A novel quality evaluation method for magnolia bark using electronic nose and colorimeter data with multiple statistical algorithms

Li, Jiahui; Shao, Yuanyang; Yao, Yuebao; Yu, Yuetong; Cao, Guangzhao; Zou, Hui-Qin; Yan, Yong-Hong

doi:10.1016/j.jtcms.2020.03.004

Cited by 9 publications

(9 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A portable e-nose and a Fourier-transform NIR (FT-NIR) spectrometer were used to assess the quality of this by-product during the storage. In [35], this study was aimed to develop a novel technique for comprehensive and accurate identification of the original locations of Hou Po. Also, to predict the contents of the relevant chemical elements.…”

Section: B System Developmentmentioning

confidence: 99%

“…The e-nose techniques have been demonstrated to be a valuable assessment and authentication approach for meat adulteration detection because of the high accuracy and efficiency of this method [36,45,49]. Also, in the discrimination of oranges according to their geographical origins [51], recognizing the origin locations of the Hou Po samples [35], and detecting and recognizing fresh and moldy apples [54]. In [3], e-nose systems with high ability in classification can be helpful in off flavor detection.…”

Section: A Motivation Related Diagnostic Accuracymentioning

confidence: 99%

See 1 more Smart Citation

A Review on Electronic Nose: Coherent Taxonomy, Classification, Motivations, Challenges, Recommendations and Datasets

et al. 2021

View full text Add to dashboard Cite

Context: Quality Control (QC) has been constantly an essential concern in many fields like food industry production, medical drugs, environmental protection, and so on. An odor or flavor, as a global fingerprint, can be implemented as a non-invasive mechanism for quality assurance. This computer-based approach can assure accurate detection and precise identification of the product quality or manufactured goods.Objective: This paper aims to achieve a systematic review about e-nose by introducing the achievements made by researchers in this area, to summarize their findings, to provide motivations and challenges to new researchers in the field of e-nose. Methods:The articles that were being utilized in the e-nose field were systematically achieved using three search engines: The online library of IEEE Explore, Web of Science and Science Direct for time span of 7 years (from 2013 to 2020). Both medical literature reviews and technical reviews were considered in the criteria of the research for wider understanding in the field of e-nose. The articles were categorized according to the objective of the research and projected into four classes. Upon completion of screening process 333 research papers using the exclusion and inclusion conditions, as the final set 54 articles were selected. Results:The taxonomy of this research was classified into four categories. The first one included the suggested methods that introduced the utilization of the e-nose for classification purposes (9/54 papers). The second category comprises the methods related to the development of e-nose (24/54 papers). The third one included the review studies about the e-nose (8/54 papers). The fourth group comprises comparative studies and evaluation (13/54 papers).Discussion: This systematic review contributes for a clearer understanding and a full insight in the e-nose research field by surveying and categorizing pertinent research efforts.

show abstract

Section: B System Developmentmentioning

confidence: 99%

Section: A Motivation Related Diagnostic Accuracymentioning

confidence: 99%

A Review on Electronic Nose: Coherent Taxonomy, Classification, Motivations, Challenges, Recommendations and Datasets

et al. 2021

View full text Add to dashboard Cite

show abstract

“…In [34], feature extraction of citrus juice during storage for electronic nose based on cellular neural network was developed. In [35], a novel quality evaluation method for magnolia bark was proposed by using electronic nose and colorimeter data with the multiple statistical algorithms. In [36], the authors made the comprehensive research on principles and recent advances in electronic nose for quality inspection of agricultural and food products.…”

Section: The Related Applicationsmentioning

confidence: 99%

Using Recurrent Neural Network to Optimize Electronic Nose System with Dimensionality Reduction

Zou

2020

Electronics

View full text Add to dashboard Cite

Electronic nose is an electronic olfactory system that simulates the biological olfactory mechanism, which mainly includes gas sensor, data pre-processing, and pattern recognition. In recent years, the proposals of electronic nose have been widely developed, which proves that electronic nose is a considerably important tool. However, the most recent studies concentrate on the applications of electronic nose, which gradually neglects the inherent technique improvement of electronic nose. Although there are some proposals on the technique improvement, they usually pay attention to the modification of gas sensor module and barely consider the improvement of the last two modules. Therefore, this paper optimizes the electronic nose system from the perspective of data pre-processing and pattern recognition. Recurrent neural network (RNN) is used to do pattern recognition and guarantee accuracy rate and stability. Regarding the high-dimensional data pre-processing, the method of locally linear embedding (LLE) is used to do dimensionality reduction. The experiments are made based on the real sensor drift dataset, and the results show that the proposed optimization mechanism not only has higher accuracy rate and stability, but also has lower response time than the three baselines. In addition, regarding the usage of RNN model, the experimental results also show its efficiency in terms of recall ratio, precision ratio, and F1 value.

show abstract

“…Several studies have conducted useful explorations on the origin division, special processing, and quality evaluation of MOC using electronic nose, electronic tongue, and chemical composition analysis. Li [38] developed different discriminant models for differentiating the origins of MOC using a combination of an electronic nose and colorimeter. It was indicated that the random forest classifier combined with the tenfold cross-validation exhibited the highest accuracy, providing a promising method for quantitatively evaluating the quality of MOC.…”

Section: Introductionmentioning

confidence: 99%

Application of Multiple-Source Data Fusion for the Discrimination of Two Botanical Origins of Magnolia Officinalis Cortex Based on E-Nose Measurements, E-Tongue Measurements, and Chemical Analysis

Jing

Zhao

et al. 2022

Molecules

View full text Add to dashboard Cite

Magnolia officinalis Rehd. et Wils. and Magnolia officinalis Rehd. et Wils. var. biloba Rehd. et Wils, as the legal botanical origins of Magnoliae Officinalis Cortex, are almost impossible to distinguish according to their appearance traits with respect to medicinal bark. The application of AFLP molecular markers for differentiating the two origins has not yet been successful. In this study, a combination of e-nose measurements, e-tongue measurements, and chemical analyses coupled with multiple-source data fusion was used to differentiate the two origins. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) were applied to compare the discrimination results. It was shown that the e-nose system presented a good discriminant ability with a low classification error for both LDA and QDA compared with e-tongue measurements and chemical analyses. In addition, the discriminating capacity of LDA for low-level fusion with original data, similar to a combined system, was superior or equal to that acquired individually with the three approaches. For mid-level fusion, the combination of different principals extracted by PCA and variables obtained on the basis of PLS-VIP exhibited an analogous discrimination ability for LDA (classification error 0.0%) and was significantly superior to QDA (classification error 1.67–3.33%). As a result, the combined e-nose, e-tongue, and chemical analysis approach proved to be a powerful tool for differentiating the two origins of Magnoliae Officinalis Cortex.

show abstract

A novel quality evaluation method for magnolia bark using electronic nose and colorimeter data with multiple statistical algorithms

Cited by 9 publications

References 28 publications

A Review on Electronic Nose: Coherent Taxonomy, Classification, Motivations, Challenges, Recommendations and Datasets

A Review on Electronic Nose: Coherent Taxonomy, Classification, Motivations, Challenges, Recommendations and Datasets

Using Recurrent Neural Network to Optimize Electronic Nose System with Dimensionality Reduction

Application of Multiple-Source Data Fusion for the Discrimination of Two Botanical Origins of Magnolia Officinalis Cortex Based on E-Nose Measurements, E-Tongue Measurements, and Chemical Analysis

Contact Info

Product

Resources

About