ArticleRSC advances2021
A novel electronic nose for the detection and classification of pesticide residue on apples.
Article in RSC advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 51 citations in OpenAlex.
- Non-Destructive Detection of Fruit Quality: Technologies, Applications and Prospects.Foods (Basel, Switzerland) · 2025Review
- Effects of Moisture Content Gradient on Alfalfa Silage Quality, Odor, and Bacterial Community Revealed by Electronic Nose and GC-MS.Microorganisms · 2025Article
- Rapid and Simultaneous Detection of Petroleum Hydrocarbons and Organic Pesticides in Soil Based on Electronic Nose.Sensors (Basel, Switzerland) · 2025Article
- Advancements in Intelligent Sensing Technologies for Food Safety Detection.Research (Washington, D.C.) · 2025Review
- Advancing One Health implementation and technological innovation in China: a technology assessment approach.Science in One Health · 2025Review
- Overcoming the Limits of Cross-Sensitivity: Pattern Recognition Methods for Chemiresistive Gas Sensor Array.Nano-micro letters · 2024Review
- Advancements in Improving Selectivity of Metal Oxide Semiconductor Gas Sensors Opening New Perspectives for Their Application in Food Industry.Sensors (Basel, Switzerland) · 2023Review
- Determination of procymidone residues in rapeseed oil based on olfactory visualization technology.Food chemistry: X · 2023Article
- Improving Recognition Accuracy of Pesticides in Groundwater by Applying TrAdaBoost Transfer Learning Method.Sensors (Basel, Switzerland) · 2023Article
- Review
- Breath Analysis of COVID-19 Patients in a Tertiary UK Hospital by Optical Spectrometry: The E-Nose CoVal Study.Biosensors · 2023Article
- Emerging biotechnology applications in natural product and synthetic pharmaceutical analyses.Acta pharmaceutica Sinica. B · 2022Review
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 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Excessive pesticide residues are a serious problem faced by food regulatory authorities, suppliers, and consumers. To assist with this challenge, this work aimed to develop a method of detecting and classifying pesticide residue on fruit samples using an electronic nose, through the application of three different data-recognition algorithms. The apple samples carried various concentrations of two known pesticides, namely cypermethrin and chlorpyrifos. Data collection was performed using a PEN3 electronic nose equipped with 10 metal oxide semiconductor (MOS) sensors. In order to classify and analyze these pesticide residues on the apple samples, principal component analysis (PCA), linear discriminant analysis (LDA), and support vector machine (SVM) results were combined with sensor output responses to realize MOS sensor array data visualization. The results indicated that all three data-recognition algorithms accurately identified the pesticide residues in the apple samples, with the PCA algorithm exhibiting the best classification and discrimination ability. Consequently, this work has shown that the MOS electronic nose, in combination with data-recognition algorithms, can provide support for the rapid and non-destructive identification of pesticide residues in fruits and can provide an effective tool for the detection of pesticide residues in agricultural products.
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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.