Application of Fourier-Transform Infrared Spectroscopy for the Assessment of Wine Spoilage Indicators: A Feasibility Study.

Wine aroma is one of the most frequently used and explored quality indicators. Typically, its assessment involves estimating the volatile composition of wine or highly trained assessors conducting sensory analysis. However, current methodologies rely on slow, expensive and complicated analytical procedures. Additionally, sensory evaluation is inherently subjective in nature. Therefore, the aim of this work is to verify the feasibility of using FTIR spectroscopy as a fast and easy methodology for the early detection of some of the most common off-odors in wines. FTIR spectroscopy was combined with partial least squares (PLS) regression for the simultaneous measurement of isoamyl alcohol, isobutanol, 1-hexanol, butyric acid, isobutyric acid, decanoic acid, ethyl acetate, furfural and acetoin. The precision and accuracy of developed calibration models (R2P > 0.90, range error ratio > 12.1 and RPD > 3.1) proved the ability of the proposed methodology to quantify the aforementioned compounds.

Dual Classification Approach for the Rapid Discrimination of Metabolic Syndrome by FTIR.

Metabolic syndrome is a complex of interrelated risk factors for cardiovascular disease and diabetes. Thus, new point-of-care diagnostic tools are essential for unambiguously distinguishing MetS patients, providing results in rapid time. Herein, we evaluated the potential of Fourier transform infrared spectroscopy combined with chemometric tools to detect spectra markers indicative of metabolic syndrome. Around 105 plasma samples were collected and divided into two groups according to the presence of at least three of the five clinical parameters used for MetS diagnosis. A dual classification approach was studied based on selecting the most important spectral variable and classification methods, linear discriminant analysis (LDA) and SIMCA class modelling, respectively. The same classification methods were applied to measured clinical parameters at our disposal. Thus, the classification's performance on reduced spectra fingerprints and measured clinical parameters were compared. Both approaches achieved excellent discrimination results among groups, providing almost 100% accuracy. Nevertheless, SIMCA class modelling showed higher classification performance between MetS and no MetS for IR-reduced variables compared to clinical variables. We finally discuss the potential of this method to be used as a supportive diagnostic or screening tool in clinical routines.

An NMR-based lipidomic approach to identify Parkinson's disease-stage specific lipoprotein-lipid signatures in plasma.

Disturbances in lipid composition and lipoproteins metabolism can play a crucial role in the pathogenesis of Parkinson's disease (PD) and other neurodegenerative diseases. The lipidomic strategy proposed here involves lipoprotein profiling using NMR spectroscopy and multivariate data pre-processing and analysis tools on 94 plasma samples (belonging to 38 early-stage PD patients, 10 PD-related dementia patients, 23 persons with Alzheimer's dementia, and 23 healthy control subjects) to firstly differentiate PD patients (irrespective of the stage of the disease) from persons with Alzheimer's disease (AD) as well as from controls, and then to discriminate among PD patients according to disease severity. The whole data set was subdivided into 86 training and 8 external test samples for validation purposes. A two-step classification scheme, based on linear discriminant analysis with variable selection accomplished by a stepwise orthogonalisation procedure, was proposed to optimise classification performance. Careful pre-processing of NMR signals was crucial to ensure data set quality. A total of 30 chemical shift buckets enabled differentiation between PD patients (regardless of disease severity), AD and control subjects, providing classification, cross-validation and external prediction rates of 100% in all cases. Only 15 variables were required to further discriminate between early-stage PD and PD-related dementia, again with 100% correct classifications, and internal/external predictions. The simplicity and effectiveness of the classification methodology proposed support the use of NMR spectroscopy, in combination with chemometrics, as a viable alternative diagnostic tool to conventional PD clinical diagnosis.

Novel method for holocellulose analysis of non-woody biomass wastes.

Compositional analysis of non-woody biomass is quite important to determine its possible applications. However, current standard methods developed for woody biomass compositional analysis have been revealed to be unsuitable when applied to non-woody biomass. Therefore, a novel and less-time consuming modified method which enables for a proper isolation of holocellulose in non-woody biomass samples while increasing lignin degradation has been developed. The novel method mainly consists in a treatment with sodium chlorite and glacial acetic acid at boiling point, which precludes changes in the holocellulose crystallinity degree or losses of carbohydrates, as shown by DSC, XRPD, and HPLC analysis. It was successfully applied to the determination of the structural components of 10 different non-woody biomass samples. Also, its use revealed that non-woody biomass belongs to LHC and LCH groups in the biomass structural composition ternary diagram, which are completely different than the ones the woody biomass belongs to.

Discrimination of patients with different serological evolution of HIV and co-infection with HCV using metabolic fingerprinting based on Fourier transform infrared.

Human immunodeficiency virus (HIV) is a retrovirus that weakens the immune system and permits opportunistic diseases such as hepatitis C (HCV) to enter the body. These diseases induce metabolic disorders in the patients and it is therefore logical to approach them from a holistic, functional perspective, studying the metabolome comprehensively to identify metabolic signatures associated with certain disease states. The metabolomics strategy here proposed involves metabolic fingerprinting using Fourier transform infrared spectroscopy and chemometric tools on 72 plasma samples (subdivided into 63 training and 9 test samples) to differentiate between healthy subjects and patients with different disease stages. Several options, relating to the variable selection method used in linear discriminant analysis and the number of categories being considered, were explored to optimize discrimination ability. A total of 18 bands enabled differentiation between control subjects, HIV patients and the group that encompassed patients with acquired immune deficiency syndrome (AIDS), AIDS/HCV and HIV/HCV, providing overall classification and internal prediction rates of 97.67% and 93.65%, respectively. Only 9 bands were required to further discriminate between AIDS, AIDS/HCV and HIV/HCV, with 99.20% (training) and 89.66% (cross-validation) correct classifications. The simplicity and effectiveness of the classification methodology proposed was reinforced by the satisfactory results obtained in external prediction.

Thawing as a critical pre-analytical step in the lipidomic profiling of plasma samples: New standardized protocol.

Lipid profiling is a promising tool for the discovery and subsequent identification of biomarkers associated with various diseases. However, data quality is quite dependent on the pre-analytical methods employed. To date, potential confounding factors that may affect lipid metabolite levels after the thawing of plasma for biomarker exploration studies have not been thoroughly evaluated. In this study, by means of experimental design methodology, we performed the first in-depth examination of the ways in which thawing conditions affect lipid metabolite levels. After the optimization stage, we concluded that temperature, sample volume and the thawing method were the determining factors that had to be exhaustively controlled in the thawing process to ensure the quality of biomarker discovery. Best thawing conditions were found to be: 4 °C, with 0.25 mL of human plasma and ultrasound (US) thawing. The new US proposed thawing method was quicker than the other methods we studied, allowed more features to be identified and increased the signal of the lipids. In view of its speed, efficiency and detectability, the US thawing method appears to be a simple, economical method for the thawing of plasma samples, which could easily be applied in clinical laboratories before lipid profiling studies.

Plasma lipidomic profiling method based on ultrasound extraction and liquid chromatography mass spectrometry.

Lipidomics is an emerging field in biomedical research that includes the analysis of all the lipids present in complex biological samples. To evaluate the chemical and biological diversity of lipids, lipid extraction is usually the first step toward lipidomics analysis. Nevertheless, sample preparation is still a time-consuming and error prone analytical step. Therefore, the development of simple and robust methods suitable for high-throughput lipid analysis is of great interest. This study presents a new method for exhaustive lipid fingerprinting of human blood plasma samples based on the employment of methyl tert-butyl ether (MTBE) and ultrasound (US) energy combined with liquid chromatography-electrospray ionization quadrupole-time-of-flight mass spectrometry (LC-ESIqToF-MS). First, the MTBE-US extraction step was optimized by means of experimental design methodology. After the optimization step, a comparative study was performed to assess the suitability of the proposed method. The new method allowed extraction time to be reduced to half, in comparison with previously reported methods. The proposed method also allowed increasing extraction repeatability (with RSDs below 5.55%) and efficiency (recoveries higher than 70% were obtained for all lipids evaluated). Moreover, the new proposed method enables more than 800 different features to be detected. Thus, the overall number of lipids identified with the databases for this novel extraction method (352) was the highest of the evaluated methods. The efficiency, precision, and feature detection capacity of the proposed method confirmed its suitability for the evaluation of the lipid profile of human blood plasma samples. Moreover, taking into account its simplicity, low time consumption, and compatibility with automation, the new proposed method could be a suitable alternative to previously reported methods for use in laboratories for comprehensive lipidomic profiling.

Biomass proximate analysis using thermogravimetry.

This work proposes a 25 min-last thermogravimetric method as a tool to determine biomass sample's proximate analysis data (moisture, ash, volatile matter and fixed carbon contents) just by direct measure of weight changes on each sample's TG chart. Compared with international standards commonly used to that aim, TG is a faster and easier to develop technique. Obtained results were satisfactory, with AEE under 6% for moisture and volatile matter, close to 10% for fixed carbon determination and AAD of 1.6 points for ash content.

Optimisation of a sensitive method based on ultrasound-assisted emulsification-microextraction for the simultaneous determination of haloanisoles and volatile phenols in wine.

In this study, an ultrasound-assisted emulsification-microextraction (USAEME) has been optimised for the sensitive determination of haloanisoles (2,4,6-trichloroanisole (TCA), 2,3,4,6-tetrachloroanisole (TeCA), 2,4,6-tribromoanisole (TBA) and pentachloroanisole (PCA)) and volatile phenols (4-ethylphenol (4-EP), 4-ethylguaiacol (4-EG), 4-vinylphenol (4-VP) and 4-vinylguaiacol (4-VG)) in wine using gas chromatography coupled to tandem mass spectrometry (GC-MS/MS). Optimisation of USAEME method was performed by evaluating the different parameters that influence the recovery of the analytes in the extract. Firstly, extraction solvent was selected. Subsequently its volume, the ionic strength and the time and temperature at which the extraction must be performed were simultaneously evaluated by means of experimental design methodology. After optimisation step, the analytical characteristics of the method were evaluated. Satisfactory linearity (with correlation coefficients over 0.989), repeatability (below 11.5%) and inter-day precision (below 11.7%) were obtained for all target analytes. For all the studied compounds, detection limits obtained were similar or even lower than previously reported. In addition, the developed method was successfully applied to the analysis of wine samples. To the best of our knowledge, this is the first time that USAEME has been applied to simultaneously determine the compounds responsible for cork taint and Brett character in wine.

Characterization of Spanish biomass wastes for energy use.

Energy plays an important role in the world's present and future. The best way to absorb the huge increase in energy demands is through diversification. In this context biomass appears as an attractive source for a number of environmental, economical, political and social reasons. There are several techniques used to obtain energy from biomass. Among these techniques, the most commonly used throughout the world is a thermo-chemical process to obtain heat. To optimize the combustion process in adequate reactors, a comprehensive study of the characterization of biomass fuel properties is needed, which includes proximate analysis (determination of moisture, ash, volatile and fixed carbon content), ultimate analysis (C, H, N, S and O composition) and calorimetry, focusing on biomass fuels obtained in Spain.

Headspace solid-phase microextraction for direct determination of volatile phenols in cider.

A headspace solid-phase microextraction coupled to gas chromatography-tandem mass spectrometry (GC-MS/MS) method was optimised and validated for the determination of 4-ethylguaiacol, 4-ethylphenol, 4-vinylguaiacol and 4-vinylphenol, involved in the presence of Brett character, in ciders. The influence of different parameters on extraction efficiency (fibre coating, salt addition, exposure time, extraction temperature and sample volume/total volume ratio) was evaluated. Divinylbenzene/carboxen/PDMS was selected as extraction fibre and the other optimised parameters were as follows: 10 mL of cider, temperature 70 degrees C, extraction time 60 min and addition of 0.4 g/mL of NaCl. The proposed method showed satisfactory linearity. The detection limits obtained were 0.01 microg/L for 4-ethylguaiacol, 0.02 microg/L for 4-ethylphenol, 0.08 microg/L for 4-vinylguaiacol and 0.03 microg/L for 4-vinylphenol. These detection limits were lower than those obtained in previous studies on the determination of volatile phenols in other alcoholic beverages. Good recoveries of over 95% were observed for all compounds, and the repeatability obtained was considered acceptable, ranging between 4 and 10%. To demonstrate the feasibility of the procedure, the method was applied to the analysis of commercial ciders. To our knowledge, this is the first time that the headspace solid-phase microextraction procedure has been optimised to determine specifically the Brett character responsible compounds in cider.

Valorization of onion waste and by-products: MCR-ALS applied to reveal the compositional profiles of alcoholic fermentations of onion juice monitored by near-infrared spectroscopy.

The overall purpose of the project, of which this study is a part, was to examine the feasibility of onion waste as a support-substrate for the profitable production of food-grade products. This study focused on the efficient production of ethanol from worthless onions by transforming the onion juice into onion liquor via alcoholic fermentation with the yeast Saccharomyces cerevisiae. The onion bioethanol produced could be later used as a favorable substrate for acetic fermentation to finally obtain onion vinegar. Near-infrared spectroscopy (NIRS), coupled with the multivariate curve resolution-alternating least squares (MCR-ALS) method, has been used to reveal the compositional and spectral profiles for both substrates and products of alcoholic fermentation runs, that is, total sugars, ethanol, and biomass concentration. The ambiguity associated with the ALS calculation was resolved by applying suitable inequality and equality constraints. The quality of the results provided by the NIR-based MCR-ALS methodology adopted was evaluated by several performance indicators, including the variance explained by the model, the lack of fit and the agreement between the MCR-ALS achieved solution and the results computed by applying previously validated PLS reference models. An additional fermentation run was employed to test the actual predictive ability of the ALS model developed. For all the components resolved in the fermentation system studied (i.e., total sugars, ethanol, and biomass), the final model obtained showed a high predictive ability and suitable accuracy and precision, both in calibration and external validation, confirmed by the very good agreement between the ALS responses and the reference values (the coefficient of determination was, in all cases, very close to 1, and the statistics confirmed that no significant difference was found between PLS reference models and the MCR-ALS methodology applied). Thus, the proven reliability of the MCR-ALS model presented in this study, based only on NIR measurements, makes it suitable for monitoring of the key species involved in the alcoholic fermentation of onion juice, allowing the process to be modeled and controlled in real time.

Using supercritical fluid chromatography to determine diffusion coefficients of 1,2-diethylbenzene, 1,4-diethylbenzene, 5-tert-butyl-m-xylene and phenylacetylene in supercritical carbon dioxide.

The binary diffusion of 1,2-diethylbenzene, 1,4-diethylbenzene, 5-tert-butyl-m-xylene and phenylacetylene at infinite dilution in supercritical carbon dioxide were measured between 15.0 and 35.0 MPa and in the temperature range of 313.16 to 333.16K by the Taylor-Aris chromatographic method. The effect of temperature, pressure, viscosity and density was discussed. In the case of temperature dependence, additional measurements were done for 5-tert-butyl-m-xylene from 308.16 to 398.16K at 35.0 MPa. The measured diffusivities of the four solutes were compared with the calculated ones by several predictive formulas.

Use of near-infrared spectroscopy and feature selection techniques for predicting the caffeine content and roasting color in roasted coffees.

Near-infrared spectroscopy (NIRS), combined with diverse feature selection techniques and multivariate calibration methods, has been used to develop robust and reliable reduced-spectrum regression models based on a few NIR filter sensors for determining two key parameters for the characterization of roasted coffees, which are extremely relevant from a quality assurance standpoint: roasting color and caffeine content. The application of the stepwise orthogonalization of predictors (an "old" technique recently revisited, known by the acronym SELECT) provided notably improved regression models for the two response variables modeled, with root-mean-square errors of the residuals in external prediction (RMSEP) equal to 3.68 and 1.46% for roasting color and caffeine content of roasted coffee samples, respectively. The improvement achieved by the application of the SELECT-OLS method was particularly remarkable when the very low complexities associated with the final models obtained for predicting both roasting color (only 9 selected wavelengths) and caffeine content (17 significant wavelengths) were taken into account. The simple and reliable calibration models proposed in the present study encourage the possibility of implementing them in online and routine applications to predict quality parameters of unknown coffee samples via their NIR spectra, thanks to the use of a NIR instrument equipped with a proper filter system, which would imply a considerable simplification with regard to the recording and interpretation of the spectra, as well as an important economic saving.

Monitoring of alcoholic fermentation of onion juice by NIR spectroscopy: valorization of worthless onions.

The valorization of vegetable byproducts is one of the main objectives of industry today. The project on which this study is based examined the potential usefulness of worthless onions (Allium cepa L. sp.) and overproduction to obtain several functional products with different applications in the food industry. Near-infrared (NIR) spectroscopy, combined with multivariate calibration, has been used to monitor the alcoholic fermentation of onion juice. Good results were obtained, revealing the suitability of NIR spectroscopy for controlling and optimizing this process in real time.

Multiple solid-phase microextraction in a non-equilibrium situation. Application in quantitative analysis of chlorophenols and chloroanisoles related to cork taint in wine.

Multiple HS-solid-phase microextraction (MHS-SPME) is a modification of SPME developed for quantitative analysis that avoids possible matrix effects based on an exhaustive analyte extraction from the sample. In this paper, the theory of this process associated with a non-equilibrium situation has been presented. The application of an optimised HS-SPME-based method in the analysis of chloroanisoles and chlorophenols, previously acetylated, associated with the occurrence of cork taint in different red, white and rosé wine samples, has revealed the existence of matrix effects. This fact determines the choice of standard addition as the adequate technique for the quantification of these compounds in real samples. MHS-SPME is proposed as a good alternative technique with respect to HS-SPME because it avoids matrix effects, simplifies the quantification of these compounds in real samples and reduces analysis time, providing sensitivity below chloroanisole sensory threshold with acceptable precision.

Orthogonal signal correction applied to the classification of wine and molasses vinegar samples by near-infrared spectroscopy. Feasibility study for the detection and quantification of adulterated vinegar samples.

The most common fraudulent practice in the vinegar industry is the addition of alcohol of different origins to the base wine used to produce wine vinegar with the objective of reducing manufacturing costs. The mixture is then sold commercially as genuine wine vinegar, thus constituting a fraud to consumers and an unfair practice with respect to the rest of the vinegar sector. A method based on near-infrared spectroscopy has been developed to discriminate between white wine vinegar and alcohol or molasses vinegar. Orthogonal signal correction (OSC) was applied to a set of 96 vinegar NIR spectra from both original and artificial blends made in the laboratory, to remove information unrelated to a specific response. The specific response used to correct the spectra was the extent of adulteration of the vinegar samples. Both raw and corrected NIR spectra were used to develop separate classification models using the potential functions method as a class-modeling technique. The previous models were compared to evaluate the suitability of near-infrared spectroscopy as a rapid method for discrimination between vinegar origin. The transformation of vinegar NIR spectra by means of an orthogonal signal-correction method resulted in notable improvement of the specificity of the constructed classification models. The same orthogonal correction approach was also used to perform a calibration model able to detect and quantify the amount of exogenous alcohol added to the commercial product. This regression model can be used to quantify the extent of adulteration of new vinegar samples.

Optimisation of a headspace solid-phase microextraction method for the direct determination of chloroanisoles related to cork taint in red wine.

One of the most critical problems in the enological industry is associated with cork taint. The main compounds responsible for this off-flavour are some chloroanisoles: 2,4,6-trichloroanisole (TCA), 2,3,4,6-tetrachloroanisole (TECA) and pentachloroanisole (PCA). Headspace solid-phase microextraction (HS-SPME) coupled to gas chromatography-electron capture detection has been used for the direct determination of these chloroanisoles in wine samples. After the evaluation of some parameters (desorption conditions and salt addition) that affect extraction efficiency, a screening study of six types of fibres and five extraction temperatures was performed. Then, a Doehlert matrix including the exposure time, temperature and Vs (sample volume)/Vt (total volume) ratio as experimental factors was proposed. According to the results of this design and the kinetic profiles evaluated, an analytical procedure based on HS-SPME was optimised and validated. This method can be used for the simultaneous determination at the low ng/l level of all chloroanisoles involved in cork taint, and not only TCA, which is the only compound found in the literature when SPME is proposed as the analytical technique.

Optimisation of the derivatisation reaction and subsequent headspace solid-phase microextraction method for the direct determination of chlorophenols in red wine.

An acetylation reaction for the derivatisation of the three chlorophenols involved in cork taint was optimised using a Doehlert design for direct application in wine samples. In this first step, the optimum reaction pH, by adding different amounts of KHCO3, and the required quantity of derivatisation reagent were fixed. Then a series of parameters relevant for the headspace solid-phase microextraction process, such as desorption conditions, salt addition and agitation sample were evaluated. A simultaneous study of the type of fibre and extraction temperature was performed at five levels and based on the results obtained the rest of factors (sample volume and exposition time) that could potentially affect the extraction yields were optimised by a central composite design. According to the validation of the method, we propose here, to our knowledge, the first application of solid-phase microextraction for the direct analysis of chlorophenols in red wine samples.

Classification of wine and alcohol vinegar samples based on near-infrared spectroscopy. Feasibility study on the detection of adulterated vinegar samples.

Near-infrared (NIR) spectroscopy was used to discriminate between wine vinegar (red or white) and alcohol vinegar. One orthogonal signal correction method (OSC) was applied on a set of 73 vinegar NIR spectra from both origins and artificial blends made in the laboratory in order to remove information unrelated to a specific chemical response (tartaric acid), which was selected due to its high discriminant ability to differentiate between wine vinegar and alcohol vinegar samples. These corrected NIR spectra, as well as raw NIR spectra and 14 physicochemical variables, were used to develop separate classification models using the potential functions method as a class-modeling technique. The aforementioned models were compared to evaluate the suitability of NIR spectroscopy as a rapid method for discriminating between vinegar origins. The transformation of vinegar NIR spectra by means of an orthogonal signal correction method prompted a notable improvement in the specificity of the constructed classification models. The classification model developed was then applied to artificial vinegar blends made in the laboratory to test its capacity to recognize adulterated vinegar samples.