
pmid: 21962351
The main part of the wide array of different calibration transfer methods found in literature is dedicated to two-way data arrangements (m×n matrices). Less work has been done within the area of calibration transfer for three-way data structures (m×n×l tensors) such as calibrations made for excitation-emission-matrix (EEM) fluorescence spectra. There are two possible ways to attack the problem for EEM transfer. Either the tensors are unfolded to two-way data, whereby the existing methods can be applied, or new methods dedicated to three-way calibration transfer have to be developed. This paper presents and compares both. It was possible to make a local linear pixel-based model that could be used for transfer of EEM's. This new method has a similar performance to the classical methods found in literature, direct- and piecewise direct standardization. The three-way advantages made it possible to use as few as four samples to build useable transfer models. Care has to be taken though when choosing the samples. When subset recalibration of the systems is compared to calibration transfer, better performance is seen for the transferred calibrations. Overall the three-way calibration transfer methods have a slightly better performance than the two-way methods.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 12 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
