Identification of Tobacco Types and Cigarette Brands Using an Electronic Nose Based on Conductive Polymer/Porphyrin Composite Sensors

Discriminative analysis of aroma profiles in diverse cigar products varieties through integrated sensory evaluation, GC-IMS and E-nose

Cigars, treasured for their rich aromatic profiles, occupy a notable segment in the global consumer market. The objective of this study was to characterize the volatile aroma compounds that shape the flavor profiles of six distinct varieties of Great Wall cigars, contributing to the understanding of cigar aroma analysis. Utilizing HS-GC-IMS and sensory evaluation, the study discerned the aroma profiles of GJ No. 6 (GJ), Animal from the Chinese zodiac (SX), Range Rover No. 3 Classic (JD), Miracle 132 (QJ), Sheng Shi No. 5 (SS), and Red 132 (HS) cigars. The analysis uncovered a spectrum of characteristic aromas, including tobacco, creaminess, cocoa, leather, baking, herbaceous, leathery, woodsy, and fruity notes. A total of 88 compounds were identified, categorized into 11 chemical classes, with their quantities varying among the cigars in a descending order of QJ, JD, GJ, SS, HS, and SX. 24 compounds, such as 2-heptanone, n-butanol, 2,6-dimethylpyrazine and 2-furfuryl methyl sulfide were considered as key differential components. The volatile components were effectively differentiated using principal component analysis (PCA), orthogonal partial least squares-discriminant analysis (OPLS-DA), and cluster analysis, revealing correlations between sensory attributes, key components, and electronic nose (E-nose). This research introduces a novel method for analyzing volatile aroma components in cigars, offering insights to enhance cigar quality and to foster the development of new products with unique aroma profiles.

Applications of an Electrochemical Sensory Array Coupled with Chemometric Modeling for Electronic Cigarettes

This study investigates the application of an eNose (electrochemical sensory array) device as a rapid and cost-effective screening tool to detect increasingly prevalent counterfeit electronic cigarettes, and those to which potentially hazardous excipients such as vitamin E acetate (VEA) have been added, without the need to generate and test the aerosol such products are intended to emit. A portable, in-field screening tool would also allow government officials to swiftly identify adulterated electronic cigarette e-liquids containing illicit flavorings such as menthol. Our approach involved developing canonical discriminant analysis (CDA) models to differentiate formulation components, including e-liquid bases and nicotine, which the eNose accurately identified. Additionally, models were created using e-liquid bases adulterated with menthol and VEA. The eNose and CDA model correctly identified menthol-containing e-liquids in all instances but were only able to identify VEA in 66.6% of cases. To demonstrate the applicability of this model to a commercial product, a Virginia Tobacco JUUL product was adulterated with menthol and VEA. A CDA model was constructed and, when tested against the prediction set, it was able to identify samples adulterated with menthol 91.6% of the time and those containing VEA in 75% of attempts. To test the ability of this approach to distinguish commercial e-liquid brands, a model using six commercial products was generated and tested against randomized samples on the same day as model creation. The CDA model had a cross-validation of 91.7%. When randomized samples were presented to the model on different days, cross-validation fell to 41.7%, suggesting that interday variability was problematic. However, a subsequently developed support vector machine (SVM) identification algorithm was deployed, increasing the cross-validation to 84.7%. A prediction set was challenged against this model, yielding an accuracy of 94.4%. Altered Elf Bar and Hyde IQ formulations were used to simulate counterfeit products, and in all cases, the brand identification model did not classify these samples as their reference product. This study demonstrates the eNose's capability to distinguish between various odors emitted from e-liquids, highlighting its potential to identify counterfeit and adulterated products in the field without the need to generate and test the aerosol emitted from an electronic cigarette.

Quality characterization of tobacco flavor and tobacco leaf position identification based on homemade electronic nose

A set of nine unique tobacco extract samples was analyzed using a self-developed electronic nose (E-nose) system, a commercial E-nose, and gas chromatography-mass spectrometry (GC-MS). The evaluation employed principal component analysis, statistical quality control, and soft independent modeling of class analogies (SIMCA). These multifaceted statistical methods scrutinized the collected data. Subsequently, a quality control model was devised to assess the stability of the sample quality. The results showed that the custom E-nose system could successfully distinguish between tobacco extracts with similar odors. After further training and the development of a quality control model for accepted tobacco extracts, it was possible to identify samples with normal and abnormal quality. To further validate our E-nose and extend its use within the tobacco industry, we collected and accurately classified the flavors of different tobacco leaf positions, with a remarkable accuracy rate of 0.9744. This finding facilitates the practical application of our E-nose system for the efficient identification of tobacco leaf positions.

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.

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.

Intelligent Detection and Odor Recognition of Cigarette Packaging Paper Boxes Based on a Homemade Electronic Nose

The printing process of box packaging paper can generate volatile organic compounds, resulting in odors that impact product quality and health. An efficient, objective, and cost-effective detection method is urgently needed. We utilized a self-developed electronic nose system to test four different cigarette packaging paper samples. Employing multivariate statistical methods like Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Statistical Quality Control (SQC), and Similarity-based Independent Modeling of Class Analogy (SIMCA), we analyzed and processed the collected data. Comprehensive evaluation and quality control models were constructed to assess sample stability and distinguish odors. Results indicate that our electronic nose system rapidly detects odors and effectively performs quality control. By establishing models for quality stability control, we successfully identified samples with acceptable quality and those with odors. To further validate the system's performance and extend its applications, we collected two types of cigarette packaging paper samples with odor data. Using data augmentation techniques, we expanded the dataset and achieved an accuracy rate of 0.9938 through classification and discrimination. This highlights the significant potential of our self-developed electronic nose system in recognizing cigarette packaging paper odors and odorous samples.

Design of a Multisensory Device for Tomato Volatile Compound Detection Based on a Mixed Metal Oxide-Electrochemical Sensor Array and Optical Reader

Insufficient control of tomato ripening before harvesting and infection by fungal pests produce large economic losses in world tomato production. Aroma is an indicative parameter of the state of maturity and quality of the tomato. This study aimed to design an electronic system (TOMATO-NOSE) consisting of an array of 12 electrochemical sensors, commercial metal oxide semiconductor sensors, an optical camera for a lateral flow reader, and a smartphone application for device control and data storage. The system was used with tomatoes in different states of ripeness and health, as well as tomatoes infected with Botrytis cinerea. The results obtained through principal component analysis of the olfactory pattern of tomatoes and the reader images show that TOMATO-NOSE is a good tool for the farmer to control tomato ripeness before harvesting and for the early detection of Botrytis cinerea.

Combination of the electronic nose with microbiology as a tool for rapid detection of Salmonella

Salmonella is one of the most important foodborne pathogens and its analysis in raw and processed products is mandatory in the food industry. Although microbiological analysis is the standard practice for Salmonella determination, these assays are commonly laborious and time-consuming, thus, alternative techniques based on easy operation, few manipulation steps, low cost, and reduced time are desirable. In this paper, we demonstrate the use of an e-nose based on ionogel composites (ionic liquid + gelatine + Fe3O4 particles) as a complementary tool for the conventional microbiological detection of Salmonella. We used the proposed methodology for differentiating Salmonella from Escherichia coli, Pseudomonas fluorescens, Pseudomonas aeruginosa, and Staphylococcus aureus in nonselective medium: pre-enrichment in brain heart infusion (BHI) (incubation at 35 °C, 24 h) and enrichment in tryptone soy agar (TSA) (incubation at 35 °C, 24 h), whereas Salmonella differentiation from E. coli and P. fluorescens was also evaluated in selective media, bismuth sulfite agar (BSA), xylose lysine deoxycholate agar (XLD), and brilliant green agar (BGA) (incubation at 35 °C, 24 h). The obtained data were compared by principal component analysis (PCA) and different machine learning algorithms: multilayer perceptron (MLP), linear discriminant analysis (LDA), instance-based (IBk), and Logistic Model Trees (LMT). For the nonselective media, under optimized conditions, taking merged data of BHI + TSA (total incubation time of 48 h), an accuracy of 85% was obtained with MLP, LDA, and LMT, while five separated clusters were presented in PCA, each cluster corresponding to a bacterium. In addition, for evaluation of the e-nose for discrimination of Salmonella using selective media, considering the combination of BSA + XLD and total incubation of 72 h, the PCA showed three separated and well-defined clusters corresponding to Salmonella, E. coli, and P. fluorescens, and an accuracy of 100% was obtained for all classifiers.

A Paper-Based Biomimetic Sensing Device for the Discrimination of Original and Fraudulent Cigarette Brands Using Mixtures of MoS2 Quantum Dots and Organic Dyes

In this study, we investigated the combined effects of MoS₂ QDs' catalytic properties and the colorimetric responses of organic reagents to create a sniffing device based on the sensor array concept of the mammalian olfactory system. The aim was to differentiate the volatile organic compounds (VOCs) present in cigarette smoke. The designed optical nose device was utilized for the classification of various cigarette VOCs. Unsupervised Principal Component Analysis (PCA) and supervised Linear Discriminant Analysis (LDA) methods were employed for data analysis. The LDA analysis showed promising results, with 100% accuracy in both training and cross-validation. To validate the sensor's performance, we assessed its ability to discriminate between five cigarette brands, achieving 100% accuracy in the training set and 82% in the cross-validation set. Additionally, we focused on studying four popular Iranian cigarette brands (Bahman Kootah, Omega, Montana Gold, and Williams), including fraudulent samples. Impressively, the developed sensor array achieved a perfect 100% accuracy in distinguishing these brands and detecting fraud. We further analyzed a total of 126 cigarette samples, including both original and fraudulent ones, using LDA with a matrix size of (126 × 27). The resulting LDA model demonstrated an accuracy of 98%. Our proposed analytical procedure is characterized by its efficiency, affordability, user-friendliness, and reliability. The selectivity exhibited by the developed sensor array positions it as a valuable tool for differentiating between original and counterfeit cigarettes, thus aiding in border control efforts worldwide.

Toward an Electronic Tongue Based on Surfactant-Stabilized Chemosensory Microparticles with a Dual Detection Mode

We propose a novel type of electronic tongue based on four types of monodispersed chemosensory microparticles (MPs) with a lipophilic core stabilized by a nonionic poloxamer surfactant. The lipophilic core composition was designed to achieve cross-sensitivity toward various ions and to enable spectrophotometric and/or spectrofluorimetric detection. Thus, generic anion-selective MPs, generic cation-selective MPs, as well as two types of metalloporphyrin-based MPs were fabricated and their morphology was characterized. Next, their differential sensing ability toward the discrimination of five l-tyrosine derivatives (dopamine, 3,4-dihydroxyphenylacetic acid, 3,4-dihydroxy-l-phenylalanine, normetanephrine, 4-hydroxy-3-methoxymandelic acid) was assessed. Comparison with the respective ion-selective electrode (ISE) responses was also provided to verify if the results from the potentiometric e-tongue correspond to outputs of the developed MP optode array. The recognition of dietary supplements containing l-tyrosine (l-Tyr) derivatives with the use of the MP-based e-tongue proved the potential of the developed sensing assay in pharmaceutical analysis.

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.

Development of a Low-Cost Portable Electronic Nose for Cigarette Brands Identification

In China, the government and the cigarette industry yearly lose millions in sales and tax revenue because of imitation cigarettes. Usually, visual observation is not enough to identify counterfeiting. An auxiliary analytical method is needed for cigarette brands identification. To this end, we developed a portable, low-cost electronic nose (e-nose) system for brand recognition of cigarettes. A gas sampling device was designed to reduce the influence caused by humidity fluctuation and the volatile organic compounds (VOCs) in the environment. To ensure the uniformity of airflow distribution, the structure of the sensing chamber was optimized by computational fluid dynamics (CFD) simulations. The e-nose system is compact, portable, and lightweight with only 15 cm in side length and the cost of the whole device is less than $100. Results from the machine learning algorithm showed that there were significant differences between 5 kinds of cigarettes we tested. Random Forest (RF) has the best performance with accuracy of 91.67% and K Nearest Neighbor (KNN) has the accuracy of 86.98%, which indicated that the e-nose was able to discriminate samples. We believe this portable, cheap, reliable e-nose system could be used as an auxiliary screen technique for counterfeit cigarettes.

A one-dimensional manganese(III)-porphyrin coordination polymer: crystal structure and photophysical properties

A novel manganese(III)-porphyrin complex, namely, catena-poly[[chloridomanganese(III)]-μ₂-5,10,15,20-tetrakis(pyridin-3-yl)-21H,23H-porphinato(2-)-κ⁵N²¹,N²²,N²³,N²⁴:N⁵], [MnCl(C₄₀H₂₄N₈)]_n, 1, was prepared by the hydrothermal reaction of manganese chloride with 5,10,15,20-tetrakis(pyridin-3-yl)-21H,23H-porphine. The crystal structure was determined by single-crystal X-ray diffraction. The porphyrin macrocycle exhibits a saddle-like distortion geometry. The Mn^III atom has a six-coordination geometry. Each porphyrin unit links to two neighbouring units to yield a one-dimensional coordination polymer. These chains are further interlinked by hydrogen bonds to form a two-dimensional network. The complex shows red photoluminescence emission bands in ethanol solution, which can be attributed to ligand-to-ligand charge transfer (LLCT) accompanied by partial metal-to-ligand charge transfer (MLCT), as revealed by TDDFT calculations.

Fluorescent carbon nanodots-based artificial tongue for determining and discriminating cigarettes

Smoking can cause cigarette-related diseases and pose serious threat to human health. Its dangers can be effectively controlled by discriminating cigarettes and monitoring cigarette quality. Herein, a kind of artificial tongue technique based on the indicator displacement assay (IDA) was developed and applied to determine and discriminate cigarettes and their main ingredients (saccharides, organic acids and nicotine). This method was constructed using carbon nanodots (CDs) as a fluorescent indicator and various concentrations of silver ion (Ag⁺) as a fluorescent regulator. A cigarette extracting solution was prepared to interact with an artificial tongue and produce fluorescence fingerprints. Twenty-nine kinds of cigarettes can be well discriminated in terms of category (flue-cured cigarette, blended cigarette and cigar), brand, origin (domestic or foreign cigarettes) after processing and visualizing the response fingerprints. The artificial tongue fluorescent sensor array can sensitively detect nine kinds of tobacco-based chemical ingredients and discriminate them between different concentrations. The as-prepared fluorescent artificial tongue is a promising platform for monitoring cigarette quality and controlling the harmful effects of smoking because of its cheap material requirements, simple operation, and good performance.

Quality Evaluation of Green and Dark Tea Grade Using Electronic Nose and Multivariate Statistical Analysis

Aroma assessment remains difficult and uncertain in the present sensory assessment system. It is highly desirable to develop a new assessment method to discriminate the quality of various teas in the tea market. In the present work, based on linear discriminant analysis and principal component analysis, the aroma of dry and wet samples of different Xi-hu Longjing and Pu-erh teas were tested and differentiated by electronic noses (e-nose). The results confirm that e-nose can discriminate different priced Xi-hu Longjing tea samples in the range of 80-800 RMB/500 g and varying storage years of Pu-erh tea samples. Furthermore, for the detection of both dry and wet samples of Longjing and Pu-erh teas, the results reveal that all samples have specific aroma characteristics that e-nose can recognize. More importantly, contribution analysis in sensors indicates that nitrogen oxides, methane and alcohols are the characteristic components that contribute to the fragrances of different priced Xi-hu Longjing teas, while nitrogen oxides, aromatic benzene and amines make the fragrances of Pu-erh teas with different storage years disparate. PRACTICAL APPLICATION: This work demonstrates that e-nose can rapidly distinguish tea products with different price levels and varying storage years. With the advantages of ease of use, high portability and flexibility, e-nose will be widely expanded and applied in refined processing and the development of flavored foods.

Electrochemical sensing of nicotine using CuWO4 decorated reduced graphene oxide immobilized glassy carbon electrode

A novel and selective electrochemical sensing of nicotine is studied using copper tungstate decorated reduced graphene oxide nanocomposite (CuWO₄/rGO) nafion (Nf) immobilized GC electrode (GCE). The CuWO₄/rGO nanocomposite is synthesized using sonication method and characterized by HR-TEM (High resolution transmission electron microscopy), SEM (Scanning electron microscopy), FT-IR (Fourier transform infrared spectroscopy), SAED (Selected area of electron diffraction pattern), XRD (X-ray diffraction), Raman spectroscopy, Thermo gravimetric analysis (TGA) and EDX (Energy dispersive X-ray diffraction) techniques. The CuWO₄/rGO/Nf immobilized GCE shows better electrocatalytic response for the detection of nicotine as compared to bare GCE. A better selectivity and sensitivity is achieved using CuWO₄/rGO/Nf immobilized GCE to detect 0.1 µM nicotine in the presence of 100-fold excess concentrations of different interferents. The present CuWO₄/rGO/Nf immobilized GCE electrochemical sensor exhibits an ample range of sensing from 0.1 µM to 0.9 µM and the low detection limit is found to be 0.035 µM (S/N = 3). Comparable results are achieved for the determination of nicotine in various real samples such as cigarettes (Gold flake and Wills) and urine samples with improved recoveries.

Intelligent Control of Bulk Tobacco Curing Schedule Using LS-SVM- and ANFIS-Based Multi-Sensor Data Fusion Approaches

The bulk tobacco flue-curing process is followed by a bulk tobacco curing schedule, which is typically pre-set at the beginning and might be adjusted by the curer to accommodate the need for tobacco leaves during curing. In this study, the controlled parameters of a bulk tobacco curing schedule were presented, which is significant for the systematic modelling of an intelligent tobacco flue-curing process. To fully imitate the curer’s control of the bulk tobacco curing schedule, three types of sensors were applied, namely, a gas sensor, image sensor, and moisture sensor. Feature extraction methods were given forward to extract the odor, image, and moisture features of the tobacco leaves individually. Three multi-sensor data fusion schemes were applied, where a least squares support vector machines (LS-SVM) regression model and adaptive neuro-fuzzy inference system (ANFIS) decision model were used. Four experiments were conducted from July to September 2014, with a total of 603 measurement points, ensuring the results’ robustness and validness. The results demonstrate that a hybrid fusion scheme achieves a superior prediction performance with the coefficients of determination of the controlled parameters, reaching 0.9991, 0.9589, and 0.9479, respectively. The high prediction accuracy made the proposed hybrid fusion scheme a feasible, reliable, and effective method to intelligently control over the tobacco curing schedule.

Photoelectric and flexible poly(styrene-b-ethylene/butylene-b-styrene)-zinc porphyrin–graphene hybrid composite: synthesis, performance, and mechanism

Stretchable and flexible photoelectric materials are highly desirable for the development of artificial intelligence products.