Electronic Nose Based on Carbon Nanocomposite Sensors for Clove Essential Oil Detection

Differentiating True and False Cinnamon: Exploring Multiple Approaches for Discrimination

This study presents a comprehensive literature review that investigates the distinctions between true and false cinnamon. Given the intricate compositions of essential oils (EOs), various discrimination approaches were explored to ensure quality, safety, and authenticity, thereby establishing consumer confidence. Through the utilization of physical-chemical and instrumental analyses, the purity of EOs was evaluated via qualitative and quantitative assessments, enabling the identification of constituents or compounds within the oils. Consequently, a diverse array of techniques has been documented, encompassing organoleptic, physical, chemical, and instrumental methodologies, such as spectroscopic and chromatographic methods. Electronic noses (e-noses) exhibit significant potential for identifying cinnamon adulteration, presenting a rapid, non-destructive, and cost-effective approach. Leveraging their capability to detect and analyze volatile organic compound (VOC) profiles, e-noses can contribute to ensuring authenticity and quality in the food and fragrance industries. Continued research and development efforts in this domain will assuredly augment the capacities of this promising avenue, which is the utilization of Artificial Intelligence (AI) and Machine Learning (ML) algorithms in conjunction with spectroscopic data to combat cinnamon adulteration.

Rapid Detection of Trace Nitro-Explosives under UV Irradiation by Electronic Nose with Neural Networks

The development of an electronic nose (E-nose) for rapid explosive trace detection (ETD) has been extensively studied. However, the extremely low saturated vapor pressure of explosives becomes the major obstacle for E-nose to be applied in practical environments. In this work, we innovatively combine the decomposition characteristics of nitro explosives when exposed to ultraviolet light into gas sensors for detecting explosives, and an E-nose consisting of a SnO₂/WO₃ nanocomposite-based chemiresistive sensor array with an artificial neural network is utilized to identify trace nitro-explosives by detecting their photolysis gas products, rather than the explosive molecules themselves or their saturated vapor. The ultralow detection limits for nitro-explosives can be achieved, and the detection limits toward three representative nitro-explosives of trinitrotoluene, pentaerythritol tetranitrate, and cyclotetramethylene tetranitroamine are as low as 500, 100, and 50 ng, respectively. Moreover, by extracting the features of sensor responses within 15 s, a classification system based on convolutional neural network (CNN) and long short-term memory network (LSTM) is introduced to realize fast and accurate classification. The 5-fold cross-validation results demonstrate that the CNN-LSTM model exhibits the highest classification accuracy of 97.7% compared with those of common classification models. This work realizes the detection of explosives photolysis gases using sensor technology, which provides a unique insight for the classification of trace explosives.

Physicochemical features partially explain olfactory crossmodal correspondences

During the olfactory perception process, our olfactory receptors are thought to recognize specific chemical features. These features may contribute towards explaining our crossmodal perception. The physicochemical features of odors can be extracted using an array of gas sensors, also known as an electronic nose. The present study investigates the role that the physicochemical features of olfactory stimuli play in explaining the nature and origin of olfactory crossmodal correspondences, which is a consistently overlooked aspect of prior work. Here, we answer the question of whether the physicochemical features of odors contribute towards explaining olfactory crossmodal correspondences and by how much. We found a similarity of 49% between the perceptual and the physicochemical spaces of our odors. All of our explored crossmodal correspondences namely, the angularity of shapes, smoothness of textures, perceived pleasantness, pitch, and colors have significant predictors for various physicochemical features, including aspects of intensity and odor quality. While it is generally recognized that olfactory perception is strongly shaped by context, experience, and learning, our findings show that a link, albeit small (6-23%), exists between olfactory crossmodal correspondences and their underlying physicochemical features.

Review on Sensor Array-Based Analytical Technologies for Quality Control of Food and Beverages

Food quality control is an important area to address, as it directly impacts the health of the whole population. To evaluate the food authenticity and quality, the organoleptic feature of the food aroma is very important, such that the composition of volatile organic compounds (VOC) is unique in each aroma, providing a basis to predict the food quality. Different types of analytical approaches have been used to assess the VOC biomarkers and other parameters in the food. The conventional approaches are based on targeted analyses using chromatography and spectroscopies coupled with chemometrics, which are highly sensitive, selective, and accurate to predict food authenticity, ageing, and geographical origin. However, these methods require passive sampling, are expensive, time-consuming, and lack real-time measurements. Alternately, gas sensor-based devices, such as the electronic nose (e-nose), bring a potential solution for the existing limitations of conventional methods, offering a real-time and cheaper point-of-care analysis of food quality assessment. Currently, research advancement in this field involves mainly metal oxide semiconductor-based chemiresistive gas sensors, which are highly sensitive, partially selective, have a short response time, and utilize diverse pattern recognition methods for the classification and identification of biomarkers. Further research interests are emerging in the use of organic nanomaterials in e-noses, which are cheaper and operable at room temperature.

Antifungal activity and aroma persistence of free and encapsulated Cinnamomum cassia essential oil in maize

The objective of this study was to evaluate the chemical composition and antifungal activity of free and encapsulated Cinnamomum cassia essential oil (EO) against Penicillium crustosum, Alternaria alternata, and Aspergillus flavus, and the aroma persistence in maize flour. Trans-cinnamaldehyde (TC) was identified as the major compound (86 %) in the C. cassia EO. The EO was encapsulated by spray-dryer with 45.26 % efficiency using gum arabic (GA) and maltodextrin (MD) in a ratio of 1:1 (m/m). C. cassia EO showed antifungal activity against A. alternata, A. flavus, and P. crustosum, with a minimum inhibitory concentration (MIC) of 0.5 % for both free and standard TC, and 5 % for the encapsulated EO. Fungal growth inhibition was evaluated under exposition to vapors at different concentrations of C. cassia EO and TC standard, with MIC of 6 % and 8 % against P. crustosum, 4 % and 1 % A. alternata, and 4 % A. flavus, respectively. The sensory analysis results of the free and encapsulated C. cassia EO in maize flour showed a significant difference between the treated samples in relation to the standard sample (p < 0.05). The sample with free EO has high aroma intensity persistence, while the samples treated with encapsulated EO were evaluated as being closer to the standard sample. The results suggest that the encapsulated C. cassia EOs can be used as natural alternatives to control fungi in maize flour.

Silver Thread-Based Microfluidic Platform for Detection of Essential Oils Using Impedance Spectroscopy

Essential oils (EOs) have a long tradition of use in the medical and cosmetic fields based on their versatile properties, including fungicidal, antiparasitic, and bactericidal effects. Nowadays, with the development of industry and electronics, EOs are increasingly being used in the agricultural and food industries; health industries, including pharmacy and dental medicine; and as cosmetic enhancements. The purpose of this study is to develop a compact and portable platform for the detection of EO type and the concentration levels using knitted silver threads. The method is based on measuring the variation in values of the electrical parameters of the silver threads using electrochemical impedance spectroscopy (EIS). The impedance of the solutions applied on the testing platform was measured in the frequency range from 1 Hz to 200 kHz. The platform was tested using three types of essential oils: tea tree; clary sage; and cinnamon bark oil. Increasing the concentration of essential oils resulted in increasing the electrical resistance of the platform, decreasing the capacitance, and consequently increasing the impedance. The proposed cost-effective platform can be used for the fast determination of the type and quality of essential oils.

Hydrophobic laser-induced graphene potentiometric ion-selective electrodes for nitrate sensing

Current solid-contact ion-selective electrodes (ISEs) suffer from signal-to-noise drift and short lifespans partly due to water uptake and the development of an aqueous layer between the transducer and ion-selective membrane. To address these challenges, we report on a nitrate ISE based on hydrophobic laser-induced graphene (LIG) coated with a poly(vinyl) chloride-based nitrate selective membrane. The hydrophobic LIG was created using a polyimide substrate and a double lasing process under ambient conditions (air at 23.0 ± 1.0 °C) that resulted in a static water contact angle of 135.5 ± 0.7° (mean ± standard deviation) in wettability testing. The LIG-ISE displayed a Nernstian response of - 58.17 ± 4.21 mV dec^-1 and a limit-of-detection (LOD) of 6.01 ± 1.44 µM. Constant current chronopotentiometry and a water layer test were used to evaluate the potential (emf) signal stability with similar performance to previously published work with graphene-based ISEs. Using a portable potentiostat, the sensor displayed comparable (p > 0.05) results to a US Environmental Protection Agency (EPA)-accepted analytical method when analyzing water samples collected from two lakes in Ames, IA. The sensors were stored in surface water samples for 5 weeks and displayed nonsignificant difference in performance (LOD and sensitivity). These results, combined with a rapid and low-cost fabrication technique, make the development of hydrophobic LIG-ISEs appealing for a wide range of long-term in situ surface water quality applications.

Sensing of Airborne Infochemicals for Green Pest Management: What Is the Challenge?

One of the biggest global challenges for our societies is to provide natural resources to the rapidly expanding population while maintaining sustainable and ecologically friendly products. The increasing public concern about toxic insecticides has resulted in the rapid development of alternative techniques based on natural infochemicals (ICs). ICs (e.g., pheromones, allelochemicals, volatile organic compounds) are secondary metabolites produced by plants and animals and used as information vectors governing their interactions. Such chemical language is the primary focus of chemical ecology, where behavior-modifying chemicals are used as tools for green pest management. The success of ecological programs highly depends on several factors, including the amount of ICs that enclose the crop, the range of their diffusion, and the uniformity of their application, which makes precise detection and quantification of ICs essential for efficient and profitable pest control. However, the sensing of such molecules remains challenging, and the number of devices able to detect ICs in air is so far limited. In this review, we will present the advances in sensing of ICs including biochemical sensors mimicking the olfactory system, chemical sensors, and sensor arrays (e-noses). We will also present several mathematical models used in integrated pest management to describe how ICs diffuse in the ambient air and how the structure of the odor plume affects the pest dynamics.

Sexual pheromone detection using PANI·Ag nanohybrid and PANI/PSS nanocomposite nanosensors

In this study, polyaniline/poly(styrene sulfonate) (PANI/PSS) nanocomposite and polyaniline·silver (PANI·Ag) nanohybrid thin films were obtained in cantilever nanosensors surface. The developed films were characterized in relation to topography, roughness, thickness, height, and structural properties. The topography study revealed that both films have a globular morphology, thickness and height in nanoscale. The gas sensing performance was investigated for sexual pheromone from the neotropical brown stink bug, Euschistus heros (F.). The sensitivities of both nanosensors based on PANI/PSS nanocomposite and PANI·Ag nanohybrid films were similar. The PANI·Ag nanohybrid nanosensor had a limit of detection of less than 3.1 ppq and limit of quantification of 10.05 ppq. The nanosensor layers were analyzed by UV-vis and FTIR showing the incorporation of Ag nanoparticles in the nanohybrid. We found that pheromone compound was adsorbed in sensing layer resulting in a reduction in the resonance frequency. The detection mechanism help us understand the good results of LOD, LOQ, sensitivity, selectivity and repeatability. The presented device has great potential for detection of the sexual pheromone from E. heros.

E-Tongues/Noses Based on Conducting Polymers and Composite Materials: Expanding the Possibilities in Complex Analytical Sensing

Conducting polymers (CPs) are extensively studied due to their high versatility and electrical properties, as well as their high environmental stability. Based on the above, their applications as electronic devices are promoted and constitute an interesting matter of research. This review summarizes their application in common electronic devices and their implementation in electronic tongues and noses systems (E-tongues and E-noses, respectively). The monitoring of diverse factors with these devices by multivariate calibration methods for different applications is also included. Lastly, a critical discussion about the enclosed analytical potential of several conducting polymer-based devices in electronic systems reported in literature will be offered.

Classification and Identification of Essential Oils from Herbs and Fruits Based on a MOS Electronic-Nose Technology

The frequent occurrence of adulterated or counterfeit plant products sold in worldwide commercial markets has created the necessity to validate the authenticity of natural plant-derived palatable products, based on product-label composition, to certify pricing values and for regulatory quality control (QC). The necessity to confirm product authenticity before marketing has required the need for rapid-sensing, electronic devices capable of quickly evaluating plant product quality by easily measurable volatile (aroma) emissions. An experimental MAU-9 electronic nose (e-nose) system, containing a sensor array with 9 metal oxide semiconductor (MOS) gas sensors, was developed with capabilities to quickly identify and classify volatile essential oils derived from fruit and herbal edible-plant sources. The e-nose instrument was tested for efficacy to discriminate between different volatile essential oils present in gaseous emissions from purified sources of these natural food products. Several chemometric data-analysis methods, including pattern recognition algorithms, principal component analysis (PCA), and support vector machine (SVM) were utilized and compared. The classification accuracy of essential oils using PCA, LDA and QDA, and SVM methods was at or near 100%. The MAU-9 e-nose effectively distinguished between different purified essential oil aromas from herbal and fruit plant sources, based on unique e-nose sensor array responses to distinct, essential-oil specific mixtures of volatile organic compounds (VOCs).