Data Fusion of ion Mobility Spectrometry Combined with Hierarchical Clustering Analysis for the Quality Assessment of Apple Essence

Sha, Mo; Zhang, Zhengyong; Gui, Dongdong; Li, Han; Fu, Linglin; Wang, Haiyan

doi:10.1007/s12161-017-0910-6

Cited by 8 publications

(3 citation statements)

References 22 publications

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“…HCA aims to establish the hierarchy of a cluster, which can be represented by tree structure. The root of the tree is a cluster that includes all samples, and the leaf corresponds to each sample . Agglomerative HCA was adopted for correlation study between Raman features in this study.…”

Section: Methodsmentioning

confidence: 99%

Improving Raman spectroscopic identification of rice varieties by feature extraction

Sha

Zhang

et al. 2020

J Raman Spectroscopy

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Raman spectroscopy combined with pattern recognition can identify rice varieties excellently. In this study, a method was established to select the key feature for identification model. Seventy‐two Raman spectra of three varieties of rice were analyzed. Seventy‐one independent variables and characteristic bands (420–560, 820–980, 1,000–1,200, and 1,300–1,500 cm−1) were obtained by principal component analysis (PCA). Window analysis further narrowed the range of characteristic bands (451–550, 951–1,000, and 1,351–1,450 cm−1). Hierarchical cluster analysis (HCA) obtained 30 wavenumbers with small correlation. The prediction accuracy was 91.71%, whereas the time was reduced by 10 times when these 30 wavenumbers were used to establish the identification model. The method combined PCA, window analysis, and HCA with support vector machine can be used as an effective feature extraction method to improve the efficiency for identification of rice varieties. Under the circumstances of large sample size or relatively complex data, the screening of Raman spectrum information is an important means to simplify the model and improve the prediction efficiency.

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Section: Methodsmentioning

confidence: 99%

Improving Raman spectroscopic identification of rice varieties by feature extraction

Sha

Zhang

et al. 2020

J Raman Spectroscopy

Self Cite

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“…Hierarchical Clustering Analysis (HCA) is a technique used to find the underlying structure or clustering tendency of objects through an iterative process that associates (agglomerative methods) or dissociates (divisive methods) the objects based on the information contained in the fingerprint matrix [ 39 ]. In this paper, the hierarchical agglomerative clustering method was used to cluster the habitat quality scores of different counties according to the similarity of the objects.…”

Section: Methodsmentioning

confidence: 99%

Spatial and Temporal Variations of Habitat Quality and Its Response of Landscape Dynamic in the Three Gorges Reservoir Area, China

Liu

Liao

Xiao

et al. 2022

IJERPH

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Habitat quality is an important indicator for assessing biodiversity and is critical to ecosystem processes. With urban development and construction in developing countries, habitat quality is increasingly influenced by landscape pattern changes. This has made habitat conservation to be an increasingly urgent issue. Despite the growing interest in this issue, studies that reveal the role of land use change in habitat degradation at multiple scales are still lacking. Therefore, we analyzed the spatial and temporal variations of habitat quality of the Three Gorges Reservoir area by the InVEST habitat quality model and demonstrated the responses of habitat quality to various landscape dynamics by correspondence analysis. The result showed that the habitat quality score of this area increased from 0.685 in 2000 to 0.739 in 2015 and presented a significant spatial heterogeneity. Habitat quality was significantly higher in the northeastern and southwestern parts of the reservoir area than in other regions. Meanwhile, habitat quality improved with altitude and slope, and increased for all altitude and slope zones. The habitat quality of >1000 m and >25° zone exceeds 0.8, while the habitat quality of <500 m and <15° zone is less than 0.6. Habitat quality significantly varied among landscape dynamics and was extremely sensitive to vegetation recovery and urban expansion. The vegetation restoration model of returning farmland to forest is difficult to sustain, so we suggest changing the vegetation recovery model to constructing complex vegetation community. This study helps us to better understand the effects of landscape pattern changes on habitat quality and can provide a scientific basis for formulating regional ecological conservation policies and sustainable use of land resources.

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“…However, the data fusion strategy based on clear chemical feature extraction is simple, intuitive and practical. [33][34][35] The above-described research revealed different feature regions of Raman spectra of dairy products making different contributions to the identication methods of sample categories. Aer normalization, the recognition accuracy of the model improved, and at the same time more than one spectral interval with an accuracy rate of greater than 70% was found.…”

mentioning

confidence: 98%

The statistical fusion identification of dairy products based on extracted Raman spectroscopy

Zhang

2020

RSC Adv.

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Data Fusion of ion Mobility Spectrometry Combined with Hierarchical Clustering Analysis for the Quality Assessment of Apple Essence

Cited by 8 publications

References 22 publications

Improving Raman spectroscopic identification of rice varieties by feature extraction

Improving Raman spectroscopic identification of rice varieties by feature extraction

Spatial and Temporal Variations of Habitat Quality and Its Response of Landscape Dynamic in the Three Gorges Reservoir Area, China

The statistical fusion identification of dairy products based on extracted Raman spectroscopy

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