TY - BOOK

T1 - Method Development in the Area of Multi-Block Analysis Focused on Food Analysis

AU - Biancolillo, Alessandra

PY - 2016

Y1 - 2016

N2 - In data analysis one could be interested in the relations among a number of data sets (datablocks) having different origin. In food science, this can be particularly relevant. For instance,developing a new product, one may need to understand the relation betweenphysical/chemical data, sensory data and consumer acceptance data. A further example couldbe in process monitoring, where one of the main tasks is to figure out relations amongspectroscopic measurements on raw materials and/or during the production, process settings,and the quality of end-product(s). Additionally, data blocks could have not only differentorigin, but measurements could be taken at different time points or by multi-channelinstruments. It has been demonstrated, that it is more convenient to extract information frommulti-block data sets handling all the blocks at the same time. Namely, performing datafusion by the means of multi-block methods. Several statistical and chemometric multi-blockmethods are already available. Mainly, these are natural developments and variations ofpreviously widely-used methods in multivariate analysis, but the area still needs to beexplored. This PhD project is centered on method-development and method-testing in themulti-block analysis field, with a specific focus on food analysis. Novel approaches will becompared with other well-known methods used in the same field and they will be applied bothin regression and in classification. The new methodologies will be tested on simulations andon real data. Attention will be also given to categorical input data (Paper IV). Additionally,variable selection in this context will be investigated, in order to obtain reduced sub-sets,easier to interpret (Paper II). In conclusion, due to the increasing need of handling multi-wayarrays ( i.e., structures resulting from experiments where the data are collected as a functionof more than two sources of variability), all the considerations done in the first part of thestudy, will be extended to multi-way arrays (Paper III).

AB - In data analysis one could be interested in the relations among a number of data sets (datablocks) having different origin. In food science, this can be particularly relevant. For instance,developing a new product, one may need to understand the relation betweenphysical/chemical data, sensory data and consumer acceptance data. A further example couldbe in process monitoring, where one of the main tasks is to figure out relations amongspectroscopic measurements on raw materials and/or during the production, process settings,and the quality of end-product(s). Additionally, data blocks could have not only differentorigin, but measurements could be taken at different time points or by multi-channelinstruments. It has been demonstrated, that it is more convenient to extract information frommulti-block data sets handling all the blocks at the same time. Namely, performing datafusion by the means of multi-block methods. Several statistical and chemometric multi-blockmethods are already available. Mainly, these are natural developments and variations ofpreviously widely-used methods in multivariate analysis, but the area still needs to beexplored. This PhD project is centered on method-development and method-testing in themulti-block analysis field, with a specific focus on food analysis. Novel approaches will becompared with other well-known methods used in the same field and they will be applied bothin regression and in classification. The new methodologies will be tested on simulations andon real data. Attention will be also given to categorical input data (Paper IV). Additionally,variable selection in this context will be investigated, in order to obtain reduced sub-sets,easier to interpret (Paper II). In conclusion, due to the increasing need of handling multi-wayarrays ( i.e., structures resulting from experiments where the data are collected as a functionof more than two sources of variability), all the considerations done in the first part of thestudy, will be extended to multi-way arrays (Paper III).

UR - https://soeg.kb.dk/permalink/45KBDK_KGL/fbp0ps/alma99122067355405763

M3 - Ph.D. thesis

BT - Method Development in the Area of Multi-Block Analysis Focused on Food Analysis

PB - Department of Food Science, Faculty of Science, University of Copenhagen

ER -