Gas chromatography/olfactometry and electronic nose analyses of retronasal aroma of espresso and correlation with sensory evaluation by an artificial neural network

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.

The Impact of Wet Fermentation on Coffee Quality Traits and Volatile Compounds Using Digital Technologies

Fermentation is critical for developing coffee’s physicochemical properties. This study aimed to assess the differences in quality traits between fermented and unfermented coffee with four grinding sizes of coffee powder using multiple digital technologies. A total of N = 2 coffee treatments—(i) dry processing and (ii) wet fermentation—with grinding levels (250, 350, 550, and 750 µm) were analysed using near-infrared spectrometry (NIR), electronic nose (e-nose), and headspace/gas chromatography–mass spectrometry (HS-SPME-GC-MS) coupled with machine learning (ML) modelling. Most overtones detected by NIR were within the ranges of 1700–2000 nm and 2200–2396 nm, while the enhanced peak responses of fermented coffee were lower. The overall voltage of nine e-nose sensors obtained from fermented coffee (250 µm) was significantly higher. There were two ML classification models to classify processing and brewing methods using NIR (Model 1) and e-nose (Model 2) values as inputs that were highly accurate (93.9% and 91.2%, respectively). Highly precise ML regression Model 3 and Model 4 based on the same inputs for NIR (R = 0.96) and e-nose (R = 0.99) were developed, respectively, to assess 14 volatile aromatic compounds obtained by GC-MS. Fermented coffee showed higher 2-methylpyrazine (2.20 ng/mL) and furfuryl acetate (2.36 ng/mL) content, which induces a stronger fruity aroma. This proposed rapid, reliable, and low-cost method was shown to be effective in distinguishing coffee postharvest processing methods and evaluating their volatile compounds, which has the potential to be applied for coffee differentiation and quality assurance and control.

Recent advances in analytical strategies for coffee volatile studies: Opportunities and challenges

Coffee has attracted significant research interest owing to its complex volatile composition and aroma, which imparts a pleasant sensorial experience that remains challenging to analyse and interpret. This review summarises analytical challenges associated with coffee's volatile and matrix complexity, and recent developments in instrumental techniques to resolve them. The benefits of state-of-the-art analytical techniques applied to coffee volatile analysis from experimental design to sample preparation, separation, detection, and data analysis are evaluated. Complementary method selection coupled with progressive experimental design and data analysis are vital to unravel the increasing comprehensiveness of coffee volatile datasets. Considering this, analytical workflows for conventional, targeted, and untargeted coffee volatile analyses are thus proposed considering the trends towards sorptive extraction, multidimensional gas chromatography, and high-resolution mass spectrometry. In conclusion, no single analytical method addresses coffee's complexity in its entirely, and volatile analysis must be tailored to the key objectives and concerns of the analyst.

Assessment the flavor of soybean meal hydrolyzed with Alcalase enzyme under different hydrolysis conditions by E-nose, E-tongue and HS-SPME-GC-MS

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Enzymatic hydrolysis with Alcalase reduced soybean odor substance 1-octene-3-ol.

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Excessive enzymatic hydrolysis resulted in the deterioration of the hydrolysate flavor.

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The flavour of soybean meal hydrolysates with different hydrolysis conditions could be distinguished by E-tongue.

In the present study, E-nose, E-tongue, and headspace-solid phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC–MS) technology combined with Principal Component Analysis (PCA) were employed to evaluate the flavor characteristics of the volatile and the non-volatile substances generated during the enzymatic hydrolysis of the soybean meal by Alcalase. The results showed that the enzymatic hydrolysis effectively reduced the content of soybean odorous substance 1-octene-3-ol and led to better flavor. However, the excessive enzymatic hydrolysis resulted in the deterioration of the enzymatic hydrolysates flavor. In addition, both radar graph and PCA of E-tongue were able to provide the distribution of flavor substances during the enzymatic hydrolysis of the soybean meal. These results provided a theoretical basis for the improvement of the flavors of the soybean meal and its derived products.

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.

Integrating a Low-Cost Electronic Nose and Machine Learning Modelling to Assess Coffee Aroma Profile and Intensity

Aroma is one of the main attributes that consumers consider when appreciating and selecting a coffee; hence it is considered an important quality trait. However, the most common methods to assess aroma are based on expensive equipment or human senses through sensory evaluation, which is time-consuming and requires highly trained assessors to avoid subjectivity. Therefore, this study aimed to estimate the coffee intensity and aromas using a low-cost and portable electronic nose (e-nose) and machine learning modeling. For this purpose, triplicates of nine commercial coffee samples with different intensity levels were used for this study. Two machine learning models were developed based on artificial neural networks using the data from the e-nose as inputs to (i) classify the samples into low, medium, and high-intensity (Model 1) and (ii) to predict the relative abundance of 45 different aromas (Model 2). Results showed that it is possible to estimate the intensity of coffees with high accuracy (98%; Model 1), as well as to predict the specific aromas obtaining a high correlation coefficient (R = 0.99), and no under- or over-fitting of the models were detected. The proposed contactless, nondestructive, rapid, reliable, and low-cost method showed to be effective in evaluating volatile compounds in coffee, which is a potential technique to be applied within all stages of the production process to detect any undesirable characteristics on–time and ensure high-quality products.

Detection and Differentiation of Volatile Compound Profiles in Roasted Coffee Arabica Beans from Different Countries Using an Electronic Nose and GC-MS

This paper describes the possibility of electronic nose-based detection and discrimination of volatile compound profiles of coffee from different countries roasted in a Gothot roaster under identical time and thermal regimes. The material used in the study was roasted Arabica coffee beans from Brazil, Ethiopia, Guatemala, Costa Rica, and Peru. The analyses were carried out with the use of the Agrinose electronic nose designed and constructed at the Institute of Agrophysics of the Polish Academy of Sciences in Lublin. The results of the volatile compound profile analysis provided by the Agrinose device were verified with the GC-MS technique. Chemometric tests demonstrated a dominant role of alcohols, acids, aldehydes, azines, and hydrazides in the coffee volatile compound profile. The differences in their content had an impact on the odor profile of the coffees originating from the different countries. High content of pyridine from the group of azines was detected in the coffee from Peru and Brazil despite the same roasting conditions. The results of the Agrinose analysis of volatile substances were consistent and correlated with the GC-MS results. This suggests that the Agrinose is a promising tool for selection of coffees based on their volatile compound profile.

Correlating Espresso Quality with Coffee-Machine Parameters by Means of Association Rule Mining

Coffee is among the most popular beverages in many cities all over the world, being both at the core of the busiest shops and a long-standing tradition of recreational and social value for many people. Among the many coffee variants, espresso attracts the interest of different stakeholders: from citizens consuming espresso around the city, to local business activities, coffee-machine vendors and international coffee industries. The quality of espresso is one of the most discussed and investigated issues. So far, it has been addressed by means of human experts, electronic noses, and chemical approaches. The current work, instead, proposes a data-driven approach exploiting association rule mining. We analyze a real-world dataset of espresso brewing by professional coffee-making machines, and extract all correlations among external quality-influencing variables and actual metrics determining the quality of the espresso. Thanks to the application of association rule mining, a powerful data-driven exhaustive and explainable approach, results are expressed in the form of human-readable rules combining the variables of interest, such as the grinder settings, the extraction time, and the dose amount. Novel insights from real-world coffee extractions collected on the field are presented, together with a data-driven approach, able to uncover insights into the espresso quality and its impact on both the life of consumers and the choices of coffee-making industries.

Effects of Manufacturing Processing Conditions on Retronasal-Aroma Odorants from a Milk Coffee Drink

To develop a ready-to-drink (RTD) milk coffee that retains the original coffee flavor, the effects of manufacturing processing conditions on retronasal-aroma (RA) odorants were investigated by gas chromatography-olfactometry (CharmAnalysis™) using an RA simulator (RAS). Twenty-nine of 33 odorants detected in the RAS effluent (RAS odorants) were identified. The detected odorants were classified into 19 odor-description groups. The total odor intensity (charm value, CMV) of all coffee RAS odorants decreased approximately 68% following pH adjustment, whereas the total CMV increased 6% to 7% following ultra-high-temperature sterilization. The total CMV ratio (about 83%) of the milk coffee produced using a new blending-after-sterilization (BAS) process without pH adjustment of the coffee was greater than that (approximately 56%) prepared using a conventional blending-before-sterilization (BBS) process with pH adjustment. In BAS-processed milk coffees, the total CMV ratio (91%) with infusion (INF)-sterilized reconstituted milk (r-milk) was greater than that (83%) of plate (PLT)-sterilized r-milk. Principal component analysis of odor-description CMVs indicated that the effect of coffee pH adjustment on odor characteristics was greater than that of sterilization, that BAS and BBS samples differed, and that BAS milk coffee prepared using INF sterilization was more similar to homemade milk coffee (blending unsterilized coffee without pH adjustment with PLT-sterilized milk) than milk coffee prepared using PLT sterilization. In conclusion, the BAS process using INF sterilization is superior for manufacturing RTD milk coffee that retains odor characteristics similar to targeted homemade milk coffee. PRACTICAL APPLICATION: Ready-to-drink milk coffee beverages produced using conventional blending-before-sterilization methods do not retain their original coffee flavor following adjustment of the pH of the coffee during manufacturing. The use of newly developed blending-after-sterilization methods, by contrast, produces ready-to-drink milk coffee with an aroma more similar to that of homemade milk coffee, as demonstrated using an analytical system for characterizing food product aromas. The blending-after-sterilization process is now being used in Japan to produce ready-to-drink milk coffee beverages.

Identification of Volatile Compounds and Selection of Discriminant Markers for Elephant Dung Coffee Using Static Headspace Gas Chromatography-Mass Spectrometry and Chemometrics

Elephant dung coffee (Black Ivory Coffee) is a unique Thai coffee produced from Arabica coffee cherries consumed by Asian elephants and collected from their feces. In this work, elephant dung coffee and controls were analyzed using static headspace gas chromatography hyphenated with mass spectrometry (SHS GC-MS), and chemometric approaches were applied for multivariate analysis and the selection of marker compounds that are characteristic of the coffee. Seventy-eight volatile compounds belonging to 13 chemical classes were tentatively identified, including six alcohols, five aldehydes, one carboxylic acid, three esters, 17 furans, one furanone, 13 ketones, two oxazoles, four phenolic compounds, 14 pyrazines, one pyridine, eight pyrroles and three sulfur-containing compounds. Moreover, four potential discriminant markers of elephant dung coffee, including 3-methyl-1-butanol, 2-methyl-1-butanol, 2-furfurylfuran and 3-penten-2-one were established. The proposed method may be useful for elephant dung coffee authentication and quality control.

Effects of Processing Conditions During Manufacture on Retronasal-Aroma Compounds from a Milk Coffee Drink

To develop a ready-to-drink (RTD) milk coffee retaining the original coffee flavor, the effects of processing conditions during manufacture on retronasal-arma (RA) compounds from the milk coffee were investigated by gas chromatography-mass spectrometry using an RA simulator (RAS). Thirteen of 46 detected compounds in the RAS effluent (RAS compounds) decreased significantly following pH adjustment of coffee (from pH 5.1 to 6.8) and 5 compounds increased. RAS compounds from coffee tended to decrease through the pH adjustment and subsequent sterilization. Significantly higher amounts of 13 RAS compounds were released from the milk coffee produced using a blending-after-sterilization (BAS) process without the pH adjustment than from that using a blending-before-sterilization (BBS) process with the pH adjustment. In BAS-processed milk coffee, significantly lower amounts of 8 high-volatility compounds and 1H-pyrrole were released from coffee containing infusion-sterilized (INF) milk than from coffee containing plate-sterilized (PLT) milk, whereas 3 low-volatility compounds were released significantly more from coffee using PLT milk. Principal component analysis revealed that the effect of the manufacturing process (BAS, BBS, or homemade (blending unsterilized coffee without pH adjustment with sterilized milk)) on milk coffee volatiles was larger than that of the sterilization method (INF or PLT) for milk, and that the sterilization method could result in different RAS volatile characteristics in BAS and homemade processes. In conclusion, a BAS process was found to be superior to a BBS process for the manufacture of an RTD milk coffee that retains volatile characteristics similar to that of a homemade milk coffee.

PRACTICAL APPLICATION

Ready-to-drink (RTD) milk coffee manufactured using the conventional blending-before-sterilization process does not retain its original coffee flavor due to pH adjustment of the coffee during the process. The new blending-after-sterilization (BAS) process enabled the production of RTD milk coffee whose volatiles are closer to that of homemade milk coffee, as demonstrated by the results of RAS-GC-MS analysis. The BAS process has already been applied to the manufacture of RTD milk coffees in Japan.

Coffee aroma: Chemometric comparison of the chemical information provided by three different samplings combined with GC-MS to describe the sensory properties in cup

This study is part of a wider project aiming to correlate the chemical composition of the coffee volatile fraction to its sensory properties with the end-goal of developing an instrumental analysis approach complementary to human sensory profiling. The proposed investigation strategy compares the chemical information concerning coffee aroma and flavor obtained with HS-SPME of the ground coffee and in-solution SBSE/SPME sampling combined with GC-MS to evaluate their compatibility with the cupping evaluation for quality control purposes. Roasted coffee samples with specific sensory properties were analyzed. The chemical results obtained by the three samplings were compared through multivariate analysis, and related to the samples' sensory attributes. Despite the differences between the three sampling approaches, data processing showed that the three methods provide the same kind of chemical information useful for sample discrimination, and that they could be used interchangeably to sample the coffee aroma and flavor.

Effect of a New Natural Seasoning on the Formation of Pyrazines in Barbecued Beef Patties

The formation of pyrazines in food cooking is required to obtain satisfactory “roasted” flavor. The aim of this work was to evaluate the effect of a new red wine pomace seasoning (RWPS) on the formation of pyrazines in barbecued beef patties. Five pyrazines were successfully identified, 2,5-dimethylpyrazine being the most abundant. Although 2-ethyl-5-methylpyrazine was in lower levels, it presented the highest odor units, due to its low threshold. RWPS enhanced the formation of the five identified pyrazines (2.1 times), without relevant changes in the proportion between them. The highest increase was observed for 2-ethyl-6-methylpyrazine (3 times higher than in control). These results were corroborated by electronic nose analysis, presenting RWPS beef patties higher odor intensities. Mechanisms to explain these effects are also proposed in the present work, and the high content of polyphenols seems to be the major cause of the observed results. The use of RWPS could improve the flavor characteristics of meat products or enable using less intense cooking conditions with no changes in the flavor.

Discrimination between washed Arabica, natural Arabica and Robusta coffees by using near infrared spectroscopy, electronic nose and electronic tongue analysis

BACKGROUND

The aim of this study is to investigate the feasibility of a 'holistic' approach, using near infrared (NIR) spectroscopy and electronic devices (electronic nose and electronic tongue), as instrumental tools for the classification of different coffee varieties. Analyses were performed on green coffee, on ground roasted coffee and on coffee beverage. Principal component analysis was applied on spectral and sensory data to uncover correlations between samples and variables. After variable selection, linear discriminant analysis was used to classify the samples on the basis of the three coffee classes: Robusta, natural Arabica and washed Arabica.

RESULTS

Linear discriminant analysis demonstrates the practicability of this approach: the external test set validation performed with NIR data showed 100% of correctly classified samples. Moreover, a satisfying percentage of correct classification in cross-validation was obtained for the electronic devices: the average values of correctly classified samples were 81.83% and 78.76% for electronic nose and electronic tongue, respectively.

CONCLUSION

NIR spectroscopy was shown to be a very reliable and useful tool to classify coffee samples in a fast, clean and inexpensive way compared to classical analysis, while the electronic devices could assume the role of investigating techniques to depict the aroma and taste of coffee samples.