ArticleSensors (Basel, Switzerland)2020
Assessment of Smoke Contamination in Grapevine Berries and Taint in Wines Due to Bushfires Using a Low-Cost E-Nose and an Artificial Intelligence Approach.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 45 citations in OpenAlex.
- Artificial Intelligence Techniques in Grapevine Research: A Comparative Study with an Extensive Review of Datasets, Diseases, and Techniques Evaluation.Sensors (Basel, Switzerland) · 2024Review
- Recent Advances and Future Perspectives in the E-Nose Technologies Addressed to the Wine Industry.Sensors (Basel, Switzerland) · 2024Review
- Influence of saliva on the sensory properties of US commercial smoke affected wines: Preliminary findings.Food science & nutrition · 2024Article
- An AI-powered Electronic Nose System with Fingerprint Extraction for Aroma Recognition of Coffee Beans.Micromachines · 2022Article
- Digital Assessment and Classification of Wine Faults Using a Low-Cost Electronic Nose, Near-Infrared Spectroscopy and Machine Learning Modelling.Sensors (Basel, Switzerland) · 2022Article
- Lab-on-a-chip technologies for food safety, processing, and packaging applications: a review.Environmental chemistry letters · 2022Review
- Editorial: Special Issue "Implementation of Sensors and Artificial Intelligence for Environmental Hazards Assessment in Urban, Agriculture and Forestry Systems".Sensors (Basel, Switzerland) · 2021Article
- Evaluation of Different Interstimulus Rinse Protocols on Smoke Attribute Perception in Wildfire-Affected Wines.Molecules (Basel, Switzerland) · 2021Article
- Development of a Low-Cost Electronic Nose for Detection of Pathogenic Fungi and Applying It toSensors (Basel, Switzerland) · 2021Article
- Assessment of Volatile Aromatic Compounds in Smoke Tainted Cabernet Sauvignon Wines Using a Low-Cost E-Nose and Machine Learning Modelling.Molecules (Basel, Switzerland) · 2021Article
- Techniques for Mitigating the Effects of Smoke Taint While Maintaining Quality in Wine Production: A Review.Molecules (Basel, Switzerland) · 2021Review
- Integrating a Low-Cost Electronic Nose and Machine Learning Modelling to Assess Coffee Aroma Profile and Intensity.Sensors (Basel, Switzerland) · 2021Article
- Application of a Low-Cost Electronic Nose for Differentiation between Pathogenic OomycetesSensors (Basel, Switzerland) · 2021Article
- FPGA Implementation for Odor Identification with Depthwise Separable Convolutional Neural Network.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
Bushfires are increasing in number and intensity due to climate change. A newly developed low-cost electronic nose (e-nose) was tested on wines made from grapevines exposed to smoke in field trials. E-nose readings were obtained from wines from five experimental treatments: (i) low-density smoke exposure (LS), (ii) high-density smoke exposure (HS), (iii) high-density smoke exposure with in-canopy misting (HSM), and two controls: (iv) control (C; no smoke treatment) and (v) control with in-canopy misting (CM; no smoke treatment). These e-nose readings were used as inputs for machine learning algorithms to obtain a classification model, with treatments as targets and seven neurons, with 97% accuracy in the classification of 300 samples into treatments as targets (Model 1). Models 2 to 4 used 10 neurons, with 20 glycoconjugates and 10 volatile phenols as targets, measured: in berries one hour after smoke (Model 2; R = 0.98; R
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.