Novel use of e-noses for digital agriculture, food, and beverage applications

Fuentes, Sigfredo; Viejo, Claudia Gonzalez

doi:10.1016/b978-0-323-91157-3.00004-0

Cited by 3 publications

(3 citation statements)

References 57 publications

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“…Another import technique used for the volatilome characterization of wine is electronic nose technology. This technique has been used to assess volatile aromatic compounds in agriculture and food applications, including determining products' maturity stage, pest and disease attacks, or for classification purposes [12]. An electronic nose, also known as an e-Nose, plays a significant role in wine analysis, offering a modern and efficient alternative to traditional methods.…”

Section: Introductionmentioning

confidence: 99%

Decoding the Volatile Profile of White Romanian Fetească Wines

Manolache,

Duță,

Criveanu-Stamatie

et al. 2024

Separations

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The wine’s volatilome, most of the time, defines not only its aroma, but also, its major attributes. In the case of wines, the authentication process has become imperative, in light of increased production of alcoholic beverages; consequently, reliable analytical methods have served for it. Therefore, the goal of this research was to establish the global volatile profile of traditional Romanian white wines from Fetească varieties (Fetească albă, Fetească regală) in order to identify its unique characteristics by means of a headspace solid-phase microextraction coupled with gas chromatography analysis (HS-SPME/GC-MS) and e-Nose devoted techniques. Statistics was also employed aimed at differentiating the analyzed wine by varietal groups. Consequently, 23 volatile compounds were detected and quantified in 39 Fetească white wine samples originating from various production areas (Muntenia, Oltenia, Transylvania, Banat and Dobrogea), then further classified according to their odor thresholds in five aromatic classes (floral, fruity, sweet, lactic (cheesy) and other). In addition, statistics (Principal Component Analysis (PCA), Hieratical Clustering Analysis (HCA)) were used aiming to differentiate the analyzed varietal groups. The outcomes have pointed out the existence of distinct clusters connected with ethyl esters or alcohol composition and production year, depending on each examined variety.

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Section: Introductionmentioning

confidence: 99%

Decoding the Volatile Profile of White Romanian Fetească Wines

Manolache,

Duță,

Criveanu-Stamatie

et al. 2024

Separations

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“…The use of eNoses is strongly related to applying classification techniques such as multivariate data analysis, cluster, and machine learning (ML) [22]. Among the classification techniques, ML is one of the most recent and common methods, highlighting the use of support vector machine (SVM), k-nearest neighbours (KNN), and neural networks (NN) as the most used ones [23].…”

Section: Introductionmentioning

confidence: 99%

Proposal of a New System for Essential Oil Classification Based on Low-Cost Gas Sensor and Machine Learning Techniques

Viciano-Tudela,

Parra,

Navarro-Garcia

et al. 2023

Sensors

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Essential oils are valuable in various industries, but their easy adulteration can cause adverse health effects. Electronic nasal sensors offer a solution for adulteration detection. This article proposes a new system for characterising essential oils based on low-cost sensor networks and machine learning techniques. The sensors used belong to the MQ family (MQ-2, MQ-3, MQ-4, MQ-5, MQ-6, MQ-7, and MQ-8). Six essential oils were used, including Cistus ladanifer, Pinus pinaster, and Cistus ladanifer oil adulterated with Pinus pinaster, Melaleuca alternifolia, tea tree, and red fruits. A total of up to 7100 measurements were included, with more than 118 h of measurements of 33 different parameters. These data were used to train and compare five machine learning algorithms: discriminant analysis, support vector machine, k-nearest neighbours, neural network, and naive Bayesian when the data were used individually or when hourly mean values were included. To evaluate the performance of the included machine learning algorithms, accuracy, precision, recall, and F1-score were considered. The study found that using k-nearest neighbours, accuracy, recall, F1-score, and precision values were 1, 0.99, 0.99, and 1, respectively. The accuracy reached 100% with k-nearest neighbours using only 2 parameters for averaged data or 15 parameters for individual data.

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“…An electronic nose (e-nose), also recognized as an artificial intelligent nose [7], is one of the emergent digital devices for applications in agriculture [8], food, and forestry [9]. A typical e-nose comprises an array of sensors [10] that are sensitive to different volatile organic compounds (VOC) such as ethanol, methane, and many others, depending on the aims and targets [11][12][13]. It may also have a data-processing and analysis unit containing a specific aroma database [14,15].…”

Section: Introductionmentioning

confidence: 99%

Early Detection of Fusarium oxysporum Infection in Processing Tomatoes (Solanum lycopersicum) and Pathogen–Soil Interactions Using a Low-Cost Portable Electronic Nose and Machine Learning Modeling

Feng

Viejo

Vaghefi

et al. 2022

Sensors

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The early detection of pathogen infections in plants has become an important aspect of integrated disease management. Although previous research demonstrated the idea of applying digital technologies to monitor and predict plant health status, there is no effective system for detecting pathogen infection before symptomatology appears. This paper presents the use of a low-cost and portable electronic nose coupled with machine learning (ML) models for early disease detection. Several artificial neural network models were developed to predict plant physiological data and classify processing tomato plants and soil samples according to different levels of pathogen inoculum by using e-nose outputs as inputs, plant physiological data, and the level of infection as targets. Results showed that the pattern recognition models based on different infection levels had an overall accuracy of 94.4–96.8% for tomato plants and between 94.81% and 96.22% for soil samples. For the prediction of plant physiological parameters (photosynthesis, stomatal conductance, and transpiration) using regression models or tomato plants, the overall correlation coefficient was 0.97–0.99, with very significant slope values in the range 0.97–1. The performance of all models shows no signs of under or overfitting. It is hence proven accurate and valid to use the electronic nose coupled with ML modeling for effective early disease detection of processing tomatoes and could also be further implemented to monitor other abiotic and biotic stressors.

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Novel use of e-noses for digital agriculture, food, and beverage applications

Cited by 3 publications

References 57 publications

Decoding the Volatile Profile of White Romanian Fetească Wines

Decoding the Volatile Profile of White Romanian Fetească Wines

Proposal of a New System for Essential Oil Classification Based on Low-Cost Gas Sensor and Machine Learning Techniques

Early Detection of Fusarium oxysporum Infection in Processing Tomatoes (Solanum lycopersicum) and Pathogen–Soil Interactions Using a Low-Cost Portable Electronic Nose and Machine Learning Modeling

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