Monitoring and Detection of Fruits and Vegetables Spoilage in the Refrigerator using Electronic Nose Based on Principal Component Analysis

Caya, Meo Vincent C.; Cruz, Febus Reidj G.; Fernando, Carolle Marian N.; Lafuente, R.; Malonzo, Mark Bien L; Chung, Wen-Yaw

doi:10.1109/hnicem48295.2019.9072715

Cited by 19 publications

(8 citation statements)

References 10 publications

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“…However, the selection of a model for the prediction of the quality of fruits greatly varies depending on the specific situation and intention. Similarly, monitoring gas emitted by fruits and vegetables in combination with multivariate chemometric analysis has been proven to be effective in detecting spoilage inside refrigerators [42]. The use of artificial neural networks as a machine learning model is prevalent in developing a predictive model from electronic nose data [43].…”

Section: Discussionmentioning

confidence: 99%

Spoilage Monitoring and Early Warning for Apples in Storage Using Gas Sensors and Chemometrics

Yin

Jayan

Cai

et al. 2023

Foods

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In the process of storage and cold chain logistics, apples are prone to physical bumps or microbial infection, which easily leads to spoilage in the micro-environment, resulting in widespread infection and serious post-harvest economic losses. Thus, development of methods for monitoring apple spoilage and providing early warning of spoilage has become the focus for post-harvest loss reduction. Thus, in this study, a spoilage monitoring and early warning system was developed by measuring volatile component production during apple spoilage combined with chemometric analysis. An apple spoilage monitoring prototype was designed to include a gas monitoring array capable of measuring volatile organic compounds, such as CO2, O2 and C2H4, integrated with the temperature and humidity sensor. The sensor information from a simulated apple warehouse was obtained by the prototype, and a multi-factor fusion early warning model of apple spoilage was established based on various modeling methods. Simulated annealing–partial least squares (SA-PLS) was the optimal model with the correlation coefficient of prediction set (Rp) and root mean square error of prediction (RMSEP) of 0.936 and 0.828, respectively. The real-time evaluation of the spoilage was successfully obtained by loading an optimal monitoring and warning model into the microcontroller. An apple remote monitoring and early warning platform was built to visualize the apple warehouse’s sensors data and spoilage level. The results demonstrated that the prototype based on characteristic gas sensor array could effectively monitor and warn apple spoilage.

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Section: Discussionmentioning

confidence: 99%

Spoilage Monitoring and Early Warning for Apples in Storage Using Gas Sensors and Chemometrics

Yin

Jayan

Cai

et al. 2023

Foods

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“…Prior studies proposed and evaluated tools to measure freshness for unspecified users [11][12][13][14][15][16][17][18][19][20][21][22][23][24].…”

Section: Rotten Food Items Detecting Tools Targeted At Unspecific Usersmentioning

confidence: 99%

Section: Rotten Food Items Detecting Tools Targeted At Unspecific Usersmentioning

confidence: 99%

“…Researchers used chemical sensors or kits to detect the spoilage in specific food items such as pork and chicken [11][12][13][14] or unspecified food items [15][16][17]. Some researchers used chemical sensors [11][12][13] and kits [14]. They used chemical sensors to distinguish rotten food items such as ham [11], fruits (e.g., banana, grape) [12], and pork [13].…”

Section: Rotten Food Items Detecting Tools Targeted At Unspecific Usersmentioning

confidence: 99%

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A Deep Learning–Based Rotten Food Recognition App for Older Adults: Development and Usability Study

Chun,

Yu,

Jung

2024

JMIR Form Res

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Background: Older adults are at greater risk of eating rotten fruits and of suffering food poisoning because cognitive function declines as they age, making it difficult to distinguish rotten fruits. To address this problem, researchers have developed and evaluated a tool detecting rotten food items in various ways. Nevertheless, little is known about how to create such an app to detect rotten food items to support older adults in danger of suffering from health problems from eating rotten food items.Objective: This study aims (1) to create a smartphone app that enables older adults to take a picture of food items with a camera and classifies whether the fruit is rotten or not for older adults, and (2) to evaluate the usability of the app and their perceptions of older adults about the app. Methods:We developed a smartphone app that supports older adults in determining whether the fruits are fresh enough to eat. We used several residual deep networks to check whether the collected fruit photos were fresh fruit. We recruited healthy older adults aged over 65 years (15 males and 11 females) as participants. Then, we evaluated the usability of the app and perceptions of older adults with the app through surveys and interviews. We analyzed survey responses, including an after-scenario questionnaire, as evaluation indicators of the usability of the app and collected qualitative data from interviewees for in-depth analysis of survey responses. Results:The results of this study showed that healthy older adults were satisfied with using an app that determines whether the fruit is fresh by taking a picture of the fruit but would be reluctant to use the paid app. The survey results revealed that participants tended to use the app efficiently to take pictures and determine the freshness of fruits. The qualitative data analysis revealed several categories, such as usability of the app and their perceptions about apps. Conclusions:This study suggests the possibility of developing an app that supports older adults in identifying rotten food items effectively and efficiently. Future work still remains to make the app distinguish the freshness of various food items other than the three kinds of fruits.

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“…This research [5] aims to identify food degradation in tomato-based Filipino cuisines using an Electronic Nose. This study is to construct a device with an array of sensors for detecting the gases emitted by spoiled tomato-based Filipino dishes and an Artificial Neural Network for classifying sensor data readings.…”

Section: Related Workmentioning

confidence: 99%

A Smart-home Electronic-Nose for Detecting Hazardous Gases

Moustafa

Metawie

Hany

et al. 2023

Journal of Computing and Communication

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The idea of networking household appliances and technologies has been promoted for over a decade using smart home technology. The Smart Homes Association defines "the combination of technology and services" for a higher living level as "smart home technology" through home networking. Computer tools come in a variety. Systems can also be included in smart home systems. In this essay, we present. Systems for Smart Homes that can use or include several technologies. Automatic detection of dangerous substances, early fires, smoke, and gas leaks is provided by this ground-breaking device. E-nose can recognize seven different odors with the help of various gas sensors and several machine learning techniques. Finally, if a hazardous gas spread occurs, the system sends out an alarm message that is particular to the gas or occurrence. Support Vector Machine (SVM), Logistic Regression, and Decision Tree all received an F1score of 98% from us. Random Forest and K-Nearest Neighbors both received perfect scores.

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Monitoring and Detection of Fruits and Vegetables Spoilage in the Refrigerator using Electronic Nose Based on Principal Component Analysis

Cited by 19 publications

References 10 publications

Spoilage Monitoring and Early Warning for Apples in Storage Using Gas Sensors and Chemometrics

Spoilage Monitoring and Early Warning for Apples in Storage Using Gas Sensors and Chemometrics

A Deep Learning–Based Rotten Food Recognition App for Older Adults: Development and Usability Study

A Smart-home Electronic-Nose for Detecting Hazardous Gases

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