Hyperspectral imaging technology is expected as a breakthrough to resolve the lack of objective indicators on the diagnosis of TCM syndrome because of its high sensitivity and including abundant information of the images and spectra. In view of fuzzy and complicated mappings between tongue and syndrome, aiming at the defects of the acquisition methods of tongue information and its processing mode which extracts the features by fragmenting the integrative information, a new idea is proposed that the specific spectral indices pool be extracted after acquiring the hyperspectral data cube of tongue by hyperspectral imaging technology and associating it as a whole because of the overlapping mixing of these characteristic information with syndrome in black-box mode by means of intergration of various linear and nonlinear data mining algorithms. The mechanisms of etiological factor and pathogenesis are analyzed from all angles by synthesis of specific spectral indices pool, clinical physiological and biochemical indicators and TCM indicators. Then a new mode of objective diagnosis for syndromes can be found.
Download full-text PDF |
Source |
|---|
Publication Analysis
Top Keywords
Similar Publications
Non-destructive identification of commercial jerky types based on multi-band hyperspectral imaging with machine learning.
Food Chem X
February 2025
Food Science College, Xizang Agriculture & Animal Husbandry University, R&D Center of Agricultural Products with Xizang Plateau Characteristics, The Provincial and Ministerial Co-founded Collaborative Innovation Center for R&D in Xizang Characteristic Agricultural and Animal Husbandry Resources, Nyingchi 860000, China.
Commercial jerky counterfeiting is widespread in the market. This study combined visible-near-infrared and short-wave-near-infrared hyperspectral imaging along with multiple machine learning algorithms for non-destructive identification of five types of commercial jerky products, and explored the impact of different spectral bands, algorithm selection, and optimization methods on identification performance. After data preprocessing, all models' accuracies and stability improved.
View Article and Find Full Text PDFImproving lung cancer pathological hyperspectral diagnosis through cell-level annotation refinement.
Sci Rep
March 2025
Brussel Photonics, Department of Applied Physics and Photonics, Vrije Universiteit Brussel and Flanders Make, 1050, Brussels, Belgium.
Lung cancer remains a major global health challenge, and accurate pathological examination is crucial for early detection. This study aims to enhance hyperspectral pathological image analysis by refining annotations at the cell level and creating a high-quality hyperspectral dataset of lung tumors. We address the challenge of coarse manual annotations in hyperspectral lung cancer datasets, which limit the effectiveness of deep learning models requiring precise labels for training.
View Article and Find Full Text PDFAdvances of Vis/NIRS and imaging techniques assisted by AI for tea processing.
Crit Rev Food Sci Nutr
March 2025
National Research Center of Engineering and Technology for Utilization of Botanical Functional Ingredients, Hunan Agricultural University, Changsha, P.R. China.
Tea is one of the most popular drinks due to its distinct flavor and numerous health benefits. The quality of tea is closely related to production processing. Human sensory evaluation is the conventional method for quality monitoring in tea processing.
View Article and Find Full Text PDFHyperspectral discrimination of vegetable crops grown under organic and conventional cultivation practices: a machine learning approach.
Sci Rep
March 2025
Department of Organic Agriculture, Kerala Agricultural University, Thiruvananthapuram, Kerala, 695522, India.
A verifiable and regional level method for mapping crops cultivated under organic practices holds significant promise for certifying and ensuring the quality of farm products marketed as organic. The prevailing method for the identification of organic crops involves labor-intensive manual inspections, detailed record-keeping of crop stages, and certification. Hyperspectral remote sensing is an evolving general sensing technique for extracting crop information across various scales.
View Article and Find Full Text PDFInk classification in historical documents using hyperspectral imaging and machine learning methods.
Spectrochim Acta A Mol Biomol Spectrosc
February 2025
Department of Optics, University of Granada, Faculty of Sciences, Campus Fuentenueva, s/n, Granada, 18071, Spain.
Ink identification using only spectral reflectance information poses significant challenges due to material degradation, aging, and spectral overlap between ink classes. This study explores the use of hyperspectral imaging and machine learning techniques to classify three distinct types of inks: pure metallo-gallate, carbon-containing, and non-carbon-containing inks. Six supervised classification models, including five traditional algorithms (Support Vector Machines, K-Nearest Neighbors, Linear Discriminant Analysis, Random Forest, and Partial Least Squares Discriminant Analysis) and one Deep Learning-based model, were evaluated.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!