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Researcher team questions the effectiveness of core consistency as a diagnostic tool in fluorescence analysis of complex samples. A new study suggests the need for alternative methods to accurately determine model complexity in such analyses.
Fluorescence spectroscopy combined with parallel factor analysis (PARAFAC) has revolutionized the analysis of food and beverage samples rich in autofluorescent compounds. However, a recent study published in the Journal of Chemometrics questions the reliability of a commonly used diagnostic tool in determining the model complexity for PARAFAC analysis (1). The research, conducted by a team from the University of Copenhagen, suggests that the core consistency diagnostic may be too conservative when dealing with real-world data.
PARAFAC is a multivariate analysis technique used for analyzing multi-way data, commonly known as tensors. It aims to decompose the tensor into a set of factor matrices representing different modes of variation. PARAFAC assumes that the data can be represented as the sum of multiple components, each characterized by a unique factor matrix. By estimating these factor matrices, PARAFAC enables the identification of underlying patterns and relationships within the data. The model complexity in PARAFAC is determined by the number of components or factors selected, which can be crucial for accurate interpretation and prediction. PARAFAC modeling finds applications in various fields, offering insights into complex data structures and facilitating data-driven decision-making.
In the study, led by Helene Fog Froriep Halberg, Marta Bevilacqua, and Åsmund Rinnan, the researchers emphasize the importance of accurately establishing the PARAFAC model complexity, particularly as the sample complexity increases. The core consistency diagnostic, which assists in determining the number of PARAFAC components, has been widely employed. However, the team's findings reveal that this approach may be overly cautious and other diagnostic tools should be considered.
To support their conclusion, the researchers highlight the significance of examining PARAFAC excitation and emission loadings for meaningful chemical interpretation. By evaluating these loadings, researchers can assess the chemical relevance of the obtained results and ensure accurate analysis. This examination becomes crucial when dealing with complex data from real-world samples.
The implications of this study extend beyond the field of fluorescence analysis. As fluorescence spectroscopy and PARAFAC continue to play a vital role in various applications, including quality control in the food and beverage industry, environmental monitoring, and biomedical research, it is crucial to refine the analysis techniques and diagnostic tools used. The study sheds light on the limitations of the core consistency diagnostic and advocates for a more comprehensive approach in determining the model complexity for accurate results.
This research contributes to the ongoing advancements in fluorescence analysis and PARAFAC modeling, providing valuable insights for scientists and practitioners working with complex samples containing autofluorescent compounds. By exploring alternative diagnostic tools and emphasizing the importance of chemical interpretation, researchers can enhance the accuracy and reliability of fluorescence analysis in diverse fields of study.
(1) Halberg, H. F. F.; Bevilacqua, M.; Rinnan, Å. Is core consistency a too conservative diagnostic? J. Chemom. 2023, ASAP. DOI: 10.1002/cem.3483