Mimicking Multimodal Contrast with Vertex Component Analysis of Hyperspectral CARS Images

Tabarangao, Joel T.; Slepkov, Aaron D.

doi:10.1155/2015/575807

Cited by 6 publications

(4 citation statements)

References 26 publications

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“…To our knowledge, this is the first time that such a nonlinear unmixing method is applied to evaluate nonlinear CARS data of biological samples. Previous studies in the field of Raman micro-spectroscopy used linear mixing models like the popular VCA algorithm [ 48 – 50 ], which was even applied to CARS data [ 51 ].…”

Section: Discussionmentioning

confidence: 99%

Chemical fingerprinting of single glandular trichomes of Cannabis sativa by Coherent anti-Stokes Raman scattering (CARS) microscopy

et al. 2018

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BackgroundCannabis possesses a rich spectrum of phytochemicals i.e. cannabinoids, terpenes and phenolic compounds of industrial and medicinal interests. Most of these high-value plant products are synthesised in the disk cells and stored in the secretory cavity in glandular trichomes. Conventional trichome analysis was so far based on optical microscopy, electron microscopy or extraction based methods that are either limited to spatial or chemical information. Here we combine both information to obtain the spatial distribution of distinct secondary metabolites on a single-trichome level by applying Coherent anti-Stokes Raman scattering (CARS), a microspectroscopic technique, to trichomes derived from sepals of a drug- and a fibre-type.ResultsHyperspectral CARS imaging in combination with a nonlinear unmixing method allows to identify and localise Δ9-tetrahydrocannabinolic acid (THCA) in the secretory cavity of drug-type trichomes and cannabidiolic acid (CBDA)/myrcene in the secretory cavity of fibre-type trichomes, thus enabling an easy discrimination between high-THCA and high-CBDA producers. A unique spectral fingerprint is found in the disk cells of drug-type trichomes, which is most similar to cannabigerolic acid (CBGA) and is not found in fibre-type trichomes. Furthermore, we differentiate between different cell types by a combination of CARS with simultaneously acquired two-photon fluorescence (TPF) of chlorophyll a from chloroplasts and organic fluorescence mainly arising from cell walls enabling 3D visualisation of the essential oil distribution and cellular structures.ConclusionHere we demonstrate a label-free and non-destructive method to analyse the distribution of secondary metabolites and distinguish between different cell and chemo-types with high spatial resolution on a single trichome. The record of chemical fingerprints of single trichomes offers the possibility to optimise growth conditions as well as guarantee a direct process control for industrially cultivated medicinal Cannabis plants. Moreover, this method is not limited to Cannabis related issues but can be widely implemented for optimising and monitoring all kinds of natural or biotechnological production processes with simultaneous spatial and chemical information.Electronic supplementary materialThe online version of this article (10.1186/s12870-018-1481-4) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Chemical fingerprinting of single glandular trichomes of Cannabis sativa by Coherent anti-Stokes Raman scattering (CARS) microscopy

et al. 2018

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“…The proposed feature selection method not only reduces the number of model variables but also decreases the model s complexity, thereby improving the predictive performance and robustness of the model. Based on spectral preprocessing, we utilized five conventional methods, namely genetic algorithm (GA), successive projections algorithm (SPA), UVE, CARS, and least angle regression (LARS) [28][29][30][31][32], for variable selection of the spectral data. The goal was to select appropriate spectral variables to be used in the quantitative analysis of P. massoniana seedling moisture content.…”

Section: Feature Selectionmentioning

confidence: 99%

Detection of Moisture Content of Pinus massoniana Lamb. Seedling Leaf Based on NIR Spectroscopy with a Multi-Learner Model

Xia

Liu

et al. 2023

Forests

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The growth quality of Pinus massoniana (Lamb.) seedlings is closely related to the survival rate of afforestation. Moisture content detection is an important indicator in the cultivation of forest seedlings because it can directly reflect the adaptability and growth potential of the seedlings to the soil environment. To improve the accuracy of quantitative analysis of moisture content in P. massoniana seedlings using near-infrared spectroscopy, a total of 100 P. massoniana seedlings were collected, and their near-infrared diffuse reflectance spectra were measured in the range of 2500 to 800 nm (12,000 to 4000 cm−1). An integrated learning framework was introduced, and a quantitative detection model for moisture content in P. massoniana seedlings was established by combining preprocessing and feature wavelength selection methods in chemometrics. Our results showed that the information carried by the spectra after multiple scattering correction (MSC) preprocessing had a good response to the target attribute. The stacking learning model based on the full-band spectrum had a prediction coefficient of determination R2 of 0.8819, and the prediction accuracy of moisture content in P. massoniana seedlings could be significantly improved compared to the single model. After variable selection, the spectrum processed by MSC and feature selection with uninformative variable elimination (UVE) showed good prediction effects in all models. Additionally, the prediction coefficient of determination R2 of the support vector regression (SVR)—adaptive boosting (AdaBoost)—partial least squares regression (PLSR) + AdaBoost model reached 0.9430. This indicates that the quantitative analysis model of moisture content in P. massoniana seedlings established through preprocessing, feature selection, and stacking learning models can achieve high accuracy in predicting moisture content in P. massoniana seedlings. This model can provide a feasible technical reference for the precision cultivation of P. massoniana seedlings.

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“…Unsupervised techniques such as vertex component analysis (VCA), hierarchical cluster analysis (HCA), and singular value decomposition (SVD) can separate Raman spectra without using class information, and are frequently used with Raman imaging approaches. VCA and SVD attempt to model spectra as linear combinations of component spectra (Khmaladze et al 2014, Tabarangao et al 2015, which in the ideal case would correspond to relative contrib utions from different molecular species, while HCA iteratively clusters spectra based on computed distances (Bonifacio et al 2015). Finally, regression is a type of supervised machine-learning where, instead of predicting discrete classes, the desired output is one or more variables and the input and output variables are related using machine-learning.…”

Section: Tissue Classificationmentioning

confidence: 99%

A review of Raman spectroscopy advances with an emphasis on clinical translation challenges in oncology

et al. 2016

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There is an urgent need for improved techniques for disease detection. Optical spectroscopy and imaging technologies have potential for non- or minimally-invasive use in a wide range of clinical applications. The focus here, in vivo Raman spectroscopy (RS), measures inelastic light scattering based on interaction with the vibrational and rotational modes of common molecular bonds in cells and tissue. The Raman 'signature' can be used to assess physiological status and can also be altered by disease. This information can supplement existing diagnostic (e.g. radiological imaging) techniques for disease screening and diagnosis, in interventional guidance for identifying disease margins, and in monitoring treatment responses. Using fiberoptic-based light delivery and collection, RS is most easily performed on accessible tissue surfaces, either on the skin, in hollow organs or intra-operatively. The strength of RS lies in the high biochemical information content of the spectra, that characteristically show an array of very narrow peaks associated with specific chemical bonds. This results in high sensitivity and specificity, for example to distinguish malignant or premalignant from normal tissues. A critical issue is that the Raman signal is often very weak, limiting clinical use to point-by-point measurements. However, non-linear techniques using pulsed-laser sources have been developed to enable in vivo Raman imaging. Changes in Raman spectra with disease are often subtle and spectrally distributed, requiring full spectral scanning, together with the use of tissue classification algorithms that must be trained on large numbers of independent measurements. Recent advances in instrumentation and spectral analysis have substantially improved the clinical feasibility of RS, so that it is now being investigated with increased success in a wide range of cancer types and locations, as well as for non-oncological conditions. This review covers recent advances and continuing challenges, with emphasis on clinical translation.

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Mimicking Multimodal Contrast with Vertex Component Analysis of Hyperspectral CARS Images

Cited by 6 publications

References 26 publications

Chemical fingerprinting of single glandular trichomes of Cannabis sativa by Coherent anti-Stokes Raman scattering (CARS) microscopy

Chemical fingerprinting of single glandular trichomes of Cannabis sativa by Coherent anti-Stokes Raman scattering (CARS) microscopy

Detection of Moisture Content of Pinus massoniana Lamb. Seedling Leaf Based on NIR Spectroscopy with a Multi-Learner Model

A review of Raman spectroscopy advances with an emphasis on clinical translation challenges in oncology

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