Monitoring grape ripeness using a voltammetric electronic tongue

Cold extraction process for producing a low-alcohol beer, International Pale Lager style: Evaluation and description of flavors using electronic tongue

Grains germinate, dry, and then undergo crushing before being combined with hot water to yield a sweet and viscous liquid known as wort. To enhance flavor and aroma compounds while maintaining a lower alcohol content, cold water is utilized during wort production without increasing its density. Recent years have witnessed a surge in demand for beverages with reduced alcohol content, reflecting shifting consumer preferences towards healthier lifestyles. Notably, consumers of low-alcohol beers seek products that closely mimic traditional beers. In response, batches of low-alcohol beer were meticulously crafted using a cold extraction method with room temperature water, resulting in a beer with 1.11% alcohol by volume (ABV). Sensory evaluations yielded a favorable score of 27 out of 50, indicating adherence to style standards and absence of major technical flaws. Furthermore, electronic taste profiling revealed a striking similarity between the low-alcohol beer and the benchmark International Pale Lager style, exemplified by commercial beers (5 and 0.03% ABV). Notably, the reduced-alcohol variant boasted lower caloric content compared to both standard and non-alcoholic counterparts. Consequently, the cold extraction approach emerges as a promising technique for producing low-alcohol beers within the International Pale Lager style, catering to evolving consumer preferences and health-conscious trends.

Discrimination of Maturity Stages of Cabernet Sauvignon Wine Grapes Using Visible-Near-Infrared Spectroscopy

Grape quality and ripeness play a crucial role in producing exceptional wines with high-value characteristics, which requires an effective assessment of grape ripeness. The primary purpose of this research is to explore the possible application of visible-near-infrared spectral (Vis-NIR) technology for classifying the maturity stages of wine grapes based on quality indicators. The reflection spectra of Cabernet Sauvignon grapes were recorded using a spectrometer in the spectral range of 400 nm to 1029 nm. After measuring the soluble solids content (SSC), total acids (TA), total phenols (TP), and tannins (TN), the grape samples were categorized into five maturity stages using a spectral clustering method. A traditional supervised classification method, a support vector machine (SVM), and two deep learning techniques, namely stacked autoencoders (SAE) and one-dimensional convolutional neural networks (1D-CNN), were employed to construct a discriminant model and investigate the association linking grape maturity stages and the spectral responses. The spectral data went through three commonly used preprocessing methods, and feature wavelengths were extracted using a competitive adaptive reweighting algorithm (CARS). The spectral data model preprocessed via multiplicative scattering correction (MSC) outperformed the other two preprocessing methods. After preprocessing, a comparison was made between the discriminant models established with full and effective spectral data. It was observed that the SAE model, utilizing the feature spectrum, demonstrated superior overall performance. The classification accuracies of the calibration and prediction sets were 100% and 94%, respectively. This study showcased the dependability of combining Vis-NIR spectroscopy with deep learning methods for rapidly and accurately distinguishing the ripeness stage of grapes. It has significant implications for future applications in wine production and the development of optoelectronic instruments tailored to the specific needs of the winemaking industry.

Quality Assessment and Ripeness Prediction of Table Grapes Using Visible-Near-Infrared Spectroscopy

Ripeness significantly affects the commercial values and sales of fruits. In order to monitor the change of grapes' quality parameters during ripening, a rapid and nondestructive method of visible-near-infrared spectral (Vis-NIR) technology was utilized in this study. Firstly, the physicochemical properties of grapes at four different ripening stages were explored. Data evidenced increasing color in redness/greenness (a*) and Chroma (C*) and soluble solids (SSC) content and decreasing values in color of lightness (L*), yellowness/blueness (b*) and Hue angle (h*), hardness, and total acid (TA) content as ripening advanced. Based on these results, spectral prediction models for SSC and TA in grapes were established. Effective wavelengths were selected by the competitive adaptive weighting algorithm (CARS), and six common preprocessing methods were applied to pretreat the spectra data. Partial least squares regression (PLSR) was applied to establish models on the basis of effective wavelengths and full spectra. The predictive PLSR models built with full spectra data and 1st derivative preprocessing provided the best values of performance parameters for both SSC and TA. For SSC, the model showed the coefficients of determination for calibration (RCal2) and prediction (RPre2) set of 0.97 and 0.93, respectively, the root mean square error for calibration set (RMSEC) and prediction set (RMSEP) of 0.62 and 1.27, respectively; and the RPD equal to 4.09. As for TA, the optimum values of RCal2, RPre2, RMSEC, RMSEP and RPD were 0.97, 0.94, 0.88, 1.96 and 4.55, respectively. The results indicated that Vis-NIR spectroscopy is an effective tool for the rapid and non-destructive detection of SSC and TA in grapes.

Low-Cost Sensor for Continuous Measurement of Brix in Liquids

This paper presents a Brix sensor based on the differential pressure measurement principle. Two piezoresistive silicon pressure sensors were applied to measure the specific gravity of the liquid, which was used to calculate the Brix level. The pressure sensors were mounted inside custom-built water-tight housings connected together by fixed length metallic tubes containing the power and signal cables. Two designs of the sensor were prepared; one for the basic laboratory testing and validation of the proposed system and the other for a fermentation experiment. For lab tests, a sugar solution with different Brix levels was used and readings from the proposed sensor were compared with a commercially available hydrometer called Tilt. During the fermentation experiments, fermentation was carried out in a 1000 L tank over 7 days and data was recorded and analysed. In the lab experiments, a good linear relationship between the sugar content and the corresponding Brix levels was observed. In the fermentation experiment, the sensor performed as expected but some problems such as residue build up were encountered. Overall, the proposed sensing solution carries a great potential for continuous monitoring of the Brix level in liquids. Due to the usage of low-cost pressure sensors and the interface electronics, the cost of the system is considered suitable for large scale deployment at wineries or juice processing industries.

Monofloral honey authentication by voltammetric electronic tongue: A comparison with 1H NMR spectroscopy

Proton-nuclear-magnetic-resonance-spectroscopy (¹H NMR) is the widely accepted reference method for monitoring honey adulteration; however, the need to find cheaper, faster, and more environmentally friendly methodologies makes the voltammetric-electronic-tongue (VET) a good alternative. The present study aims to demonstrate the ability of VET (in comparison with ¹H NMR) to predict the adulteration of honey with syrups. Samples of monofloral honeys (citrus, sunflower and heather, assessed by pollen analysis) simulating different levels of adulteration by adding syrups (barley, rice and corn) from 2.5 to 40% (w/w) were analyzed using both techniques. According to the indicators (slope, intercept, regression coefficient-R², root mean square error of prediction-RMSEP) of the partial-least-squares (PLS) regression models, in general terms, the performance of these models obtained by both techniques was good, with an average error lower than 5% in both cases. These results support the use of VET as a screening technique to easily detect honey adulteration with syrups.

Sensors and Instruments for Brix Measurement: A Review

Quality assessment of fruits, vegetables, or beverages involves classifying the products according to the quality traits such as, appearance, texture, flavor, sugar content. The measurement of sugar content, or Brix, as it is commonly known, is an essential part of the quality analysis of the agricultural products and alcoholic beverages. The Brix monitoring of fruit and vegetables by destructive methods includes sensory assessment involving sensory panels, instruments such as refractometer, hydrometer, and liquid chromatography. However, these techniques are manual, time-consuming, and most importantly, the fruits or vegetables are damaged during testing. On the other hand, the traditional sample-based methods involve manual sample collection of the liquid from the tank in fruit/vegetable juice making and in wineries or breweries. Labour ineffectiveness can be a significant drawback of such methods. This review presents recent developments in different destructive and nondestructive Brix measurement techniques focused on fruits, vegetables, and beverages. It is concluded that while there exist a variety of methods and instruments for Brix measurement, traits such as promptness and low cost of analysis, minimal sample preparation, and environmental friendliness are still among the prime requirements of the industry.

Detection of Red Wine Faults over Time with Flash Profiling and the Electronic Tongue

Wine faults, often caused by spoilage microorganisms, are considered negative sensory attributes, and may result in substantial economic losses. The objective of this study was to use the electronic tongue (e-tongue) and flash sensory profiling (FP) to evaluate changes in red wine over time due to the presence of different spoilage microorganisms. Merlot wine was inoculated with one of the following microorganisms: Brettanomyces bruxellensis, Lactobacillus brevis, Pediococcus parvulus, or Acetobacter pasteurianus. These wines were analyzed weekly until Day 42 using the e-tongue and FP, with microbial plate counts. Over time, both FP and e-tongue differentiated the wines. The e-tongue showed a low discrimination among microorganisms up to Day 14 of storage. However, at Day 21 and continuing to Day 42, the e-tongue discriminated among the samples with a discrimination index of 91. From the sensory FP data, assessors discriminated among the wines starting at Day 28. Non-spoilage terms were used to describe the wines at significantly higher frequency for all time points until Day 42, at which point the use of spoilage terms was significantly higher (p < 0.05). These results suggest that application of these novel techniques may be the key to detecting and limiting financial losses associated with wine faults.

Use of Nanomaterials in Food Science

The current global population is nearly 6 billion; due to this rapid population growth, there is a need to produce food in a more efficient, safe, and sustainable way, and it should be safe from the adverse effects of pathogenic organisms. A large proportion of population living in developing countries face daily food shortages as a result of environmental impacts or some other reasons like political instability, etc., while in the developed countries, food is surplus. For developing countries, the objective is to develop drought- and pest-resistant crops, with maximized yield. In developed countries, the food industry depends on consumer’s demand for fresher and healthier foodstuffs. The present chapter describes the use of nanoparticles in food science.

Using an automatic pulse voltammetric electronic tongue to verify the origin of honey from Spain, Honduras, and Mozambique

BACKGROUND

The growing need to classify the origin of honey in a simple way is leading to the development of affordable analytical equipment that is in-line and manageable, enabling rapid on-site screening. The aim of this work was therefore to evaluate whether an electronic tongue (made of four metallic electrodes: Ir, Rh, Pt, Au), based on potential multistep pulse voltammetry with electrochemical polishing, is able to differentiate between honey samples from Spain, Honduras, and Mozambique.

RESULTS

It was demonstrated, for the first time, that automatic pulse voltammetry, in combination with principal component analysis (PCA) statistical analysis, was able to differentiate honey samples from these three countries. A partial least squares (PLS) analysis predicted the level of certain physicochemical parameters, the best results being for conductivity and moisture with correlation coefficients of 0.948 and 0.879, whereas the weakest correlation was for the sugars.

CONCLUSION

The tool proposed in this study could be applied to identify the country origin of the three types of multifloral honey considered here. It also offers promising perspectives for expanding knowledge of the provenance of honey. All of this could be achieved when a comprehensive database with the information generated by this electronic tongue has been created. © 2019 Society of Chemical Industry.

Voltammetric Electronic Tongues in Food Analysis

A critical revision is made on recent applications of voltammetric electronic tongues in the field of food analysis. Relevant works are discussed dealing with the discrimination of food samples of different type, origin, age and quality and with the prediction of the concentration of key substances and significant indexes related to food quality.

Monitoring the Phenolic Ripening of Red Grapes Using a Multisensor System Based on Metal-Oxide Nanoparticles

The maturity of grapes is usually monitored by means of the sugar concentration. However, the assessment of other parameters such as the phenolic content is also important because the phenolic maturity has an important impact on the organoleptic characteristics of wines. In this work, voltammetric sensors able to detect phenols in red grapes have been developed. They are based on metal oxide nanoparticles (CeO2, NiO, and TiO2,) whose excellent electrocatalytic properties toward phenols allows obtaining sensors with detection limits in the range of 10-8 M and coefficients of variation lower than 7%. An electronic tongue constructed using a combination of the nanoparticle-based sensors is capable to monitor the phenolic maturity of red grapes from véraison to maturity. Principal Component Analysis (PCA) can be successfully used to discriminate samples according to the ripeness. Regression models performed using Partial Least Squares (PLS-1) have established good correlations between voltammetric data obtained with the electrochemical sensors and the Total Polyphenolic Index, the Brix degree and the Total Acidity, with correlation coefficients close to 1 and low number of latent variables. An advantage of this system is that the electronic tongue can be used for the simultaneous assessment of these three parameters which are the main factors used to monitor the maturity of grapes. Thus the electronic tongue based on metal oxide nanoparticles can be a valuable tool to monitor ripeness. These results demonstrate the exciting possible applications of metal oxide nanoparticles in the field of electronic tongues.

Prediction of parameters related to grape ripening by multivariate calibration of voltammetric signals acquired by an electronic tongue

An electronic tongue (ET) consisting of two voltammetric sensors, namely a poly-ethylendioxythiophene modified Pt electrode and a sonogel carbon electrode, has been developed aiming at monitoring grape ripening. To test the effectiveness of device and measurement procedures developed, samples of three varieties of grapes have been collected from veraison to harvest of the mature grape bunches. The derived musts have been then submitted to electrochemical investigation using Differential Pulse Voltammetry technique. At the same time, quantitative determination of specific analytical parameters for the evaluation of technological and phenolic maturity of each sample has been performed by means of conventional analytical techniques. After a preliminary inspection by principal component analysis, calibration models were calculated both by partial least squares (PLS) on the whole signals and by the interval partial least squares (iPLS) variable selection algorithm, in order to estimate physico-chemical parameters. Calibration models have been obtained both considering separately the signals of each sensor of the ET, and by proper fusion of the voltammetric data selected from the two sensors by iPLS. The latter procedure allowed us to check the possible complementarity of the information brought by the different electrodes. Good predictive models have been obtained for estimation of pH, total acidity, sugar content, and anthocyanins content. The application of the ET for fast evaluation of grape ripening and of most suitable harvesting time is proposed.

Application of Sensory Evaluation, HS-SPME GC-MS, E-Nose, and E-Tongue for Quality Detection in Citrus Fruits

In this study, electronic tongue (E-tongue), headspace solid-phase microextraction gas chromatography-mass spectrometer (GC-MS), electronic nose (E-nose), and quantitative describe analysis (QDA) were applied to describe the 2 types of citrus fruits (Satsuma mandarins [Citrus unshiu Marc.] and sweet oranges [Citrus sinensis {L.} Osbeck]) and their mixing juices systematically and comprehensively. As some aroma components or some flavor molecules interacted with the whole juice matrix, the changes of most components in the fruit juice were not in proportion to the mixing ratio of the 2 citrus fruits. The potential correlations among the signals of E-tongue and E-nose, volatile components, and sensory attributes were analyzed by using analysis of variance partial least squares regression. The result showed that the variables from the sensor signals (E-tongue system and E-nose system) had significant and positive (or negative) correlations to the most variables of volatile components (GC-MS) and sensory attributes (QDA). The simultaneous utilization of E-tongue and E-nose obtained a perfect classification result with 100% accuracy rate based on linear discriminant analysis and also attained a satisfying prediction with high coefficient association for the sensory attributes (R(2) > 0.994 for training sets and R(2) > 0.983 for testing sets) and for the volatile components (R(2) > 0.992 for training sets and R(2) > 0.990 for testing sets) based on random forest. Being easy-to-use, cost-effective, robust, and capable of providing a fast analysis procedure, E-nose and E-tongue could be used as an alternative detection system to traditional analysis methods, such as GC-MS and sensory evaluation by human panel in the fruit industry.

PRACTICAL APPLICATION

Being easy-to-use, cost-effective, robust, and capable of providing a fast analysis procedure, E-nose and E-tongue could be used as an alternative detection system to traditional analysis methods for characterizing food flavors. Based on those results, one can draw a conclusion that the fusion system composed of E-tongue and E-nose could guarantee a satisfying result in the prediction of sensory attributes and volatile components for fruit quality profile.

Characterization of conventional, biodynamic, and organic purple grape juices by chemical markers, antioxidant capacity, and instrumental taste profile

The objectives of this study were to characterize organic, biodynamic, and conventional purple grape juices (n = 31) produced in Europe based on instrumental taste profile, antioxidant activity, and some chemical markers and to propose a multivariate statistical model to analyze their quality and try to classify the samples from the 3 different crop systems. Results were subjected to ANOVA, correlation, and regression analysis, principal component analysis (PCA), hierarchical cluster analysis (HCA), soft independent modeling of class analogy (SIMCA), and partial least-squares discriminant analysis (PLSDA). No statistical significant differences (P > 0.05) were observed among juices from the 3 crop systems. Using PCA and HCA, no clear separation among crop systems was observed, corroborating the ANOVA data. However, PCA showed that the producing region highly affects the chemical composition, electronic tongue parameters, and bioactivity of grape juices. In this sense, when organic and biodynamic were grouped as "nonconventional" juices, SIMCA model was able to discriminate 12 out of 13 organic/biodynamic juices and 17 out of 18 conventional juices, presenting an efficiency of 93.5%, while 11 out of 13 non-conventional and 100% conventional grape juices were correctly classified using PLSDA. The use of electronic tongue and the determination of antioxidant properties and major phenolic compounds have shown to be a quick and accurate analytical approach to assess the quality of grape juices.

SPR imaging based electronic tongue via landscape images for complex mixture analysis

Electronic noses/tongues (eN/eT) have emerged as promising alternatives for analysis of complex mixtures in the domain of food and beverage quality control. We have recently developed an electronic tongue by combining surface plasmon resonance imaging (SPRi) with an array of non-specific and cross-reactive receptors prepared by simply mixing two small molecules in varying and controlled proportions and allowing the mixtures to self-assemble on the SPRi prism surface. The obtained eT generated novel and unique 2D continuous evolution profiles (CEPs) and 3D continuous evolution landscapes (CELs) based on which the differentiation of complex mixtures such as red wine, beer and milk were successful. The preliminary experiments performed for monitoring the deterioration of UHT milk demonstrated its potential for quality control applications. Furthermore, the eT exhibited good repeatability and stability, capable of operating after a minimum storage period of 5 months.