Browsing by Subject "principal component analysis"

Herbs are valued for culinary and health purposes and their metabolism and chemical composition can be influenced with LED lighting. This Master’s Thesis aimed to study how different spectra (green, blue, and white light) affect the sensory properties of hydroponically grown dill (Anethum graveolens L.) and coriander (Coriandrum sativum). The hypothesis was that green light produces more soapy and musty flavours in coriander, whereas blue light produces more citrus and typical coriander-like flavours. For dill the hypothesis was that blue and green light treatments produce stronger flavours compared to white light. A generic descriptive analysis method was chosen, and trained panels created sensory profiles for three light treatment and one commercial coriander and dill samples. Intensities of smell, taste and flavour attributes were evaluated using a line scale (0 = not at all to 10 = extremely) in three replicates. Study was conducted during the COVID-19 pandemic in the sensory laboratory conditions (ISO 8589). One-way ANOVA showed that light treatments had only slight impact on the sensory profiles of coriander and dill. In coriander blue light produced significantly lower lemon odour intensity compared to green light treatment. In dill total odour intensity was significantly lower in blue light sample compared to white light and commercial samples. Otherwise, one-way ANOVA did not show significant differences between samples. However, principal component analysis (PCA) implied that samples differed. Two-way ANOVA results showed that neither panel worked uniformly and deviation among intensity scores was observed. Herb samples proved to be rather difficult to evaluate and more extensive training could have improved panel’s performance. In future consumer study could be performed to study if spectrum affects the hedonic response to these herbs.

Study research how finnish farmers react to risk. Outcome is that finnish farmers are in average risk averse, and they weight lower probabilities more than high. Questionnaire was sent to 5 000 farmers, which 820 farmers sent their answer. Questionnaire included questions related to principal component analysis to confirm reliability. After analysis there were to principal components, which were compared in regression analysis with risk parameters alfa (value function parameter) and gamma (weighting function) with farmer's background information. Two principal components were not significant when alfa or gamma was dependent variable. Production sector was significant variable when weighting function parameter gamma acted as dependent variable. Age, amount of field owned or farms location did not have any meaning in attitudes towards risk. Study research how finnish farmers react to risk. Outcome is that finnish farmers are in average risk averse, and they weight lower probabilities more than high. Questionnaire was sent to 5 000 farmers, which 820 farmers sent their answer. Questionnaire included questions related to principal component analysis to confirm reliability. After analysis there were to principal components, which were compared in regression analysis with risk parameters alfa (value function parameter) and gamma (weighting function) with farmer's background information. Two principal components were not significant when alfa or gamma was dependent variable. Production sector was significant variable when weighting function parameter gamma acted as dependent variable. Age, amount of field owned or farms location did not have any meaning in attitudes towards risk.

Eutrophication and harmful substances of anthropogenic origin threaten the state of the Baltic Sea and especially its geochemistry and oxygen levels near the seafloor. Water exchange between the Baltic Sea and the Atlantic Ocean can affect oxygen circulation and sedimentation rates, but they are considered very sporadic and it is unclear how the water circulation and flow rates affect element concentrations and sediment deposition in the near seafloor environments. One of the less studied basins is the Western Gulf of Finland and its seafloor environment. During the 2019 voyage, the seafloor located to the south of the city of Hanko was investigated through bathymetric sounding tools and other measurements in which element concentration and sediment deposition rate data was acquired. The sounding revealed a large channel cutting the seafloor which was hypothesized to influence the nearbottom conditions. The obtained data consisted of samples from 13 short, 40 cm sediment cores which were analysed for 137Cs activity, organic content, and grain size distribution. The goal of the thesis was to determine the intensity of water exchange taking place in the seabed channels between the mid-Baltic Sea and the Western Gulf of Finland and investigate the effect of the seafloor channel and flow rates on sediment and element deposition, their relationships, and how they affect the overall conditions in the study area. These relationships were analyzed through spatial and statistical methods by utilizing GIS-tools to interpolate the data obtained from the study locations by using the Inverse Distance Weighting (IDW) method, and by multielement analyses in the R-environment, namely Principal Component Analysis (PCA) and Partial Least Regression (PLS) to analyze grain size and element concentration correlations and combine them with obtained flow rate data. The results showed strong correlation in flow rate intensities between the Western Gulf of Finland and the mid-Baltic Sea, and they are strongly linked with sedimentation and element deposition rates. However, no long-term trend was identified for the seafloor channel velocity frequencies. The Cs-activity shows stronger sedimentation activity on the western side of the seafloor channel. The overall element and sediment deposition in the study area was largely controlled by monthly and seasonal current velocity fluctuations among other processes. The element concentration comparison showed weakened oxygen conditions in the study area with increased eutrophication and carbon burial since the 1950s. The principal Component Analysis showed smaller grain sizes (0.15 - 2 mm) having a stronger influence on the datasets with Mo, N, and C providing largest variation in the data. Interpolation showed oxygen, pH, and H2S to have more fluctuation in the study area, which can indicate changes in the vertical gradients in each sample point. It could also be determined that other measured concentrations, such as temperature, turbidity, and salinity do not respond very sensitively to water inflow fluctuations or sedimentation rate changes. The results indicate that harmful substances and eutrophication are most likely going to increase in the near-bottom environment in the Western Gulf of Finland, contributed by anthropogenic activity. Water exchange is likely to become more and more uneven, thus affecting the flow rate effects to the sediment deposition in the Baltic Sea. Further studies are needed to link these processes to large-scale global changes and the general state of changes happening in the Baltic Sea and its surrounding areas. The seafloor of the Western Gulf of Finland could also be studied further to gain a better understanding of longer timescale changes on the seafloor channel currents, and element and sediment deposition rates.

A population is said to be genetically structured when it can be divided into subpopulations based on genetic differences between the individuals. As in case of Finland for example, the population has been shown to consist of genetic subpopulations that correspond strongly to geographical subgroups. Such information may be interesting when seeking answers to questions related to the settlement and migration history of some population. Information about genetic population structure is also required for example in studies looking for associations between genetic variants and some inheritable disease to ensure that the groups with and without diagnosis of the disease resemble each other genetically except for the genetic variants causing the disease. In my thesis, I have compared how two different mathematical models, principal component analysis (PCA) and generative topographic mapping (GTM), visualize ancestry and identify genetic structure in Finnish population. PCA was introduced already in 1901, and nowadays it is a standard tool in identifying genetic structure and visualizing ancestry. GTM instead was published relatively recently, in 1998, and has not yet been applied in population structure studies as widely than PCA. Both PCA and GTM transform high-dimensional data to a low-dimensional, interpretable representation where relationships between observations of the data are summarized. In case of data containing genetic heterogeneity between individuals, this representation gives a visual approximation of the genetic structure of the population. However, Hèlèna A. Gaspar and Gerome Breen found in 2018 that GTM is able to classify ancestry of populations from around the world more accurately than PCA: the differences recognized by PCA were mainly between geographically most distant populations, while GTM detected also more their subpopulations. My aims in the thesis were to examine whether applying the methods for Finnish data would give similar results, and to give thorough presentations of the mathematical background for both the methods. I also discuss how the results fit into what is currently known about the genetic population structure in Finland. The study results are based on data from the FINRISK Study Survey collected by the National Institute for Health and Welfare (THL) in 1992-2012 and include 35 499 samples. After performing quality control on the data, I analysed the data with SmartPCA program and ugtm Python package implementing PCA and GTM, respectively. The final results have been presented for such 2010 individuals that participated the FINRISK Study Survey in 1997 and whose both parents were born close to each other. I have assigned the individuals into distinct geographical subgroups according to the birthplaces of their mothers to find out whether PCA and GTM identify individuals having a similar geographical origin to be genetically close to each other. Based on the results, the genetic structure in Finland is clearly geographically clustered, which fits into what is known from earlier studies. The results were also similar to those observed by Gaspar and Breen: Both the methods identified the genetic substructure but GTM was able to recognize more subtle differences in ancestry between the geographically defined subgroups than PCA. For example, GTM discovered the group corresponding to the region of Northern Ostrobothnia to consist of four smaller separate subgroups, while PCA interpreted the individuals with a Northern Ostrobothnian origin to be genetically rather homogeneous. Locating these individuals on the map of Finland according to the birthplaces of their mothers reveals that they also make four geographical clusters corresponding to the genetic subpopulations detected by GTM. As a final conclusion I state that GTM is a noteworthy alternative to PCA for studying genetic population structure, especially when it comes to identifying substructures from a population that PCA may interpret to be genetically homogeneous. I also note that the reason why GTM generally seems to be capable of more fine-grained clustering than PCA, is probably that PCA as a linear model may cause more bias to the results than GTM which accounts for also non-linear relationships when transforming the data into a more interpretable form.