Jesús Lozano

This study presents the development and preliminary analytical evaluation of a portable electronic nose (E-nose) for differentiating rice-based beverages containing 0, 5, 10, and 20% (w/v) Cassia grandis pulp. The system was evaluated through optimization of the operating conditions, assessment of short-term repeatability and robustness, exploratory chemometric analysis, and complementary volatile characterization by headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME-GC-MS). The optimized conditions comprised a sample volume of 20 mL, a headspace equilibration time of 10 min, an adsorption period of 60 s, and a desorption period of 30 s. Under these conditions, the sensor responses exhibited relative standard deviations (RSDs) ranging from 1.88 to 3.67% during ten consecutive measurements. Robustness experiments involving deliberate variations in the operating parameters yielded RSDs between 0.87 and 2.48%, although reducing the desorption time to 20 s decreased the mean sensor response by 5.29%. Three independent beverage preparations were produced for each formulation, and ten E-nose measurements were obtained per formulation, resulting in 40 instrumental measurements. Principal component analysis (PCA) accounted for 96.18% of the total variance in the first two components (PC1: 87.04%; PC2: 9.14%) and revealed differences in sensor-response patterns among formulations. The control (A0) and the formulation containing 20% C. grandis pulp (A20) occupied distinct regions of the PCA space, whereas A5 and A10 exhibited greater similarity. Complementary HS-SPME-GC-MS analysis revealed formulation-dependent differences in chromatographic peak areas, particularly for terpene-related compounds. For example, the linalool peak area increased from 18.34 × 10⁶ in A0 to 535.50 × 10⁶ arbitrary units in A20. Nevertheless, preparation-grouped cross-validation and independent predictive validation could not be established from the available experimental records.

Preventive conservation requires environmental information capable of identifying conditions that may contribute to the deterioration of cultural heritage collections. This work presents a low-cost IoT node for continuous formaldehyde monitoring inside museum display cases. The system is based on an ESP32-S3 and integrates a Sensirion SFA30 electrochemical formaldehyde sensor together with environmental and generic VOC sensors (MOX). Measurements are transmitted through Wi-Fi to a private server for remote storage and visualisation. The node was preliminarily evaluated under controlled static-headspace exposure to formaldehyde, formic acid and acetic acid. The dedicated SFA30 channel showed a concentration-dependent response to formaldehyde, while the auxiliary sensing channels exhibited different responses to the three analytes.

A portable electronic nose based on modern digital metal oxide (MOX) gas sensors and programmable temperature modulation was developed and validated. The system integrates four modern commercially available MOX sensors capable of generating temperature-dependent odor fingerprints and multidimensional sensor responses compared with conventional fixed-temperature operation. The performance of the device was assessed in terms of sensor stability, repeatability, and pattern-recognition capability under long-term operation. As a proof of concept, the electronic nose was applied to the discrimination of Extra Virgin Olive Oil and pomace oil. Repeatability analysis using the Root Mean Squared Error (RMSE) demonstrated stable responses across one month of measurements. Temperature-modulated signals were processed using Principal Component Analysis (PCA) and classified with k-Nearest Neighbors (KNNs) and Multilayer Perceptrons (MLPs), achieving 100% accuracy after selecting the most repeatable sensor. These results highlight the robustness and analytical potential of temperature-modulated digital MOX sensors and demonstrate the feasibility of a compact and highly reproducible electronic-nose platform suitable for complex odor-analysis tasks in real-world applications.

This study evaluated the potential of electronic nose (E-nose) technology to discriminate Spanish-style green table olives spoiled by different bacterial strains. Microbial growth, physicochemical properties, sensory attributes, and volatile organic compounds (VOCs) profiles were analyzed to assess spoilage patterns. The results indicated strain-dependent microbial survival during incubation, with Bacillus cereus and Enterobacter cloacae showing the highest tolerance. Inoculated olives exhibited significant changes in color, texture, pH, phenolic content, and antioxidant activity compared to the Control. Sensory evaluation revealed a reduction in positive attributes and the emergence of defects such as cooked, rancid, and woody aromas, particularly in olives inoculated with B. cereus and Escherichia coli. VOC analysis confirmed these alterations, showing strain-specific increases in aldehydes, phenols, and esters, along with reductions in alcohols and acids. Principal component analysis (PCA) of E-nose data successfully distinguished two groups—spoiled and non-spoiled samples—explaining 84.8% of variance, while Partial Least Squares Discriminant Analysis (PLS-DA) achieved a classification accuracy of 90.4%. These findings highlight the E-nose as a rapid, non-destructive, and reliable tool for detecting bacterial spoilage in table olives, with potential applications in quality control and early spoilage detection.

The presence of toxigenic Aspergillus strains in dried figs and the resulting mycotoxin contamination represent a major challenge for the commercialisation of this product. This study assessed the performance of an electronic nose (E-nose) for detecting Aspergillus spp. contamination in dried figs through changes in volatile organic compound (VOC) profiles. Nine Aspergillus strains, including A. flavus, A. niger, and A. awamori, were inoculated into separate dried fig batches, alongside a control group. VOC profiles were characterised using gas chromatography–mass spectrometry (GC-MS), and E-nose sensor responses were recorded. A strong correlation was observed between sensor signals and the distinct aromatic profiles associated with each Aspergillus species. Linear discriminant analysis (LDA) applied to E-nose data successfully differentiated among Aspergillus species with an accuracy of 84.5%, while external validation achieved recognition rates above 96% for distinguishing healthy from contaminated samples. These findings highlight the potential of E-nose technology, combined with LDA, as a rapid and non-destructive tool for detecting Aspergillus contamination in dried figs. This approach offers a promising solution for enhancing food safety and quality control in the dried fig industry, enabling early detection and mitigation of fungal and mycotoxigenic contamination.

Food fraud, particularly in the olive oil sector, represents a pressing concern within the agri-food industry, with implications for consumer trust and product authenticity. Certified products like Protected Designation of Origin (PDO) Extra Virgin Olive Oil (EVOO) are premium products that undergo strict quality controls, must comply with specific production regulations, and generally have a higher market price. These characteristics make them particularly vulnerable to economically motivated adulteration. In this study, the adulteration of PDO EVOO with Olive Pomace Oil (POO) and Olive Oil (OO) was investigated through a combined analytical approach. A traditional technique, gas chromatography–mass spectrometry (GC-MS) combined with solid-phase microextraction (SPME), was employed alongside an innovative method based on an electronic nose equipped with metal oxide semiconductor (MOX) sensors. GC-MS analysis enabled the identification of characteristic volatile compounds, providing a detailed chemical fingerprint of the different oil samples. Concurrently, the MOX sensor array successfully detected variations in the volatile profiles released by the adulterated oils, demonstrating its potential as a rapid and cost-effective screening tool. The complementary use of both techniques highlighted the reliability of MOX sensors in differentiating authentic PDO EVOO from adulterated samples and underscored their applicability in routine quality control and fraud prevention strategies.

This study investigated the ability of an electronic nose system (E-nose) to detect early signs of fungal contamination in the red plum variety ‘Black Splendor’. We focused on identifying changes in volatile organic compounds (VOCs) that occur with decay. For this purpose, we compared two groups of plums: a control group (healthy plums) and a group inoculated with Monilinia laxa. VOCs from both groups were analyzed and quantified using gas chromatography/mass spectrometry (GC/MS). In parallel, E-nose signals were recorded at two key moments of fungal development: an early and an intermediate phase. The results revealed a strong correlation between E-nose signals and the aromatic profile characteristic of fungal contamination in plums. Linear discriminant analysis (LDA) models, developed from the E-nose data, achieved 100% differentiation between healthy and infected samples. Furthermore, these models discriminated with 100% accuracy between healthy plums and those with incipient contamination. These findings demonstrate that E-nose technology serves as a reliable, non-destructive approach for real-time assessment of plum quality throughout storage.

Food fraud, particularly in the olive oil sector, represents a pressing concern within the agri-food industry, with implications for consumer trust and product authenticity. Certified products like Protected Designation of Origin (PDO) extra virgin olive oil (EVOO) are premium products that undergo strict quality controls, must comply with specific production regulations, and generally have a higher market price. These characteristics make them particularly vulnerable to economically motivated adulteration. In this study, the adulteration of PDO EVOO with olive pomace oil and refined olive oil was investigated through a combined analytical approach. A traditional technique, gas chromatography–mass spectrometry with solid-phase micro-extraction detection (GC-MS SPME), was employed alongside an innovative method based on an electronic nose equipped with metal oxide semiconductor (MOX) sensors. GC-MS analysis enabled the identification of characteristic volatile compounds, providing a detailed chemical fingerprint of the different oil samples. Concurrently, the MOX sensor array successfully detected variations in the volatile profiles released by the adulterated oils, demonstrating its potential as a rapid and cost-effective screening tool. The complementary use of both techniques highlighted the reliability of MOX sensors in differentiating authentic PDO EVOO from adulterated samples and underscored their applicability in routine quality control and fraud prevention strategies.

This study evaluates the application of an electronic nose (E-nose) system as a non-destructive tool for the early detection of Monilinia laxa infection in yellow nectarines (Prunus persica var. nectarine, cv. “Kinolea”) through the analysis of volatile organic compounds (VOCs). Two experimental groups were established: a control group of healthy fruit and a treatment group inoculated with the pathogen. The VOCs emitted by both groups were identified and quantified using gas chromatography-mass spectrometry (GC-MS). Simultaneously, the responses of the E-nose were recorded at three critical stages of fungal development: early, intermediate, and advanced. The electronic nose used consists of a set of 11 commercial metal oxide semiconductor (MOX) sensors. The signals from these sensors showed a strong correlation with the VOC profiles associated with fungal deterioration. Linear discriminant analysis (LDA) models based on E-nose data successfully distinguished between healthy and infected samples with 97% accuracy. Furthermore, the system accurately classified samples into three stages of contamination—control, early infection, and advanced infection—with 96% classification accuracy. These findings demonstrate that E-nose technology is an effective, rapid, and non-invasive method for the real-time monitoring of post-harvest fungal contamination in nectarines, offering significant potential for improving quality control during storage and distribution.