ArticleFood chemistry: X2023
The effect of data fusion on improving the accuracy of olive oil quality measurement.
Article in Food chemistry: X, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Machine Learning Modeling forFoods (Basel, Switzerland) · 2026Article
- Food circular economy and safety considerations in waste management of urban manufacturing side streams.NPJ science of food · 2024Review
- Overcoming the Limits of Cross-Sensitivity: Pattern Recognition Methods for Chemiresistive Gas Sensor Array.Nano-micro letters · 2024Review
- Design of an Artificial Intelligence of Things-Based Sesame Oil Evaluator for Quality Assessment Using Gas Sensors and Deep Learning Mechanisms.Foods (Basel, Switzerland) · 2023Article
- Nanotechnology and E-Sensing for Food Chain Quality and Safety.Sensors (Basel, Switzerland) · 2023Review
- Monitoring the major taste components during black tea fermentation using multielement fusion information in decision level.Food chemistry: X · 2023Article
- Proposal of a New System for Essential Oil Classification Based on Low-Cost Gas Sensor and Machine Learning Techniques.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Olive oil is one of the healthiest and most nutritious edible oils, and it has a great potential to be adulterated. In this research, fraud samples of olive oil were detected with six different classification models by fusion of two methods of E-nose and ultrasound. The samples were prepared in six categories of adulteration. The E-nose system included eight various sensors. 2 MHz probes were used in through transmission ultrasound system. Principal Component Analysis method was used to reduce features and six classification models were used for classification. Feature with the greatest influence in the classification was "percentage of ultrasonic amplitude loss." It was found that the ultrasound system's data had worked more effectively than the E-nose system. Results showed that the ANN method was recognized as the most effective classifier with the highest accuracy (95.51%). The accuracy of classification in all the classification models significantly increased with data fusion.
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.