Application of electronic senses to characterize espresso coffees brewed with different thermal profiles

Electronic Noses: From Gas-Sensitive Components and Practical Applications to Data Processing

Artificial olfaction, also known as an electronic nose, is a gas identification device that replicates the human olfactory organ. This system integrates sensor arrays to detect gases, data acquisition for signal processing, and data analysis for precise identification, enabling it to assess gases both qualitatively and quantitatively in complex settings. This article provides a brief overview of the research progress in electronic nose technology, which is divided into three main elements, focusing on gas-sensitive materials, electronic nose applications, and data analysis methods. Furthermore, the review explores both traditional MOS materials and the newer porous materials like MOFs for gas sensors, summarizing the applications of electronic noses across diverse fields including disease diagnosis, environmental monitoring, food safety, and agricultural production. Additionally, it covers electronic nose pattern recognition and signal drift suppression algorithms. Ultimately, the summary identifies challenges faced by current systems and offers innovative solutions for future advancements. Overall, this endeavor forges a solid foundation and establishes a conceptual framework for ongoing research in the field.

A comprehensive insight on modern green analyses for quality control determination and processing monitoring in coffee and cocoa seeds

Green analysis is defined as the analysis of chemicals in a manner where sample extraction and analysis are performed with least amounts of steps, low hazardous materials, while maintaining efficiency in terms of analytes detection. Coffee and cocoa represent two of the most popular and valued beverages worldwide in addition to their several products i.e., cocoa butter, chocolates. This study presents a comprehensive overview of green methods used to evaluate cocoa and coffee seeds quality compared to other conventional techniques highlighting advantages and or limitations of each. Green techniques discussed in this review include solid phase microextraction, spectroscopic techniques i.e., infra-red (IR) spectroscopy and nuclear magnetic resonance (NMR) besides, e-tongue and e-nose for detection of flavor. The employment of multivariate data analysis in data interpretation is also highlighted in the context of identifying key components pertinent to specific variety, processing method, and or geographical origin.

Graphene oxide nanocomposites based room temperature gas sensors: A review

Over the last few decades, various volatile organic compounds (VOCs) have been widely used in the processing of building materials and this practice adversely affected the environment i.e. both indoor and outdoor air quality. A cost-effective solution for detecting a wide range of VOCs by sensing approaches includes chemiresistive, optical and electrochemical techniques. Room temperature (RT) chemiresistive gas sensors are next-generation technologies desirable for self-powered or battery-powered instruments utilized in monitoring emissions that are associated with indoor/outdoor air pollution and industrial processes. In this review, a state-of-the-art overview of chemiresistive gas sensors is provided based on their attractive analytical characteristics such as high sensitivity, selectivity, reproducibility, rapid assay time and low fabrication cost. The review mainly discusses the recent advancement and advantages of graphene oxide (GO) nanocomposites-based chemiresistive gas sensors and various factors affecting their sensing performance at RT. Besides, the sensing mechanisms of GO nanocomposites-based chemiresistive gas sensors derived using metals, transition metal oxides (TMOs) and polymers were discussed. Finally, the challenges and future perspectives of GO nanocomposites-based RT chemiresistive gas sensors are addressed.

Acids in coffee: A review of sensory measurements and meta-analysis of chemical composition

Coffee contains a variety of organic acids (OAs) and chlorogenic acids (CGAs) that contribute to overall sensory properties. Large variations in preparation and measurement methodology across the literature complicate interpretation of general trends. Here, we perform a systematic review and meta-analysis of the published literature to elucidate the concentrations of OAs and CGAs in both Coffea arabica (arabica) and Coffea canephora (robusta), for both green coffee and roasted coffee at multiple roast levels. A total of 129 publications were found to report acid concentration measurements, yielding 8,634 distinct data points. Analysis of the full data set reveals several trends. First, roasted robusta has considerably more acidic compounds than arabica with 2 to 5 times as much total OAs, and much larger amounts of formic and acetic acid. As for CGAs, in both arabica and robusta 5-CQA is the major component, and progressive roasting decreases the concentration of all CGAs. The total amount of CGA present was more dependent on roast level than the type of coffee (arabica vs. robusta). Overall, this meta-analysis suggests that the increases in certain OAs with roast level might play more of a role in the sensory profile of dark roast coffees than previously suspected.

Characterization of the Aroma Profile and Main Key Odorants of Espresso Coffee

Espresso coffee (EC) is a common coffee preparation technique that nowadays is broadly widespread all over the globe. Its popularity is in part attributed to the intense aroma and pleasant flavor. Many researchers have studied and reviewed the aroma of the coffee, but there is a lack of specific review focused on EC aroma profile even if it is intensively investigated. Thus, the objective of the current review was to summarize the aroma profile of EC and how different preparation variables can affect EC flavor. Moreover, a collection of diverse analytical procedures for volatile analysis was also reported. The findings of this survey showed that the volatile fraction of EC is extremely complex, but just some compounds are responsible for the characteristic aroma of the coffee, such as some aldehyde, ketones, furanones, furans, sulfur compounds, pyrazines, etc. In addition, during preparation, some variables, e.g., temperature and pressure of water, granulometry of the coffee particle, and brew ratio, can also modify the aroma profile of this beverage, and therefore its quality. A better understanding of the aroma fraction of EC and how the preparation variables should be adjusted according to desired EC would assist coffee workers in obtaining a higher quality product.

Electronic Eye Based on RGB Analysis for the Identification of Tequilas

The present work reports the development of a biologically inspired analytical system known as Electronic Eye (EE), capable of qualitatively discriminating different tequila categories. The reported system is a low-cost and portable instrumentation based on a Raspberry Pi single-board computer and an 8 Megapixel CMOS image sensor, which allow the collection of images of Silver, Aged, and Extra-aged tequila samples. Image processing is performed mimicking the trichromatic theory of color vision using an analysis of Red, Green, and Blue components (RGB) for each image's pixel. Consequently, RGB absorbances of images were evaluated and preprocessed, employing Principal Component Analysis (PCA) to visualize data clustering. The resulting PCA scores were modeled with a Linear Discriminant Analysis (LDA) that accomplished the qualitative classification of tequilas. A Leave-One-Out Cross-Validation (LOOCV) procedure was performed to evaluate classifiers' performance. The proposed system allowed the identification of real tequila samples achieving an overall classification rate of 90.02%, average sensitivity, and specificity of 0.90 and 0.96, respectively, while Cohen's kappa coefficient was 0.87. In this case, the EE has demonstrated a favorable capability to correctly discriminated and classified the different tequila samples according to their categories.

Pretreatment methods affecting the color, flavor, bioactive compounds, and antioxidant activity of jujube wine

In the case of wine production, the selection of optimal pretreatment methods and starter cultures are the 2 key points before fermentation. In this research, the fresh jujube was separately underwent alcoholic fermentation at 20°C with 3 different pretreatment methods (with peel, without peel, and juice) and 5 different starter cultures, respectively. Color analysis, electronic sense analysis, bioactive compound analysis, and antioxidant activity analysis combined with multivariate statistical analysis were applied to evaluated the effects of pretreatment methods and starter cultures on the overall quality of jujube wine. It was found that both pretreatment methods and starter cultures have effects on the quality of jujube wines, in which pretreatment methods have much more significant effects. The jujube wines fermented with different pretreatment methods were classified clearly by their overall quality, and that of the jujube wines fermented with peel was the best among all, since it can not only enhance the color and flavor quality of the wine, but also maximize the preservation of bioactive compounds and antioxidant activity of jujube for better consumer acceptance. This will provide a theoretical reference and application basis for the quality improvement of jujube wine.

Influence of Cooking Conditions on Nutritional Properties and Sensory Characteristics Interpreted by E-Senses: Case-Study on Selected Vegetables

This study investigates the effects of three cooking methods (boiling, steaming and microwaving) on the nutritional and physical properties of cauliflowers, carrots and sweet potatoes; e-senses were applied to interpret the sensory characteristics according to physico-chemical aspects. The nutritional quality was evaluated by determining the concentrations of key components and the antioxidant activity; e-sense data, combined with texture parameters, were processed by a principal component analysis. The cooking method and time significantly influenced the quality of the three products. Boiling, which detrimentally affected ascorbic acid, total phenolic concentration and antioxidant activity, enhanced carotene accessibility. Steaming produced losses in ascorbic acid, increasing total phenolics and carotenoids. Microwaving resulted in minor changes in ascorbic acid concentrations, preserved carotenoids and increased total phenolics. The nutritional quality was better preserved or enhanced using shorter cooking times. The elaboration of the data collected by the e-senses showed a clear evolution according to the cooking method and time. The results helped to determine the cooking method that best preserves the nutritional properties of the vegetables, highlighting the applicability of rapid instrumental methods to interpret the evolution of sensory characteristics.

Gas Sensors Based on Chemi-Resistive Hybrid Functional Nanomaterials

Chemi-resistive sensors based on hybrid functional materials are promising candidates for gas sensing with high responsivity, good selectivity, fast response/recovery, great stability/repeatability, room-working temperature, low cost, and easy-to-fabricate, for versatile applications. This progress report reviews the advantages and advances of these sensing structures compared with the single constituent, according to five main sensing forms: manipulating/constructing heterojunctions, catalytic reaction, charge transfer, charge carrier transport, molecular binding/sieving, and their combinations. Promises and challenges of the advances of each form are presented and discussed. Critical thinking and ideas regarding the orientation of the development of hybrid material-based gas sensor in the future are discussed.

Emerging Technologies Based on Artificial Intelligence to Assess the Quality and Consumer Preference of Beverages

Beverages is a broad and important category within the food industry, which is comprised of a wide range of sub-categories and types of drinks with different levels of complexity for their manufacturing and quality assessment. Traditional methods to evaluate the quality traits of beverages consist of tedious, time-consuming, and costly techniques, which do not allow researchers to procure results in real-time. Therefore, there is a need to test and implement emerging technologies in order to automate and facilitate those analyses within this industry. This paper aimed to present the most recent publications and trends regarding the use of low-cost, reliable, and accurate, remote or non-contact techniques using robotics, machine learning, computer vision, biometrics and the application of artificial intelligence, as well as to identify the research gaps within the beverage industry. It was found that there is a wide opportunity in the development and use of robotics and biometrics for all types of beverages, but especially for hot and non-alcoholic drinks. Furthermore, there is a lack of knowledge and clarity within the industry, and research about the concepts of artificial intelligence and machine learning, as well as that concerning the correct design and interpretation of modeling related to the lack of inclusion of relevant data, additional to presenting over- or under-fitted models.

Development of an extraction method for the quantification of lignans in espresso coffee by using HPLC-MS/MS triple quadrupole

Lignans are polyphenolic compounds that are considered phytoestrogens for their plant origins, and they possess different biological activities. Three different extraction methods, ie, "dilute and shoot", acidic hydrolysis, and enzymatic digestion, have been compared for extracting lignans (secoisolariciresinol (SECO), matairesinol (MAT), and lariciresinol (LARI)) from espresso coffee (EC) by using high-performance liquid chromatography (HPLC) tandem mass spectrometry (MS/MS). The best recovery values (SECO: 97%, LARI: 98%, and MAT: 93%) were obtained by using enzymatic hydrolysis with Clara-Diastase at 10% (w/v), keeping the sample at 37°C for 3 hours. For this reason, this method has been chosen and then applied to quantify lignans in 9 different EC samples from 5 different geographical origins (Brazil, Colombia, El Salvador, Ethiopia, and India). Secoisolariciresinol and LARI were found in all EC samples from 27.9 to 52.0 μg L^-1 and from 5.3 to 27.8 μg L^-1 respectively, contrary to MAT that it was not possible to detect it in each type of coffee. This method confirms the high specificity and sensitivity of MS/MS system for detecting bioactives in complex matrix such as coffee.

Comparative evaluation of the volatile profiles and taste properties of roasted coffee beans as affected by drying method and detected by electronic nose, electronic tongue, and HS-SPME-GC-MS

In this study, room-temperature drying, solar drying, heat pump drying (HPD), hot-air drying, and freeze drying were applied to investigate the volatile profiles and taste properties of roasted coffee beans by using electronic nose, electronic tongue, and headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS). Results indicated that the drying process markedly affected pH, total titratable acidity, total solids, and total soluble solids. Significant differences existed among all samples based on drying method; and the HPD method was superior for preserving ketones, phenols, and esters. Principal component analysis (PCA) combined with E-nose and E-tongue radar charts as well as the fingerprint of HS-SPME-GC-MS could clearly discriminate samples from different drying methods, with results obtained from hierarchical cluster analysis (the Euclidean distance is 0.75) being in agreement with those of PCA. These findings may provide a theoretical basis for the dehydration of coffee beans and other similar thermo-sensitive agricultural products.

E-nose, e-tongue and e-eye for edible olive oil characterization and shelf life assessment: A powerful data fusion approach

The aim of this work was to investigate the applicability of e-senses (electronic nose, electronic tongue and electronic eye) for the characterization of edible olive oils (extra virgin, olive and pomace) and for the assessment of extra virgin olive oil and olive oil quality decay during storage at different temperatures. In order to obtain a complete description of oil samples, physico-chemical analyses on quality and nutritional parameters were also performed. Data were processed by PCA and a targeted data processing flow-sheet has been applied to physico-chemical and e-senses dataset starting from data pre-processing introducing an innovative normalization method, called t0 centering. On e-senses data a powerful mid-level data fusion approach has been employed to extract relevant information from different analytical sources combining their individual contributions. On physico-chemical data, an alternative approach for grouping extra virgin olive oil and olive oil samples on the basis of their freshness was applied and two classes were identified: fresh and oxidized. A k-NN classification rule was developed to test the performance of e-senses to classify samples in the two classes of freshness and the average value of correctly classified samples was 94%. Results demonstrated that the combined application of e-senses and the innovative data processing strategy allows to characterize edible olive oils of different categories on the basis of their sensorial properties and also to follow the evolution during storage of extra-virgin olive oil and olive oil sensorial properties thus assessing the quality decay of oils.

Assessment of the Indoor Odour Impact in a Naturally Ventilated Room

Indoor air quality influences people’s lives, potentially affecting their health and comfort. Nowadays, ventilation is the only technique commonly used for regulating indoor air quality. CO₂ is the reference species considered in order to calculate the air exchange rates of indoor environments. Indeed, regarding air quality, the presence of pleasant or unpleasant odours can strongly influence the environmental comfort. In this paper, a case study of indoor air quality monitoring is reported. The indoor field tests were conducted measuring both CO₂ concentration, using a photoacoustic multi-gas analyzer, and odour trends, using an electronic nose, in order to analyze and compare the information acquired. The indoor air monitoring campaign was run for a period of 20 working days into a university room. The work was focused on the determination of both CO₂ and odour emission factors (OEF) emitted by the human activity and on the evaluation of the odour impact in a naturally ventilated room. The results highlighted that an air monitoring and recycling system based only on CO₂ concentration and temperature measurements might be insufficient to ensure a good indoor air quality, whereas its performances could be improved by integrating the existing systems with an electronic nose for odour detection.