Search Results (477)

A chili pepper variety and origin detection system that integrates a field-programmable gate array (FPGA) with an electronic nose (e-nose) is proposed in this paper to address the issues of variety confusion and origin ambiguity in the chili pepper market. The system uses the AIRSENSE PEN3 e-nose from Germany to collect gas data from thirteen different varieties of chili peppers and two specific varieties of chili peppers originating from seven different regions. Model training is conducted via the proposed lightweight convolutional neural network ChiliPCNN. By combining the strengths of a convolutional neural network (CNN) and a multilayer perceptron (MLP), the ChiliPCNN model achieves an efficient and accurate classification process, requiring only 268 parameters for chili pepper variety identification and 244 parameters for origin tracing, with 364 floating-point operations (FLOPs) and 340 FLOPs, respectively. The experimental results demonstrate that, compared with other advanced deep learning methods, the ChiliPCNN has superior classification performance and good stability. Specifically, ChiliPCNN achieves accuracy rates of 94.62% in chili pepper variety identification and 93.41% in origin tracing tasks involving Jiaoyang No. 6, with accuracy rates reaching as high as 99.07% for Xianjiao No. 301. These results fully validate the effectiveness of the model. To further increase the detection speed of the ChiliPCNN, its acceleration circuit is designed on the Xilinx Zynq7020 FPGA from the United States and optimized via fixed-point arithmetic and loop unrolling strategies. The optimized circuit reduces the latency to 5600 ns and consumes only 1.755 W of power, significantly improving the resource utilization rate and processing speed of the model. This system not only achieves rapid and accurate chili pepper variety and origin detection but also provides an efficient and reliable intelligent agricultural management solution, which is highly important for promoting the development of agricultural automation and intelligence. Full article

With the development of various new types of instrumental aroma sensing technologies, there is a need for methodologies that help developers and users evaluate the performance of the different devices. This study introduces a simple method that uses standard coffee beverages, reproducible worldwide, thus allowing users to compare aroma sensing devices and technologies globally. Eight different variations of commercial coffee capsules were used to brew espresso coffees (40 mL), consisting of either Arabica coffee or a blend of Robusta and Arabica coffee, covering a wide range of sensory attributes. The AlphaMOS Astree electronic tongue (equipped with sensors based on chemically modified field-effect transistor technology) and the AlphaMOS Heracles NEO and the Volatile Scout3 electronic noses (both using separation technology based on gas chromatography) were used to describe the taste and odor profiles of the freshly brewed coffee samples and also to compare them to the various sensory characteristics declared on the original packaging, such as intensity, roasting, acidity, bitterness, and body. Linear discriminant analysis (LDA) results showed that these technologies were able to classify the samples similarly to the pattern of the coffees based on the human sensory characteristics. In general, the arrangement of the different coffee types in the LDA results—i.e., the similarities and dissimilarities in the types based on their taste or smell—was the same in the case of the Astree electronic tongue and the Heracles electronic nose, while slightly different arrangements were found for the Scout3 electronic nose. The results of the Astree electronic tongue and those of the Heracles electronic nose showed the taste and smell profiles of the decaffeinated coffees to be different from their caffeinated counterparts. The Heracles and Scout3 electronic noses provided high accuracies in classifying the samples based on their odor into the sensory classes presented on the coffee capsules’ packaging. Despite the technological differences in the investigated devices, the introduced coffee test could assess the similarities in the taste and odor profiling capacities of the aroma fingerprinting technologies. Since the coffee capsules used for the test can be purchased all over the world in the same quality, these coffees can be used as global standard samples during the comparison of different devices applying different measurement technologies. The test can be used to evaluate instrumentational and data analytical developments worldwide and to assess the potential of novel, cost-effective, accurate, and rapid solutions for quality assessments in the food and beverage industry. Full article

The early detection of liver cirrhosis (LC) is crucial due to its high morbidity and mortality in advanced stages. Reliable, non-invasive diagnostic tools are essential for timely intervention. Exhaled human breath, reflecting metabolic changes, offers significant potential for disease diagnosis. This paper focuses on the emerging role of sensor array-based volatile organic compounds (VOCs) analysis of exhaled breath, particularly using electronic nose (e-nose) technology to differentiate LC patients from healthy controls (HCs). This study included 55 participants: 27 LC patients and 28 HCs. Sensor’s measurement data were analyzed using machine learning techniques, such as principal component analysis (PCA), discriminant function analysis (DFA), and support vector machines (SVMs) that were utilized to uncover meaningful patterns and facilitate accurate classification of sensor-derived information. The diagnostic accuracy was thoroughly assessed through receiver operating characteristic (ROC) curve analysis, with specific emphasis on assessing sensitivity and specificity metrics. The e-nose effectively distinguished LC from HC, with PCA explaining 92.50% variance and SVMs achieving 100% classification accuracy. This study demonstrates the significant potential of e-nose technology towards VOCs analysis in exhaled breath, as a valuable tool for LC diagnosis. It also explores feature extraction methods and suitable algorithms for effectively distinguishing between LC patients and controls. This research provides a foundation for advancing breath-based diagnostic technologies for early detection and monitoring of liver cirrhosis. Full article

Assessing the quality of fresh produce is essential to ensure a safe and satisfactory product. Methods to monitor the quality of fresh produce exist; however, they are often expensive, time-consuming, and sometimes require the destruction of the sample. Electronic Nose (E-Nose) technology has been established to track the ripeness, spoilage, and quality of fresh produce. Our study developed a freshness monitoring system for tomatoes, combining E-Nose technology with storage condition monitoring, color analysis, and weight-loss tracking. Different post-purchase scenarios were investigated, focusing on the influence of temperature and mechanical damage on shelf life. Support Vector Classifier (SVC) and k-Nearest Neighbor (kNN) were applied to classify storage scenarios and storage days, while Support Vector Regression (SVR) and kNN regression were used for predicting storage days. By using a data fusion approach with Linear Discriminant Analysis (LDA), the SVC achieved an accuracy of 72.91% in predicting storage days and an accuracy of 86.73% in distinguishing between storage scenarios. The kNN yielded the best regression results, with a Mean Absolute Error (MAE) of 0.841 days and a coefficient of determination of 0.867. The results highlight the method’s potential to predict storage scenarios and storage days, providing insight into the product’s remaining shelf life. Full article

Benzene, toluene, ethylbenzene, and xylene (BTEX) are widespread volatile organic compounds commonly present in fuels and various industrial materials. Their release into the atmosphere significantly contributes to air pollution, prompting strict regulatory concentration limits in ambient air. In this work, we introduce Multiphoton Electron Extraction Spectroscopy (MEES) as an innovative technique for the sensitive, selective, and online detection and quantitation of BTEX compounds under ambient conditions. MEES employs tunable UV laser pulses to induce the resonant ionization of target molecules under a high electrical field, with subsequent measurement of the generated photocurrent. We now demonstrate the method’s ability to detect BTEX in ambient air, at part-per-trillion (ppt) concentration range, providing distinct spectral signatures for each compound, including individual xylene isomers. The technique represents a significant advancement in BTEX monitoring, with potential applications in environmental sensing and industrial air quality control. Full article

The evaluation of aroma-active profiles in dry-cured hams is crucial for determining quality, flavor, consumer acceptance, and economic value. This study characterized the volatile compounds in five varieties of dry-cured hams using gas chromatography-mass spectrometry-olfactometry (GC-MS-O) and an electronic nose (E-Nose). In total, 78 volatile compounds were identified across five varieties of dry-cured hams. A total of 29 compounds were recognized as aroma-active compounds. Odor description, intensity, and hedonic assessment were employed to evaluate these compounds. Black Hoof Cured Ham and Special-grade Xuan-Zi Ham contained higher levels of favorable compounds such as nonanal, 5-butyldihydro-2(3H)-furanone, and 2,6-dimethylpyrazine, contributing to sweet and popcorn-like notes. In contrast, Fei-Zhong-Wang Ham and Liang-Tou-Wu Ham exhibited higher proportions of off-odor compounds with lower hedonic scores. A principal component analysis clearly separated the five hams based on their aroma-active profiles, and a correlation analysis between E-Nose sensor responses and GC-MS-O data demonstrated a strong discriminatory ability for specific samples. These findings offer valuable insights into the chemical and sensory differentiation of dry-cured hams and provide a scientific basis for quality control, product development, and future improvements in E-Nose sensor design and intelligent aroma assessment. Full article

This study investigated aroma changes in Hui Li red Sichuan pepper across five different harvesting times within their typical optimum period based on 24 traditional solar terms, employing sensory evaluation, electronic nose (E-nose), gas chromatography-ion mobility spectrometry (GC-IMS) combined with relative odour activity value (ROAV) and partial least squares discriminant analysis (PLS-DA). Sensory analysis indicated that peppers were characterised by green, citrus, minty, sweet, woody, and peppery numbing aroma attributes. E-nose revealed the greatest aroma difference in peppers occurred between the early and late optimum harvest stages. GC-IMS identified 71 volatile compounds, with esters being the most abundant. Six key compounds identified were crucial for distinguishing peppers harvested at different times. Findings provided a valuable contribution to decide the optimal harvest window for Hui Li red Sichuan peppers, maximising their applications in the seasoning industry. Full article

Cyprinus carpio, Parabramis pekinensis, Aristichthys nobilis, and Lateolabrax maculatus were systematically evaluated as crucial components of Chinese aquaculture with substantial market demand. Texture profile analysis (TPA) showed C. carpio had maximal hardness, while L. maculatus displayed optimal elasticity. Nutrient composition analysis revealed that the highest crude protein content was identified in L. maculatus, while a higher crude lipid level was recorded in C. carpio. Fatty acid profiling established L. maculatus as a superior source of monounsaturated fatty acids (MUFAs), whereas P. pekinensis was distinguished by its polyunsaturated fatty acid (PUFA) content. Volatile compounds were comprehensively analyzed using an electronic nose (e-nose) coupled with HS-SPME-GC-MS, resulting in the identification of 59 flavor compounds. Molecular docking demonstrated that hydrogen bonding and π–π stacking were identified as critical mechanisms governing flavor perception. These findings offer valuable information that can support improvements in aquaculture management practices and help inform consumer choices regarding fish quality. Full article

In recent years, the consumers’ demand for healthy foods has been increased. To address the dietary related diseases, the food products enriched with mushroom or colored rice were promoted. The effects of Tricholoma matsutake powder and colored rice flour on baking quality and volatile aroma compound of cookies were investigated. Texture analyzer, and electronic nose (e-nose) were used to analyze the physicochemical, structural, and digestibility properties and volatile aroma compound of cookie. With the content of Tricholoma matsutake powder and colored rice flour increased, the hardness and free amino acid content increased. Cookie in terms of weaker network structure, relatively crispy cookie texture, and better in vitro digestion activity was obtained with appropriate amount replacement. The cookie sample contained with 5% Tricholoma matsutake and 20% red rice exhibited acceptable hardness and lowest starch hydrolysis rate. The volatile aroma compounds were also affected by the wheat flour substitution. The results indicated that Tricholoma matsutake powder and colored rice flour substitution improved the baking quality of cookie. Full article

With the rising demand for improved living standards and environmental protection, malodor pollution has emerged as a critical concern for both the public and regulatory authorities. Accurate prediction of malodor gas composition is essential for effective environmental monitoring and safety management. However, existing online malodor detection systems often suffer from short-term sensor drift, compromising their accuracy and long-term stability. To address these challenges, this study proposes an advanced electronic nose (e-nose) detection framework based on a time series data analysis. This study presents a novel approach utilizing a multi-channel sensor array for gas sampling, which establishes a robust mapping relationship between sensor response patterns and gas concentration distributions. To address the challenges of sensor drift and enhance system stability, we propose an innovative Encoder-Decoder architecture IED-CNN-LSTM incorporating external compensation mechanisms. Experimental results demonstrate that the proposed IED-CNN-LSTM model outperforms conventional methods significantly in both prediction accuracy and long-term stability. The framework achieves enhanced feature extraction from sensor time series data, enabling more precise and reliable detection of malodorous compounds. This research contributes an effective solution for real-time environmental monitoring applications while offering substantial improvements in both performance metrics and practical implementation for industrial and regulatory scenarios. Full article

Field-based diagnostic technologies which aid in the early detection of bovine respiratory disease (BRD) are of great need, given the rising attention related to animal welfare and antimicrobial stewardship. This induced BRD study followed 12 Holstein calves through pre-challenge (day 1–3) and post-challenge (day 6–13) periods with daily sampling of nasal secretions with nasal swabs and expired air with air collection bags for determination of BRD status by use of an electronic nose (eNose). Animals were challenged with bovine herpes virus-1 (BHV-1) on day 3 following sample collection and Mannheimia haemolytica on day 5. Results demonstrated a high degree of accuracy for the eNose in correctly classifying pre-challenge samples for nasal swabs (93.5%) and expired air (96.8%). Post-challenge correct classification by the eNose was 97.8% for nasal swabs and 72.5% for expired air samples. Logistical regression was used to determine the probability of agreement between eNose classification and actual animal BRD status by study day. The largest discrepancy between nasal swab and expired air samples fell on days 6 and 7, immediately following the bacterial challenge. The eNose demonstrated potential as a field-based diagnostic tool for the detection of BRD with nasal swabs as the optimal sample type. Full article

The rapid detection and quantification of microbial quantity and aflatoxin are crucial for food safety and quality. In order to achieve rapid detection, nutmeg with mildew, but with difficult-to-observe mildew characteristics, was selected as the research object. Its intrinsic component (dehydrodiisoeugenol) and exogenous noxious substances (the total number of colonies and aflatoxin B₁) were determined to clarify their changes during the mold process. Subsequently, electronic nose (E-nose) was employed to analyze the odor of nutmeg and was combined with six machine learning algorithms to establish a classification model for samples with different degrees of mold. Finally, three algorithms were chosen as the preferred options to establish the prediction models of indicator content, which can not only identify whether nutmeg is edible but also measure each index. The results demonstrate the enormous potential of E-nose for real-time detection for assessing food safety. In terms of qualitative analysis, the established classification model can achieve a more than 90% true positive rate, suggesting that E-nose could identify early mildew. In quantitative analysis, E-nose combined with Back Propagation Neural Network achieved the highest prediction accuracy, since the correlation coefficient between the predicted value and the measured value of aflatoxin B₁ is 0.9776, the TAMC is 0.9443, and the TYMC is 0.9685. This study provides a reference for the rapid and comprehensive quality evaluation of mildew-prone nutmeg, and it confirms that E-nose can be applied as a quick and simple technology. Full article

The evaluation of region-specific aroma characteristics in green tea remains critical for quality control. This study systematically analyzed eight Tongcheng Xiaohua tea samples (standard and premium batches) originating from four distinct regions using sensory analysis, electronic nose (E-nose), headspace solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME-GC-MS), and chemometrics. The E-nose results demonstrated that the volatile characteristics of Tongcheng Xiaohua tea exhibit distinct geographical signatures, confirming the regional specificity of its aroma. HS-SPME-GC-MS identified 66 volatile metabolites across samples, with 18 key odorants (OAV > 1) including linalool, geraniol, (Z)-jasmone, and β-ionone driving aroma profiles. The partial least squares–discriminant analysis (PLS-DA) model, combined with variable importance in projection (VIP) scores and OAV, identified seven compounds that effectively differentiate the origins, among which α-pinene and β-cyclocitral emerged as novel markers imparting unique regional characteristics. Further comparative analysis between standard and premium grades revealed 2-methyl butanal, 3-methyl butanal, and dimethyl sulfide as main differential metabolites. Notably, the influence of geographical origin on metabolite profiles was found to be more significant than batch effects. These findings establish a robust analytical framework for origin traceability, quality standardization, and flavor optimization in tea production, providing valuable insights for the tea industry. Full article

Sepsis is a severe systemic condition due to an extreme response of the body to an infection. It is responsible for a significant number of deaths worldwide, and is still difficult to diagnose early. In this study, a system was developed for exhaled breath sampling in mechanically ventilated patients at the intensive care unit (ICU), together with a custom-made electronic nose (e-Nose) device for detecting sepsis in exhaled breath. The diagnostic performance of this system was evaluated in an animal sepsis model. Ten pigs (LPS group) were administered lipopolysaccharide (LPS) to induce a systemic inflammatory response. Nine other pigs received a placebo solution (control group). Exhaled breath samples were collected in Nalophan^TM bags and stored for temperature and humidity equilibration before e-Nose analysis. Measurements were corrected for the effects of different fractions of inspired oxygen (FiO₂) on e-Nose sensors. Two classification models using e-Nose and physiological measurements were developed and compared. One hour after LPS administration, the e-Nose data model with FiO₂ correction showed a higher accuracy (76.2% (95% confidence interval (CI) [58.0, 94.2])) than the physiological data model (59.0% (95% CI [39.5, 79.5])), indicating the potential of the early detection of sepsis with an e-Nose. Full article

This study investigates the impact of using Lactiplantibacillus plantarum and Saccharomyces cerevisiae, either individually or in co-culture, on the fermentation of rose beverage. We comprehensively analyzed the resulting changes in quality characteristics and volatile compound profiles. Fermentation significantly altered the physicochemical properties, appearance, color, and free amino acid/organic acid content. Both microbial strains significantly increased total polyphenols and flavonoid content, with co-fermentation exhibiting a more pronounced effect compared to single-strain fermentations. Furthermore, the volatile compounds in rose beverages fermented with different microorganisms were characterized by an electronic nose (E-nose) and headspace–solid-phase microextraction coupled with gas chromatography–mass spectrometry (HS-SPME/GC-MS). E-nose analysis demonstrated distinct volatile profiles distinguishing the four fermentation samples. HS-SPME/GC-MS identified a total of 245 volatile compounds, among which alcohols constituted the most abundant class. Integrating GC-MS data with odor activity value (OAV ≥ 1) analysis pinpointed 34 key aroma compounds. Partial least-squares discriminant analysis (PLS-DA) based on variable importance in projection (VIP) identified eight key volatile markers: eugenol, phenylethyl alcohol, (E)-3,7-dimethyl-2,6-octadienoic acid, methyleugenol, ethyl octanoate, citronellol, D-citronellol, and 2,4-bis(1,1-dimethylethyl)phenol. These findings provide valuable insights into the microbial influence on rose beverage quality and offer a theoretical basis for optimizing industrial fermentation processes. Full article