Flash Gas Chromatography Research Articles

Aroma Potential of German Riesling Winegrapes during Late-Stage Ripening

The “aromatic maturity” of winegrapes is not fully understood, particularly during the later stages of ripening. The contribution of grapes to wine aroma has historically been challenging to determine, given most aroma compounds originate from nonvolatile precursors. In this study, an analytical approach previously developed for red winegrapes was adapted to assess the “aroma potential” of Riesling from two vineyards in Essenheim and Durbach, Germany, during the 2022 vintage, by extracting and hydrolyzing aroma precursors in an anoxic model wine matrix. Following sensory and chemical analyses of the hydrolysates using flash profiling and gas chromatography, a multiple factor analysis revealed vineyard- and ripening-dependent changes to aroma, even after total soluble solids had plateaued. As samples matured, green apple and fresh/vegetal aromas were prominent among the Durbach hydrolysates, likely due to persistent concentrations of hexanol. Hydrolysates from both vineyards nonetheless developed more pronounced citrus fruit, tropical fruit, and floral aromas, reflecting increased concentrations of various norisoprenoids and terpenoids. Findings suggest delaying harvest past technological maturity could confer greater aromatic intensity and complexity. The analytical approach used here appears promising for future studies on other grape varieties and other factors that could influence aroma, such as viticultural practices and environmental conditions.

Screening tools combined with multivariate data analysis to predict or confirm virgin olive oil classification by the Panel test

AbstractA particular aspect of quality control of virgin olive oil (VOO) is the mandatory application, together with chemical and instrumental determinations, of a standardized and official method for sensory assessment. The latter, known as Panel test, is carried out by trained assessors and contributes to the classification of VOOs into three commercial categories (extra virgin, virgin, and lampante). One drawback of this method is related to the large number of samples to be analyzed, compared to the work capacity of a sensory panel, especially during the selection for purchase by companies that blend and market virgin oils and the quality control conducted by the authorities to verify the declared commercial category. For this reason, it is helpful to develop and validate robust and rapid screening methods, based on volatile fingerprints, to preclassify each sample into one of the three commercial categories. Considering the strict relation between volatile compounds and the main sensory attributes (fruity and defects), a gas‐chromatographic volatile fingerprint can be the right choice. In this paper, the comparison of two emerging techniques, namely, headspace‐gas chromatography‐ion mobility spectrometry (HS‐GC‐IMS) and flash‐gas chromatography (FGC), applied on a sample set of 49 VOOs, using calibrations previously built with a larger number of samples, is presented. The number of correctly classified samples, with respect to the commercial category determined by the Panel test, was satisfactory and comparable (92% for HS‐GC‐IMS, and 94% for FGC), confirming the effectiveness of both methods and the robustness of the predictive models.Practical Applications: The demand for rapid screening tools to reduce the number of samples to be assessed by the Panel test has increased in recent years. The validation of robust models and their joint adoption by companies that market VOOs as well as official control bodies could reduce nonconformities and increase the batches of VOO being controlled, thus better protecting the consumer. Therefore, it is desirable to have different tools available to analyze volatile compounds, together with the associated calibration models, along with detailed instructions for their application, to have different alternatives that suit the equipment of individual laboratories.

A study of volatiles of young citrus fruits from four areas based on GC–MS and flash GC e-nose combined with multivariate algorithms

The present study has successfully established a scientific and precise approach for distinguishing the geographical origins of young citrus fruits (Qingpi) from four primary production regions in China, using gas chromatography-mass spectrometry (GC–MS) and flash gas chromatography electronic nose (flash GC e-nose) to analyze the volatile composition and odor characteristics. Through the application of chemometric analysis, a clear differentiation among Qingpi samples was established using GC–MS. Additionally, the application of flash GC e-nose facilitated the extraction of flavor information, which enabled the discrimination of geographical origins. Several flavor components were identified as significant factors for origin certification. Furthermore, two pattern recognition algorithms were employed to achieve high accuracy in regional identification. The results of this investigation demonstrate that the amalgamation of multivariate chemometrics and algorithms can proficiently discern the sources of those young citrus fruits. The findings of this research can provide a reference for the assessment of quality control in food and other agricultural commodities in the times ahead.

PSVIII-12 Hexanal, as Identified Through Flash Gas Chromatography Electronic Nose, Strongly Correlates with Pv in Rendered Chicken Meal Samples, Although Additional Compounds Influence Aromatic Palatability

Abstract Samples of rendered chicken meal (CM) and chicken by-product meal (CB) were obtained from a commercial plant with samples representing various storage time points and a range of peroxide values from < 10 (1), 20-50 (2), 50-100 (3), 100-200 (4). Samples were utilized in a novel aromatic palatability trial with 20 working Labrador Retrievers (n = 10 males and n = 10 females). Aggregated interaction time showed the most pronounced difference between the lowest PV sample and the highest PV for both CB and CM (P < 0.0001, 0.001). Both CB1 and CM1 were significantly different from all other samples (P = 0.05). Samples were also sent to a third party for HERACLES Neo flash gas chromatography electronic nose analysis (e-nose). Analysis of e-nose chromatograms showed good discrimination between samples (DI = 97) with the largest spatial differences between the low and high PV samples. Chromatogram peaks were tentatively identified according to their Kovats index and the AlphaChemBase database. Component 1 (X-axis) accounted for 99.4% of the difference while component 2 (Y-axis) showed 0.54% of the difference. The strongest odor, as determined by the highest area peak was hexanal which produces a fatty and fishy smell. When evaluated in a bivariate model hexanal vs PV was well correlated on a 2P curve (R2= 0.986, P < 0.0001). However, hexanal was less correlated with aromatic palatability (R2 = 0.616. P = 0.09). Therefore, a primary least squares model was created to better display the relationship between palatability and oxidation. Utilizing two factors, the PLSR model explained 72% of the variation in the e-nose peak area data (X) and 90% of the variation in the PV*palatability (Y). Of the 61 aromatic compounds identified, the model identified 45 compounds with a VIP over 0.8. Both PV and palatability were well-fitted by the model as indicated by their position between the outer two ellipses (R2 = 75% to 100%). In the model, Hexanal had a VIP of 1.1 suggesting it was an important component, although other compounds had significantly better VIP scores and are likely to better predict palatability. Therefore, these data show aromatic hexanal is an accurate predictor of the oxidation of a product, although it may not provide the full picture regarding palatability.

Rapid determination of the storage time of cold-pressed berry seed oils using flash gas chromatography E-Nose coupled with chemometrics

Since oils containing a high content of polyunsaturated fatty acids are susceptible to oxidation, it is necessary to monitor the degree of deterioration during storage, e.g. by measuring the volatile compounds. This study aimed to assess volatile profiles of berry seed oils in terms of the authenticity and the deterioration assessment using flash gas chromatography (FGC E-Nose) combined with chemometrics. Berry seed oils (raspberry, blackcurrant, strawberry, chokeberry), obtained from three different suppliers and stored for a one year in brown bottles at room temperature, were analysed after 0, 3, 6, 9 and 12 months of storage. Principal component analysis enabled separation of oil samples by different berry types, suppliers and storage times. To predict the storage time, partial least square (PLS) models were built for each type of berry oil. Determination coefficients (R2) in cross-validation ranged from 0.842 (RMSECV = 1.69 months) to 0.969 (RMSECV = 0.75 months). Selecting the specific regions of chromatograms improved the residual prediction deviation (RPD) to values between 2.8 and 5.7, which indicated the suitability of the PLS models to predict the storage time in the quality control of berry oils.

E-eye, flash GC E-nose and HS-GC-MS combined with chemometrics to identify the adulterants and geographical origins of Ziziphi Spinosae Semen.

Ziziphi Spinosae Semen (ZSS), a valuable seed food, has faced increasing authenticity issues. In this study, the adulterants and geographical origins of ZSS were successfully identified by electronic eye, flash gas chromatography electronic nose (Flash GC e-nose) and headspace gas chromatography-mass spectrometry (HS-GC-MS). As a result, there were color differences between ZSS and adulterants, mainly represented by the a* value of ZSS was less than adulterants. In ZSS, 29 and 32 compounds were detected by Flash GC e-nose and HS-GC-MS. Spicy, sweety, fruity and herbal were the main flavor of ZSS. Five compounds were determined to be responsible for flavor differences between different geographical origins. In the HS-GC-MS analysis, the relative content of Hexanoic acid was the highest in ZSS from Hebei and Shandong, while 2,4-Decadien-1-ol was the highest in Shaanxi. Overall, this study provided a meaningful strategy for addressing authenticity problems of ZSS and other seed foods.

Rapid identification and quantification of vegetable oil adulteration in raw milk using a flash gas chromatography electronic nose combined with machine learning

Rapid identification and quantification of vegetable oil adulteration in raw milk using a flash gas chromatography electronic nose combined with machine learning

Rapid identification for the species discrimination of Curcumae Rhizoma using spectrophotometry and flash gas chromatography e-nose combined with chemometrics

Curcumae Rhizoma (CR) is a multi-origin herbal medicine from Cucurma kwangsiensis, Cucurma phaeocaulis , and Cucurma wenyujin . These three species are difficult to distinguish because of their similar morphological characteristics. In this study, a species discrimination strategy that combines spectrophotometry and flash gas chromatography (GC) e-nose was proposed. Principal component analysis (PCA) based on color parameters exhibited an overlap among the three species, which could be distinguished by color reference ranges and Bayes discriminant. Chemometric models were built based on odor information. The fingerprint revealed 18, 17, and 18 common peaks of C. kwangsiensis, C. phaeocaulis , and C. wenyujin , respectively. Heatmap and PCA visualized differences among the three varieties. With the accuracy of the validation set being 100%, linear discriminant analysis was used to identify different varieties rapidly and accurately. Meanwhile, 14 components, such as decanal, were determined to be chemical markers ( P < 0.05, VIP > 1) for the three species in partial least squares discriminant analysis. Finally, the Pearson test indicated that various components and color parameters were strongly related. In conclusion, spectrophotometry and flash GC e-nose combined with chemometrics can be used to identify CR from different species rapidly and accurately.

Evaluation of Three Marine Algae on Degradability, In Vitro Gas Production, and CH4 and CO2 Emissions by Ruminants

Livestock production systems are responsible for producing ~18% of the total anthropogenic greenhouse gas (GHG) emissions. Numerous alternatives, such as feed additives or supplements, have been proposed to meet these challenges. Marine algae have been proposed for gas reduction in ruminants; nevertheless, there are still very few experimental reports. Thus, the objective of the current study was to identify the volatile organic compounds (VOCs) in three marine algae—Kelp (Macrocystis pyrifera), Ulva (Ulva spp.), and Silk (Mazzaella spp.)—and to test their in vitro ruminal fermentation characteristics, gas profiles, and ability to mitigate biogas production. The evaluation of the VOCs in marine algae was performed using a flash gas chromatography electronic nose (FGC-E-Nose). The in vitro study was elaborated through in vitro incubation and gas production. The data obtained were analyzed using a completely randomized design. In total, forty-three volatile compounds were identified for Kelp algae, thirty-eight were identified for Ulva algae, and thirty-six were identified for Silk algae; the compounds were from different chemical families and included aromas, alcohols, aldehydes, phenolics, carboxylic acids, esters, and nutraceutical properties. Dry matter degradability was significantly (p &lt; 0.05) affected by the algae type. The cumulative ruminal gas production was different (p &lt; 0.05) between treatments. Kelp algae presented a major (V; p &lt; 0.05) volume of gas produced compared to the other algae. Lag time (l; p &lt; 0.05) was increased by Kelp alga; however, there were no differences (p&gt;0.05) between the Silk and Ulva algae. The gas production rate was higher (s; p &lt; 0.05) for Silk algae compared to the others. Ulva and Silk algae demonstrated a significant (p &lt; 0.05) decrease in carbon dioxide emissions. Nevertheless, Kelp algae reduced the proportional methane (CH4) production (p &lt; 0.05) after 48 h of incubation, with the lowest emission rate of 47.73%. In conclusion, algae have numerous bio compounds that provide some properties for use in ruminant diets as additives to reduce methane and carbon dioxide emissions.

A novel technique for detection of vanaspati ( hydrogenated fat ) in cow ghee ( clarified butter fat ) using flash gas chromatography electronic nose combined with chemometrics

This study aimed to detect various proportions of vanaspati mixed with cow ghee using an electronic nose (e-nose) based on fast gas chromatography. The e-nose apparatus was equipped with a non-polar and medium polar column used to generate two chromatograms simultaneously along with the volatile compound information. Further investigation on the adulteration detection in ghee was carried out using chemometric analysis such as principal component analysis (PCA), discriminant function analysis (DFA), and soft-independent modeling of class analogy (SIMCA). An outstanding differentiation among all the samples was obtained using PCA and DFA with 99.85% and 99.83% classification accuracy in the e-nose dataset. SIMCA presented a validation score of 76, indicating SIMCA could also be a potential method to detect higher levels of adulteration in ghee. The validation study shows good agreement between FGCEN and GC–MS methods. Practical applications Cow ghee is frequently adulterated with vanaspati, due to its high market demand. The existing methods for adulteration detection are time-consuming, and require tedious sample preparation and expertise in these fields. The e-nose based on fast GC combined with chemometric analysis turned out to be a reliable and promising technique for various proportions of adulteration detection in pure cow ghee. The technique was efficient enough to get the volatile compound information from the samples in a fast-performing way. The chemometrics used has shown to be effective to confirm adulterations in ghee.

Detection of soft-refined oils in extra virgin olive oil using data fusion approaches for LC-MS, GC-IMS and FGC-Enose techniques: The winning synergy of GC-IMS and FGC-Enose

Extra virgin olive oil (EVOO) is frequently adulterated by mixing it with soft refined oils (SROO). The differentiation of EVOO from its blends with SROO is not possible with the most common approaches, and, for this reason, the discriminating power of liquid chromatography-high resolution mass spectrometry (LC-MS), gas-chromatography ion mobility spectrometry (GC-IMS) and flash gas-chromatography electronic nose (FGC-Enose) was examined previously. Here, the combination of the above-mentioned techniques for an improvement in classification power of the methods is explored. A total of 43 commercial EVOOs and 18 illegal mixtures of SROO with EVOO were previously analysed by LC-(+/−)MS, GC-IMS and FGC-Enose. Low-level and mid-level data fusion of the four datasets were performed. The merged unique fingerprints were submitted to partial least squared discriminant analysis (PLS-DA), and the extrapolated most informative variables were used to build support vector machine (SVM) classifiers. Statistical indicators were calculated and compared to find out the best classifier. The results of PLS-DA-SVM strategies on the combination of datasets demonstrated that, after low-level data fusion, the discriminatory capability of the two merged GC-based techniques was remarkably improved as compared to the individual techniques. This indicates that merging the datasets before PLS-DA better retrieves the most informative variables and, thus, enhances group separation and classification of unknowns. The combination of LC(+/−)MS datasets, both by mid- and low-level data fusion, did not show significant enhancement in terms of discrimination of EVOO from SROO as compared to the individual LC(+)MS matrix. The low-level combination of the four datasets (LC(+/−)MS, GC-IMS, FGC-Enose) was successful, although this laborious option is not a viable path in industry quality assurance. This study primarily provides new paths for the authentication of EVOO, taking advantage of merging multimodal LC-(+/−)MS, GC-IMS and FGC-Enose data, with consequent improvement in the performances of the classification models. The most promising results were achieved by the low-level data fusion of GC-IMS and FGC-Enose data.

Electronic Noses and Their Applications for Sensory and Analytical Measurements in the Waste Management Plants-A Review.

Waste management plants are one of the most important sources of odorants that may cause odor nuisance. The monitoring of processes involved in the waste treatment and disposal as well as the assessment of odor impact in the vicinity of this type of facilities require two different but complementary approaches: analytical and sensory. The purpose of this work is to present these two approaches. Among sensory techniques dynamic and field olfactometry are considered, whereas analytical methodologies are represented by gas chromatography–mass spectrometry (GC-MS), single gas sensors and electronic noses (EN). The latter are the core of this paper and are discussed in details. Since the design of multi-sensor arrays and the development of machine learning algorithms are the most challenging parts of the EN construction a special attention is given to the recent advancements in the sensitive layers development and current challenges in data processing. The review takes also into account relatively new EN systems based on mass spectrometry and flash gas chromatography technologies. Numerous examples of applications of the EN devices to the sensory and analytical measurements in the waste management plants are given in order to summarize efforts of scientists on development of these instruments for constant monitoring of chosen waste treatment processes (composting, anaerobic digestion, biofiltration) and assessment of odor nuisance associated with these facilities.

Storage time of nut spreads using flash gas chromatography E-nose combined with multivariate data analysis

The quality assessment, in terms of lipid oxidative status, of food products stored in the long-term is of great importance, especially those with a high lipid content. Specifically, companies working in this sector need feasible, simple, and fast techniques that are suitable for quality or process control. Herein, a fingerprinting approach, based on headspace analysis carried out by flash gas chromatography electronic nose (FGC E-nose) and multivariate data analysis was applied to pistachio and gianduja spreads. These samples, differently packaged, were stored in climatic chambers at 40 °C for 180 days and their headspace fraction was analyzed periodically for a total of 15 sampling times. Principal component analysis showed a clear separation according to the packaging type for both pistachio and gianduja samples. Partial least squares regression models were developed to predict the storage time considering the aggregated data (R2 up to 0.985, RMSEP = 6.16 days) or separately (R2 up to 0.989, RMSEP = 5.71 days). Based on the obtained residual prediction deviation (RPD from 4.4 to 8.5 in prediction), the models can be considered suitable for use in quality control in an industrial environment.

Detection of soybean oil adulteration in cow ghee (clarified milk fat): an ultrafast study using flash gas chromatography electronic nose coupled with multivariate chemometrics

Cow ghee is one of the expensive edible fats in the dairy sector. Ghee is often adulterated with low-priced edible oils, like soybean oil, owing to its high market demand. The existing adulteration detection methods are time-consuming, requiring sample preparation and expertise in these fields. The possibility of detecting soybean oil adulteration (from 10% to 100%) in pure cow ghee was investigated in this study. The fingerprint information of volatile compounds was collected using a flash gas chromatography electronic nose (FGCEN) instrument. The classification results were studied using the pattern recognition chemometric models principal component analysis (PCA), soft independent modelling of class analogy (SIMCA), and discriminant function analysis (DFA). The most powerful fingerprint odor of all the samples identified from FGCEN analysis was acetaldehyde (Z)-4-heptenal, 2-propanol, ethyl propanoate, and pentan-2-one. The odor analysis investigation was accomplished with an average analysis time of 90 s. A clear differentiation of all the samples with an excellent classification accuracy of more than 99% was achieved with the PCA and DFA chemometric methods. However, the results of the SIMCA model showed that SIMCA could only be used to detect ghee adulteration at higher concentration levels (30% to 100%). The validation study shows good agreement between FGCEN and gas chromatography-mass spectrometry methods. The methodology demonstrated coupled with PCA and DFA methods for adulteration detection in ghee using FGCEN apparatus has been an efficient and convenient technique. This study explored the capability of the FGCEN instrument to tackle the adulteration problems in ghee. © 2022 Society of Chemical Industry.

Physicochemical parameters combined flash GC e-nose and artificial neural network for quality and volatile characterization of vinegar with different brewing techniques

Vinegar is a kind of traditional fermented food, there are significant variances in quality and flavor due to differences in raw ingredients and processes. The quality assessment and flavor characteristics of 69 vinegar samples with 5 brewing processes were analyzed by physicochemical parameters combined with flash gas chromatography (GC) e-nose. The evaluation system of quality and the detection method of flavor profile were established. 17 volatile flavor compounds and potential flavor differential compounds of each brewing process were identified. The artificial neural network (ANN) analysis model was established based on the physicochemical parameters and the analysis of flash GC e-nose. Although the physicochemical parameters were more intuitive in quality evaluating, the flash GC e-nose could better reflect the flavor characteristics of vinegar samples and had better fitting, prediction and discrimination ability, the correct rates of training and prediction of flash GC e-nose trained ANN model were 98.6% and 96.7%, respectively.

Photochemical reaction kinetics and mechanism of bisphenol A with K2S2O8 in aqueous solution: a laser flash photolysis study

The photochemical reaction kinetics and mechanism of bisphenol A (BPA) with potassium persulfate (K2S2O8) were investigated by using 266 nm laser flash photolysis and gas chromatography mass spectrum (GC-MS) technique. Sulfate radical (SO4•−), generated upon K2S2O8 photolysis, reacted with BPA with the overall rate constant of (1.61 ± 0.15) × 109 L mol−1 s−1, and two main reaction mechanisms were involved. One was addition channel to generate BPA–SO4•− adduct with a specific second-order rate constant of (1.09 ± 0.15) × 109 L mol−1 s−1. Molecular oxygen was involved in the decay of the BPA–SO4•− adduct with a rate constant of (1.28 ± 0.14) × 108 L mol−1 s−1. Another channel was the formation of BPA’s phenoxyl radical, likely derived from a deprotonation of the cation radical (BPA•+) generated from single electron transfer reactions. The specific rate constant of BPA’s phenoxyl radical formation was determined to be (6.16 ± 0.08) × 108 L mol−1 s−1. The overall rate constant was in line with the sum of aforementioned two specific rate constants for two main reaction channels. By comparing these rate constants, it was indicated that SO4•− addition channel accounted for ∼65% (1.09/1.61) to the overall reaction, and phenoxyl radical formation accounted for only ∼35% (0.62/1.61). The transformation products of BPA were identified by using GC-MS including 4-isopropylphenol, 4-isopropenylphenol, and 2,4-di-tert-butylphenol, and the reaction mechanism was proposed. These results may provide microscopic kinetics and mechanism information on BPA degradation using SO4•−-based advanced oxidation processes.

GC-IMS and FGC-Enose fingerprint as screening tools for revealing extra virgin olive oil blending with soft-refined olive oils: A feasibility study

The undeclared blending of EVOO with soft-refined oils is one of the main issue in the olive oil sector. Despite the efforts, reliable markers related to soft-refinement processes have not been found yet. In the present work, two rapid headspace-based techniques, namely gas-chromatography ion mobility spectrometry and flash gas chromatography electronic nose, were proposed and tested as rapid screening tools for the detection of this fraud practice. Since real counterfeited samples are not commercially available, soft refined and deacidified olive oils were recreated at a laboratory scale and mixed with EVOO at different percentages. Commercial EVOOs sampled over three harvesting seasons (2015/2016, 2016/2017 and 2017/2018), along with the in-house prepared blends, were analysed by means of the above-mentioned techniques. SIMCA was chosen as classification algorithm to discern the illicit mix from the authentic EVOOs. Both the analytical techniques exhibited notable robustness and stability over the time in terms of intra- and inter-day reproducibility. Concerning the samples discrimination, the final outcome was found to be greatly affected by the inclusion (or exclusion) of the EVOO15/16 group in the model training. When EVOO from more recent harvests (i.e. EVOO16/17, EVOO17/18) were used to calibrate the model, a practically 100% specificity was achieved by both the techniques and even the lowest-percentage adulterated samples (i.e. 10%) were recognized to be non-authentic EVOOs. On the other hand, poorer classification was achieved including the EVOO15/16 in the model training. The present work demonstrated that focusing on the volatile fraction might be the right strategy to overcome the lack of clear and specific process-related marker formed upon soft-refinement processes. At the same time, it highlighted how the EVOO chemical (in)stability would be a crucial aspect to be considered in the development of fingerprinting methods.

Flash Gas Chromatography in Tandem with Chemometrics: A Rapid Screening Tool for Quality Grades of Virgin Olive Oils.

This research aims to develop a classification model based on untargeted elaboration of volatile fraction fingerprints of virgin olive oils (n = 331) analyzed by flash gas chromatography to predict the commercial category of samples (extra virgin olive oil, EVOO; virgin olive oil, VOO; lampante olive oil, LOO). The raw data related to volatile profiles were considered as independent variables, while the quality grades provided by sensory assessment were defined as a reference parameter. This data matrix was elaborated using the linear technique partial least squares-discriminant analysis (PLS-DA), applying, in sequence, two sequential classification models with two categories (EVOO vs. no-EVOO followed by VOO vs. LOO and LOO vs. no-LOO followed by VOO vs. EVOO). The results from this large set of samples provide satisfactory percentages of correctly classified samples, ranging from 72% to 85%, in external validation. This confirms the reliability of this approach in rapid screening of quality grades and that it represents a valid solution for supporting sensory panels, increasing the efficiency of the controls, and also applicable to the industrial sector.

Compliance with EU vs. extra-EU labelled geographical provenance in virgin olive oils: A rapid untargeted chromatographic approach based on volatile compounds

Many studies have shown that geographic origin is one of the most influencing factors in consumers’ choice of olive oil. To avoid misleading consumers, European regulation has established specific rules to report the geographical origin of extra virgin (EVOOs) and virgin olive oils (VOOs) on the product label, even if an official analytical procedure to verify the origin has not been yet defined. In this work, a flash gas chromatography (FGC) untargeted approach based on volatile compounds, followed by a chemometric data analysis, is proposed for discrimination of EVOOs and VOOs according to their geographical origin (EU and extra-EU). A set of 210 samples was analyzed and two different classification techniques were used, one linear (Partial Least Square-Discriminant Analysis, PLS-DA) and one non-linear (Artificial Neural Network, ANN). The two models were also validated using an external data set. Satisfactory results were obtained for both chemometric approaches: with PLS-DA, 89% and 81% of EU and extra-EU samples, respectively, were correctly classified; for ANN, the percentages were 92% and 88%, respectively. These results confirm the reliability of the method as a rapid approach to discriminate EVOOs and VOOs according to their geographical provenance.

Impact of cooling rates on the flavor of cooked rice during storage

This study aimed to evaluate the impact of cooling rates (0.19, 1.27, 1.74 and 2.88°C/min) on the flavor of cooked rice during storage. The results obtained from flash gas chromatography electronic nose and sensory analysis showed that higher cooling rates prolonged rice fragrance retention, whereas faster deterioration of flavor acceptability of cooked rice was observed at lower cooling rates during storage. The contribution of 43 flavor compounds in cooked rice was validated in three parallel tests by gas chromatography-mass spectrometry combined with principal component analysis, which indicated that 2-acetyl-1-pyrroline and alcohols contributed to the fragrance, while aliphatic aldehydes (at high concentration), benzene derivatives, and acids were responsible for flavor deterioration. Partial least squares regression further identified that eight flavor compounds [(E,E)-2,4-decadienal, 2-butyl-2-octenal, 1-hexanol, 2-propyl-1-pentanol, 2-butyl-furan, geranylacetone, 2-acetylthiazole, and 2-acetyl-1-pyrroline] had significantly positive contributions to sensory evaluations, while nine flavor compounds [hexanal, octanal, (E)-2-octenal, decanal, (E)-5-methyl-4-hepten-3-one, n-hexadecanoic acid, ethylbenzene, o-xylene and p-xylene] had significantly negative contributions to sensory evaluations during storage (p < 0.05). The water migration and/or starch crystallization of cooked rice during storage affected by cooling rates were supposed to affect the release of the flavor compounds. These findings could provide a reference for the flavor preservation of instant rice.