A Machine Learning Method for the Quantitative Detection of Adulterated Meat Using a MOS-Based E-Nose

Huang, Chang-quan; Gu, Yu

doi:10.3390/foods11040602

Cited by 42 publications

(13 citation statements)

References 41 publications

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“…However, regarding the SSC prediction, the PCR, MLR, and PLS models exhibited low accuracy (R 2 = 0.64–0.66), while the SVR model managed to predict the SSC of potatoes with high precision (93%). The results of this research (about acidity) were consistent with the reports by Huang and Gu [ 60 ], who used a sensor array and machine learning (SVR) to distinguish pork-fake beef with an accuracy of 92%. In another research, Wu et al [ 61 ] utilized an E-nose to detect and predict the contamination of sweet potato with C. Fimbriata with a respective accuracy of 65 and 66% for PLS and PCR, which is very close to the results of this research on the SSC.…”

Section: Resultssupporting

confidence: 89%

Investigating Changes in pH and Soluble Solids Content of Potato during the Storage by Electronic Nose and Vis/NIR Spectroscopy

Khorramifar

Sharabiani

Karami

et al. 2022

Foods

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Potato is an important agricultural product, ranked as the fourth most common product in the human diet. Potato can be consumed in various forms. As customers expect safe and high-quality products, precise and rapid determination of the quality and composition of potatoes is of crucial significance. The quality of potatoes may alter during the storage period due to various phenomena. Soluble solids content (SSC) and pH are among the quality parameters experiencing alteration during the storage process. This study is thus aimed to assess the variations in SSC and pH during the storage of potatoes using an electronic nose and Vis/NIR spectroscopic techniques with the help of prediction models including partial least squares (PLS), multiple linear regression (MLR), principal component regression (PCR), support vector regression (SVR) and an artificial neural network (ANN). The variations in the SSC and pH are ascending and significant. The results also indicate that the SVR model in the electronic nose has the highest prediction accuracy for the SSC and pH (81, and 92%, respectively). The artificial neural network also managed to predict the SSC and pH at accuracies of 83 and 94%, respectively. SVR method shows the lowest accuracy in Vis/NIR spectroscopy while the PLS model exhibits the best performance in the prediction of the SSC and pH with respective precision of 89 and 93% through the median filter method. The accuracy of the ANN was 85 and 90% in the prediction of the SSC and pH, respectively.

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Section: Resultssupporting

confidence: 89%

Investigating Changes in pH and Soluble Solids Content of Potato during the Storage by Electronic Nose and Vis/NIR Spectroscopy

Khorramifar

Sharabiani

Karami

et al. 2022

Foods

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show abstract

“…(6) Return the optimal kernel parameters and regularisation coefficients to KELM to implement the ISHO-KELM model. The beef dataset is a publicly available dataset based on the PEN3 E-nose instrument by Huang and Gu [55] from Beijing University of Chemical Technology, where the researchers mixed minced beef with minced pork in seven different weight ratios (0%, 10%, 20%, 30%, 40%, 50%, and 60%) to simulate adulteration conditions. High This dataset included seven adulterated proportions of meat, with 30 sets of measurements for each, for a total of 210 sets of meat measurements.…”

Section: Isho-kelmmentioning

confidence: 99%

A food quality detection method based on electronic nose technology

Wang,

Chen,

Chen

et al. 2024

Meas. Sci. Technol.

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Food quality detection is of great importance for human health and industrial production. Currently, the common detection methods are difficult to achieve the need for fast, accurate, and non-destructive detection. In this work, an electronic nose (E-nose) detection method based on the combination of Convolutional Neural Network combined with Wavelet Scattering Network (CNN-WSN) and Improved Seahorse Optimizes Kernel Extreme Learning Machine (ISHO-KELM) is proposed for identifying the quality level of a variety of food products. In the feature extraction part, the abstract features of Convolutional Neural Network (CNN) are fused with the scattering features of Wavelet Scattering Network (WSN), and the obtained CNN-WSN fusion features can characterize the original information of the food quality effectively. In the classifier design and decision-making section, chaotic mapping is used to initialize the population in the Seahorse Optimisation Algorithm (SHO), avoiding the problem that SHO may fall into local optimal solutions. The kernel parameters and regularisation coefficients of the Kernel Extreme Learning Machine (KELM) model were then optimized by improving the locomotion, predation, and reproduction behaviors of the hippocampal populations, which solved the problem of the difficult selection of the key parameters in the model, and thus improved the accuracy and generalization of the overall model. To validate the effectiveness of the proposed food quality detection model, the E-nose system was first built and milk quality data were collected independently, and then tested on two publicly available food quality datasets as well as a self-collected milk quality dataset, respectively. The experimental results show that the food quality detection method proposed in this work has good quality assessment effect on different datasets.

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“…A one-dimensional convolutional neural network (1DCNN) and random forest regressor (RFR) which is as combined known as 1DCNN-RFR was proposed by Changquan Huang and Yu Gu 2022 [58]. This 1DCNN-RFR method is being used for the quantity recognition and detection of pork meat adulterated using ENose data.…”

Section: Enose Parts: Pre-processing and Post-processing Techniquesmentioning

confidence: 99%

E-Nose design and structures from statistical analysis to application in robotic: a compressive review

Moshayedi

Khan

Yang

et al. 2023

EAI Endorsed Trans AI Robotics

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Since 1982, the olfactory system of creatures has piqued the interest of academics who seek to create a comparable system. Despite its mysterious nature, the first stage has been successfully completed with the development of the E-nose. Its extended applications have opened new doors for researchers, ranging from food quality testing to bomb detection and even, more recently, identifying those infected with the coronavirus. In this talk, we will review the structure and sensor behavior of the E-nose, as well as its applications, such as odour source localization and various applications in agriculture. The challenge of odour identification has prompted researchers to employ robots with sensors to investigate and locate odour sources. The present study aims to synthesize documented research and provide a fresh perspective on odour localization research efforts and tests conducted. The study highlights previous attempts to equip robots with sensors to explore the real indoor or outdoor environment. Initially, a review was conducted to investigate various aspects of the sector and the obstacles involved.

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A Machine Learning Method for the Quantitative Detection of Adulterated Meat Using a MOS-Based E-Nose

Cited by 42 publications

References 41 publications

Investigating Changes in pH and Soluble Solids Content of Potato during the Storage by Electronic Nose and Vis/NIR Spectroscopy

Investigating Changes in pH and Soluble Solids Content of Potato during the Storage by Electronic Nose and Vis/NIR Spectroscopy

A food quality detection method based on electronic nose technology

E-Nose design and structures from statistical analysis to application in robotic: a compressive review

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