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multivariate curve resolution alternating least squares method  (Aquaterra gmbh)

 
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    Aquaterra gmbh multivariate curve resolution alternating least squares method
    Multivariate Curve Resolution Alternating Least Squares Method, supplied by Aquaterra gmbh, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/alternating+least+squares+algorithm/multivariate+curve+resolution+alternating+least+squares+method/pm19951753-80-10-31
    Average 90 stars, based on 1 article reviews
    multivariate curve resolution alternating least squares method - by Bioz Stars, 2026-11
    90/100 stars

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    Environmental Monitoring:

    Article Title: Multivariate curve resolution of organic pollution patterns in the Ebro River surface water-groundwater-sediment-soil system.
    Article Snippet: Multivariate curve resolution alternating least squares (MCR-ALS) is shown to be a powerful chemometric method for the analysis of environmental monitoring data sets.. It allows for the investigation, resolution, identification, and description of pollution patterns distributed over a particular geographical area, time and environmental compartment.. An integrated interpretation of the main features characterizing pollution patterns of organic contaminants affecting the Ebro River basin (Catalonia, NE Spain) is attempted using the results obtained by MCR-ALS analysis of surface water, groundwater, sediment eywords:



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    Image Search Results


    Non-negative Matrix Factorization of a typical record from a P1 animal. A Grayscale reference image followed by the 13 features required to capture 90% of the variance of the original data, displayed in descending order of relative prominence in sleep vs wake. Note the smaller total number of features required capture the same fraction of variance of the data compared to P6-P8 and the more even weighting of those features between sleep (8) and wake (5), reflecting the more evenly distributed complexity of activity between the two states at P1. Scale bar represents 1mm. B: Time vectors and records as in Fig. 5. C: Relationship between number of features (NMF Rank) and fraction of variance captures for P6-P8 vs P1 animals. On average, fewer features are required to capture equal variances at P1. D-F: Bivariate distributions of Feature Area vs. Sleep Score at P6-P8 (D) and P1 (E), and the Sleep-Wake difference plot (F). Development during the first postnatal week is characterized by added features with wider spread (20–40% area coverage) in sleep (Sleep Score 0.5–1.2). P-values determined using permutation test.

    Journal: Developmental neurobiology

    Article Title: Large-scale waves of activity in the neonatal mouse brain in vivo occur almost exclusively during sleep cycles

    doi: 10.1002/dneu.22901

    Figure Lengend Snippet: Non-negative Matrix Factorization of a typical record from a P1 animal. A Grayscale reference image followed by the 13 features required to capture 90% of the variance of the original data, displayed in descending order of relative prominence in sleep vs wake. Note the smaller total number of features required capture the same fraction of variance of the data compared to P6-P8 and the more even weighting of those features between sleep (8) and wake (5), reflecting the more evenly distributed complexity of activity between the two states at P1. Scale bar represents 1mm. B: Time vectors and records as in Fig. 5. C: Relationship between number of features (NMF Rank) and fraction of variance captures for P6-P8 vs P1 animals. On average, fewer features are required to capture equal variances at P1. D-F: Bivariate distributions of Feature Area vs. Sleep Score at P6-P8 (D) and P1 (E), and the Sleep-Wake difference plot (F). Development during the first postnatal week is characterized by added features with wider spread (20–40% area coverage) in sleep (Sleep Score 0.5–1.2). P-values determined using permutation test.

    Article Snippet: The resulting matrix was factored into a series of paired spatial and temporal components, using the MatLab implementation of the alternating least squares algorithm for NMF.

    Techniques: Activity Assay