ArticleSensors (Basel, Switzerland)2021
A Machine Learning Method for Classification and Identification of Potato Cultivars Based on the Reaction of MOS Type Sensor-Array.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Advanced hybrid 3DCNN-SGAN framework for high-precision gas mixture analysis with sensor arrays.Scientific reports · 2026Article
- Machine Learning-Based Identification of Mating Type and Metalaxyl Response inMicroorganisms · 2024Article
- EfficientRMT-Net-An Efficient ResNet-50 and Vision Transformers Approach for Classifying Potato Plant Leaf Diseases.Sensors (Basel, Switzerland) · 2023Article
- Determination of the effect of abiotic stress on the oxidative potential of edible potato tubers.Scientific reports · 2023Article
- Environmental Engineering Applications of Electronic Nose Systems Based on MOX Gas Sensors.Sensors (Basel, Switzerland) · 2023Review
- Spotting of Volatile Signatures through GC-MS Analysis of Bacterial and Fungal Infections in Stored Potatoes (Foods (Basel, Switzerland) · 2023Article
- Article
- Investigating Changes in pH and Soluble Solids Content of Potato during the Storage by Electronic Nose and Vis/NIR Spectroscopy.Foods (Basel, Switzerland) · 2022Article
- A Green Analytical Method Combined with Chemometrics for Traceability of Tomato Sauce Based on Colloidal and Volatile Fingerprinting.Molecules (Basel, Switzerland) · 2022Article
- Application of MOS Gas Sensors Coupled with Chemometrics Methods to Predict the Amount of Sugar and Carbohydrates in Potatoes.Molecules (Basel, Switzerland) · 2022Article
- Impact of Coffee Bean Roasting on the Content of Pyridines Determined by Analysis of Volatile Organic Compounds.Molecules (Basel, Switzerland) · 2022Article
- Comparison of Cheese Aroma Intensity Measured Using an Electronic Nose (E-Nose) Non-Destructively with the Aroma Intensity Scores of a Sensory Evaluation: A Pilot Study.Sensors (Basel, Switzerland) · 2021Article
- Recent Progress in Smart Electronic Nose Technologies Enabled with Machine Learning Methods.Sensors (Basel, Switzerland) · 2021Review
- Characteristic-Aroma-Component-Based Evaluation and Classification of Strawberry Varieties by Aroma Type.Molecules (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
In response to one of the most important challenges of the century, i.e., the estimation of the food demands of a growing population, advanced technologies have been employed in agriculture. The potato has the main contribution to people's diet worldwide. Therefore, its different aspects are worth studying. The large number of potato varieties, lack of awareness about its new cultivars among farmers to cultivate, time-consuming and inaccurate process of identifying different potato cultivars, and the significance of identifying potato cultivars and other agricultural products (in every food industry process) all necessitate new, fast, and accurate methods. The aim of this study was to use an electronic nose, along with chemometrics methods, including PCA, LDA, and ANN as fast, inexpensive, and non-destructive methods for detecting different potato cultivars. In the present study, nine sensors with the best response to VOCs were adopted. VOCs sensors were used at various VOCs concentrations (1 to 10,000 ppm) to detect different gases. The results showed that a PCA with two main components, PC1 and PC2, described 92% of the total samples' dataset variance. In addition, the accuracy of the LDA and ANN methods were 100 and 96%, respectively.
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Registered trials
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