Feasibility of an electronic nose to differentiate commercial Spanish wines elaborated from the same grape variety

Differentiation through E-nose and GC-FID data modeling of rosé sparkling wines elaborated via traditional and Charmat methods

BACKGROUND

The growing demand for rosé sparkling wine has led to an increase in its production. Traditional or Charmat wine-making influence the aromatic profiles in wine. An analysis such as gas chromatography makes an accurate assessment of wines based on volatile detection but is resource intensive. On the other hand, the electronic nose (E-nose) has emerged as a versatile tool, offering rapid, cost-effective discrimination of wines, and contributing insights into quality and production processes because of its aptitude to perform a global aromatic pattern evaluation. In the present study, rosé sparkling wines were produced using both methods and major volatile compounds and polyols were measured. Wines were tested by E-nose and predictive modelling was performed to distinguish them.

RESULTS

Volatile profiles showed differences between Charmat and traditional methods, especially at 5 months of aging. A partial least square discriminant analysis (PLS-DA) was carried out on E-nose detections, obtaining a model that describes 94% of the variability, separating samples in different clusters and correctly identifying different classes. The differences derived from PLS-DA clustering agree with the results obtained by gas-chromatography. Moreover, a principal components regression model was built to verify the ability of the E-nose to non-destructively predict the amount of different volatiles analyzed.

CONCLUSION

Production methods of Rosé sparkling wine affect the final wine aroma profiles as a result of the differences in terms of volatiles. The PLS-DA of the data obtained with E-nose reveals that distinguishing between Charmat and traditional methods is possible. Moreover, predictive models using gas chromatography-flame ionization detection analysis and E-nose highlight the possibility of fast and efficient prediction of volatiles from the E-nose. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Differentiating True and False Cinnamon: Exploring Multiple Approaches for Discrimination

This study presents a comprehensive literature review that investigates the distinctions between true and false cinnamon. Given the intricate compositions of essential oils (EOs), various discrimination approaches were explored to ensure quality, safety, and authenticity, thereby establishing consumer confidence. Through the utilization of physical-chemical and instrumental analyses, the purity of EOs was evaluated via qualitative and quantitative assessments, enabling the identification of constituents or compounds within the oils. Consequently, a diverse array of techniques has been documented, encompassing organoleptic, physical, chemical, and instrumental methodologies, such as spectroscopic and chromatographic methods. Electronic noses (e-noses) exhibit significant potential for identifying cinnamon adulteration, presenting a rapid, non-destructive, and cost-effective approach. Leveraging their capability to detect and analyze volatile organic compound (VOC) profiles, e-noses can contribute to ensuring authenticity and quality in the food and fragrance industries. Continued research and development efforts in this domain will assuredly augment the capacities of this promising avenue, which is the utilization of Artificial Intelligence (AI) and Machine Learning (ML) algorithms in conjunction with spectroscopic data to combat cinnamon adulteration.

Application progress of intelligent flavor sensing system in the production process of fermented foods based on the flavor properties

Fermented foods are sensitive to the production conditions because of microbial and enzymatic activities, which requires intelligent flavor sensing system (IFSS) to monitor and optimize the production process based on the flavor properties. As the simulation system of human olfaction and gustation, IFSS has been widely used in the field of food with the characteristics of nondestructive, pollution-free, and real-time detection. This paper reviews the application of IFSS in the control of fermentation, ripening, and shelf life, and the potential in the identification of quality differences and flavor-producing microbes in fermented foods. The survey found that electronic nose (tongue) is suitable to monitor fermentation process and identify food authenticity in real time based on the changes of flavor profile. Gas chromatography-ion mobility spectrometry and nuclear magnetic resonance technology can be used to analyze the flavor metabolism of fermented foods at various production stages and explore the correlation between flavor substances and microorganisms.

HPLC-DAD-MS and Antioxidant Profile of Fractions from Amontillado Sherry Wine Obtained Using High-Speed Counter-Current Chromatography

In the present work, the polyphenolic profile of a complex matrix such as Amontillado sherry has been processed by means of high-speed counter-current chromatography (HSCCC) and characterized by HPLC-DAD-MS. An Amberlite XAD-7 column was used to obtain the wine extract, and three different biphasic solvent systems were applied for HSCCC separation: MTBE (methyl tert-butyl ether)/n-butanol/acetonitrile/water (1.1/3/1.1/5+0.1% trifluoroacetic acid), MTBE/n-butanol/acetonitrile/water (2/2/1/5), and hexane/ethyl acetate/ethanol/water (1/5/1/5). As a result, 42 phenolic compounds and furanic derivatives have been identified by means of HPLC-DAD-MS, with 11 of them being identified for the first time in Sherry wines: 3-feruloylquinic acid, isovanillin, ethyl vanillate, furoic acid, dihydro-p-coumaric acid, 6-O-feruloylglucose, ethyl gallate, hydroxytyrosol, methyl protocatechuate, homoveratric acid and veratraldehyde. In addition, the antioxidant capacity (ABTS) of the obtained fractions was determined, revealing higher values in those fractions in which compounds such as gallic acid, protocatechuic acid, protocatechualdehyde, trans-caftaric acid, syringic acid, isovanillin or tyrosol, among others, were present. This is the first time that HSCCC has been used to characterize the phenolic composition of Sherry wines.

Integration of a low-cost electronic nose and a voltammetric electronic tongue for red wines identification

The purpose of this present study was to develop a rapid and effective approach for identification of red wines that differ in geographical origins, brands, and grape varieties, a multi-sensor fusion technology based on a novel cost-effective electronic nose (E-nose) and a voltammetric electronic tongue (E-tongue) was proposed. The E-nose sensors was created using porphyrins or metalloporphyrins, pH indicators and Nile red printed on a C2 reverse phase silica gel plate. The voltammetric E-Tongue with six metallic working electrodes, namely platinum, gold, palladium, tungsten, titanium, and silver was employed to sense the taste of red wines. Principal component analysis (PCA) was utilized for dimensionality reduction and decorrelation of the raw sensors datasets. The fusion models derived from extreme learning machine (ELM) were built with PCA scores of E-nose and tongue as the inputs. Results showed superior performance (100% recognition rate) using combination of odor and taste sensors than individual artificial systems. The results suggested that fusion of the novel cost-effective E-nose created and voltammetric E-tongue coupled with ELM has a powerful potential in rapid quality evaluation of red wine.

Using an electronic nose and volatilome analysis to differentiate sparkling wines obtained under different conditions of temperature, ageing time and yeast formats

Effect of yeast inoculation format (F), temperature (T), and "on lees" ageing time (t) factors were evaluated on the composition of sparkling wines by a quantitative fingerprint obtained from volatile metabolites and the response of an electronic nose (E-nose). Wines elaborated according the traditional method at 10 and 14 °C, free cells and yeast biocapsules formats were monitored at 15 and 24 months of ageing time. Sixty-six volatiles identified and quantified in the eight sampling lots were subjected to a pattern recognition technique. A dual criterion based on univariate (ANOVA) and multivariate analysis (PLS-DA) through the variable importance projection (VIP) values, allowed to identify ten volatiles as potential markers for T factor, eleven for t and twelve for F factors. The discriminant models based on E-nose dataset enable a 100% correct classification of samples, in relation with t and F factors and the 83% for T factor.

Freshness Evaluation of Three Kinds of Meats Based on the Electronic Nose

The aim of this study was to use an electronic nose set up in our lab to detect and predict the freshness of pork, beef and mutton. Three kinds of freshness, including fresh, sub-fresh and putrid, was established by human sensory evaluation and was used as a reference for the electronic nose’s discriminant factor analysis. The principal component analysis results showed the electronic nose could distinguish well pork, beef and mutton samples with different storage times. In the PCA figures, three kinds of meats samples all presented an approximate parabola trend during 7 days’ storage time. The discriminant factor analysis showed electronic nose could distinguish and judge well the freshness of samples (accuracy was 89.5%, 84.2% and 94.7% for pork, beef and mutton, respectively). Therefore, the electronic nose is promising for meat fresh detection application.

Rapid detection of adulterated peony seed oil by electronic nose

Peony seed oil has recently been introduced as a high-quality food oil. Because the high price of peony seed oil may tempt unscrupulous merchants to dilute it with cheaper substitutes, a rapid detection method for likely adulterants is required. In this study, the fatty acid composition of peony seed oil and four less expensive edible oils (soybean oil, corn oil, sunflower oil, and rapeseed oil) were measured by gas chromatography mass spectrometry. Peony oil adulterated by other edible oils was assessed using iodine values to estimate the extent of adulteration. Adulteration was also measured using an electronic nose (E-nose) combined with principal component analysis (PCA) or linear discriminant analysis (LDA). Results indicated that peony seed oil was highly enriched in α-linolenic acid. Although the iodine value can be used to detect some adulterants by measuring unsaturation, it was not able to detect all four potential adulterants. In contrast, the E-nose can rapidly identify adulterated peony seed oil by sampling vapor. Data analyses using PCA and LDA show that LDA more effectively clusters the data, discriminates between pure and adulterated oil, and can detect adulteration at the 10% level. E-nose combined with LDA suitable for detection of peony seed oil adulteration.

Preparación, Caracterización y Aplicación de sensores de Pd-SnO como nariz electrónica en la clasificación de vinos peruanos y su comparación con técnicas cromatográficas

Se prepararon sensores basados en SnO dopados con paladio (0, 1, 3, 5 y 7%) mediante el método  de  impregnación húmeda.  Para  caracterizarlos,  se  usaron las técnicas  de espectroscopía infrarroja con transformada de Fourier (FTIR), adsorción y desorción de N2  (BET), difracción de rayos X (XRD), microscopía electrónica de barrido (SEM) y espectroscopia de energía dispersiva de rayos X (EDX). Los sensores se evaluaron con etanol para que conformaran, junto a dos sensores  comerciales,  una  nariz  electrónica (E-nose) que  lograse  detectar  componentes volátiles  del  aroma  en  vinos  peruanos. Los  resultados  fueron  interpretados  a través  del análisis  de  componentes principales  (PCA)  con  la  finalidad  de buscar  una  técnica  que complementase la información recolectada por la cromatografía de gases (GC) y la cromatografía líquida (HPLC), y justamente comparando los PCAs obtenidos del GC y el HPLC con los resultantes de la E-nose se encontró que estos últimos clasificaban mejor las muestras. Fue posible diferenciar tanto vinos de la misma uva como de distinta cepa. Además, se logró detectar vinos adulterados, lo cual contribuye a la industria vitivinícola en el control de su producción con la finalidad de mejorar la calidad de esta bebida para el consumidor.

A novel method to quantify the activity of alcohol acetyltransferase Using a SnO2-based sensor of electronic nose

Alcohol acetyltransferase (AATFase) extensively catalyzes the reactions of alcohols to acetic esters in microorganisms and plants. In this work, a novel method has been proposed to quantify the activity of AATFase using a SnO2-based sensor of electronic nose, which was determined on the basis of its higher sensitivity to the reducing alcohol than the oxidizing ester. The maximum value of the first-derivative of the signals from the SnO2-based sensor was therein found to be an eigenvalue of isoamyl alcohol concentration. Quadratic polynomial regression perfectly fitted the correlation between the eigenvalue and the isoamyl alcohol concentration. The method was used to determine the AATFase activity in this type of reaction by calculating the conversion rate of isoamyl alcohol. The proposed method has been successfully applied to determine the AATFase activity of a cider yeast strain. Compared with GC-MS, the method shows promises with ideal recovery and low cost.

Colorimetric Sensor Array for White Wine Tasting

A colorimetric sensor array was developed to characterize and quantify the taste of white wines. A charge-coupled device (CCD) camera captured images of the sensor array from 23 different white wine samples, and the change in the R, G, B color components from the control were analyzed by principal component analysis. Additionally, high performance liquid chromatography (HPLC) was used to analyze the chemical components of each wine sample responsible for its taste. A two-dimensional score plot was created with 23 data points. It revealed clusters created from the same type of grape, and trends of sweetness, sourness, and astringency were mapped. An artificial neural network model was developed to predict the degree of sweetness, sourness, and astringency of the white wines. The coefficients of determination (R2) for the HPLC results and the sweetness, sourness, and astringency were 0.96, 0.95, and 0.83, respectively. This research could provide a simple and low-cost but sensitive taste prediction system, and, by helping consumer selection, will be able to have a positive effect on the wine industry.

Predicting the composition of red wine blends using an array of multicomponent Peptide-based sensors

Differential sensing using synthetic receptors as mimics of the mammalian senses of taste and smell is a powerful approach for the analysis of complex mixtures. Herein, we report on the effectiveness of a cross-reactive, supramolecular, peptide-based sensing array in differentiating and predicting the composition of red wine blends. Fifteen blends of Cabernet Sauvignon, Merlot and Cabernet Franc, in addition to the mono varietals, were used in this investigation. Linear Discriminant Analysis (LDA) showed a clear differentiation of blends based on tannin concentration and composition where certain mono varietals like Cabernet Sauvignon seemed to contribute less to the overall characteristics of the blend. Partial Least Squares (PLS) Regression and cross validation were used to build a predictive model for the responses of the receptors to eleven binary blends and the three mono varietals. The optimized model was later used to predict the percentage of each mono varietal in an independent test set composted of four tri-blends with a 15% average error. A partial least square regression model using the mouth-feel and taste descriptive sensory attributes of the wine blends revealed a strong correlation of the receptors to perceived astringency, which is indicative of selective binding to polyphenols in wine.

Detecting internal quality of peanuts during storage using electronic nose responses combined with physicochemical methods

In this study, the changes in the quality of unshelled peanuts and peanut kernels during storage were analyzed using an electronic nose (e-nose). The physicochemical indexes (acid and peroxide values) of peanut kernels were tested by traditional method as a reference. The storage time of peanut kernels increases from left to right in the cluster analysis plot based on the physicochemical indexes. The "maximum values", "area values", and "70th s values" methods were applied to extract the feature data from the e-nose responses. Principal component analysis (PCA) results indicated that the "70th s values" method produced the most accurate results, furthermore, unshelled peanut and peanut kernel samples presented similar characteristics in the PCA plots; the partial least squares regression (PLSR) results showed that the features of unshelled peanuts and peanut kernels are highly correlated with acid and peroxide values, respectively.

Discrimination of chicken seasonings and beef seasonings using electronic nose and sensory evaluation

This study examines the feasibility of electronic nose as a method to discriminate chicken and beef seasonings and to predict sensory attributes. Sensory evaluation showed that 8 chicken seasonings and 4 beef seasonings could be well discriminated and classified based on 8 sensory attributes. The sensory attributes including chicken/beef, gamey, garlic, spicy, onion, soy sauce, retention, and overall aroma intensity were generated by a trained evaluation panel. Principal component analysis (PCA), discriminant factor analysis (DFA), and cluster analysis (CA) combined with electronic nose were used to discriminate seasoning samples based on the difference of the sensor response signals of chicken and beef seasonings. The correlation between sensory attributes and electronic nose sensors signal was established using partial least squares regression (PLSR) method. The results showed that the seasoning samples were all correctly classified by the electronic nose combined with PCA, DFA, and CA. The electronic nose gave good prediction results for all the sensory attributes with correlation coefficient (r) higher than 0.8. The work indicated that electronic nose is an effective method for discriminating different seasonings and predicting sensory attributes.

Electronic nose and isotope ratio mass spectrometry in combination with chemometrics for the characterization of the geographical origin of Italian sweet cherries

Sweet cherries from two Italian regions, Apulia and Emilia Romagna, were analysed using electronic nose (EN) and isotope ratio mass spectrometry (IRMS), with the aim of distinguishing them according to their geographic origin. The data were elaborated by statistical techniques, examining the EN and IRMS datasets both separately and in combination. Preliminary exploratory overviews were performed and then linear discriminant analyses (LDA) were used for classification. Regarding EN, different approaches for variable selection were tested, and the most suitable strategies were highlighted. The LDA classification results were expressed in terms of recognition and prediction abilities and it was found that both EN and IRMS performed well, with IRMS showing better cross-validated prediction ability (91.0%); the EN-IRMS combination gave slightly better results (92.3%). In order to validate the final results, the models were tested using an external set of samples with excellent results.

Paper microfluidics for red wine tasting

A smartphone takes a digital image of the paper microfluidic chip, after a red wine sample filled the entire 8 channels (each pre-loaded with a different chemical dye). The PCA score plot shows good statistical difference among the 6 red wine samples, where PC1 corresponded to the sweetness and PC2 to the bodyness of a red wine. The PCA model was validated with an additional 4 red wine samples from a different manufacturer.