Qualitative and Quantitative Detection of Food Adulteration Using a Smart E-Nose

Pulluri, Kranthi Kumar; Kumar, Vaegae Naveen

doi:10.3390/s22207789

Cited by 17 publications

(5 citation statements)

References 53 publications

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“…Similarly, the electronic nose was able to differentiate between different proportions of pork forequarter, chicken breast, and chicken skin in beef brisket, with accuracies of 87.82%, 99.04%, and 98.57%, respectively [86]. The recognition accuracy of fake beef rolls adulterated with different proportions of pork and duck can reach more than 95% [87], and that of beef samples adulterated with different proportions of pork can be as accurate as 97.4% to 99.99% [88][89][90]. It can be seen that, although beef adulteration means are complex and diverse, the electronic nose can still be used on the beef according to the unique volatile substances emitted by the different meats to distinguish them effectively to detect the adulteration of different types and different quantities of other meats to provide technical support.…”

Section: Application Of Electronic Nose In Meat Adulteration Detectionmentioning

confidence: 99%

Research Progress of Electronic Nose and Near-Infrared Spectroscopy in Meat Adulteration Detection

Sun,

Wang,

Jia

2024

Chemosensors

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China is a large consumer of meat and meat products. People’s daily diets include a variety of meat, but meat food adulteration problems are common. This paper discusses the research progress of the electronic nose and near-infrared spectroscopy in the field of meat adulteration detection. Through the study of dozens of related papers in recent years, it has been found that the use of the electronic nose and near-infrared spectroscopy for meat detection has the advantages of speed, a nondestructive nature, high sensitivity, strong quantitative analysis, high automation, a wide applicability, an improved product quality, and cost reduction over the traditional detection, but it may be limited in detecting the adulteration of a specific meat, and there are issues with the life and stability of the sensors of the electronic nose in the process of detection, along with the problems of the high requirements for the modeling of the data of near-infrared spectroscopy. This paper takes adulterated meat as the research object and briefly summarizes the detection principles of the electronic nose and near-infrared spectroscopy, as well as the types of sensors applied in the electronic nose. The research progress of the electronic nose and near-infrared detection technology in meat adulteration assessment is reviewed, the advantages and disadvantages of the two in practical application are analyzed, the classification of pattern recognition methods and their applications in meat identification are described, and the feasibility and practical significance of the joint application of the two in meat adulteration detection are envisioned. Meanwhile, the challenges faced by the two in meat detection are pointed out.

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Section: Application Of Electronic Nose In Meat Adulteration Detectionmentioning

confidence: 99%

Research Progress of Electronic Nose and Near-Infrared Spectroscopy in Meat Adulteration Detection

Sun,

Wang,

Jia

2024

Chemosensors

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“…The deep learning model implemented in the study achieved a classification accuracy of 90%, while accuracies of up to 98.74% and 89.49% were also achievable using the random forest and MLP feed-forward neural network classifiers, respectively. Elsewhere, the work undertaken by Pulluri and Kumar [111] involved the development of a smart electronic nose (SE-Nose) designed to rapidly detect and measure food adulteration, particularly in recognizing the presence of pork in beef. The methodology employed classification models to do qualitative analysis of adulteration and regression models to conduct quantitative analysis.…”

Section: Ai and ML In The Detection Of Food Fraudmentioning

confidence: 99%

Enhancing Food Integrity through Artificial Intelligence and Machine Learning: A Comprehensive Review

Gbashi,

Njobeh

2024

Applied Sciences

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Herein, we examined the transformative potential of artificial intelligence (AI) and machine learning (ML) as new fronts in addressing some of the pertinent challenges posed by food integrity to human and animal health. In recent times, AI and ML, along with other Industry 4.0 technologies such as big data, blockchain, virtual reality, and the internet of things (IoT), have found profound applications within nearly all dimensions of the food industry with a key focus on enhancing food safety and quality and improving the resilience of the food supply chain. This paper provides an accessible scrutiny of these technologies (in particular, AI and ML) in relation to food integrity and gives a summary of their current advancements and applications within the field. Key areas of emphasis include the application of AI and ML in quality control and inspection, food fraud detection, process control, risk assessments, prediction, and management, and supply chain traceability, amongst other critical issues addressed. Based on the literature reviewed herein, the utilization of AI and ML in the food industry has unequivocally led to improved standards of food integrity and consequently enhanced public health and consumer trust, as well as boosting the resilience of the food supply chain. While these applications demonstrate significant promise, the paper also acknowledges some of the challenges associated with the domain-specific implementation of AI in the field of food integrity. The paper further examines the prospects and orientations, underscoring the significance of overcoming the obstacles in order to fully harness the capabilities of AI and ML in safeguarding the integrity of the food system.

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“…To maintain consistency in the lengths of the data segments, it is necessary to upsample the sliced data to match the original sequence length. Methods similar to WS have been applied in related studies of E-nose systems [22,31].…”

Section: Data Augmentationmentioning

confidence: 99%

E-Nose: Time–Frequency Attention Convolutional Neural Network for Gas Classification and Concentration Prediction

Jiang,

Li,

et al. 2024

Sensors

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In the electronic nose (E-nose) systems, gas type recognition and accurate concentration prediction are some of the most challenging issues. This study introduced an innovative pattern recognition method of time–frequency attention convolutional neural network (TFA-CNN). A time–frequency attention block was designed in the network, aiming to excavate and effectively integrate the temporal and frequency domain information in the E-nose signals to enhance the performance of gas classification and concentration prediction tasks. Additionally, a novel data augmentation strategy was developed, manipulating the feature channels and time dimensions to reduce the interference of sensor drift and redundant information, thereby enhancing the model’s robustness and adaptability. Utilizing two types of metal-oxide-semiconductor gas sensors, this research conducted qualitative and quantitative analysis on five target gases. The evaluation results showed that the classification accuracy could reach 100%, and the coefficient of the determination (R2) score of the regression task was up to 0.99. The Pearson correlation coefficient (r) was 0.99, and the mean absolute error (MAE) was 1.54 ppm. The experimental test results were almost consistent with the system predictions, and the MAE was 1.39 ppm. This study provides a method of network learning that combines time–frequency domain information, exhibiting high performance in gas classification and concentration prediction within the E-nose system.

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Qualitative and Quantitative Detection of Food Adulteration Using a Smart E-Nose

Cited by 17 publications

References 53 publications

Research Progress of Electronic Nose and Near-Infrared Spectroscopy in Meat Adulteration Detection

Research Progress of Electronic Nose and Near-Infrared Spectroscopy in Meat Adulteration Detection

Enhancing Food Integrity through Artificial Intelligence and Machine Learning: A Comprehensive Review

E-Nose: Time–Frequency Attention Convolutional Neural Network for Gas Classification and Concentration Prediction

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