@article{MAKHILLJEAS202116119509,
    title = {Statistical Analysis of Morphological Growth phases of Cyanobacteria},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {16},
    number = {1},
    pages = {6-17},
    year = {2021},
    issn = {1816-949x},
    doi = {jeasci.2021.6.17},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2021.6.17},
    author = {Sabeeha and},
    keywords = {Cyanobacteria,Linear regression,Scatter plots,Correlation,Time series,Chlorophyll,Growth Phases,Statistics,Errorbars,Biomass,Training model},
    abstract = {An automatic generic tool is developed to
identify the morphological growth phases of
microbiological data types using computer-vision and
statistical modelling techniques. In algae phage (phage)
typing, representative profiles of morphological growth
stages of different algae types are extracted. Present
systems rely on the subjective reading of the growth
profiles by a human expert which is time consuming and
prone to errors. The statistical methodology existing in
this work, provides for an automated, objective and robust
analysis of the visual image data, along with the facility
to cope with increasing data volumes. Validation is
performed by comparison to an expert manual
segmentation and labelling of the growth phage profiles.
The statistical analysis performed on time series data
extracted is important for understanding relationships
between parameters, provides insight to the growth curve
of micro algae and cyanobacteria (correlation) and an
essential step to forecast yield of biomass, etc. or predict
the duration to achieve a certain yield of a pigment or
protein, etc., for commercial applications. There are a
number of methods for modelling time series data and
being able to predict specific values; specifically,
regression analysis and Analysis of Variance (ANOVA)
are foremost among them. Computation of the correlation
coefficient aids in better understanding the relationships
that exist between various parameters that evolve with
time and change with different phases of the growth of the
organism (and cyanobacteria). This study focuses on
statistical techniques for analysis of time series data.}
    }