Design of temperature and humidity sensors for an electronic nose used in rotten food

Benabdellah, Nihad; Bourhaleb, Mohammed; Nasri, M’barek; Benazzi, Naima; Dahbi, Sanae

doi:10.1109/eitech.2016.7519652

Cited by 17 publications

(6 citation statements)

References 3 publications

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“…Humidity sensor: A humidity sensor is a sensor that senses humidity and converts the information into an electrical current. Humidity sensors are available in a range of shapes and sizes, as well as with different features ( 40 ). The Humidity sensor first senses any humidity or moisture in the fruits or vegetables and it sends some value to the microcontroller if any moisture content is detected then an alert message sends to our mobile phone if the moisture content is not detected then again taking values from the microcontroller.…”

Section: Architecturementioning

confidence: 99%

An Artificial Intelligence Approach Toward Food Spoilage Detection and Analysis

Sonwani

Bansal

Alroobaea

et al. 2022

Front. Public Health

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Aiming to increase the shelf life of food, researchers are moving toward new methodologies to maintain the quality of food as food grains are susceptible to spoilage due to precipitation, humidity, temperature, and a variety of other influences. As a result, efficient food spoilage tracking schemes are required to sustain food quality levels. We have designed a prototype to track food quality and to manage storage systems at home. Initially, we have employed a Convolutional Neural Network (CNN) model to detect the type of fruit and veggies. Then the proposed system monitors the gas emission level, humidity level, and temperature of fruits and veggies by using sensors and actuators to check the food spoilage level. This would additionally control the environment and avoid food spoilage wherever possible. Additionally, the food spoilage level is informed to the customer by an alert message sent to their registered mobile numbers based on the freshness and condition of the food. The model employed proved to have an accuracy rate of 95%. Finally, the experiment is successful in increasing the shelf life of some categories of food by 2 days.

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Section: Architecturementioning

confidence: 99%

An Artificial Intelligence Approach Toward Food Spoilage Detection and Analysis

Sonwani

Bansal

Alroobaea

et al. 2022

Front. Public Health

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“…A recent investigation of the disease burden in 2017 revealed that ∼544.9 million people suffer from chronic respiratory diseases globally . Monitoring humidity is crucial in many industries including those of food processing, semiconductor manufacturing, packaging, agriculture, and medicine. − Respiration is vital to sustaining human life, and the exhaled breath is a treasure trove that contains a vast variety of components such as water vapor, carbon dioxide, and volatile organic components, which can reveal vital information on health. , Apart from analyzing these components, measurement of the tidal volume and breath rate assists in determining the physiological state of the individual. A humidity sensor that is flexible, fast-responsive, affordable, and mobile phone-enabled can be deployed for continuous monitoring of breath rate and can eventually help determine the status of health.…”

Section: Introductionmentioning

confidence: 99%

Toward Continuous Breath Monitoring on a Mobile Phone Using a Frugal Conducting Cloth-Based Smart Mask

et al. 2022

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A frugal humidity sensor that can detect changes in the humidity of exhaled breath of individuals has been fabricated. The sensor comprises a humiditysensitive conducting polymer that is in situ formed on a cloth that acts as a substrate. Interdigitated silver electrodes were screen-printed on the modified cloth, and conducting threads connected the electrodes to the measurement circuit. The sensor's response to changing humidity was measured as a voltage drop across the sensor using a microcontroller. The sensor was capable of discerning between fast, normal, and slow breathing based on the response time. A response time of ∼1.3 s was observed for fast breathing. An Android-based mobile application was designed to collect sensor data via Bluetooth for analysis. A time series classification algorithm was implemented to analyze patterns in breathing. The sensor was later stitched onto a face mask, transforming it into a smart mask that can monitor changes in the breathing pattern at work, play, and sleep.

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“…Various systems for food monitoring have been developed that rely on embedded circuits to detect food quality deterioration, called electric noses [8]- [10]. These systems show high reliability by measuring the biochemical changes in the environment around the monitored food through a series of sensors attached to the device.…”

Section: Introductionmentioning

confidence: 99%

Self-Powered Food Assessment System Using LSTM Network and 915 MHz RF Energy Harvesting

Kim

Lam

et al. 2021

IEEE Access

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This study proposes a self-powered food monitoring system that benefits from far-field RF Energy harvesting and deep learning techniques. Recent smart IoT systems for food quality management mainly focused on the appearance of Total Volatile Organic Compounds (TVOCs) during food preservation, which has the limitations of large power consumption, complex configuration, and low accuracy. Different from these methods, we aim to measure the gradual increase in air pressure inside food packages caused by gas emissions during food quality deterioration. With this new approach, the designed sensor circuit's energy consumption is reduced significantly than in conventional systems. The sensor module's operation power is supplied by an RF energy harvester capable of converting electromagnetic waves in space into electrical energy. We adopted a Yagi three elements as a receiver antenna in the RF energy scavenging module to enhance the harvested power and transmission distance. The designed antenna has a high gain of 6.54dBi in the direction of maximum radiation and voltage standing wave ratio (VSWR) better than 1.3 in approximately 60 MHz band (890-950 MHz). To demonstrate the feasibility of the proposed system, a set of experiments were conducted with different sorts of food such as pork, chicken, and fish. Raw data of food storage temperature, air pressure, and storage time obtained from the battery-less sensor module were analyzed and utilized to assess food quality changes. Several classification models, including LSTM, 1D-CNN, MLP, and SVM were developed and trained on the pork, chicken, and fish datasets to predict different food quality states. Experimental results show that the LSTM classifier, which can extract temporal characteristics from the dataset, achieves the best accuracy of above 99% on all three datasets. The food classification performance is investigated based on training accuracy and confusion matrix.

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Design of temperature and humidity sensors for an electronic nose used in rotten food

Cited by 17 publications

References 3 publications

An Artificial Intelligence Approach Toward Food Spoilage Detection and Analysis

An Artificial Intelligence Approach Toward Food Spoilage Detection and Analysis

Toward Continuous Breath Monitoring on a Mobile Phone Using a Frugal Conducting Cloth-Based Smart Mask

Self-Powered Food Assessment System Using LSTM Network and 915 MHz RF Energy Harvesting

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