the shooting distance was studied. By selecting the blue frame of multispectral images (i.e. the blue frame in the red-green-blue (RGB) system which falls at 470 nm), the amount of pixels containing GSR particles was accounted based on the intensity of pixels in that frame. Results demonstrated that the number of pixels containing GSR exponentially decreases with the shooting distance from 30 to 220 cm following a particular exponential equation. However, the targets shot at the shortest distance (10 cm) did not satisfy the above equation, probably due to the noticeable differences of the GSR-pattern of these targets (e.g. high presence of soot). Then, the equation was applied to validation samples to estimate the shooting distances, obtaining results with an error below 10%.

2 Tablas.-- 6 Figuras, The use of data fusion methodologies has increased at the same rhythm as the capability of modern analytical laboratories of measuring sample from multiple sources. Almost all data fusion strategies can be grouped into three levels, they fuse the data differently with the sole aim of obtaining a better response (qualitative or quantitative) than that obtained by the instruments individually. One of the major key points for the data fusion methodologies to succeed is the understanding of the data structure obtained from a particular instrument. This point is not exhaustively commented in the literature focused on data fusion, sometimes paying too much attention to the algorithms instead. This manuscript explains data fusion from the structure of the different data obtained by different analytical platforms. Special attention will be given to the nature of the data and the relationships between the samples and the variables, as well as within the variables., The authors are grateful to Univ. Nacional de La Pampa, and CONICET (Consejo Nacional de Investigaciones Científicas y Técnicas, (Project PIP-2015 Nº 0111) and ANPCyT (Agencia Nacional de Promoción Científica y Tecnológica, (Projects PICT 2017-0340 and PICT-2018-04496) for financial support.

Identification of semen stains is a critical step in evidence analysis in cases of sexual assaults (rape, child molestation, sexual harassments, etc). Presumptive and confirmatory methodologies that are nondestructive and can be used at crime scenes are extremely needed to help forensic experts to collect useful evidences without compromising the integrity of the samples. In this work, we put forward the high potential of a fast, nondestructive and noninvasive methodology like Near Infrared Hyperspectral Imaging (HSI-NIR), together with adequate multivariate methodologies for exploratory data analysis and classification. Due to the high absorption of the substrate, an exhaustive data analysis is performed in order to test the most suitable tool for the detection and discrimination of semen stains in diverse fabrics. Samples of human semen, lubricants, breast milk and animal semen were placed on fabrics of different colors and compositions to create a stain and hyperspectral images were acquired. As identification tools, Principal Component Analysis and MCR-ALS were used. Moreover, Classification techniques such as PLS-DA, sPLS-DA and SVM-DA were used in order to differentiate human semen from other substances. Although the methodology developed depend on the substrate used, it has been proven that HSI-NIR can be used as presumptive and confirmatory method of analysis in the search for semen stains.

A new methodology based on Near Infrared-hyperspectral imaging and Principal Components Analysis (PCA) was developed and accurately validated to model the degradations kinetics and to estimate the multivariate accelerated shelf life (MASLT) of chia seeds (Salvia hispanica). Chia seeds were stored 180 days at 25, 35 and 45 °C, observing fatty acid degradation and an increase in the acidity. PC1 scores and kinetic charts were built fitting the time-related PC1 scores versus time by a fused kinetic model (R2 > 0.85). The spectra of chia seeds where acidity increased at 75% from initial value were used to calculate the cut-off value (−0.9853). The shelf life estimations were 1300, 798 and 90 days for chia seeds stored at 25, 35 and 45 °C, respectively. For the first time, a reliable methodology is proposed to validate that all samples were correctly predicted using PC1 scores.

This work firstly investigates the use of MRI, fractal algorithms and data mining techniques to determine pork quality parameters non-destructively. The main objective was to evaluate the capability of fractal algorithms (Classical Fractal algorithm, CFA; Fractal Texture Algorithm, FTA and One Point Fractal Texture Algorithm, OPFTA) to analyse MRI in order to predict quality parameters of loin. In addition, the effect of the sequence acquisition of MRI (Gradient echo, GE; Spin echo, SE and Turbo 3D, T3D) and the predictive technique of data mining (Isotonic regression, IR and Multiple linear regression, MLR) were analysed. Both fractal algorithm, FTA and OPFTA are appropriate to analyse MRI of loins. The sequence acquisition, the fractal algorithm and the data mining technique seems to influence on the prediction results. For most physico-chemical parameters, prediction equations with moderate to excellent correlation coefficients were achieved by using the following combinations of acquisition sequences of MRI, fractal algorithms and data mining techniques: SE-FTA-MLR, SE-OPFTA-IR, GE-OPFTA-MLR, SE-OPFTA-MLR, with the last one offering the best prediction results. Thus, SE-OPFTA-MLR could be proposed as an alternative technique to determine physico-chemical traits of fresh and dry-cured loins in a non-destructive way with high accuracy.

This study explores the effect of different settings on beer fermentation process applying an interval-based version of ASCA on FT-IR data. Three main factors (yeast type, temperature, fermentation time) are included in the experimental design, being high sources of variation in brewing and strictly interdependent; thus, difficult to be studied through a univariate approach. The three-factor full factorial design leads to a spectral multi-set data, with a total of 12 independent fermentations, which is explored combining ASCA and an interval adaptation of ASCA (interval-ASCA or i-ASCA). The ASCA models calculated on two separate regions (2900–2250 cm −1 and 1500–980 cm −1 ) shows differences for average time levels and the interaction between yeast types and time linked to carbon dioxide, maltose consumption and ethanol production, respectively. To better investigate the punctual influence of the studied factors on the so-called IR fingerprint region, permutation testing of ASCA in variable intervals is investigated. The analysis highlights the significant effect not only of the fermentation in all intervals considered, but also the role of other factors, such as time × yeast, yeast and temperature, in smaller variable regions. The proposed approach demonstrates how interval-ASCA on FT-IR data, isolating the variation in the data according to the experimental design used, allows a rapid and accurate test for parameter control in beer manufacturing.

Solid-state transitions at the surface of pharmaceutical solid dosage forms (SDF) were monitored using multi-series hyperspectral imaging (HSI) along with Multivariate Curve Resolution – Alternating Least Squares (MCR-ALS) and Parallel Factor Analysis (PARAFAC and PARAFAC2). First, the solid-state transformation due to the dehydration of the monohydrate forms of piroxicam and lactose to their respective anhydrate counterparts in tablets were monitored using temperature series NIR-HSI. PARAFAC and MCR-ALS solutions were hampered by the lack of strict trilinearity of the pixels among the unfolded series NIR-images (three-way array) and due to rotational ambiguity (augmented matrix), respectively, while PARAFAC2 resolved satisfactorily the profile of the corresponding compounds in the pixels in the series NIR-images. Next, the amorphous-to-crystalline transitions were monitored in solid dispersion of indomethacin with polyvinylpyrrolidone using time series MIR-HSI. MCR-ALS properly resolved the known solid-state forms of the drug in the pixels of the series MIR-images, while PARAFAC and PARAFAC2 failed to properly resolve all the drug forms in the series MIR-images due to i) strict trilinearity leak in the three-way array and ii) the mandatory constant cross-product AkTAk over the k series MIR-images (A is the loadings of the shift mode), respectively. The highlighting of the advantages and limitation of the corresponding curve resolution methods stressed their potential applicability when handling multi-series HSI to study solid-state transitions in pharmaceutical SDFs.

Bread staling is one of the most costly food deterioration processes. This study presents an in-depth, multivariate, statistical assessment of the differences in the staling process of white wheat bread as a function of storage time, usage of maltogenic α-amylases and spatial position in the loaf by texture measurements and non-linear fitting (Avrami). This study demonstrates the effects of anti-staling enzymes upon bread staling, where significant changes in the spatial staling kinetics occur. While the spatial development of staling is reduced in the outer crumb by anti-staling enzymes, the staling is retarded in the middle. The Avrami model suggests that this happens by two different competing mechanisms: one which increases the initial staling rate, and one which slows the convergence towards the limiting hardness. The two enzyme treated breads differed widely in early and ultimate resilience, despite the fact that they were adjusted to provide the same ultimate hardness.

[EN] The internal quality of nectarines (Prunus persica L. Batsch var. nucipersica) cv. 'Big Top' (yellow flesh) and 'Magique' (white flesh) has been inspected using hyperspectral transmittance imaging. Hyperspectral images of intact fruits were acquired in the spectral range of 630-900 nm using transmittance mode during their ripening under controlled conditions. The detection of split pit disorder and classification according to an established firmness threshold were performed using PLS-DA. The prediction of the Internal Quality Index (IQI) related to ripeness was performed using PLS-R. The most important variables were selected using interval-PLS. As a result, an accuracy of 94.7% was obtained in the detection of fruits with split pit of the 'Big Top' cultivar. Accuracies of 95.7% and 94.6% were achieved in the classification of the 'Big Top' and 'Magique' cultivars, respectively, according to the firmness threshold. The internal quality was predicted through the IQI with R-2 values of 0.88 and 0.86 for the two cultivars. The results obtained indicate the great potential of hyperspectral transmittance imaging for the assessment of the internal quality of intact nectarines., This work was partially funded by INIA and FEDER funds through project RTA2015-00078-00-00. Sandra Munera thanks INIA for the FPI-INIA grant num. 43 (CPR2014-0082), partially supported by European Union FSE funds.

Spain is one of the major producers of high-quality wine vinegars having three protected designations of origin (a.k.a. PDOs): “Vinagre de Jerez”, “Vinagre de Condado de Huelva” and “Vinagre de Montilla-Moriles”. Their high prices due to their high quality and their high production costs explain the need for developing an adequate quality control technique and the interest in extensive characterization in order to capture the identity of each denomination. In this framework, methodologies based on non-targeted techniques, such as spectroscopies, are becoming popular in food authentication. Thus, for improving vinegar quality assessment, fusion of data blocks obtained from the same samples but different analytical techniques could be a good strategy, since the quantity and quality of sample knowledge could be enhanced providing new insights into the differentiation of vinegars. Therefore, the aim of this manuscript is the development of a multi-platform methodology and a model able to classify the Spanish wine vinegar PDOs. Sixty-five PDO wine vinegars were analyzed by four spectroscopic techniques: Fourier-transform mid-infrared spectroscopy (MIR), near infrared spectroscopy (NIR), multidimensional fluorescence spectroscopy (EEM) and proton nuclear magnetic resonance (1H-NMR). Two different data fusion strategies were evaluated: Mid-level data fusion with different preprocessing, and Common Component and Specific Weights analysis multiblock method. Exploratory and classification analysis on the data from individual techniques were also performed and compared with data fusion models. The data fusion models improved the classification, providing a more efficient differentiation, than the models based on single methods, and supporting the approach to combine these methods to achieve synergies for an optimized PDO differentiation. Junta de Andalucía P12-AGR-1601

A non-destructive methodology based on Fourier Transformed Infrared Spectroscopy (FTIR) is proposed in this research to estimate the age of documents of different ages. Due the variability in the samples caused by their different chemical compositions, chemometric approaches were proposed to build one unique regression model able to determine the age of the paper regardless of its composition. PLS models were built employing Generalized Least Squares Weighting (GLSW) and Orthogonal Least Squares (OLS) filters to reduce the variability of samples from the same year. Afterwards, sparse PLS, which is an extension of the PLS model including a variable selection step, was applied to compare its performance with the preprocessing filters. All techniques proposed were compared to the initial PLS models, showing the potential of the chemometric approaches applied to FTIR data to estimate the age of unknown documents., CAPES (Coordenação de aperfeiçoamento de pessoal de nível superior)

The quantitative determination of atmospheric organic carbon (OC), elemental carbon (EC) and total carbon (TC) is of utmost relevance due to its influence on global warming, visibility degradation, and human health. Thermal-optical transmission (TOT) is the most common analytical method used to analyze them. Nevertheless, it is destructive, time-consuming, and only a small portion of the sample is analyzed. Here, near infrared spectroscopy coupled with a hyperspectral detector (HSI-NIR), together with multivariate data analysis, comes as a reliable alternative. This work aims at establishing a methodology for the quantitation of the carbonaceous content in atmospheric particulate matter (PM) fusing HSI-NIR and multivariate data analysis, and being validated with TOT analysis. Principal Component Analysis and Multivariate Curve Resolution analysis, used as qualitative tools, confirmed concentration trends in the PM samples, and contributions from the chemical composition of both the filter material and aerosol samples. The Partial Least Squares (PLS) regression models developed were optimized using interval Partial Least Squares (iPLS) selection and showed a high correlation between the HSI-NIR spectra and TOT measurements: R2Cal = 0.99, 0.94 and 0.99 for the OC, EC and TC contents, respectively. Finally, the spatial distribution of the carbonaceous content in the filter samples was assessed using a homogeneity percentage (%H). This showed values higher than 50% for all cases, which indicated that no distributional inhomogeneities were observed in the filters and corroborated the efficient operation of the sampling system. This study demonstrated the feasibility of using HSI-NIR for the quantitative assessment of carbonaceous particulate matter in air pollution monitoring.

Browning in sparkling wines was assessed by the use of excitation-emission fluorescence spectroscopy combined with PARAllel FACtor analysis (PARAFAC). Four different cava sparkling wines were monitored during an accelerated browning process and subsequently storage. Fluorescence changes observed during the accelerated browning process were monitored and compared with other conventional parameters: absorbance at 420nm (A420) and the content of 5-hydroxymethyl-2-furfural (5-HMF). A high similarity of the spectral profiles for all sparkling wines analyzed was observed, being explained by a four component PARAFAC model. A high correlation between the third PARAFAC factor (465/530nm) and the commonly used non-enzymatic browning indicators was observed. The fourth PARAFAC factor (280/380nm) gives us also information about the browning process following a first order kinetic reaction. Hence, excitation-emission fluorescence spectroscopy, together with PARAFAC, provides a faster alternative for browning monitoring to conventional methods, as well as useful key indicators for quality control.

4 Tablas.-- 5 Figuras, High-quality wine vinegars have been registered in Spain under protected designation of origin (PDO): “Vinagre de Jerez”, “Vinagre de Condado de Huelva” and “Vinagre de Montilla-Moriles”. The raw material, production and aging processes determine their quality and their aromatic composition. Vinegar volatile profile is usually analyzed by gas chromatography–mass spectrometry (GC–MS), being necessary a previous extraction step. Thus, three different sampling methods (Headspace solid phase microextraction “HS-SPME”, Headspace stir bar sorptive extraction “HSSE” and Dynamic headspace extraction “DHS”) were studied for the analysis of the volatile composition of Spanish PDO wine vinegars. Multivariate curve resolution (MCR) was used to solve chromatographic problems, improving the results obtained. Principal component analysis (PCA) showed that not all the sampling methods were equally suitable for the characterization and differentiation between PDOs and categories, being HSSE the technique that made able the best vinegar characterization., The authors would like to thank the Spanish Regulatory Councils of the wine vinegar PDOs for their invaluable help with the acquisition of the samples. This work was supported by “Consejería de Economía, Innovación y Ciencia, Junta de Andalucía” [P12-AGR-1601]; and the FPU scholarship of “Ministerio de Educación, Cultura y Deporte” [FPU014/01247].

30 Páginas; 3 Tablas; 3 Figuras, This work assesses the potential of multidimensional fluorescence spectroscopy combined with chemometrics for characterization and authentication of Spanish Protected Designation of Origin (PDO) wine vinegars. Seventy-nine vinegars of different categories (aged and sweet) belonging to the Spanish PDOs “Vinagre de Jerez”, “Vinagre de Montilla-Moriles” and “Vinagre de Condado de Huelva”, were analyzed by excitation-emission fluorescence spectroscopy. A visual assessment of fluorescence landscapes pointed out different trends with vinegar categories. PARAllel FACtor analysis (PARAFAC) extracted the potential fluorophores and their values in the PDO vinegars. This information, coupled with different classification methods (Partial Least Square Discrimination Analysis “PLS-DA” and Support Vectors Machines “SVM”), was able to discriminate the wine vinegar category within each PDO, for which SVM models obtained better results (>92% of classification). In each category, SVM also allows the differentiation between PDOs. The proposed methodology could be used as an analysis method for the authentication of Spanish PDO wine vinegars., The authors want to thank “Consejería de Economía, Innovación y Ciencia” of the “Junta de Andalucía” for the financial support provided through the project P12-AGR-1601, and the Spanish Regulatory Councils of the wine vinegar PDOs for their invaluable help with the acquisition of the samples for this study. In addition, authors want to thanks “Ministerio de Educación, Cultura y Deporte” (MECD, Spanish Government) for the FPU pre-doctoral fellowship and UPV/EHU (University of the Basque Country) for the grant received from the program ‘‘Contratación de Doctores Recientes”.

This work presents the development and validation of a multivariate method for quantitation of 6 potentially allergenic substances (PAS) related to fragrances by ultrasound-assisted emulsification microextraction coupled with HPLC-DAD and PARAFAC2 in the presence of other 18 PAS. The objective is the extension of a previously proposed univariate method to be able to determine the 24 PAS currently considered as allergens. The suitability of the multivariate approach for the qualitative and quantitative analysis of the analytes is discussed through datasets of increasing complexity, comprising the assessment and validation of the method performance. PARAFAC2 showed to adequately model the data facing up different instrumental and chemical issues, such as co-elution profiles, overlapping spectra, unknown interfering compounds, retention time shifts and baseline drifts. Satisfactory quality parameters of the model performance were obtained (R2≥0.94), as well as meaningful chromatographic and spectral profiles (r≥0.97). Moreover, low errors of prediction in external validation standards (below 15% in most cases) as well as acceptable quantification errors in real spiked samples (recoveries from 82 to 119%) confirmed the suitability of PARAFAC2 for resolution and quantification of the PAS. The combination of the previously proposed univariate approach, for the well-resolved peaks, with the developed multivariate method allows the determination of the 24 regulated PAS.

Traditionally, the quality traits of meat products have been estimated by means of physico-chemical methods. Computer vision algorithms on MRI have also been presented as an alternative to these destructive methods since MRI is non-destructive, non-ionizing and innocuous. The use of fractals to analyze MRI could be another possibility for this purpose. In this paper, a new fractal algorithm is developed, to obtain features from MRI based on fractal characteristics. This algorithm is called OPFTA (One Point Fractal Texture Algorithm). Three fractal algorithms were tested in this study: CFA (Classical fractal algorithm), FTA (Fractal texture algorithm) and OPFTA. The results obtained by means of these three fractal algorithms were correlated to the results obtained by means of physico-chemical methods. OPFTA and FTA achieved correlation coefficients higher than 0.75 and CFA reached low relationship for the quality parameters of loins. The best results were achieved for OPFTA as fractal algorithm (0.837 for lipid content, 0.909 for salt content and 0.911 for moisture). These high correlation coefficients confirm the new algorithm as an alternative to the classical computational approaches (texture algorithms) in order to compute the quality parameters of meat products in a non-destructive and efficient way.

5 Figuras.-- 2 Tablas, Characterization of wine vinegars qualified with a Protected Designation of Origin (PDO) is crucial to certify their quality and authenticity. Spectroscopic techniques as Fourier transform mid infrared spectroscopy (FTIR) with Attenuated Total Reflectance (ATR) has been applied to investigate its potential as a rapid, cost-effective and non-destructive tool for characterizing different categories of high-quality vinegars. Spectra from 67 wine vinegars belonging to the PDOs ¿Vinagre de Jerez¿ and ¿Vinagre Condado de Huelva¿, including their different established categories, were analyzed in the 4000e600 cm-1 infrared region. Changes associated to categories were observed in the region 1800e900 cm-1. These changes were assigned to certain compounds that increase during aging (e.g. acetic acids, alcohols, esters) or are characteristic of Pedro Ximenez category (e.g. sugars, furfural). Principal component analysis carried out on the most relevant spectral features, revealed that aging of vinegars clearly affect the ATRFTIR spectra obtained in each PDO., This work was supported by the “Consejería de Economía, Innovación y Ciencia, Junta de Andalucía” under the project P12-AGR-1601 and by a FPU scholarship of the Ministerio de Educación, Cultura y Deporte (FPU014/01247).

Due to the differences in terms of both price and quality, the availability of effective instrumentation to discriminate between Arabica and Robusta coffee is extremely important. To this aim, the use of multispectral imaging systems could provide reliable and accurate real-time monitoring at relatively low costs. However, in practice the implementation of multispectral imaging systems is not straightforward: the present work investigates this issue, starting from the outcome of variable selection performed using a hyperspectral system. Multispectral data were simulated considering four commercially available filters matching the selected spectral regions, and used to calculate multivariate classification models with Partial Least Squares-Discriminant Analysis (PLS-DA) and sparse PLS-DA. Proper strategies for the definition of the training set and the selection of the most effective combinations of spectral channels led to satisfactory classification performances (100% classification efficiency in prediction of the test set).

One of the main issues of hyperspectral imaging data is to unravel the relevant, yet overlapped, huge amount of information contained in the spatial and spectral dimensions. When dealing with the application of multivariate models in such high-dimensional data, sparsity can improve the interpretability and the performance of the model. In this chapter, we will introduce the exploration of hyperspectral images using a sparse version of the well-known principal component analysis method to demonstrate how the derived models can reveal very useful spectral zones. In particular, we will present two practical applications related to different issues: the separation among groups of homogeneous samples and the identification of outlier pixels in the spatial domain. For both case studies, guidance to the identification of the proper level of sparsity will be provided and, furthermore, we will show how sparsity can improve the chemical interpretation of the results.

This work aims at the development of a method based on FT-NIR spectroscopy and multivariate analysis for the identification and quantification of minced beef meat adulteration with turkey meat. Samples were analyzed as raw, frozen-thawed and cooked. Different multivariate regression and class-modeling strategies were evaluated. PLS regression models with R(2) in prediction higher than 0.884 and RMSEP lower than 10.8% were developed. PLS-DA applied to discriminate each type of sample in two classes (adulteration threshold=20%) showed values of sensitivity and specificity in prediction higher than 0.84 and 0.76, respectively. Thus, the study demonstrates that FT-NIR spectroscopy coupled with suitable chemometric strategies is a reliable tool for the identification and quantification of minced beef adulteration with turkey meat not only in fresh products, but also in frozen-thawed and cooked samples. This achievement is of crucial importance in the meat industry due to the increasing number of processed meat products, in which technological treatments can mask a possible inter-species adulteration.

Vegetation is a useful matrix for the quantification of atmospheric pollutants such as semi-volatile organic compounds (SVOCs). In particular, pine needles stand out as effective biomonitors due to the excellent uptake properties of their waxy layer. Having previously validated an original and reliable method to analyse pesticides in pine needles, our work team set the objective of this study to determine the levels of 18 pesticides in Pinus pinea needles collected in 12 different sampling sites in Portugal. These compounds were selected among a total of 70 pesticides by previous chemical scoring, developed to assess their probability to occur in the atmosphere. The risk of exposure was evaluated by the binomial chemical score/frequency of occurrence in the analysed samples. Levels and trends of the chemical families and target of the pesticides were obtained regarding the type of land occupation of the selected sites, including the use of advanced statistics (principal component analysis, PCA). Finally, some correlations with several characteristics of the sampling sites (population, energy consumption, meteorology, etc.) were also investigated.