Nonequilibrium sensing of volatile compounds using active and passive analyte delivery.

Although sensor technologies have allowed us to outperform the human senses of sight, hearing, and touch, the development of artificial noses is significantly behind their biological counterparts. This largely stems from the sophistication of natural olfaction, which relies on both fluid dynamics within the nasal anatomy and the response patterns of hundreds to thousands of unique molecular-scale receptors. We designed a sensing approach to identify volatiles inspired by the fluid dynamics of the nose, allowing us to extract information from a single sensor (here, the reflectance spectra from a mesoporous one-dimensional photonic crystal) rather than relying on a large sensor array. By accentuating differences in the nonequilibrium mass-transport dynamics of vapors and training a machine learning algorithm on the sensor output, we clearly identified polar and nonpolar volatile compounds, determined the mixing ratios of binary mixtures, and accurately predicted the boiling point, flash point, vapor pressure, and viscosity of a number of volatile liquids, including several that had not been used for training the model. We further implemented a bioinspired active sniffing approach, in which the analyte delivery was performed in well-controlled 'inhale-exhale' sequences, enabling an additional modality of differentiation and reducing the duration of data collection and analysis to seconds. Our results outline a strategy to build accurate and rapid artificial noses for volatile compounds that can provide useful information such as the composition and physical properties of chemicals, and can be applied in a variety of fields, including disease diagnosis, hazardous waste management, and healthy building monitoring.

VOC-based detection of prostate cancer using an electronic nose and ion mobility spectrometry: A novel urine-based approach.

BACKGROUND: Many diseases leave behind specific metabolites which can be detected from breath and urine as volatile organic compounds (VOC). Our group previously described VOC-based methods for the detection of bladder cancer and urinary tract infections. This study investigated whether prostate cancer can be diagnosed from VOCs in urine headspace. METHODS: For this pilot study, mid-stream urine samples were collected from 56 patients with histologically confirmed prostate cancer. A control group was formed with 53 healthy male volunteers matched for age who had recently undergone a negative screening by prostate-specific antigen (PSA) and digital rectal exam. Headspace measurements were performed with the electronic nose Cyranose 320TM. Statistical comparison was performed using principal component analysis, calculating Mahalanobis distance, and linear discriminant analysis. Further measurements were carried out with ion mobility spectrometry (IMS) to compare detection accuracy and to identify potential individual analytes. Bonferroni correction was applied for multiple testing. RESULTS: The electronic nose yielded a sensitivity of 77% and specificity of 62%. Mahalanobis distance was 0.964, which is indicative of limited group separation. IMS identified a total of 38 individual analytical peaks, two of which showed significant differences between groups (p < 0.05). To discriminate between tumor and controls, a decision tree with nine steps was generated. This model led to a sensitivity of 98% and specificity of 100%. CONCLUSIONS: VOC-based detection of prostate cancer seems feasible in principle. While the first results with an electronic nose show some limitations, the approach can compete with other urine-based marker systems. However, it seems less reliable than PSA testing. IMS is more accurate than the electronic nose with promising sensitivity and specificity, which warrants further research. The individual relevant metabolites identified by IMS should further be characterized using gas chromatography/mass spectrometry to facilitate potential targeted rapid testing.

Predicting the quality attributes related to geographical growing regions in red-fleshed kiwifruit by data fusion of electronic nose and computer vision systems.

The ability of a data fusion system composed of a computer vision system (CVS) and an electronic nose (e-nose) was evaluated to predict key physiochemical attributes and distinguish red-fleshed kiwifruit produced in three distinct regions in northern Iran. Color and morphological features from whole and middle-cut kiwifruits, along with the maximum responses of the 13 metal oxide semiconductor (MOS) sensors of an e-nose system, were used as inputs to the data fusion system. Principal component analysis (PCA) revealed that the first two principal components (PCs) extracted from the e-nose features could effectively differentiate kiwifruit samples from different regions. The PCA-SVM algorithm achieved a 93.33% classification rate for kiwifruits from three regions based on data from individual e-nose and CVS. Data fusion increased the classification rate of the SVM model to 100% and improved the performance of Support Vector Regression (SVR) for predicting physiochemical indices of kiwifruits compared to individual systems. The data fusion-based PCA-SVR models achieved validation R2 values ranging from 90.17% for the Brix-Acid Ratio (BAR) to 98.57% for pH prediction. These results demonstrate the high potential of fusing artificial visual and olfactory systems for quality monitoring and identifying the geographical growing regions of kiwifruits.

Molecules that Generate Fingerprints: A New Class of Fluorescent Sensors for Chemical Biology, Medical Diagnosis, and Cryptography.

Fluorescent molecular sensors, often referred to as "turn-on" or "turn-off" fluorescent probes, are synthetic agents that change their fluorescence signal in response to analyte binding. Although these sensors have become powerful analytical tools in a wide range of research fields, they are generally limited to detecting only one or a few analytes. Pattern-generating fluorescent probes, which can generate unique identification (ID) fingerprints for different analytes, have recently emerged as a new class of luminescent sensors that can address this limitation. A unique characteristic of these probes, termed ID-probes, is that they integrate the qualities of conventional small-molecule-based fluorescent sensors and cross-reactive sensor arrays (often referred to as chemical, optical, or electronic noses/tongues). On the one hand, ID-probes can discriminate between various analytes and their combinations, akin to array-based analytical devices. On the other hand, their minute size enables them to analyze small-volume samples, track dynamic changes in a single solution, and operate in the microscopic world, which the macroscopic arrays cannot access.Here, we describe the principles underlying the ID-probe technology, as well as provide an overview of different ID-probes that have been developed to date and the ways they can be applied to a wide range of research fields. We describe, for example, ID-probes that can identify combinations of protein biomarkers in biofluids and in living cells, screen for several protein inhibitors simultaneously, analyze the content of Aß aggregates, as well as ensure the quality of small-molecule and biological drugs. These examples highlight the relevance of this technology to medical diagnosis, bioassay development, cell and chemical biology, and pharmaceutical quality assurance, among others. ID-probes that can authorize users and protect secret data are also presented and the mechanisms that enable them to hide (steganography), encrypt (cryptography), and prevent access to (password protection) information are discussed.The versatility of this technology is further demonstrated by describing two types of probes: unimolecular ID-probes and self-assembled ID-probes. Probes from the first type can operate inside living cells, be recycled, and their initial patterns can be more easily obtained in a reproducible manner. The second type of probes can be readily modified and optimized, allowing one to prepare various different probes from a much wider range of fluorescent reporters and supramolecular recognition elements. Taken together, these developments indicate that the ID-probe sensing methodology is generally applicable, and that such probes can better characterize analyte mixtures or process chemically encoded information than can the conventional fluorescent molecular sensors. We therefore hope that this review will inspire the development of new types of pattern-generating probes, which would extend the fluorescence molecular toolbox currently used in the analytical sciences.

Advancing accuracy in breath testing for lung cancer: strategies for improving diagnostic precision in imbalanced data.

BACKGROUND: Breath testing using an electronic nose has been recognized as a promising new technique for the early detection of lung cancer. Imbalanced data are commonly observed in electronic nose studies, but methods to address them are rarely reported. OBJECTIVE: The objectives of this study were to assess the accuracy of electronic nose screening for lung cancer with imbalanced learning and to select the best mechanical learning algorithm. METHODS: We conducted a case‒control study that included patients with lung cancer and healthy controls and analyzed metabolites in exhaled breath using a carbon nanotube sensor array. The study used five machine learning algorithms to build predictive models and a synthetic minority oversampling technique to address imbalanced data. The diagnostic accuracy of lung cancer was assessed using pathology reports as the gold standard. RESULTS: We enrolled 190 subjects between 2020 and 2023. A total of 155 subjects were used in the final analysis, which included 111 lung cancer patients and 44 healthy controls. We randomly divided samples into one training set, one internal validation set, and one external validation set. In the external validation set, the summary sensitivity was 0.88 (95% CI 0.84-0.91), the summary specificity was 1.00 (95% CI 0.85-1.00), the AUC was 0.96 (95% CI 0.94-0.98), the pAUC was 0.92 (95% CI 0.89-0.96), and the DOR was 207.62 (95% CI 24.62-924.64). CONCLUSION: Electronic nose screening for lung cancer is highly accurate. The support vector machine algorithm is more suitable for analyzing chemical sensor data from electronic noses.

Cross-site validation of lung cancer diagnosis by electronic nose with deep learning: a multicenter prospective study.

BACKGROUND: Although electronic nose (eNose) has been intensively investigated for diagnosing lung cancer, cross-site validation remains a major obstacle to be overcome and no studies have yet been performed. METHODS: Patients with lung cancer, as well as healthy control and diseased control groups, were prospectively recruited from two referral centers between 2019 and 2022. Deep learning models for detecting lung cancer with eNose breathprint were developed using training cohort from one site and then tested on cohort from the other site. Semi-Supervised Domain-Generalized (Semi-DG) Augmentation (SDA) and Noise-Shift Augmentation (NSA) methods with or without fine-tuning was applied to improve performance. RESULTS: In this study, 231 participants were enrolled, comprising a training/validation cohort of 168 individuals (90 with lung cancer, 16 healthy controls, and 62 diseased controls) and a test cohort of 63 individuals (28 with lung cancer, 10 healthy controls, and 25 diseased controls). The model has satisfactory results in the validation cohort from the same hospital while directly applying the trained model to the test cohort yielded suboptimal results (AUC, 0.61, 95% CI: 0.47─0.76). The performance improved after applying data augmentation methods in the training cohort (SDA, AUC: 0.89 [0.81─0.97]; NSA, AUC:0.90 [0.89─1.00]). Additionally, after applying fine-tuning methods, the performance further improved (SDA plus fine-tuning, AUC:0.95 [0.89─1.00]; NSA plus fine-tuning, AUC:0.95 [0.90─1.00]). CONCLUSION: Our study revealed that deep learning models developed for eNose breathprint can achieve cross-site validation with data augmentation and fine-tuning. Accordingly, eNose breathprints emerge as a convenient, non-invasive, and potentially generalizable solution for lung cancer detection. CLINICAL TRIAL REGISTRATION: This study is not a clinical trial and was therefore not registered.

An electronic nose can identify humans by the smell of their ear.

Terrestrial mammals identify conspecifics by body odor. Dogs can also identify humans by body odor, and in some instances, humans can identify other humans by body odor as well. Despite the potential for a powerful biometric tool, smell has not been systematically used for this purpose. A question arising in the application of smell to biometrics is which bodily odor source should we measure. Breath is an obvious candidate, but the associated humidity can challenge many sensing devices. The armpit is also a candidate source, but it is often doused in cosmetics. Here, we test the hypothesis that the ear may provide an effective source for odor-based biometrics. The inside of the ear has relatively constant humidity, cosmetics are not typically applied inside the ear, and critically, ears contain cerumen, a potent source of volatiles. We used an electronic nose to identify 12 individuals within and across days, using samples from the armpit, lower back, and ear. In an identification setting where chance was 8.33% (1 of 12), we found that we could identify a person by the smell of their ear within a day at up to ~87% accuracy (~10 of 12, binomial P < 10-5), and across days at up to ~22% accuracy (~3 of 12, binomial P < 0.012). We conclude that humans can indeed be identified from the smell of their ear, but the results did not imply a consistent advantage over other bodily odor sources.

Electronic Tongue Based on ZnO/ITO@glass for Electrochemical Monitoring of Spiciness Levels.

Capsaicin, a chemical compound present in chili peppers, is widely acknowledged as the main contributor to the spicy and hot sensations encountered during consumption. Elevated levels of capsaicin can result in meals being excessively spicy, potentially leading to health issues, such as skin burning, irritation, increased heart rate and circulation, and discomfort in the gastrointestinal system and even inducing nausea or diarrhea. The level of spiciness that individuals can tolerate may vary, so what may be considered incredibly hot for one person could be mild for another. To ensure food safety, human healthcare, regulatory compliance, and quality control in spicy food products, capsaicin levels must be measured. For these purposes, a reliable and stable sensor is required to quantify the capsaicin level. To leverage the effect of zinc oxide (ZnO), herein, we demonstrated the one-step fabrication process of an electronic tongue (E-Tongue) based on an electrochemical biosensor for the determination of capsaicin. ZnO was electrodeposited on the indium tin oxide (ITO) surface. The biosensor demonstrated the two notable linear ranges from 0.01 to 50 µM and from 50 to 500 µM with a limit of detection (LOD) of 2.1 nM. The present study also included the analysis of real samples, such as green chilis, red chili powder, and dried red chilis, to evaluate their spiciness levels. Furthermore, the E-Tongue exhibited notable degrees of sensitivity, selectivity, and long-term stability for a duration of more than a month. The development of an E-Tongue for capsaicin real-time monitoring as a point-of-care (POC) device has the potential to impact various industries and improve safety, product quality, and healthcare outcomes.

Volatilome: Smells like microbial spirit.

In recent years, the study of volatile compounds has sparked interest due to their implications in signaling and the enormous variety of bioactive properties attributed to them. Despite the absence of analysis methods standardization, there are a multitude of tools and databases that allow the identification and quantification of volatile compounds. These compounds are chemically heterogeneous and their diverse properties are exploited by various fields such as cosmetics, the food industry, agriculture and medicine, some of which will be discussed here. In virtue of volatile compounds being ubiquitous and fast chemical messengers, these molecules mediate a large number of interspecific and intraspecific interactions, which are key at an ecological level to maintaining the balance and correct functioning of ecosystems. This review briefly summarized the role of volatile compounds in inter- and intra-specific relationships as well as industrial applications associated with the use of these compounds that is emerging as a promising field of study.

Electronic Nose for Improved Environmental Methane Monitoring.

Reducing emissions of the key greenhouse gas methane (CH4) is increasingly highlighted as being important to mitigate climate change. Effective emission reductions require cost-effective ways to measure CH4 to detect sources and verify that mitigation efforts work. We present here a novel approach to measure methane at atmospheric concentrations by means of a low-cost electronic nose strategy where the readings of a few sensors are combined, leading to errors down to 33 ppb and coefficients of determination, R2, up to 0.91 for in situ measurements. Data from methane, temperature, humidity, and atmospheric pressure sensors were used in customized machine learning models to account for environmental cross-effects and quantify methane in the ppm-ppb range both in indoor and outdoor conditions. The electronic nose strategy was confirmed to be versatile with improved accuracy when more reference data were supplied to the quantification model. Our results pave the way toward the use of networks of low-cost sensor systems for the monitoring of greenhouse gases.

Carbamoylase-based impedimetric electronic tongue for rapid detection of paralytic shellfish toxins.

Phytotoxins produced by marine microalgae, such as paralytic shellfish toxins (PSTs), can accumulate in bivalve molluscs, representing a human health concern due to the life-threatening symptoms they cause. To avoid the commercialization of contaminated bivalves, monitoring programs were established in the EU. The purpose of this work is the implementation of a PST transforming enzyme-carbamoylase-in an impedimetric test for rapid simultaneous detection of several carbamate and N-sulfocarbamoyl PSTs. Carbamoylase hydrolyses carbamate and sulfocarbamoyl toxins, which may account for up to 90% of bivalve toxicity related to PSTs. Conformational changes of carbamoylase accompanying enzymatic reactions were probed by Fourier transform mid-infrared spectroscopy (FT-MIR) and electrochemical impedance spectroscopy (EIS). Furthermore, a combination of EIS with a metal electrode and a carbamoylase-based assay was employed to harness changes in the enzyme conformation and adsorption on the electrode surface during the enzymatic reaction as an analytical signal. After optimization of the working conditions, the developed impedimetric e-tongue could quantify N-sulfocarbamoyl toxins with a detection limit of 0.1 µM. The developed e-tongue allows the detection of these toxins at concentration levels observed in bivalves with PST toxicity close to the regulatory limit. The quantification of a sum of N-sulfocarbamoyl PSTs in naturally contaminated mussel extracts using the developed impedimetric e-tongue has been demonstrated.

Transparent, flexible, and eco-friendly starch-based films for reversible optoelectronic noses for food spoilage monitoring in smart packaging.

A reversible optoelectronic nose is presented consisting of ten acid-base indicators incorporated into a starch-based film, covering a wide pH range. The starch substrate is odorless, biocompatible, flexible, and exhibits high tensile resistance. This optical artificial olfaction system was used to detect the early stages of food decomposition by exposing it to the volatile compounds produced during the spoialge process of three food products (beef, chicken, and pork). A smartphone was used to capture the color changes caused by intermolecular interactions between each dye and the emitted volatiles over time. Digital images were processed to generate a differential color map, which uses the observed color shifts to create a unique signature for each food product. To effectively discriminate among different samples and exposure times, we employed chemometric tools, including hierarchical cluster analysis (HCA) and principal component analysis (PCA). This approach detects food deterioration in a practical, cost-effective, and user-friendly manner, making it suitable for smart packaging. Additionally, the use of starch-based films in the food industry is preferable due to their biocompatibility and biodegradability characteristics.

Structure Optimization and Data Processing Method of Electronic Nose Bionic Chamber for Detecting Ammonia Emissions from Livestock Excrement Fermentation.

In areas where livestock are bred, there is a demand for accurate, real-time, and stable monitoring of ammonia concentration in the breeding environment. However, existing electronic nose systems have slow response times and limited detection accuracy. In this study, we introduce a novel solution: the bionic chamber construction of the electronic nose is optimized, and the sensor response data in the chamber are analyzed using an intelligent algorithm. We analyze the structure of the biomimetic chamber and the surface airflow of the sensor array to determine the sensing units of the system. The system employs an electronic nose to detect ammonia and ethanol gases in a circulating airflow within a closed box. The captured signals are processed, followed by the application of classification and regression models for data prediction. Our results suggest that the system, leveraging the biomimetic chamber, offers rapid gas detection response times. A high classification prediction accuracy, with a determination coefficient R2 value of 0.99 for single-output regression and over 0.98 for multi-output regression predictions, is achieved by incorporating a backpropagation (BP) neural network algorithm. These outcomes demonstrate the effectiveness of the electronic nose, based on an optimized bionic chamber combined with a BP neural network algorithm, in accurately detecting ammonia emitted during livestock excreta fermentation, satisfying the ammonia detection requirements of breeding farms.

Influence of the Degree of Fruitiness on the Quality Assessment of Virgin Olive Oils Using Electronic Nose Technology.

The electronic nose is a non-invasive technology suitable for the analysis of edible oils. One of the practical applications in the olive oil industry is the classification of virgin oils based on their sensory characteristics. Notwithstanding that this technology, at this stage, cannot realistically replace the currently used methods, it is fruitful for a preliminary analysis of the oil quality. This work makes use of this technology to develop a methodology for the detection of the threshold by which an extra-virgin olive oil (EVOO) drops into the virgin olive oil (VOO) category. With this aim, two features were studied: the level of fruitiness level and the type of defect. The results showed a greater influence of the level of fruitiness than the type of defect in the determination of the detection threshold. Furthermore, three of the sensors (S2, S7 and S9) of the commercial e-nose PEN3 were identified as the most discriminating in the classification between EVOO and VOO oils.

Methods for Estimating the Detection and Quantification Limits of Key Substances in Beer Maturation with Electronic Noses.

To evaluate the suitability of an analytical instrument, essential figures of merit such as the limit of detection (LOD) and the limit of quantification (LOQ) can be employed. However, as the definitions k nown in the literature are mostly applicable to one signal per sample, estimating the LOD for substances with instruments yielding multidimensional results like electronic noses (eNoses) is still challenging. In this paper, we will compare and present different approaches to estimate the LOD for eNoses by employing commonly used multivariate data analysis and regression techniques, including principal component analysis (PCA), principal component regression (PCR), as well as partial least squares regression (PLSR). These methods could subsequently be used to assess the suitability of eNoses to help control and steer processes where volatiles are key process parameters. As a use case, we determined the LODs for key compounds involved in beer maturation, namely acetaldehyde, diacetyl, dimethyl sulfide, ethyl acetate, isobutanol, and 2-phenylethanol, and discussed the suitability of our eNose for that dertermination process. The results of the methods performed demonstrated differences of up to a factor of eight. For diacetyl, the LOD and the LOQ were sufficiently low to suggest potential for monitoring via eNose.

Recent Advances and Future Perspectives in the E-Nose Technologies Addressed to the Wine Industry.

Electronic nose devices stand out as pioneering innovations in contemporary technological research, addressing the arduous challenge of replicating the complex sense of smell found in humans. Currently, sensor instruments find application in a variety of fields, including environmental, (bio)medical, food, pharmaceutical, and materials production. Particularly the latter, has seen a significant increase in the adoption of technological tools to assess food quality, gradually supplanting human panelists and thus reshaping the entire quality control paradigm in the sector. This process is happening even more rapidly in the world of wine, where olfactory sensory analysis has always played a central role in attributing certain qualities to a wine. In this review, conducted using sources such as PubMed, Science Direct, and Web of Science, we examined papers published between January 2015 and January 2024. The aim was to explore prevailing trends in the use of human panels and sensory tools (such as the E-nose) in the wine industry. The focus was on the evaluation of wine quality attributes by paying specific attention to geographical origin, sensory defects, and monitoring of production trends. Analyzed results show that the application of E-nose-type sensors performs satisfactorily in that trajectory. Nevertheless, the integration of this type of analysis with more classical methods, such as the trained sensory panel test and with the application of destructive instrument volatile compound (VOC) detection (e.g., gas chromatography), still seems necessary to better explore and investigate the aromatic characteristics of wines.

Analysis of Wheat Grain Infection by Fusarium Mycotoxin-Producing Fungi Using an Electronic Nose, GC-MS, and qPCR.

Fusarium graminearum and F. culmorum are considered some of the most dangerous pathogens of plant diseases. They are also considerably dangerous to humans as they contaminate stored grain, causing a reduction in yield and deterioration in grain quality by producing mycotoxins. Detecting Fusarium fungi is possible using various diagnostic methods. In the manuscript, qPCR tests were used to determine the level of wheat grain spoilage by estimating the amount of DNA present. High-performance liquid chromatography was performed to determine the concentration of DON and ZEA mycotoxins produced by the fungi. GC-MS analysis was used to identify volatile organic components produced by two studied species of Fusarium. A custom-made, low-cost, electronic nose was used for measurements of three categories of samples, and Random Forests machine learning models were trained for classification between healthy and infected samples. A detection performance with recall in the range of 88-94%, precision in the range of 90-96%, and accuracy in the range of 85-93% was achieved for various models. Two methods of data collection during electronic nose measurements were tested and compared: sensor response to immersion in the odor and response to sensor temperature modulation. An improvement in the detection performance was achieved when the temperature modulation profile with short rectangular steps of heater voltage change was applied.

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.

Noninvasive Diabetes Detection through Human Breath Using TinyML-Powered E-Nose.

Volatile organic compounds (VOCs) in exhaled human breath serve as pivotal biomarkers for disease identification and medical diagnostics. In the context of diabetes mellitus, the noninvasive detection of acetone, a primary biomarker using electronic noses (e-noses), has gained significant attention. However, employing e-noses requires pre-trained algorithms for precise diabetes detection, often requiring a computer with a programming environment to classify newly acquired data. This study focuses on the development of an embedded system integrating Tiny Machine Learning (TinyML) and an e-nose equipped with Metal Oxide Semiconductor (MOS) sensors for real-time diabetes detection. The study encompassed 44 individuals, comprising 22 healthy individuals and 22 diagnosed with various types of diabetes mellitus. Test results highlight the XGBoost Machine Learning algorithm's achievement of 95% detection accuracy. Additionally, the integration of deep learning algorithms, particularly deep neural networks (DNNs) and one-dimensional convolutional neural network (1D-CNN), yielded a detection efficacy of 94.44%. These outcomes underscore the potency of combining e-noses with TinyML in embedded systems, offering a noninvasive approach for diabetes mellitus detection.

Evaluating the Non-Invasive Measurement of Apple Aroma Using Electronic Nose Device through Comparison with Direct Mass Spectrometry, Sugar Content, and Ripeness Measurements.

To compare apple aroma intensities, apples were analyzed from the calyx side (on the opposite side of the stem) using an electronic nose (e-nose) sensor device and direct mass spectrometry. The results indicated that the sensor value tended to increase in accordance with the total intensity of apple aroma components measured by direct mass spectrometry. In addition, the e-nose sensor values for apple aroma did not correlate with the sugar content and ripeness measurements using optical sensors. Moreover, the relative standard deviations of repeatability and intermediate precision in the measurement of apple flavor (apple lip balm) were within 1.36-9.96%. Similar to the utilization of sugar content and ripeness values, the aroma measured from the calyx side can be potentially used for apple evaluation.