Autofluorescence, the endogenous fluorescence present in cells and tissues, has historically been considered a nuisance in biomedical imaging. Many endogenous fluorophores, specifically, collagen, elastin, nicotinamide adenine dinucleotide, and flavin adenine dinucleotide (FAD), are found throughout the human body. In fluorescence imaging scenarios, these signals can be prohibitive as they can outcompete signals introduced for diagnostic purposes. However, autofluorescence also contains information that has diagnostic value. Recent advances in hyperspectral imaging have allowed the acquisition of significantly more data in a shorter time period by scanning the excitation spectra of fluorophores. The reduced acquisition time and increased signal-to-noise ratio allow for separation of significantly more fluorophores than previously possible. We propose to utilize excitation-scanning hyperspectral imaging of autofluorescence to differentiate neoplastic lesions from surrounding non-neoplastic "normal" tissue. The spectra of isolated autofluorescent molecules are obtained using a custom inverted microscope (TE-2000, Nikon Instruments) with an Xe arc lamp and thin-film tunable filter array (VersaChrome, Semrock, Inc.). Scans utilize excitation wavelengths from 360 to 550 nm in 5-nm increments. The resultant molecule-specific spectra are used to analyze hyperspectral image stacks from normal and neoplastic colorectal tissues. Due to a limited number of samples, neoplastic tissues examined here are a pool of both colorectal adenocarcinoma and adenomatous polyps. The hyperspectral images are analyzed with ENVI software and custom MATLAB scripts, including linear spectral unmixing. Initial results indicate the ability to separate signals of endogenous fluorophores and measure the relative concentrations of fluorophores among healthy and diseased states, in this case, normal colon versus neoplastic colon. These results suggest pathology-specific changes to endogenous fluorophores can be detected using excitation-scanning hyperspectral imaging. Future work will focus on expanding the library of pure molecules, exploring histogram distance metrics as a means for identifying deviations in spectral signatures, and examining more defined disease states.
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http://dx.doi.org/10.1117/1.JBO.24.2.021207 | DOI Listing |
Zhongguo Zhong Yao Za Zhi
December 2024
Jiangsu Dualix Spectral Imaging Co., Ltd. Wuxi 214000, China.
This study aims to establish a rapid and non-destructive method for recognizing the origins and cultivation patterns of Astragali Radix. A hyperspectral imaging system(spectral ranges: 400-1 000 nm, 900-1 700 nm; detection time: 15 s) was used to examine the samples of Astragali Radix with different origins and cultivation patterns. The collected hyperspectral datasets were highly correlated and numerous, which required the establishment of stable and reliable dimension reduction and classification models.
View Article and Find Full Text PDFZhongguo Zhong Yao Za Zhi
December 2024
Key Laboratory of Modern Preparation of TCM,Ministry of Education, Jiangxi University of Chinese Medicine Nanchang 330004, China National Key Laboratory of Creation of Modern Chinese Medicine with Classical Formulas Nanchang 330004, China.
In recent years, with the increasing societal focus on drug quality and safety, quality issues have become a major challenge faced by the pharmaceutical industry, directly impacting consumer health and market trust. By combining multispectral imaging technology with machine learning, it is possible to achieve rapid, non-destructive, and precise detection of traditional Chinese medicine(TCM) preparations, thereby revolutionizing traditional detection methods and developing more convenient and automated solutions. This paper provides a comprehensive review of the current applications of rapid, non-destructive detection techniques based on machine learning algorithms in the field of TCM preparations.
View Article and Find Full Text PDFChem Biodivers
January 2025
Department of Horticultural Science, Faculty of Agriculture, Jahrom University, Jahrom, Iran.
The approaches used to determine the medicinal properties of the plants are often destructive, labor-intensive, time-consuming, and expensive, making it impossible to analyze their quality analysis online. Performance of hyperspectral imaging (HSI) integrated with intelligent techniques to overcome these problems was investigated in this research. For this purpose, three classification methods-support vector machine, random forest (RF), and extreme gradient boosting-were studied for the classification of plants in three classes of medicinal, edible, and ornamental for the organs of leaf, stem, flower, and root.
View Article and Find Full Text PDFCurr Res Food Sci
December 2024
Department of Food and Nutrition, University of Helsinki, P.O. Box 66, 00014, Helsinki, Finland.
Modified Atmosphere Packaging (MAP) is a conventional method used to prolong the shelf-life of fresh-cut vegetables, including lettuce. However, MAP-stored lettuce remains perishable, and its deterioration mechanism is not fully understood. Here, we utilized non-targeted LC-MS metabolomics to evaluate the effects of cutting and extended storage time on metabolite profiles of lettuce stored in MAP.
View Article and Find Full Text PDFSci Rep
January 2025
School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.
In this study, we used desert soil from Gansu, China, as a sample to propose a method for designing hyperspectral stealth coatings against desert soil backgrounds within the spectral range of 400-2500 nm, and the corresponding coating was prepared. Firstly, the correlation between the composition and typical spectral detected characteristics of the desert soil was systematically analyzed. It was found that the color and the spectrum of the desert soil in the range of 400-1000 nm were influenced by different types of iron oxides.
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