A dedicated electronic nose combined with chemometric methods for detection of adulteration in sesame oil

Hosseini, Seyed Hossein; Minaei, Saeid; Beheshti, Babak

doi:10.1007/s13197-023-05792-2

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…Many studies demonstrated the widespread use of e-nose in assessing food quality-related properties, including the detection of Salmonella (Gonçalves et al, 2023), the quality status of fruits (Buratti et al, 2018;Qiu et al, 2014Qiu et al, , 2015, the quality of oils (Hosseini et al, 2023), and the detection of contaminated foods (Feyzioglu & Taspinar, 2023;Putri et al, 2023;Tian et al, 2023Tian et al, ). et al, 2019.…”

Section: Figurementioning

confidence: 99%

An Embedded Machine Learning-Based Spoiled Leftover Food Detection Device for Multiclass Classification

Wan Azman,

Ku Azir,

Mohd Khairuddin

2024

JICT

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Food waste’s negative environmental repercussions are causing it to become a global concern. Several studies have examined thefactors influencing food waste behaviour and management. This work was motivated by the lack of previous research on machinelearning and electronic noses to detect contamination from leftover cooked food. This work proposes using machine learning algorithms and electronic nose technology to recognise and forecast the contamination in leftover cooked food. After five days of storage, the freshness of cooked leftovers was evaluated using an electronic nose combined with machine learning algorithms. Most food samples used in this work were from Malaysian’s leftover lunch and dinner dishes. Four (4) gas sensors—MQ-2, MQ-136, MQ-137, and MQ-138—are used in developing the electronic nose to identify the presence of gas in the food sample. The data from the gas sensors was analysed using machine learning methods, namely Random Forest, k-nearest Neighbors, Support Vector Machine, and Linear Discriminant Analysis. Based on the results, a multi-classification technique yielded a greater accuracy rate in classifying and identifying the level of contamination in the cooked food leftovers, with average accuracy ranging from 90 percent to 100 percent. In conclusion, the work demonstrates a novel method for using machine learning algorithms to classify, identify, and predict the contamination level of leftover cooked food, contributing to reducing food waste generated primarily by Malaysians.

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Section: Figurementioning

confidence: 99%

An Embedded Machine Learning-Based Spoiled Leftover Food Detection Device for Multiclass Classification

Wan Azman,

Ku Azir,

Mohd Khairuddin

2024

JICT

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Enhancing Food Authentication through E-Nose and E-Tongue Technologies: Current Trends and Future Directions

Mahanti,

Shivashankar,

Chhetri

et al. 2024

Trends in Food Science & Technology

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Design of an Artificial Intelligence of Things-Based Sesame Oil Evaluator for Quality Assessment Using Gas Sensors and Deep Learning Mechanisms

Ku,

Lung,

Chi

2023

Foods

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Traditional oil quality measurement is mostly based on chemical indicators such as acid value, peroxide value, and p-anisidine value. This process requires specialized knowledge and involves complex steps. Hence, this study designs and proposes a Sesame Oil Quality Assessment Service Platform, which is composed of an Intelligent Sesame Oil Evaluator (ISO Evaluator) and a Cloud Service Platform. Users can quickly assess the quality of sesame oil using this platform. The ISO Evaluator employs Artificial Intelligence of Things (AIoT) sensors to detect changes in volatile gases and the color of the oil during storage. It utilizes deep learning mechanisms, including Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) to determine and evaluate the quality of the sesame oil. Evaluation results demonstrate that the linear discriminant analysis (LDA) value is 95.13. The MQ2, MQ3, MQ4, MQ7, and MQ8 sensors have a positive correlation. The CNN combined with an ANN model achieves a Mean Absolute Percentage Error (MAPE) of 8.1820% for predicting oil quality, while the LSTM model predicts future variations in oil quality indicators with a MAPE of 0.44%. Finally, the designed Sesame Oil Quality Assessment Service Platform effectively addresses issues related to digitization, quality measurement, supply quality observation, and scalability.

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A dedicated electronic nose combined with chemometric methods for detection of adulteration in sesame oil

Cited by 3 publications

References 23 publications

An Embedded Machine Learning-Based Spoiled Leftover Food Detection Device for Multiclass Classification

An Embedded Machine Learning-Based Spoiled Leftover Food Detection Device for Multiclass Classification

Enhancing Food Authentication through E-Nose and E-Tongue Technologies: Current Trends and Future Directions

Design of an Artificial Intelligence of Things-Based Sesame Oil Evaluator for Quality Assessment Using Gas Sensors and Deep Learning Mechanisms

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