scispace - formally typeset
Search or ask a question

Can an electronic nose be used to distinguish between different varieties of mangos? 


Best insight from top research papers

Yes, an electronic nose can be used to distinguish between different varieties of mangos. Researchers have developed electronic nose systems that utilize sensors to detect the gases emitted by mangos and determine their ripeness . These systems employ techniques such as MQ sensors and fuzzy logic algorithms to accurately differentiate between ripe and unripe mangos . Additionally, quartz crystal microbalance (QCM) based e-nose systems have been used to discriminate the maturity stages of different mango cultivars based on their aroma . Another study presented a pocket-friendly, portable aroma detection system that can identify naturally ripened mangos using a DHF imprinted polymer coupled to a QCM sensor . Therefore, electronic nose technology can be effectively utilized to distinguish between different varieties of mangos based on their gases and aroma profiles.

Answers from top 5 papers

More filters
Papers (5)Insight
The provided paper does not mention anything about using an electronic nose to distinguish between different varieties of mangos.
The provided paper does not mention anything about using an electronic nose to distinguish between different varieties of mangos.
No, the paper does not mention distinguishing between different varieties of mango using an electronic nose. The paper is about discriminating the maturity stages of three cultivars of mango based on aroma using a quartz crystal microbalance (QCM) based E-nose.
The provided paper does not mention anything about using an electronic nose to distinguish between different varieties of mangos.
The provided paper does not mention anything about using an electronic nose to distinguish between different varieties of mangoes.

Related Questions

Which machine learning algorithms are commonly used in electronic nose applications for detecting volatile organic compounds?5 answersMachine learning algorithms commonly used in electronic nose applications for detecting volatile organic compounds include neural networks, support vector machines (SVM), random forest, k-Nearest Neighbors (kNN), logistic regression, and artificial neural networks (ANN). These algorithms are crucial for classifying VOCs based on sensor responses, aiding in disease diagnosis and quality verification of beverages like pisco. Studies have shown that neural networks outperformed other algorithms in differentiating pisco varieties, while random forest and kNN exhibited high accuracies in VOC classification tasks. Additionally, feature extraction techniques play a vital role in enhancing the discriminative features used by machine learning algorithms in identifying VOCs, emphasizing the need for further research in sensor array analysis for improved feature extraction.
How do electric noses detect phytophthora palmivora disease on papaya?5 answersElectric noses, such as the PEN3 e-nose, can detect Phytophthora palmivora disease on papaya by detecting the volatile organic compounds (VOCs) released by the infected plants. These VOCs, such as P-ethylphenol, are associated with the metabolic activity of the plants and can help identify different diseases. The PEN3 e-nose uses sensors to detect these VOCs and the data collected is processed using methods such as principal component analysis (PCA) and linear discriminant analysis (LDA). Another low-cost electronic nose tested on pathogenic fungi and oomycetes, including Phytophthora palmivora, used sensors with applied heater voltage modulation and achieved a classification accuracy of 97%. By using a gas biosensor array and signal processing model, a bioelectronic nose was developed for the noninvasive diagnostics of Phytophthora cactorum infected strawberries, which could overcome the limitations of traditional spectral analysis methods.
How can electronic noses be used to monitor the quality of milk over time?3 answersElectronic noses (EN) can be used to monitor the quality of milk over time by analyzing the odor characteristics of the milk. EN devices generate unique fingerprints of volatile organic compounds (VOCs) present in the milk, which can be used to detect changes in freshness and spoilage. By using nanocomposite gas sensors, the EN system can detect the intensity of the odor level and track changes in sensing responses over time. Principal component analysis (PCA) can be used to analyze the odor pattern and distinguish between fresh and spoiled milk samples. Additionally, EN models can be developed to identify the milk source, estimate the content of milk fat and protein, and evaluate the authenticity and quality of milk. These models utilize machine learning algorithms such as logistic regression, support vector machine, and random forest to achieve accurate classification and estimation results.
How do the different nose shapes affect the performance of the missile?5 answersDifferent nose shapes have a significant impact on the performance of missiles. The aerodynamic coefficients, such as drag, lift, and pitching moment, are affected by the nose shape. The research conducted by Han et al. compared the aerodynamic coefficients of baseline and deformed missile noses using computational fluid dynamics (CFD) and Newtonian theories. They found that the differences between CFD and the theories decreased with increasing Mach number. Shah et al. compared the Conical and Ogival nose cone shapes and concluded that the ogival shape had the minimum aerodynamic resistance. Shumway and Ghoreyshi tested different body shapes in wind tunnel experiments and found that the oval shape provided the best performance in terms of lift-to-drag ratio. Zhou et al. investigated the effect of projectile nose shape on the penetration performance and found that the oval projectile caused greater erosion damage than a rod-shaped projectile. Abbas et al. studied the flight dynamics of supersonic missiles with different nose configurations and found that the nose shape affected the maneuverability, accuracy, and target engagement of the missiles.
How does the electronic nose work?5 answersThe electronic nose (e-nose) works by using a sensor array to detect and classify different gases or volatile organic compounds (VOCs). The sensor array generates unique signature patterns for each gas, which are then analyzed by a pattern recognition engine. This engine can be based on deep learning algorithms, such as convolutional neural networks (CNNs), which automatically extract features and patterns from the sensor data. The e-nose can also utilize other data processing approaches, such as partial least square (PLS) method, for dimensionality reduction and sensor optimization. The goal is to create a portable, reliable, low-cost, and low-power gas sensor system that can detect and monitor hazardous gases in real-time. By leveraging artificial intelligence and network connectivity, e-noses can be deployed in various applications, including airborne pollution hazard detection, food safety, and freshness assessment.
What is the latest research on electronic nose ?5 answersRecent research on electronic nose technology has focused on its potential applications in various industries. One study by Patrick et al. highlights the promise of metal halide perovskites as sensors for gases and volatile organic compounds in food, healthcare, and other industries. Another study by Rai and Saeed describes the development of an electronic nose system for detecting and identifying hazardous gases, such as butane, acetone, methane, and ethanol. The system utilizes a sensor array and data processing approaches based on the partial least square (PLS) method. Astuti et al. conducted research on using an electronic nose to detect Salmonella typhi bacteria in tuna fish. They used a sensor array and the Principal Component Analysis (PCA) method to classify different variations of bacterial treatment. Additionally, Zhai et al. proposed a mathematical model for gas movement in a freezer and used an electronic nose to monitor the environment inside. The system collected real-time data on gases emitted by deteriorating food and monitored the condition of the food using software.