☆ 4.7 Article

Assessment of fish adulteration using SnO2 nanopetal-based gas sensor and machine learning

FOOD CHEMISTRY (2024)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Engineering, Electrical & Electronic

Hierarchical Zinc Stannate Nanoneedle-Based Sensitive Detection of Formaldehyde

Snehanjan Acharyya et al.

Summary: This paper presents the synthesis of nanodimensional needle-like hierarchical structure of zinc stannate (ZnSnO3) material through a simple and low-cost hydrothermal process. The morphology and structural properties of the nanomaterial were characterized and validated. Gas-sensing experiments showed that the nanoneedles exhibited admirable sensitivity, fast kinetics, complete reversibility, and good selectivity and reproducibility towards formaldehyde. The sensing mechanism was explained using adsorption theory and electron depletion layer modulation.

ACS APPLIED ELECTRONIC MATERIALS (2023)

添加到收藏夹

Article Chemistry, Physical

Copper ferrite inverse spinel-based highly sensitive and selective chemiresistive gas sensor for the detection of formalin adulteration in fish

Preeti Lata Mahapatra et al.

Summary: This study reports a gas sensor based on semi-conducting Copper ferrite (CuFe2O4) inverse spinel nanoparticles for the detection of formalin adulteration in fish. The sensor exhibits high sensitivity, rapid response and recovery times, good selectivity, and excellent long-term stability, making it suitable for commercial applications.

JOURNAL OF ALLOYS AND COMPOUNDS (2023)

添加到收藏夹

Article Engineering, Electrical & Electronic

Adulterated Fish Recognition Employing SnO2 Nanostructure-Based Chemiresistive Sensor

Bidesh Mahata et al.

Summary: The letter describes a chemiresistive formaldehyde sensor based on flowerlike SnO2 nanostructure for detecting formalin adulteration in fish. The sensing material was prepared using a low-temperature hydrothermal technique and characterized using XRD and FESEM. The sensor device was fabricated by drop casting the sensing material on interdigitated electrodes built on an alumina substrate using a micropipette. The sensor exhibited notable sensitivity towards formaldehyde with a theoretical limit of detection of 870 ppb. It also demonstrated significant distinction between fresh and adulterated fish samples when exposed to fish-emitted vapors.

IEEE SENSORS LETTERS (2023)

添加到收藏夹

Review Food Science & Technology

Fish quality evaluation by sensor and machine learning: A mechanistic review

Rehan Saeed et al.

Summary: This study highlights the novel applications of sensors in fish quality monitoring, as well as the role of machine learning in quality management and prediction. Current challenges include the precision, sensitivity, flexibility, and low power consumption of the sensors.

FOOD CONTROL (2022)

添加到收藏夹

Review Food Science & Technology

A review of the presence of formaldehyde in fish and seafood

B. K. K. K. Jinadasa et al.

Summary: Fish is a great source of high-quality protein, good fat, vitamins, and minerals, and can lower blood pressure and reduce the risk of heart attack or stroke. However, the illegal addition of formaldehyde to extend shelf life is a common problem, and it is classified as a human carcinogen. There is also a natural formation pathway of formaldehyde in fish and seafood. Current studies show that the reported levels of formaldehyde in fish are often higher than the recognized safety level, highlighting the need for efforts to measure indigenous formaldehyde levels and strengthen regulations to prevent illegal addition of formaldehyde.

FOOD CONTROL (2022)

添加到收藏夹

Article Chemistry, Analytical

Ultra-selective tin oxide-based chemiresistive gas sensor employing signal transform and machine learning techniques

Snehanjan Acharyya et al.

Summary: In this study, a metal-oxide based chemiresistive sensor was integrated with soft computing tools for selective detection of gases. The sensor exhibited remarkable sensing performance towards various volatile organic compounds (VOCs) with the help of signal processing, feature extraction, and machine learning. The extracted features were inputted into machine learning algorithms to qualitatively discriminate among different VOCs and quantitatively estimate their concentrations. The approach achieved excellent classification accuracy (best average accuracy: 96.84%).

ANALYTICA CHIMICA ACTA (2022)

添加到收藏夹

Review Chemistry, Analytical

A Review on Advanced Sensing Materials for Agricultural Gas Sensors

Calvin Love et al.

Summary: This work provides a comprehensive review of sensing materials for agricultural monitoring applications, discussing their performance characteristics and commercial viability. It suggests utilizing multiple sensing materials to detect a single target gas to mitigate selectivity issues.

SENSORS (2021)

添加到收藏夹

Article Chemistry, Multidisciplinary

Selective Discrimination of VOCs Applying Gas Sensing Kinetic Analysis over a Metal Oxide-Based Chemiresistive Gas Sensor

Snehanjan Acharyya et al.

Summary: Semiconducting metal oxide-based gas sensors lack selectivity due to responsiveness to various gases. Gas sensing kinetic analysis was employed to precisely identify volatile organic compounds, with excellent sensor response and high sensitivity observed. The Eley-Rideal model was utilized for theoretical fitting and calculation of characteristic kinetic properties to discriminate among different VOCs.

ACS SENSORS (2021)

添加到收藏夹

Article Engineering, Electrical & Electronic

E-Nose: Multichannel Analog Signal Conditioning Circuit With Pattern Recognition for Explosive Sensing

Vijay S. Palaparthy et al.

IEEE SENSORS JOURNAL (2020)

添加到收藏夹

Article Chemistry, Analytical

Single resistive sensor for selective detection of multiple VOCs employing SnO2 hollowspheres and machine learning algorithm: A proof of concept

Snehanjan Acharyya et al.

SENSORS AND ACTUATORS B-CHEMICAL (2020)

添加到收藏夹

Article Chemistry, Analytical

Evolution of breath analysis based on humidity and gas sensors: Potential and challenges

Huiling Tai et al.

SENSORS AND ACTUATORS B-CHEMICAL (2020)

添加到收藏夹

Article Engineering, Electrical & Electronic

Fabrication of Microsensor for Detection of Low-Concentration Formaldehyde Gas in Formalin-Treated Fish

Surajit Das et al.

IEEE TRANSACTIONS ON ELECTRON DEVICES (2020)

添加到收藏夹

Review Food Science & Technology

Quality Assessment of Chilled and Frozen Fish-Mini Review

Ana M. Duarte et al.

FOODS (2020)

添加到收藏夹

Article Engineering, Chemical

Enhanced formaldehyde sensing performance based on Ag@WO3 2D nanocomposite

Huimin Yu et al.

POWDER TECHNOLOGY (2019)

添加到收藏夹

Article Chemistry, Multidisciplinary

Chemiresistive Sensor Array and Machine Learning Classification of Food

Vera Schroeder et al.

ACS SENSORS (2019)

添加到收藏夹

Review Engineering, Electrical & Electronic

Air Pollution Monitoring Using Near Room Temperature Resistive Gas Sensors: A Review

Ruma Ghosh et al.

IEEE TRANSACTIONS ON ELECTRON DEVICES (2019)

添加到收藏夹

Review Chemistry, Multidisciplinary

Gas sensing mechanisms of metal oxide semiconductors: a focus review

Haocheng Ji et al.

NANOSCALE (2019)

添加到收藏夹

Article Engineering, Electrical & Electronic

Photon-Assisted Ultra-Selective Formaldehyde Sensing by Defect Induced NiO-Based Resistive Sensor

Sayan Dey et al.

IEEE SENSORS JOURNAL (2018)

添加到收藏夹

Article Chemistry, Physical

Facile synthesis cedar-like SnO2 hierarchical micro-nanostructures with improved formaldehyde gas sensing characteristics

Hai Yu et al.

JOURNAL OF ALLOYS AND COMPOUNDS (2017)

添加到收藏夹

Article Chemistry, Multidisciplinary

One-Minute Fish Freshness Evaluation by Testing the Volatile Amine Gas with an Ultrasensitive Porous-Electrode-Capped Organic Gas Sensor System

Liang-Yu Chang et al.

ACS SENSORS (2017)

添加到收藏夹

Review Materials Science, Ceramics

Detection of hazardous volatile organic compounds (VOCs) by metal oxide nanostructures-based gas sensors: A review

A. Mirzaei et al.

CERAMICS INTERNATIONAL (2016)

添加到收藏夹

Article Materials Science, Multidisciplinary

Hydrothermal synthesis of flower-like SnO2 architectures with superior gas sensing properties

He Zhang et al.

MATERIALS LETTERS (2015)

添加到收藏夹

Review Computer Science, Artificial Intelligence

Deep learning in neural networks: An overview

Juergen Schmidhuber

NEURAL NETWORKS (2015)

添加到收藏夹

Review Chemistry, Analytical

Nanostructured metal oxides/hydroxides-based electrochemical sensor for monitoring environmental micropollutants

Xin-Yao Yu et al.

TRENDS IN ENVIRONMENTAL ANALYTICAL CHEMISTRY (2014)

添加到收藏夹

Article Food Science & Technology

Analysis of free and bound formaldehyde in squid and squid products by gas chromatography-mass spectrometry

Tai-Sheng Yeh et al.

JOURNAL OF FOOD AND DRUG ANALYSIS (2013)

添加到收藏夹

Article Materials Science, Multidisciplinary

SnO2-nanowires grown by catalytic oxidation of tin sputtered thin films for formaldehyde detection

I. Castro-Hurtado et al.

THIN SOLID FILMS (2012)

添加到收藏夹

Article Chemistry, Multidisciplinary

Porous SnO2 nanospheres as sensitive gas sensors for volatile organic compounds detection

Zhipeng Li et al.

NANOSCALE (2011)

添加到收藏夹

Review Toxicology

Cancer effects of formaldehyde: a proposal for an indoor air guideline value

Gunnar Damgard Nielsen et al.

ARCHIVES OF TOXICOLOGY (2010)

添加到收藏夹

Article Chemistry, Multidisciplinary

A Simple and Highly Sensitive Colorimetric Detection Method for Gaseous Formaldehyde

Liang Feng et al.

JOURNAL OF THE AMERICAN CHEMICAL SOCIETY (2010)

添加到收藏夹

Article Chemistry, Analytical

Theory of power laws for semiconductor gas sensors

Noboru Yamazoe et al.

SENSORS AND ACTUATORS B-CHEMICAL (2008)

添加到收藏夹

Article Chemistry, Applied

Induced formation of dimethylamine and formaldehyde by lizardfish (Saurida micropectoralis) kidney trimethylamine-N-oxide demethylase

S Benjakul et al.

FOOD CHEMISTRY (2004)

添加到收藏夹

Article Biochemistry & Molecular Biology

Mechanism of biosynthesis of trimethylamine oxide from choline in the teleost tilapia, Oreochromis niloticus, under freshwater conditions

N Niizeki et al.

COMPARATIVE BIOCHEMISTRY AND PHYSIOLOGY B-BIOCHEMISTRY & MOLECULAR BIOLOGY (2002)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Random forests

L Breiman

MACHINE LEARNING (2001)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.