Instrumental methods (spectroscopy, electronic nose, and tongue) as tools to predict taste and aroma in beverages: advantages and limitations

A review on natural sweeteners, sweet taste modulators and bitter masking compounds: structure-activity strategies for the discovery of novel taste molecules

Growing demand for the tasty and healthy food has driven the development of low-calorie sweeteners, sweet taste modulators, and bitter masking compounds originated from natural sources. With the discovery of human taste receptors, increasing numbers of sweet taste modulators have been identified through human taste response and molecular docking techniques. However, the discovery of novel taste-active molecules in nature can be accelerated by using advanced spectrometry technologies based on structure-activity relationships (SARs). SARs explain why structurally similar compounds can elicit similar taste qualities. Given the characterization of structural information from reported data, strategies employing SAR techniques to find structurally similar compounds become an innovative approach to expand knowledge of sweeteners. This review aims to summarize the structural patterns of known natural non-nutritive sweeteners, sweet taste enhancers, and bitter masking compounds. Innovative SAR-based approaches to explore sweetener derivatives are also discussed. Most sweet-tasting flavonoids belong to either the flavanonols or the dihydrochalcones and known bitter masking molecules are flavanones. Based on SAR findings that structural similarities are related to the sensory properties, innovative methodologies described in this paper can be applied to screen and discover the derivatives of taste-active compounds or potential taste modulators.

Flavour by design: food-grade lactic acid bacteria improve the volatile aroma spectrum of oat milk, sunflower seed milk, pea milk, and faba milk towards improved flavour and sensory perception

BACKGROUND

The global market of plant-based milk alternatives is continually growing. Flavour and taste have a key impact on consumers' selection of plant-based beverages. Unfortunately, natural plant milks have only limited acceptance. Their typically bean-like and grassy notes are perceived as "off-flavours" by consumers, while preferred fruity, buttery, and cheesy notes are missing. In this regard, fermentation of plant milk by lactic acid bacteria (LAB) appears to be an appealing option to improve aroma and taste.

RESULTS

In this work, we systematically studied LAB fermentation of plant milk. For this purpose, we evaluated 15 food-approved LAB strains to ferment 4 different plant milks: oat milk (representing cereal-based milk), sunflower seed milk (representing seed-based milk), and pea and faba milk (representing legume-based milk). Using GC‒MS analysis, flavour changes during anaerobic fermentations were studied in detail. These revealed species-related and plant milk-related differences and highlighted several well-performing strains delivered a range of beneficial flavour changes. A developed data model estimated the impact of individual flavour compounds using sensory scores and predicted the overall flavour note of fermented and nonfermented samples. Selected sensory perception tests validated the model and allowed us to bridge compositional changes in the flavour profile with consumer response.

CONCLUSION

Specific strain-milk combinations provided quite different flavour notes. This opens further developments towards plant-based products with improved flavour, including cheesy and buttery notes, as well as other innovative products in the future. S. thermophilus emerged as a well-performing strain that delivered preferred buttery notes in all tested plant milks. The GC‒MS-based data model was found to be helpful in predicting sensory perception, and its further refinement and application promise enhanced potential to upgrade fermentation approaches to flavour-by-design strategies.

Bimetallic bionic taste sensor for perception of the synergistic effect of umami substances

Synergistic effect is one of the main properties of umami substances, elucidating the synergistic effect of umami is of great significance in the food industry. In this study, a bimetallic bionic taste sensor was developed to evaluate the synergistic effect of umami substances based on the perceptual mechanism of the human taste system. The Venus flytrap domain of T1R1 which is in charge of recognizing umami ligands was employed as the sensing element and self-assembled on the bimetallic nanomaterial (MoS₂-PtPd) by Au-S bonding, the binding of receptors and ligands is characterized by changes of electrical signals. The sensor had good linearity (R² > 0.99) and wide detection range in the detection of different kinds of umami substances (amino acids, nucleotides, organic acids, umami peptides) with detection limits as low as 0.03 pM. Comparing with electronic tongues, the sensor owned multiple characteristics of human taste system and could recognize the presence of synergistic effect of umami substances in a variety of real samples. Moreover, the differences in synergistic effect at different concentrations and ratios were also explored, the findings showed that the synergistic effect was more obvious at lower concentrations and balanced ratios of multiple umami substances added. The strategy would afford a promising platform for in-depth research on the mechanism of synergistic effect and multifunctional industrial applications.

Near-infrared spectroscopy analysis of wines through bottles to assess quality traits and provenance

Due to increased fraud rates through counterfeiting and adulteration of quality wines, it is important to develop novel non-destructive techniques to assess wine quality and provenance. Therefore, our research group developed a novel method using near-infrared (NIR) spectroscopy (1596-2396 nm) coupled with machine learning (ML) modeling to assess wine vintages and quality traits based on the intensity of sensory descriptors through the bottle. These were developed using samples from an Australian vineyard for Shirazwines. Models resulted in high accuracy 97% for classification (vintages) and R=0.95 regression (sensory quality traits). The proposed method will allow to assess authenticity and sensory quality traits of any wines in the market without the need to open the bottles, which is rapid, accurate, effective, and convenient. Furthermore, currently, there are low-cost NIR devices available in the market with the required spectral range and sensitivity, which can be affordable for winemakers and retailers that can be used with the ML models proposed here.

A Comparison between the Egg Yolk Flavor of Indigenous 2 Breeds and Commercial Laying Hens Based on Sensory Evaluation, Artificial Sensors, and GC-MS

The focus of this study was to compare the yolk flavor of eggs from laying hens of Chinese indigenous and commercial, based on detection of volatile compounds, fatty acids, and texture characteristics determination, using sensory evaluation, artificial sensors (electronic nose (E-nose), electronic tongue (E-tongue)), and gas chromatography-mass spectrometry (GC-MS). A total of 405 laying hens (Hy-Line Brown (n = 135), Xueyu White (n = 135), and Xinyang Blue (n = 135)) were used for the study, and 540 eggs (180 per breed) were collected within 48 h of being laid and used for sensory evaluation and the instrument detection of yolk flavor. Our research findings demonstrated significant breed differences for sensory attributes of egg yolk, based on sensory evaluation and instrument detection. The milky flavor, moisture, and compactness scores (p < 0.05) of egg yolk from Xueyu White and Xinyang Blue were significantly higher than that of Hy-Line Brown. The aroma preference scores of Xinyang Blue (p < 0.05) were significantly higher, compared to Hy-Line Brown and Xueyu White. The sensor responses of WIW and W2W from E-nose and STS from E-tongue analysis were significantly higher foe egg yolks of Hy-Line Brown (p < 0.05), compared to that of Xueyu White and Xinyang Blue. Additionally, the sensor responses of umami from E-tongue analysis, was significantly higher for egg yolks of Xueyu White (p < 0.05), compared to that of Hy-Line Brown and Xinyang Blue. Besides, the contents of alcohol and fatty acids, such as palmitic acid, oleic acid, and arachidonic acid, in egg yolk were positively correlated with egg flavor. The texture analyzer showed that springiness, gumminess, and hardness of Hy-Line Brown and Xueyu White (p < 0.05) were significantly higher, compared to Xinyang Blue. The above findings demonstrate that the egg yolk from Chinese indigenous strain had better milky flavor, moisture, and compactness, as well as better texture. The egg yolk flavors were mainly due to presence of alcohol and fatty acids, such as palmitic acid, oleic acid, and arachidonic acid, which would provide research direction on improvement in egg yolk flavor by nutrition. The current findings validate the strong correlation between the results of egg yolk flavor and texture, based on sensory evaluation, artificial sensors, and GC-MS. All these indicators would be beneficial for increased preference for egg yolk flavor by consumers and utilization by food processing industry, as well as a basis for the discrimination of eggs from different breeds of laying hens.

Mass spectrometry-based electronic nose to authenticate 100% Italian durum wheat pasta and characterization of volatile compounds

Headspace solid-phase microextraction (HS-SPME) coupled with mass spectrometry-based electronic nose (MS-eNose), in combination with multivariate statistical analysis was used as untargeted method for the rapid authentication of 100% Italian durum wheat pasta. Among the tested classification models, i.e. PCA-LDA, PLS-DA and SVMc, SVMc provided the highest accuracy results in both calibration (90%) and validation (92%) processes. Potential markers discriminating pasta samples were identified by HS-SPME/GC-MS analysis. Specifically, the content of a pattern of 8 out of 59 volatile organic compounds (VOCs) was significantly different between samples of 100% Italian durum wheat pasta and pasta produced with durum wheat of different origins, most of which were related to different lipidic oxidation in the two classes of pasta. The proposed MS-eNose method is a rapid and reliable tool to be used for authenticating Italian pasta useful to promote its typicity and preserving consumers from fraudulent practices.

Following drug degradation and consequent taste deterioration of an oral reconstituted paediatric suspension during dosing interval via electronic tongue

Background

The taste of oral liquid dosage forms is a crucial factor that impacts pediatric patient compliance. Taste of suspensions can be typically evaluated by human volunteers. Recently, the electronic tongue (ET) has been proven as an emerging tool that could be useful to follow up various formulations' properties like taste and composition. This study aimed to evaluate the potential use of ET in assessing the taste deterioration of reconstituted oral suspensions and compare the results obtained with the typical in vivo panel taste method.

Methods

Four commercially available brands of amoxicillin/ clavulanic acid suspensions (one brand and three generic formulations) were reconstituted and stored in refrigerator to assess their taste on a daily basis. The taste of these products was assessed using Alpha-Astree ET and the obtained results were compared with those obtained from an in vivo panel taste assessment using a hedonic panel test (the 5-point hedonic scale).

Results

All evaluated suspensions exhibited similar trends. ET and in vivo analysis indicated low taste scores for all evaluated suspensions immediately after reconstitution, possibly due to the incomplete dissolution of sucrose. The scores for all formulations were higher on day 2, followed by a steady state for the next two days. After that, a significant decay in the scores was observed in the fifth day for all evaluated suspensions. ET results were in excellent agreement with the results obtained via in vivo panel test method.

Conclusion

The ET seems to be promising for testing the taste of pharmaceutical liquid preparations and evaluate possible deterioration upon storage or after reconstitution. It may provide a platform to avoid the involvement of pediatric volunteers in clinical evaluation and can be employed as a quality control tool during manufacturing.

Digital Assessment and Classification of Wine Faults Using a Low-Cost Electronic Nose, Near-Infrared Spectroscopy and Machine Learning Modelling

The winemaking industry can benefit greatly by implementing digital technologies to avoid guesswork and the development of off-flavors and aromas in the final wines. This research presents results on the implementation of near-infrared spectroscopy (NIR) and a low-cost electronic nose (e-nose) coupled with machine learning to detect and assess wine faults. For this purpose, red and white base wines were used, and treatments consisted of spiked samples with 12 faults that are traditionally formed in wines. Results showed high accuracy in the classification models using NIR and e-nose for red wines (94–96%; 92–97%, respectively) and white wines (96–97%; 90–97%, respectively). Implementing new and emerging digital technologies could be a turning point for the winemaking industry to become more predictive in terms of decision-making and maintaining and increasing wine quality traits in a changing and challenging climate.

Research progress in the screening and evaluation of umami peptides

Umami is an important element affecting food taste, and the development of umami peptides is a topic of interest in food-flavoring research. The existing technology used for traditional screening of umami peptides is time-consuming and labor-intensive, making it difficult to meet the requirements of high-throughput screening, which limits the rapid development of umami peptides. The difficulty in performing a standard measurement of umami intensity is another problem that restricts the development of umami peptides. The existing methods are not sensitive and specific, making it difficult to achieve a standard evaluation of umami taste. This review summarizes the umami receptors and umami peptides, focusing on the problems restricting the development of umami peptides, high-throughput screening, and establishment of evaluation standards. The rapid screening of umami peptides was realized based on molecular docking technology and a machine learning method, and the standard evaluation of umami could be realized with a bionic taste sensor. The progress of rapid screening and evaluation methods significantly promotes the study of umami peptides and increases its application in the seasoning industry.

Tensiometry as a Simple Analytical Method for Quantification of Solubility and Release of Aroma Molecules in Aqueous Media

Dynamic tensiometry is shown to be a high-potential analytical tool in assessing physico-chemical characteristics of fragrance molecules, such as solubility limit, volatility as well as much rarely assessed interfacial activity of these amphiphilic molecules. Surface tension of aqueous solutions of selected essential oils has been measured as a function of time and fragrance concentration using maximum bubble pressure method. The effect of the temperature and saline solution on the rate of dissolution in water was assessed. Dynamic surface tension turned to be sensitive to the composition of fragrances, as demonstrated on examples of natural and synthetic mixtures. Furthermore, presented work reveals the possibility of maximum bubble pressure tensiometry method to quantify the amount of fragrance compositions in flavored salts, including the artificially aged carrier samples. Suggested here analytical approach can be used for the detection of the purity of essential oils, for the optimization of compositions and of the manufacturing processes of fragrances-containing products, as well as for the assessment of the release/evaporation of fragrances from carrier systems.

Identification of multi-concentration aromatic fragrances with electronic nose technology using a support vector machine

Due to the concentration effect, there is a major challenge for the electronic nose system to identify different odor samples with multiple concentrations. The development of artificial intelligence provides new ways to solve such problems. This article attempts to use support vector machine (SVM) technology to distinguish four fragrance samples with three concentrations, including roman chamomile, jasmine, lavender, and orange. The responses of these samples were collected by an 11-sensor electronic nose. After baseline correction, data smoothing, and removal of non-responsive sensors, the signals of 8 sensors were used for subsequent model analysis. Due to the concentration effect, when the primary signal intensities were used as features, the electronic nose cannot distinguish between different aroma types (accuracy less than 50%). When the normalized maximum signal intensity X_mr was used, the accuracy of the model was greatly improved. Graphic analysis and PCA showed that the normalized feature effectively eliminates the concentration effect, and appropriately reducing some sensors can enhance the ability to distinguish odors. The SVM correctly classified all 14 aromas when feeding 8 sets of data to train the radial kernel C-classification SVM. This showed that the cross-interference of the sensors was reduced, and the resolving power of the electronic nose was enhanced after the feature reduction.

Sensory evaluation, chemical structures, and threshold concentrations of bitter-tasting compounds in common foodstuffs derived from plants and maillard reaction: A review

The bitterness of foodstuffs is often associated with toxicity, which negatively influences product acceptability. However, bitter compounds have many benefits, and a slight bitter taste is sometimes favored. In this review, we summarize the methods used to isolate and evaluate the taste of bitter compounds in different foods. The chemical structures and threshold concentrations of these compounds are also recapped. Although the structures and thresholds of many bitter compounds have been confirmed, further studies are needed to develop detailed bitter-masking strategies and establish the relation between functional groups (hetero-cyclic substituents and bonding types) and taste quality. Furthermore, a comprehensive bitterness database and chemometric data must be provided in order to quickly assess the bitterness of unfamiliar products.

From consumers' science to food functionality-Challenges and opportunities for vibrational spectroscopy

Current available methods used to measure or estimate the composition, functionality, and sensory properties of foods and food ingredients are destructive and time consuming. Therefore, new approaches are required by both the food industry and R&D organizations. Recent years have witnessed a steady growth on the applications and utilization of vibrational spectroscopy techniques [near (NIR), mid infrared (MIR), Raman] to analyse or estimate several properties in a wide range of foods and food ingredients. This chapter will provide with an overview of vibrational spectroscopy techniques, the combination of these techniques with multivariate data analysis, and examples on the use of these techniques to measure composition, and functional properties in a wide range of foods.

Developing non-invasive bladder cancer screening methodology through potentiometric multisensor urine analysis

We report on the feasibility study exploring the potential of a simple electrochemical multisensor system as a tool for distinguishing between urine samples from patients with confirmed bladder cancer (36 samples) and healthy volunteers (51 samples). The potentiometric sensor responses obtained in urine samples were employed as the input data for various machine learning classification algorithms (logistic regression, random forest, extreme gradient boosting classifier, support vector machine, and voting classifier). The performance metrics of the classifiers were evaluated via Monte-Carlo cross-validation. The best model combining all the acquired data from the people aged 19-88 with different tumor grades and malignancies, including patients with recurrent bladder cancer, yielded 72% accuracy, 71% sensitivity, and 58% specificity. It was found that these metrics can be improved to 76% accuracy, 80% sensitivity, and 75% specificity when only a limited age group (50-88 years of age) is considered. Taking into account the simplicity of the proposed screening method, this technique appears to be a promising tool for further research.

Novel rhodamine probe for colorimetric and fluorescent detection of Fe3+ ions in aqueous media with cellular imaging

A novel rhodamine-pyridine conjugated spectroscopic probe RhP was synthesized and its X-ray single crystalline properties were revealed with tabulation. The RhP displayed a distinct pale-pink colorimetric and "turn-on" fluorescent response to Fe³⁺ in aqueous media [H₂O:DMSO (95:5, v/v)] than that of other interfering ions. During the Fe³⁺ recognition, the absorption (UV-Vis) and photoluminescence (PL) spectral studies revealed new peaks at 561 and 592 nm, respectively. The 1:1 stoichiometry and binding sites were verified by Job's plot, ESI-mass, and ¹H NMR titrations. Subsequently, LOD and binding constant for RhP + Fe³⁺ complex were estimated as 102.3 nM and 6.265 × 10⁴ M^-1 from linear fitting and Benesi-Hildebrand plots, correspondingly. Sensor reversibility of RhP + Fe³⁺ by EDTA was demonstrated by UV/PL and TRPL investigations. Moreover, the photoinduced energy transfer mechanism and band gap changes were established from the DFT interrogations. Lastly, cellular imaging studies were carried out to authenticate the real applicability of RhP in Fe³⁺ detection.

Vibrational coupling to hydration shell - Mechanism to performance enhancement of qualitative analysis in NIR spectroscopy of carbohydrates in aqueous environment

Vibrational coupling between carbohydrates and the hydration shell is unveiled as the underlying mechanism that improves wavenumber-selectively the carbohydrate discrimination performance by near-infrared (NIR) spectroscopy. The investigation is based on measurement of six carbohydrates (fructose, glucose, mannose, ribose, xylose and sorbitol) in aqueous solution in different concentration levels (5 mg/L, ~0.03 mmol/dm^-3 and 20 mg/L, ~0.1 mmol/dm^-3). The results of multivariate classification are interpreted by quantum mechanical NIR spectra simulations. The simulation unveils that the phenomenon is vibration-selective and thus wavenumber-selective, and leads to an enhancement of the qualitative information contained in the specific spectral regions. The location of these regions and the related performance correspond fully to the appearance and magnitude of the unveiled cooperative vibration effect.

Non-Destructive Technologies for Detecting Insect Infestation in Fruits and Vegetables under Postharvest Conditions: A Critical Review

In the last two decades, food scientists have attempted to develop new technologies that can improve the detection of insect infestation in fruits and vegetables under postharvest conditions using a multitude of non-destructive technologies. While consumers' expectations for higher nutritive and sensorial value of fresh produce has increased over time, they have also become more critical on using insecticides or synthetic chemicals to preserve food quality from insects' attacks or enhance the quality attributes of minimally processed fresh produce. In addition, the increasingly stringent quarantine measures by regulatory agencies for commercial import-export of fresh produce needs more reliable technologies for quickly detecting insect infestation in fruits and vegetables before their commercialization. For these reasons, the food industry investigates alternative and non-destructive means to improve food quality. Several studies have been conducted on the development of rapid, accurate, and reliable insect infestation monitoring systems to replace invasive and subjective methods that are often inefficient. There are still major limitations to the effective in-field, as well as postharvest on-line, monitoring applications. This review presents a general overview of current non-destructive techniques for the detection of insect damage in fruits and vegetables and discusses basic principles and applications. The paper also elaborates on the specific post-harvest fruit infestation detection methods, which include principles, protocols, specific application examples, merits, and limitations. The methods reviewed include those based on spectroscopy, imaging, acoustic sensing, and chemical interactions, with greater emphasis on the noninvasive methods. This review also discusses the current research gaps as well as the future research directions for non-destructive methods' application in the detection and classification of insect infestation in fruits and vegetables.

Portable and field-deployed surface plasmon resonance and plasmonic sensors

Plasmonic sensors are ideally suited for the design of small, integrated, and portable devices that can be employed in situ for the detection of analytes relevant to environmental sciences, clinical diagnostics, infectious diseases, food, and industrial applications. To successfully deploy plasmonic sensors, scaled-down analytical devices based on surface plasmon resonance (SPR) and localized surface plasmon resonance (LSPR) must integrate optics, plasmonic materials, surface chemistry, fluidics, detectors and data processing in a functional instrument with a small footprint. The field has significantly progressed from the implementation of the various components in specifically designed prism-based instruments to the use of nanomaterials, optical fibers and smartphones to yield increasingly portable devices, which have been shown for a number of applications in the laboratory and deployed on site for environmental, biomedical/clinical, and food applications. A roadmap to deploy plasmonic sensors is provided by reviewing the current successes and by laying out the directions the field is currently taking to increase the use of field-deployed plasmonic sensors at the point-of-care, in the environment and in industries.

A Colorimetric Artificial Olfactory System for Airborne Improvised Explosive Identification

The detection of ultralow or nonvolatile target analytes remains a significant challenge for artificial olfactory systems even after decades of development, which severely limits their widespread application. To overcome this challenge, an artificial olfactory system based on a colorimetric hydrogel array is constructed for the first time as a universal representative. As an effective extension of conventional artificial olfactory systems that integrates the merits of its predecessors, the proposed system accurately mimics olfactory mucosa and specific odorant binding proteins using hydrogels endowed with specific colorimetric reagents for the detection of hypochlorite, chlorate, perchlorate, urea, and nitrate. Therefore, the proposed system is capable of detecting and discriminating between these five airborne improvised explosive microparticulates with a detection limit as low as 39.4 pg. Additionally, the system demonstrates good reusability over ten cycles, rapid response time of ≈0.2 s, and excellent discrimination properties, despite significant variation. This proof-of-concept study on colorimetric artificial olfactory systems yields a novel strategy for the direct and discriminative detection of nonvolatile airborne microparticulates.

Analytical Figures of Merit for Multisensor Arrays

Multisensor arrays employing various sensing principles are a rapidly developing field of research as they allow simple and inexpensive quantification of various parameters in complex samples. Quantitative analysis with such systems is based on multivariate regression techniques, and deriving of traditional analytical figures of merit (e.g., sensitivity, selectivity, limit of detection, and limit of quantitation) for such systems is not obvious and straightforward. Nevertheless, it is absolutely needed for further development of the multisensor research field and for introducing these instruments into the general context of analytical chemistry. Here, we report on the protocol for calculation of sensitivity, selectivity, and detection limits for multisensor arrays. The results are provided and discussed in detail for several real-world data sets.

Colorimetric Ethanol Indicator Based on Instantaneous, Localized Wetting of a Photonic Crystal

Easy-to-use sensors for ethanol solutions have broad applications ranging from monitoring alcohol quality to combating underage drinking. Although there are a number of electronic and colorimetric sensors available for determining alcohol concentration, there is currently no device that can concurrently provide a prompt, well-defined, quickly recoverable readout and remain readily affordable. Here, we developed a field-ready, colorimetric indicator that provides a fast, clear identification of ethanol-water mixtures between 0 and 40% based on the discoloration of a wetted photonic crystal. We cooperatively exploit the iridescence and the geometrical gating in silica inverse opal films (IOFs), together with a fine-tuned surface chemistry gradient, to distinguish ethanol concentrations by their wettability patterns in the different segments of the IOFs. The resultant all-in-one colorimetric sensor delivers a striking and instantaneous optical response at an ethanol concentration as low as 5%. We further improve the ease of use by seamlessly integrating this colorimetric platform with drinking glassware (a glass stirrer and a vial). This research provides an optimal means for colorimetric ethanol detection and is a step toward the immersible sensing of diverse molecules (e.g., biomarkers) in aqueous solutions without expensive laboratory tests.

Volatile Composition and Sensory Properties of Mead

Mead is a traditional beverage that results from the alcoholic fermentation of diluted honey performed by yeasts. Although the process of mead production has been optimized in recent years, studies focused on its sensory properties are still scarce. Therefore, the aim of this work was to analyse the sensory attributes of mead produced with free or immobilized cells of the Saccharomyces cerevisiae strains QA23 and ICV D47, and to establish potential correlations with its volatile composition. In the volatile composition of mead, the effect of yeast condition was more important than the strain. In respect to sensory analysis, the most pleasant aroma descriptors were correlated with mead obtained with free yeast cells, independently of the strain. Both sensory analysis and volatile composition indicates that the most pleasant mead was produced by free yeast cells. Although this study has provided a significant contribution, further research on the sensory quality of mead is still needed.

Modified Diamide and Phosphine Oxide Extracting Compounds as Membrane Components for Cross-Sensitive Chemical Sensors

This research is devoted to the development and study of novel cross-sensitive sensors based on modified extracting ligands. According to the previous results of liquid extraction studies, the chemical modification of membrane active components would change the analytical characteristics of a sensor comprising them. The sensing elements of the studied sensors consisted of various derivatives of N,N,N′,N′-tetraoctyldiamide of diglycolic acid (TODGA) and di-phenyl-N,N-di-i-sobutylcarbamoylmethylen phoshine oxide (CMPO) used as neutral carriers, CCD (chlorinated cobalt dicarbollide) as a lipophilic additive, different plasticizers, and poly(vinyl chloride) (PVC) as a polymer. TODGA-based sensors demonstrated a stable and reproducible response towards rare earth cations in acidic media (pH = 2). Changing the concentrations and ratio of neutral carriers and the lipophilic additive, it is possible to modify the sensitivity and selectivity of the sensors towards the same target ions. Bonded ligands, such as cobalt dicarbollide covalently attached to TODGA and CMPO, exhibited lower selectivity and sensitivity to rare earth cations. A possibility to vary the cross-sensitivity patterns of the sensors in a wide range might be of great interest for the development of multisensor systems allowing the simultaneous determination of several analytes in multicomponent solutions.

Geographical origin traceability of Cabernet Sauvignon wines based on Infrared fingerprint technology combined with chemometrics

Mid-infrared (MIR) and near-infrared (NIR) spectroscopy combined with chemometrics were explored to classify Cabernet Sauvignon wines from different countries (Australia, Chile and China). Commercial wines (n = 540) were scanned in transmission mode using MIR and NIR, and their characteristic fingerprint bands were extracted at 1750-1000 cm⁻¹ and 4555-4353 cm⁻¹. Through the identification system of Tri-step infrared spectroscopy, the correlation between macroscopic chemical fingerprints and geographical regions was explored more deeply. Furthermore, Principal component analysis (PCA), soft independent modelling of class analogy (SIMCA) and discriminant analysis (DA) based on MIR and NIR spectra were used to visualize or discriminate differences between samples and to realize geographical origin traceability of Cabernet Sauvignon wines. Through “external test set (n = 157)” validation, SIMCA models correctly classified 97%, 97% and 92% of Australian, Chilean and Chinese Cabernet Sauvignon wines, while the DA models correctly classified 86%, 85% and 77%, respectively. Based on unique digital fingerprints of spectroscopy (FT-MIR and FT-NIR) associated with chemometrics, geographical origin traceability was achieved in a more comprehensive, effective and rapid manner. The developed database models based on IR fingerprint spectroscopy with chemometrics could provide scientific basis and reference for geographical origin traceability of Cabernet Sauvignon wines (Australia, Chile and China).

Analysis of musts and wines by means of a bio-electronic tongue based on tyrosinase and glucose oxidase using polypyrrole/gold nanoparticles as the electron mediator

A bioelectronic tongue (bioET) based on combinations of enzymes (tyrosinase and glucose oxidase) and polypyrrole (Ppy) or polypyrrole/AuNP (Ppy/AuNP) composites was build up and applied to the analysis and discrimination of musts and wines. Voltammetric responses of the array of sensors demonstrated the effectiveness of polymers as electron mediators and the existence of favorable synergistic effects between Ppy and the AuNPs. Using Principal Component Analysis and Parallel Factor Analysis it was possible to discriminate musts according to the °Brix and TPI (Total Polyphenol Index), and wines according to the alcoholic degree and TPI. Partial Least Squares provided good correlations between the bioET output and traditional chemical parameters. Moreover, Support Vector Machines permitted to predict the TPI and the alcoholic degree of wines, from data provided by the bioET in the corresponding grapes. This result opens the possibility to predict wine characteristics from the beginning of the vinification process.

A Simple Procedure to Assess Limit of Detection for Multisensor Systems

Currently, there are no established procedures for limit of detection (LOD) evaluation in multisensor system studies, which complicates their correct comparison with other analytical techniques and hinders further development of the method. In this study we propose a simple and visually comprehensible approach for LOD estimation in multisensor analysis. The suggested approach is based on the assessment of evolution of mean relative error values in calibration series with growing analyte concentration. The LOD value is estimated as the concentration starting from which MRE values become stable from sample to sample. This intuitive procedure was successfully tested with a variety of real data from potentiometric multisensor systems.

Breakthrough Potential in Near-Infrared Spectroscopy: Spectra Simulation. A Review of Recent Developments

Near-infrared (12,500–4,000 cm⁻¹; 800–2,500 nm) spectroscopy is the hallmark for one of the most rapidly advancing analytical techniques over the last few decades. Although it is mainly recognized as an analytical tool, near-infrared spectroscopy has also contributed significantly to physical chemistry, e.g., by delivering invaluable data on the anharmonic nature of molecular vibrations or peculiarities of intermolecular interactions. In all these contexts, a major barrier in the form of an intrinsic complexity of near-infrared spectra has been encountered. A large number of overlapping vibrational contributions influenced by anharmonic effects create complex patterns of spectral dependencies, in many cases hindering our comprehension of near-infrared spectra. Quantum mechanical calculations commonly serve as a major support to infrared and Raman studies; conversely, near-infrared spectroscopy has long been hindered in this regard due to practical limitations. Advances in anharmonic theories in hyphenation with ever-growing computer technology have enabled feasible theoretical near-infrared spectroscopy in recent times. Accordingly, a growing number of quantum mechanical investigations aimed at near-infrared region has been witnessed. The present review article summarizes these most recent accomplishments in the emerging field. Applications of generalized approaches, such as vibrational self-consistent field and vibrational second order perturbation theories as well as their derivatives, and dense grid-based studies of vibrational potential, are overviewed. Basic and applied studies are discussed, with special attention paid to the ones which aim at improving analytical spectroscopy. A remarkable potential arises from the growing applicability of anharmonic computations to solving the problems which arise in both basic and analytical near-infrared spectroscopy. This review highlights an increased value of quantum mechanical calculations to near-infrared spectroscopy in relation to other kinds of vibrational spectroscopy.

Discrimination of geographical origin of oranges (Citrus sinensis L. Osbeck) by mass spectrometry-based electronic nose and characterization of volatile compounds

An untargeted method using headspace solid-phase microextraction coupled to electronic nose based on mass spectrometry (HS-SPME/MS-eNose) in combination with chemometrics was developed for the discrimination of oranges of three geographical origins (Italy, South Africa and Spain). Three multivariate statistical models, i.e. PCA/LDA, SELECT/LDA and PLS-DA, were built and relevant performances were compared. Among the tested models, SELECT/LDA provided the highest prediction abilities in cross-validation and external validation with mean values of 97.8% and 95.7%, respectively. Moreover, HS-SPME/GC-MS analysis was used to identify potential markers to distinguish the geographical origin of oranges. Although 28 out of 65 identified VOCs showed a different content in samples belonging to different classes, a pattern of analytes able to discriminate simultaneously samples of three origins was not found. These results indicate that the proposed MS-eNose method in combination with multivariate statistical analysis provided an effective and rapid tool for authentication of the orange's geographical origin.

Electronic Tongue for Brand Uniformity Control: A Case Study of Apulian Red Wines Recognition and Defects Evaluation †

The potentiometric electronic tongue system has been tested as a potential analytical tool for brand uniformity control of monoculture Apulian red wines (Primitivo and Negroamaro). The sensor array was composed of eight porphyrin coatings obtained by electrochemical polymerization process and was employed for both wines discrimination and quantitative detection of wine defect compounds: "off-odour" 3-(methylthio)-propanol; isoamyl alcohol fusel oil; benzaldehyde (marker of the yeast activity) and acetic acid (marker of vinegar formation). PLS-DA applied to Electronic tongue output data has permitted a correct discrimination of more than 70% of analysed wines in respect to the original brand affiliation. Satisfactory PLS1 predictions were obtained in real wine samples; with R² = 0.989 for isoamyl alcohol and R² = 0.732 for acetic acid. Moreover; the possibility to distinguish wine samples on the base of permitted levels of fault compounds content was shown.

Determining degree of roasting in cocoa beans by artificial neural network (ANN)-based electronic nose system and gas chromatography/mass spectrometry (GC/MS)

BACKGROUND

Roasting is a critical step in chocolate processing, where moisture content is decreased and unique flavors and texture are developed. The determination of the degree of roasting in cocoa beans is important to ensure the quality of chocolate. Determining the degree of roasting relies on human specialists or sophisticated chemical analyses that are inaccessible to small manufacturers and farmers. In this study, an electronic nose system was constructed consisting of an array of gas sensors and used to detect volatiles emanating from cocoa beans roasted for 0, 20, 30 and 40 min. The several signals were used to train a three-layer artificial neural network (ANN). Headspace samples were also analyzed by gas chromatography/mass spectrometry (GC/MS), with 23 select volatiles used to train a separate ANN.

RESULTS

Both ANNs were used to predict the degree of roasting of cocoa beans. The electronic nose had a prediction accuracy of 94.4% using signals from sensors TGS 813, 826, 822, 830, 830, 2620, 2602 and 2610. In comparison, the GC/MS predicted the degree of roasting with an accuracy of 95.8%.

CONCLUSION

The electronic nose system is able to predict the extent of roasting, as well as a more sophisticated approach using GC/MS. © 2018 Society of Chemical Industry.

Identification of Tobacco Types and Cigarette Brands Using an Electronic Nose Based on Conductive Polymer/Porphyrin Composite Sensors

Three tobacco types (Burley, Flue Cured, and Oriental) and eight cigarette brands were unequivocally identified using an electronic nose formed by only three sensors based on a single novel conducting polymer (PF-BTB) doped with different porphyrins (H₂TPP, H₂TPFP, and H₂BTBOP). The synthesis and characterization of the polymer are also discussed. Small changes in the porphyrin structure caused significant changes in the electrical conductance response patterns of the sensors upon exposure to complex chemical matrixes, representing a novel approach for tuning the selectivity of chemiresistive sensors for e-nose application. This e-nose is fast, cheap, reliable, can be easily operated, and could be a valuable tool for border agents fighting cigarette smuggling around the world, helping them prevent losses of millions in tax revenues and sales.

Fingerprinting Green Curry: An Electrochemical Approach to Food Quality Control

The detection and identification of multiple components in a complex sample such as food in a cost-effective way is an ongoing challenge. The development of on-site and rapid detection methods to ensure food quality and composition is of significant interest to the food industry. Here we report that an electrochemical method can be used with an unmodified glassy carbon electrode for the identification of the key ingredients found within Thai green curries. It was found that green curry presents a fingerprint electrochemical response that contains four distinct peaks when differential pulse voltammetry is performed. The reproducibility of the sensor is excellent as no surface modification is required and therefore storage is not an issue. By employing particle swarm optimization algorithms the identification of ingredients within a green curry could be obtained. In addition, the quality and freshness of the sample could be monitored by detecting a change in the intensity of the peaks in the fingerprint response.

Classification of Bitter Orange Essential Oils According to Fruit Ripening Stage by Untargeted Chemical Profiling and Machine Learning

The quality and composition of bitter orange essential oils (EOs) strongly depend on the ripening stage of the citrus fruit. The concentration of volatile compounds and consequently its organoleptic perception varies. While this can be detected by trained humans, we propose an objective approach for assessing the bitter orange from the volatile composition of their EO. The method is based on the combined use of headspace gas chromatography–mass spectrometry (HS-GC-MS) and artificial neural networks (ANN) for predictive modeling. Data obtained from the analysis of HS-GC-MS were preprocessed to select relevant peaks in the total ion chromatogram as input features for ANN. Results showed that key volatile compounds have enough predictive power to accurately classify the EO, according to their ripening stage for different applications. A sensitivity analysis detected the key compounds to identify the ripening stage. This study provides a novel strategy for the quality control of bitter orange EO without subjective methods.

Non-contact identification and differentiation of illicit drugs using fluorescent films

Sensitive and rapid identification of illicit drugs in a non-contact mode remains a challenge for years. Here we report three film-based fluorescent sensors showing unprecedented sensitivity, selectivity, and response speed to the existence of six widely abused illicit drugs, including methamphetamine (MAPA), ecstasy, magu, caffeine, phenobarbital (PB), and ketamine in vapor phase. Importantly, for these drugs, the sensing can be successfully performed after 5.0 × 10⁵, 4.0 × 10⁵, 2.0 × 10⁵, 1.0 × 10⁵, 4.0 × 10⁴, and 2.0 × 10² times dilution of their saturated vapor with air at room temperature, respectively. Also, presence of odorous substances (toiletries, fruits, dirty clothes, etc.), water, and amido-bond-containing organic compounds (typical organic amines, legal drugs, and different amino acids) shows little effect upon the sensing. More importantly, discrimination and identification of them can be realized by using the sensors in an array way. Based upon the discoveries, a conceptual, two-sensor based detector is developed, and non-contact detection of the drugs is realized.

Sensitive and rapid identification of illicit drugs in a non-contact mode remains a challenge. Here, the authors report three film-based fluorescent sensors showing remarkable sensitivity, selectivity and response speed to six widely abused illicit drugs in vapor phase.