Laura Aceña

Data fusion methodologies for food and beverage authentication and quality assessment – A review

The ever increasing interest of consumers for safety, authenticity and quality of food commodities has driven the attention towards the analytical techniques used for analyzing these commodities. In recent years, rapid and reliable sensor, spectroscopic and chromatographic techniques have emerged that, together with multivariate and multiway chemometrics, have improved the whole control process by reducing the time of analysis and providing more informative results. In this progression of more and better information, the combination (fusion) of outputs of different instrumental techniques has emerged as a means for increasing the reliability of classification or prediction of foodstuff specifications as compared to using a single analytical technique. Although promising results have been obtained in food and beverage authentication and quality assessment, the combination of data from several techniques is not straightforward and represents an important challenge for chemometricians. This review provides a general overview of data fusion strategies that have been used in the field of food and beverage authentication and quality assessment.

Application of FT-MIR Spectroscopy for Fast Control of Red Grape Phenolic Ripening

The content of phenolic compounds determines the state of phenolic ripening of red grapes and is a key criterion in setting the harvest date to produce quality red wines. In this study, the feasibility of Fourier transform mid-infrared (FT-MIR) spectroscopy combined with partial least-squares (PLS) regression to quantify phenolic compounds is reported. The reference methods used for quantifying these compounds (which were evaluated as total phenolic compounds, total anthocyanins, and condensed tannins) were the usual ones used in cellars that employed UV-vis spectroscopy. To take into account the high natural variability of grapes when building the calibration models, fresh grapes from six varieties, at different phenolic ripening states were harvested during three vintages. Destemmed and crushed grapes were subjected to an accelerated extraction process and used as calibration standards. A total of 192 extracts (objects) were obtained, and these were divided into a training set (106 objects) and a test set (86 objects) to evaluate the predictive ability of the models. Among the different MIR regions of the extract raw spectra, those that provided the highest variability on the absorption were selected. The results showed that the best PLS regression model was the one obtained when working in the region of 1168-1457 cm(-1) because it gave the most accurate and robust prediction for total phenolic compounds (RMSEP%=4.3 and RPD=4.5), total anthocyanins (RMSEP%=5.9 and RPD=3.5), and condensed tannins (RMSEP%=5.8 and RPD=3.8). Therefore, it can be concluded that FT-MIR spectroscopy can be a fast and reliable technique for monitoring the phenolic ripening in red grapes during the harvest period.

Discrimination and sensory description of beers through data fusion

Beer samples of the same brand and commercialized as a same product, but brewed in four different factories were analyzed with three techniques, an MS e-nose, a mid-IR optical-tongue and a UV-visible, to see if the factories show differences and to find out if the differences found could be attributed to different sensory properties. The data from the three instruments were fused to improve the ability of classification with respect to the individual use of the techniques. Two levels of data fusion were studied: low and mid level fusion, and the classification was performed by linear discriminant analysis (LDA). Mid-level fusion provided better classification results (above 95% correct classification) than those of low-level fusion and also than those obtained when using the individual techniques. Moreover, by means of the score and loading plots obtained by Fisher-LDA, it was possible to interpret the chemical information provided by the three techniques, and we were able to relate the variables associated to each sensor to the main compounds responsible of the sensory perception.

Quantification of phenolic compounds during red winemaking using FT-MIR spectroscopy and PLS-regression.

We present a rapid method to quantify phenolic compounds all during the red winemaking process using Fourier transform mid-infrared (FT-MIR) spectroscopy and chemometrics. To get the reference values, we used the usual UV–vis spectroscopy methods, and the compounds studied were evaluated as total phenolic compounds (TPC), total anthocyanins (TA), and condensed tannins (CT). Sampling from five different grape varieties (Merlot, Tempranillo, Syrah, Cariñena, and Cabernet sauvignon), harvested at different ripening states, and monitored over 10 days of vinification produced a total of 600 spectra. These were used to build and validate four different predictive models by partial least-squares (PLS) regression. The spectral regions selected for each model were between 979 and 2989 cm(–1), and when selecting the most suitable one in each case, good values of performance parameters were obtained (R2(val) > 0.95 and RPD > 4.0 for TPC; R2(val) > 0.90 and RPD > 3.0 for TA; R2(val) < 0.8 and RPD < 3.0 for CT). Furthermore, also more specific PLS regression models for each phenolic parameter and each grape variety were developed using different regions with results similar to those obtained when dealing with all of the grape varieties. It is concluded that FT-MIR spectroscopy together with multivariate calibration could be a rapid and valuable tool for wineries to carry out the monitoring of phenolic compound extraction during winemaking.

Olive oil sensory defects classification with data fusion of instrumental techniques and multivariate analysis (PLS-DA).

Three instrumental techniques, headspace-mass spectrometry (HS-MS), mid-infrared spectroscopy (MIR) and UV-visible spectrophotometry (UV-vis), have been combined to classify virgin olive oil samples based on the presence or absence of sensory defects. The reference sensory values were provided by an official taste panel. Different data fusion strategies were studied to improve the discrimination capability compared to using each instrumental technique individually. A general model was applied to discriminate high-quality non-defective olive oils (extra-virgin) and the lowest-quality olive oils considered non-edible (lampante). A specific identification of key off-flavours, such as musty, winey, fusty and rancid, was also studied. The data fusion of the three techniques improved the classification results in most of the cases. Low-level data fusion was the best strategy to discriminate musty, winey and fusty defects, using HS-MS, MIR and UV-vis, and the rancid defect using only HS-MS and MIR. The mid-level data fusion approach using partial least squares-discriminant analysis (PLS-DA) scores was found to be the best strategy for defective vs non-defective and edible vs non-edible oil discrimination. However, the data fusion did not sufficiently improve the results obtained by a single technique (HS-MS) to classify non-defective classes. These results indicate that instrumental data fusion can be useful for the identification of sensory defects in virgin olive oils.

Determination of roasted pistachio (Pistacia vera L.) key odorants by headspace solid-phase microextraction and gas chromatography-olfactometry.

Key odorants in roasted pistachio nuts have been determined for the first time. Two different pistachio varieties (Fandooghi and Kerman) have been analyzed by means of headspace solid-phase microextraction (HS-SPME) and gas chromatography-olfactometry (GCO). The aroma extract dilution analyses (AEDA) applied have revealed 46 and 41 odor-active regions with a flavor dilution (FD) factor≥64 for the Fandooghi and the Kerman varieties, respectively, and 39 of them were related to precisely identified compounds. These included esters, pyrazines, aldehydes, acids, furans, and phenols. The results show that the Fandooghi variety presents, not only more odor-active regions but also higher FD factors than the Kerman variety that can lead to the conclusion that the first variety has a richer aromatic profile than the second one. The descriptive sensory analysis (DSA) showed that the roasted, chocolate/coffee, and nutty attributes were rated significantly higher in the Fandooghi variety, whereas the green attribute was significantly higher in the Kerman one.

Identification of olive oil sensory defects by multivariate analysis of mid infrared spectra

Mid-infrared (MIR) spectra (4000-600 cm(-1)) of olive oils were analyzed using chemometric methods to identify the four main sensorial defects, musty, winey, fusty and rancid, previously evaluated by an expert sensory panel. Classification models were developed using partial least squares discriminant analysis (PLS-DA) to distinguish between extra-virgin olive oils (defect absent) and lower quality olive oils (defect present). The most important spectral ranges responsible for the discrimination were identified. PLS-DA models were able to discriminate between defective and high quality oils with predictive abilities around 87% for the musty defect and around 77% for winey, fusty and rancid defects. This methodology advances instrumental determination of results previously only achievable with a human test panel.

Comparative study of two extraction techniques to obtain representative aroma extracts for being analysed by gas chromatography-olfactometry: application to roasted pistachio aroma.

This research paper presents a comparative study of two different extraction and concentration techniques to obtain representative pistachio aroma extracts: the traditional direct solvent extraction (DSE) followed by high-vacuum transfer (HVT) and the headspace solid-phase microextraction (HS-SPME). The results showed that, although both techniques provide accurate information about the aromatic composition that will be perceived by the consumer, the precision in terms of within-day repeatability and between-days repeatability (intermediate precision) of the chromatographic areas presented better values for HS-SPME than for DSE-HVT. Moreover the solvent-free HS-SPME allows the extraction of more odour-active regions, requires very little sample handling and shorter time for sampling.

Chemical characterization of commercial Sherry vinegar aroma by headspace solid-phase microextraction and gas chromatography-olfactometry.

The sensorial representativeness of the headspace solid-phase microextraction (HS-SPME) aroma extract from commercial Sherry vinegars has been determined by direct gas chromatography-olfactometry (D-GCO). Extracts obtained under optimal conditions were used to characterize the aroma of these vinegars by means of GCO and aroma extract dilution analysis (AEDA). Among the 37 different odorants determined, 13 of them were identified for the first time in Sherry vinegars: 2 pyrazines (3-isopropyl-2-methoxypyrazine, 3-isobutyl-2-methoxypyrazine), 2 sulfur compounds (methanethiol, dimethyl trisulfide), 1 unsaturated ketone (1-octen-3-one), 1 norisoprenoid (β-damascenone), 1 ester (ethyl trans-cinnamate) and 6 aldehydes (2- and 3-methylbutanal, octanal, nonanal, (E)-2-nonenal and (E,E)-2,4-decadienal). The determination of the odor thresholds in a hydroacetic solution together with the quantitative analysis-which was also performed using the simple and fast SPME technique-allowed obtaining the odor activity values (OAV) of the aromatic compounds found. Thus, a first pattern of their sensory importance on commercial Sherry vinegar aroma was provided.

Prediction of olive oil sensory descriptors using instrumental data fusion and partial least squares (PLS) regression

Headspace-Mass Spectrometry (HS-MS), Fourier Transform Mid-Infrared spectroscopy (FT-MIR) and UV-Visible spectrophotometry (UV-vis) instrumental responses have been combined to predict virgin olive oil sensory descriptors. 343 olive oil samples analyzed during four consecutive harvests (2010-2014) were used to build multivariate calibration models using partial least squares (PLS) regression. The reference values of the sensory attributes were provided by expert assessors from an official taste panel. The instrumental data were modeled individually and also using data fusion approaches. The use of fused data with both low- and mid-level of abstraction improved PLS predictions for all the olive oil descriptors. The best PLS models were obtained for two positive attributes (fruity and bitter) and two defective descriptors (fusty and musty), all of them using data fusion of MS and MIR spectral fingerprints. Although good predictions were not obtained for some sensory descriptors, the results are encouraging, specially considering that the legal categorization of virgin olive oils only requires the determination of fruity and defective descriptors.