Cited by Rapid Identification of Fritillaria Spp. Using Multi-Wavebands Spectroscopy and Multi-Source Data Fusion Strategies

A Clustering Algorithm Based on Local Relative Density DOI

Yujuan Zou, Zhijian Wang, Xiangchen Wang

et al.

Electronics, Journal Year: 2025, Volume and Issue: 14(3), P. 481 - 481

Published: Jan. 24, 2025

DBSCAN and DPC are typical density-based clustering algorithms. These two algorithms have their drawbacks, such as difficulty in when there significant differences density between clusters. This study proposes a algorithm, RDBSCAN, which is based on local relative density, drawing the extension strategy of allocation mechanism DPC. The algorithm first uses k-nearest neighbors to calculate original then sorts points descending order this density. It selects point with highest from unprocessed center next cluster. Based center, RDBSCAN calculates determines core objects, performs cluster expansion. Drawing DPC, secondary for clusters that too small complete final clustering. Comparative experiments using eight other were conducted, test results show ranks performance metrics among all synthetic datasets second real-world datasets.

Language: Английский

Citations

Rapid and accurate identification of Panax ginseng origins based on data fusion of near-infrared and laser-induced breakdown spectroscopy DOI

Jiacong Ping,

Nan Hao,

Xuting Guo

et al.

Food Research International, Journal Year: 2025, Volume and Issue: 204, P. 115925 - 115925

Published: Feb. 7, 2025

Language: Английский

Citations

Characterizing Chinese saffron Origin, Age and grade using VNlR hyperspectral imaging and Machine learning DOI

Jiahui Wu,

Jing Nie,

Hu Hao

et al.

Food Research International, Journal Year: 2025, Volume and Issue: 202, P. 115585 - 115585

Published: Jan. 2, 2025

Language: Английский

Citations

Analysis of Volatile Compounds and Vintage Discrimination of Raw Pu-erh Tea Based on GC-IMS and GC-MS Combined with Data Fusion DOI

Haoran Huang, Xinyu Chen, Ying Wang

et al.

Journal of Chromatography A, Journal Year: 2025, Volume and Issue: 1743, P. 465683 - 465683

Published: Jan. 14, 2025

Language: Английский

Citations

Data integrity of food and machine learning: Strategies, advances and prospective DOI

Chenming Li, Jieqing Li,

Yuanzhong Wang

et al.

Food Chemistry, Journal Year: 2025, Volume and Issue: unknown, P. 143831 - 143831

Published: March 1, 2025

Language: Английский

Citations

Rapid identification of Fritillaria spp. using multi-wavebands spectroscopy and multi-source data fusion strategies DOI

Yuchen Tang,

Wennan Nie,

Yao Zhang

et al.

Journal of Applied Research on Medicinal and Aromatic Plants, Journal Year: 2025, Volume and Issue: unknown, P. 100636 - 100636

Published: April 1, 2025

Language: Английский

Citations

Rapid and chemical-free technique based on hyperspectral imaging combined with artificial intelligence for monitoring quality and shelf life of dried shrimp DOI

Ubonrat Siripatrawan, Yoshio Makino

Food Research International, Journal Year: 2025, Volume and Issue: unknown, P. 116422 - 116422

Published: April 1, 2025

Language: Английский

Citations

The Prediction of Kiwi Quality Attributes Based on Multi-Source Data Fusion Comprehensive Analysis Model Using HSI and FHSI DOI

Yuchen Xiao,

Dongyu Yuan, Zhiyong Zou

et al.

Journal of Food Composition and Analysis, Journal Year: 2025, Volume and Issue: unknown, P. 107645 - 107645

Published: April 1, 2025

Language: Английский

Citations

Unraveling volatile metabolites in pigmented onion (Allium cepa L.) bulbs through HS-SPME/GC–MS-based metabolomics and machine learning DOI

Kaiqi Cheng,

Jin-Chang Xiao,

Jingyuan He

et al.

Frontiers in Nutrition, Journal Year: 2025, Volume and Issue: 12

Published: April 22, 2025

Introduction Colored onions are favored by consumers due to their distinctive aroma, rich phytochemical content, and diverse biological activities. However, comprehensive analyses of profiles volatile metabolites remain limited. Methods In this study, total phenols, flavonoids, anthocyanins, carotenoids, antioxidant activities three colored onion bulbs were evaluated. Volatile identified using headspace solid-phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME/GC-MS). Multivariate statistical analyses, feature selection techniques (SelectKBest, LASSO), machine learning models applied further analyze classify the metabolite profiles. Results Significant differences in composition observed among types. A 243 detected, sulfur compounds accounting for 51-64%, followed organic acids derivatives (4-19%). analysis revealed distinct profiles, 19 key as biomarkers. Additionally, 33 38 selected SelectKBest LASSO, respectively. The features LASSO enabled clear differentiation types via PCA, UMAP, k-means clustering. Among four tested, random forest model achieved highest classification accuracy (1.00). SHAP confirmed 20 potential markers. Conclusion findings suggest that combination HS-SPME/GC-MS learning, particularly algorithm, is a powerful approach characterizing classifying onions. This method holds quality assessment breeding applications.

Language: Английский

Citations

Combining with acid-base titration, HPLC, ATR-FTIR and chemometrics to study the effects of sulfur fumigation on medicinal and edible starchy samples DOI

Yuchen Tang, Jianyu Zhang, Ying Xu

et al.

Journal of Food Composition and Analysis, Journal Year: 2024, Volume and Issue: 137, P. 106967 - 106967

Published: Nov. 14, 2024

Language: Английский

Citations