Electronic nose and its application in the food industry: a review

Wang, Mingyang; Chen, Yinsheng

doi:10.1007/s00217-023-04381-z

Cited by 6 publications

(3 citation statements)

References 352 publications

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“…Noise is often unavoidable when using E-nose systems for detection and is usually distributed in the high-frequency range. Examples include electromagnetic interference, airflow disturbances, and mechanical vibrations from outside the system, or flicker noise, scattering noise, and Gaussian white noise from circuits inside the system [49]. Therefore, the CNN method alone is susceptible to interference from noise in the high-frequency information, which can greatly reduce the detection accuracy of the model.…”

Section: Cnn-wsnmentioning

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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Section: Cnn-wsnmentioning

confidence: 99%

A food quality detection method based on electronic nose technology

Wang,

Chen,

Chen

et al. 2024

Meas. Sci. Technol.

Self Cite

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“…ENs are essential for maintaining product safety and quality in the food and beverage industries because they can identify odor changes that could be signs of adulteration, contamination, or spoiling. When it comes to determining whether perishable foods are fresh or not, as well as spotting flavor irregularities, ENs offer insightful information that supports manufacturers in upholding strict guidelines for product integrity and customer pleasure [68]. ENs are also used in environmental monitoring to track industrial emissions, identify contaminants, and evaluate air quality.…”

Section: Applications Of Electronic Nose (En) Technology Across Indus...mentioning

confidence: 99%

Smart coffee aromas: A literature review on electronic nose technologies for quality assessment

Ira Mae Gallo Caray,

King Paulo Ramos Ditchon,

Edwin Remeroso Arboleda

2023

World J. Adv. Res. Rev.

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One of the most important aspects of coffee's sensory experience is its scent, which is affected by roasting, microbial contamination, and place of origin. Electronic noses (ENs), which provide quick and precise identification of intricate odor patterns linked to microbial activity, have become highly effective instruments for evaluating coffee odors. This study examines the most recent uses of EN technology for evaluating coffee scent and emphasizes how important it is for assuring the safety and quality of the final product. The integration of ENs with artificial neural networks (ANNs) improves their precision in fragrance profile prediction and classification. Moreover, real-time smell profile monitoring is made possible by automated systems that use ENs, which helps with quality control in the coffee manufacturing process. Beyond the coffee business, EN technology is used in many other areas, including food safety, medical diagnostics, and environmental monitoring. Through innovation in fragrance analysis and quality control, this multidisciplinary approach promotes customer confidence and product integrity across industries.

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“…To do so, the information feature to be input into the system must be simple enough to lower the computational cost for data preprocessing prior to classification. Real-time inference of an eNose is of prior importance in different applications where they show their true relevance, such as for instant detection of environmental pollutants and chemical hazards [31][32][33], high-throughput food-quality assessment [34][35][36], and their compact integration into smartphones for disease early diagnosis [37][38][39]. In most of these applications, a rapid response of the system is often required without users being able to routinely declare when samples of interest are being exposed.…”

Section: Introductionmentioning

confidence: 99%

A Temporal Filter to Extract Doped Conducting Polymer Information Features from an Electronic Nose

Haj Ammar,

Boujnah,

Baron

et al. 2024

Electronics

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Identifying relevant machine learning features for multi-sensing platforms is both an applicative limitation to recognize environments and a necessity to interpret the physical relevance of transducers’ complementarity in their information processing. Particularly for long acquisitions, feature extraction must be fully automatized without human intervention and resilient to perturbations without significantly increasing the computational cost of a classifier. In this study, we investigate the relative resistance and current modulation of a 24-dimensional conductimetric electronic nose, which uses the exponential moving average as a floating reference in a low-cost information descriptor for environment recognition. In particular, we identified that depending on the structure of a linear classifier, the ‘modema’ descriptor is optimized for different material sensing elements’ contributions to classify information patterns. The low-pass filtering optimization leads to opposite behaviors between unsupervised and supervised learning: the latter favors longer integration of the reference, allowing the recognition of five different classes over 90%, while the first one prefers using the latest events as its reference to cluster patterns by environment nature. Its electronic implementation shall greatly diminish the computational requirements of conductimetric electronic noses for on-board environment recognition without human supervision.

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Electronic nose and its application in the food industry: a review

Cited by 6 publications

References 352 publications

A food quality detection method based on electronic nose technology

A food quality detection method based on electronic nose technology

Smart coffee aromas: A literature review on electronic nose technologies for quality assessment

A Temporal Filter to Extract Doped Conducting Polymer Information Features from an Electronic Nose

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