A novel framework for analyzing MOS E-nose data based on voting theory: Application to evaluate the internal quality of Chinese pecans

Jiang, Shui; Wang, Jun; Wang, Yongwei; Cheng, Sibo

doi:10.1016/j.snb.2016.11.074

Cited by 49 publications

(26 citation statements)

References 24 publications

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“…Classifying storage quality of Chinese pecans and predicting fatty acids content [72] MO x gas sensor b) SVM, FNN f )…”

Section: Classificationmentioning

confidence: 99%

“…Electronic chemical noses are commonly used to assess the quality of food in this category. Jiang et al [72] developed two models to classify the freshness quality of Carya cathayensis (Chinese pecans) at different storage times and quantitative predictions of its fatty acid profiles. Five distinct features Figure 11.…”

Section: Precision Agriculture and Food Sciencementioning

confidence: 99%

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Machine Learning‐Enabled Smart Sensor Systems

Ren

et al. 2020

Advanced Intelligent Systems

114

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Recent advancements and major breakthroughs in machine learning (ML) technologies in the past decade have made it possible to collect, analyze, and interpret an unprecedented amount of sensory information. A new era for “smart” sensor systems is emerging that changes the way that conventional sensor systems are used to understand the world. Smart sensor systems have taken advantage of classic and emerging ML algorithms and modern computer hardware to create sophisticated “smart” models that are tailored specifically for sensing applications and fusing diverse sensing modalities to gain a more holistic appreciation of the system being monitored. Herein, a review of the recent sensing applications, which harness ML enabled smart sensor systems, is presented. First well‐known ML algorithms implemented in smart sensor systems for practical sensing applications are discussed. Subsequent sections summarize the practical sensing applications under two major categories: physical and chemical sensing and visual imaging sensing describing how the sensor technologies are coupled with ML “smart” models and how these systems achieve practical benefits. Finally, an outlook on the current trajectory and challenges that will be faced by future smart sensing systems and the opportunities that may be unlocked is provided.

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“…Classifying storage quality of Chinese pecans and predicting fatty acids content [72] MO x gas sensor b) SVM, FNN f )…”

Section: Classificationmentioning

confidence: 99%

Section: Precision Agriculture and Food Sciencementioning

confidence: 99%

Machine Learning‐Enabled Smart Sensor Systems

Ren

et al. 2020

Advanced Intelligent Systems

114

View full text Add to dashboard Cite

show abstract

“…In previous studies, the maximum response values, maximum integral values, maximum differential values, and the maximum slop values were always taken as the feature data [47,48]. The complete flavor information of the sample could not be presented by just one feature, and the most useful information could be abandoned during the feature extracting process.…”

Section: Responses Presentation and Feature Data Extractionmentioning

confidence: 99%

Collaborative Analysis on the Marked Ages of Rice Wines by Electronic Tongue and Nose based on Different Feature Data Sets

Zhang

Shao

Qiu

et al. 2020

Sensors

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Aroma and taste are the most important attributes of alcoholic beverages. In the study, the self-developed electronic tongue (e-tongue) and electronic nose (e-nose) were used for evaluating the marked ages of rice wines. Six types of feature data sets (e-tongue data set, e-nose data set, direct-fusion data set, weighted-fusion data set, optimized direct-fusion data set, and optimized weighted-fusion data set) were used for identifying rice wines with different wine ages. Pearson coefficient analysis and variance inflation factor (VIF) analysis were used to optimize the fusion matrixes by removing the multicollinear information. Two types of discrimination methods (principal component analysis (PCA) and locality preserving projections (LPP)) were used for classifying rice wines, and LPP performed better than PCA in the discrimination work. The best result was obtained by LPP based on the weighted-fusion data set, and all the samples could be classified clearly in the LPP plot. Therefore, the weighted-fusion data were used as independent variables of partial least squares regression, extreme learning machine, and support vector machines (LIBSVM) for evaluating wine ages, respectively. All the methods performed well with good prediction results, and LIBSVM presented the best correlation coefficient (R2 ≥ 0.9998).

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“…In recent years, as a reliable, time-saving, and cost-efficient technique, the Electronic Nose (E-nose) has been applied in many fields, including aided medical diagnosis [1,2], food engineering [3], environmental control [4,5], and explosive detection [6]. Specifically, Metal Oxide Semiconductor (MOS) gas sensors, which have the advantage of cross-sensitivity, broad spectrum response, and low-cost, have been widely used in conjunction with the E-nose [7]. Identification of an unknown odor is the core content of the research and application of the E-nose.…”

Section: Introductionmentioning

confidence: 99%

A Suppression Method of Concentration Background Noise by Transductive Transfer Learning for a Metal Oxide Semiconductor-Based Electronic Nose

Liu

et al. 2020

Sensors

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Signal drift caused by sensors or environmental changes, which can be regarded as data distribution changes over time, is related to transductive transfer learning, and the data in the target domain is not labeled. We propose a method that learns a subspace with maximum independence of the concentration features (MICF) according to the Hilbert-Schmidt Independence Criterion (HSIC), which reduces the inter-concentration discrepancy of distributions. Then, we use Iterative Fisher Linear Discriminant (IFLD) to extract the signal features by reducing the divergence within classes and increasing the divergence among classes, which helps to prevent inconsistent ratios of different types of samples among the domains. The effectiveness of MICF and IFLD was verified by three sets of experiments using sensors in real world conditions, along with experiments conducted in the authors’ laboratory. The proposed method achieved an accuracy of 76.17%, which was better than any of the existing methods that publish their data on a publicly available dataset (the Gas Sensor Drift Dataset). It was found that the MICF-IFLD was simple and effective, reduced interferences, and deftly managed tasks of transfer classification.

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A novel framework for analyzing MOS E-nose data based on voting theory: Application to evaluate the internal quality of Chinese pecans

Cited by 49 publications

References 24 publications

Machine Learning‐Enabled Smart Sensor Systems

Machine Learning‐Enabled Smart Sensor Systems

Collaborative Analysis on the Marked Ages of Rice Wines by Electronic Tongue and Nose based on Different Feature Data Sets

A Suppression Method of Concentration Background Noise by Transductive Transfer Learning for a Metal Oxide Semiconductor-Based Electronic Nose

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