Comprehensive adulteration detection of sesame oil based on characteristic markers

Chen, Zhe; Fu, Jiashun; Dou, Xinjing; Deng, Zhuowen; Wang, Xuefang; Ma, Fang; Yu, Li; Yun, Yong‐Huan; Li, Peiwu; Zhang, Liangxiao

doi:10.1016/j.fochx.2023.100745

Cited by 6 publications

(5 citation statements)

References 37 publications

(39 reference statements)

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“…There were 28 fatty acids in the peanut oil single adulteration and multiple adulteration dataset matrix; more detailed information on the relative content ranges in different kinds of oils can be found in the previous study (Zhang et al, 2014). Moreover, in order to explore the feasibility and validation of the proposed method, its practical application was demonstrated on sesame oils from the market which has already been confirmed by chemical markers in our previous study (Chen et al, 2023).…”

Section: Discussionmentioning

confidence: 68%

“…Besides, in our case, the predicted results of the developed method were compared with the traditional OCPLS identification method, which showed the same results. Finally, the method could identify the sesame oils from the practical market (Chen et al, 2023), compared with the markers-based method, it provided a screening strategy for market inspection with the advantage of fast and low cost. This method could also be applied to other oils or foods, offering a new idea and solution for the inspection in market surveillance of untargeted adulteration in practice.…”

Section: Discussionmentioning

confidence: 99%

“…The sesame oils verified by the chemical markers in our previous study (Chen et al, 2023) were used to validate this method. Similarly, fatty 2), it means that the number of adulterated sesame oils is 7.…”

Section: Sesame Oil Authenticationmentioning

confidence: 99%

“…The identification results of the practical sesame oil adulteration: (a) the accumulated absolute centered residual (ACR) of the remaining test dataset for Monte Carlo models (k = 0.4); (b) average predicted ACR of each sample from the selected models with highest accumulated ACR (k = 0.4, N t = 494); (c) the accumulated ACR of the remaining test dataset for Monte Carlo models (k = 0.5); (d) average predicted ACR of each sample from the selected models with highest accumulated ACR (k = 0.5, N t = 107).used to identify the adulterated oil with soybean oil (No.63)(Chen et al, 2023). The practical application of the sesame oil authentication case provides evidence of the feasibility of detecting oil adulteration in practical market surveillance.…”

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confidence: 99%

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Adulteration detection of edible oil by one‐class classification and outlier detection

Dou,

Tu,

et al. 2024

Food Frontiers

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Edible oil adulteration is a mostly practiced phenomenon. However, the traditional discriminant methods fail to detect oil adulteration involving more than one adulterant. Recently, one‐class classifiers were built for food or oil authentication. Unfortunately, as it is hard to determine the application domain of the one‐class classifier, high prediction error was obtained for real samples in market surveillance. In this study, a new method was developed based on one‐class classification and outlier detection for edible oil adulteration detection in market surveillance. The model population was constructed using Monte Carlo sampling of unidentified inspected samples to select the plateau region exhibiting the highest accumulated absolute centered residual (ACR) values. Subsequently, the number of models in the plateau region was validated by the theoretical ones calculated by the classical probability model. The models in the plateau region with the highest cumulative accumulated ACR values were used to identify adulterated oils. Furthermore, the cross‐validation was conducted by comparing identification results from two different Monte Carlo sampling ratios to ensure the accuracy of our method. Both single adulteration and multiple adulteration of peanut oils were prepared to validate our method. Moreover, this method was used to detect adulteration of sesame oils, which have already been identified by the markers in our previous study. The validation results of three datasets indicated that this method could effectively identify adulterated samples and therefore provide a novel solution for inspecting potential adulteration in practice.

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Section: Discussionmentioning

confidence: 68%

Section: Discussionmentioning

confidence: 99%

Section: Sesame Oil Authenticationmentioning

confidence: 99%

mentioning

confidence: 99%

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Adulteration detection of edible oil by one‐class classification and outlier detection

Dou,

Tu,

et al. 2024

Food Frontiers

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“…Liu et al, develop a visual array sensor for sesame oil adulteration detection, reaching a 100% classification accuracy [24]. Chen et al, create a model for detecting sesame oil adulteration using fatty acids, phytosterols, and tocopherols and employ chemometric techniques for effective screening and verification [25]. These methods involve the utilization of headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS) and various spectroscopic techniques, along with analytical algorithms, such as Terahertz spectra and Long Short-Term Memory (LSTM), combined with chemometrics for identifying adulteration and assessing oil quality.…”

Section: Introductionmentioning

confidence: 99%

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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Two flavors in adulterated sesame oil: discovery, confirmation, and content regularity study

Liu,

Chen,

Lin

et al. 2024

Anal. Methods

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Comprehensive adulteration detection of sesame oil based on characteristic markers

Cited by 6 publications

References 37 publications

Adulteration detection of edible oil by one‐class classification and outlier detection

Adulteration detection of edible oil by one‐class classification and outlier detection

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

Two flavors in adulterated sesame oil: discovery, confirmation, and content regularity study

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