Organic Vapor Sensors for Food Quality Assessment

2000 ◽  
Vol 77 (3) ◽  
pp. 338 ◽  
Author(s):  
Colin L. Honeybourne
Keyword(s):  
Quality Assessment ◽  
Food Quality ◽  
Organic Vapor ◽  
Vapor Sensors

Nanotechnology for Food Packaging and Food Quality Assessment

2017 ◽  
pp. 149-204 ◽  
Author(s):  
Marco Rossi ◽  
Daniele Passeri ◽  
Alberto Sinibaldi ◽  
Mariglen Angjellari ◽  
Emanuela Tamburri ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Food Quality ◽  
Food Packaging

Food quality assessment by NIR hyperspectral imaging

2010 ◽  
Author(s):  
Martin B. Whitworth ◽  
Samuel J. Millar ◽  
Astor Chau
Keyword(s):  
Quality Assessment ◽  
Hyperspectral Imaging ◽  
Food Quality

Organic vapor sensors based on functionalized macroporous Si using single and double-side electrochemical etching

2012 ◽  
Vol 90 ◽  
pp. 55-58 ◽  
Author(s):  
A. Ramírez-Porras ◽  
B.D. Fahlman ◽  
J.P. Badilla ◽  
V. López
Keyword(s):  
Electrochemical Etching ◽  
Organic Vapor ◽  
Vapor Sensors

scholarly journals Organic Vapor Sensors: Ordered‐Assembly Conductive Nanowires Array with Tunable Polymeric Structure for Specific Organic Vapor Detection (Small 27/2019)

Small ◽  
2019 ◽  
Vol 15 (27) ◽  
pp. 1970142
Author(s):  
Xiangyu Jiang ◽  
Xiqi Zhang ◽  
Yuchen Wu ◽  
Yunqi Li ◽  
Jinhui Pang ◽  
...  
Keyword(s):  
Vapor Detection ◽  
Polymeric Structure ◽  
Organic Vapor ◽  
Vapor Sensors ◽  
Ordered Assembly ◽  
Nanowires Array

scholarly journals Trace‐Level, Multi‐Gas Detection for Food Quality Assessment Based on Decorated Silicon Transistor Arrays

2020 ◽  
Vol 32 (21) ◽  
pp. 1908385 ◽  
Author(s):  
Zhen Yuan ◽  
Mallika Bariya ◽  
Hossain M. Fahad ◽  
Jingbo Wu ◽  
Rui Han ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Food Quality ◽  
Gas Detection ◽  
Trace Level ◽  
Silicon Transistor

scholarly journals Sensor Failure Tolerable Machine Learning-Based Food Quality Prediction Model

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3173 ◽  
Author(s):  
Aydin Kaya ◽  
Ali Seydi Keçeli ◽  
Cagatay Catal ◽  
Bedir Tekinerdogan
Keyword(s):  
Machine Learning ◽  
Quality Assessment ◽  
Food Quality ◽  
Physical Aging ◽  
Quality Prediction ◽  
Sensor Failure ◽  
Linear Discriminant ◽  
Cut Quality ◽  
Automated Simulation

For the agricultural food production sector, the control and assessment of food quality is an essential issue, which has a direct impact on both human health and the economic value of the product. One of the fundamental properties from which the quality of the food can be derived is the smell of the product. A significant trend in this context is machine olfaction or the automated simulation of the sense of smell using a so-called electronic nose or e-nose. Hereby, many sensors are used to detect compounds, which define the odors and herewith the quality of the product. The proper assessment of the food quality is based on the correct functioning of the adopted sensors. Unfortunately, sensors may fail to provide the correct measures due to, for example, physical aging or environmental factors. To tolerate this problem, various approaches have been applied, often focusing on correcting the input data from the failed sensor. In this study, we adopt an alternative approach and propose machine learning-based failure tolerance that ignores failed sensors. To tolerate for the failed sensor and to keep the overall prediction accuracy acceptable, a Single Plurality Voting System (SPVS) classification approach is used. Hereby, single classifiers are trained by each feature and based on the outcome of these classifiers, and a composed classifier is built. To build our SPVS-based technique, K-Nearest Neighbor (kNN), Decision Tree, and Linear Discriminant Analysis (LDA) classifiers are applied as the base classifiers. Our proposed approach has a clear advantage over traditional machine learning models since it can tolerate the sensor failure or other types of failures by ignoring and thus enhance the assessment of food quality. To illustrate our approach, we use the case study of beef cut quality assessment. The experiments showed promising results for beef cut quality prediction in particular, and food quality assessment in general.

Integration of a porous wood-based triboelectric nanogenerator and gas sensor for real-time wireless food-quality assessment

Nano Energy ◽  
2021 ◽  
Vol 83 ◽  
pp. 105833 ◽  
Author(s):  
Chenchen Cai ◽  
Jilong Mo ◽  
Yanxu Lu ◽  
Ni Zhang ◽  
Zhengyang Wu ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Real Time ◽  
Gas Sensor ◽  
Food Quality ◽  
Triboelectric Nanogenerator

A bottom-up approach to design wearable and stretchable smart fibers with organic vapor sensing behaviors and energy storage properties

2018 ◽  
Vol 6 (28) ◽  
pp. 13633-13643 ◽  
Author(s):  
Ifra Marriam ◽  
Xingping Wang ◽  
Mike Tebyetekerwa ◽  
Guoyin Chen ◽  
Fatemeh Zabihi ◽  
...  
Keyword(s):  
Energy Storage ◽  
Bottom Up ◽  
Organic Vapor ◽  
Vapor Sensing ◽  
Energy Storage Properties ◽  
Vapor Sensors ◽  
Storage Properties

Wetspinning and electrospinning were employed in a bottom-up approach to fabricate fibers for organic vapor sensors and energy storage.

Biosensors for Food Quality Assessment

2005 ◽  
Author(s):  
Michele Del Carlo ◽  
Mihaela Nistor ◽  
Dario Compagnone ◽  
Bo Mattiasson ◽  
Elisabeth Csöregi
Keyword(s):  
Quality Assessment ◽  
Food Quality
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