PubMed Health⌕ Search

PubMed · 7626746

Separation of overlapping spectra from evolving systems using factor analysis. 4. Fluorescence spectra of hematoporphyrin IX.

Abstract

Fluorescence spectra of hematoporphyrin IX (Hp) in water and in aqueous SDS solutions are obtained in the pH range 0.1 to 13 to determine the ionisation state of the molecule as a function of pH. In water, the spectra are complicated by aggregation which is quite severe near pH 4. In aqueous SDS, the aggregation is much less violent. Factor analysis (FA) is used to identify five species in the fluorescence spectra in each series of solutions. The distribution curve of these species as a function of pH is also obtained. By comparing the spectra and the distribution curve of Hp with those of HPPEEA, an ethanolamide derivative of Hp that does not contain the carboxylic groups (Part 3), the species are identified. For Hp in water we have obtained the following species: the dication in two allotropic forms in the pH range 0 to 5; the monocation (with the charge on an imino nitrogen) in the pH range 2 to 7; and the free base in the pH range 3.5 to 13. The monocation observed by the second derivative technique revealed three subspecies. For Hp in aqueous SDS we have obtained the following species; one dication in the pH range 0 to near 4; one monocation (with the charge on an imino nitrogen) in the pH range 0.5 to 9; three free bases (with no charge on the imino nitrogen) in the pH range 4 to 13. Of the latter, one species is the neutral molecule, another is a dianion (with the charges on the carboxylic side chains), and the third one appearing at pH higher than 10 is an allotropic form of the dianion.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

C Chapados, D Girard, M Trudel, M Ringuet. 1995. Separation of overlapping spectra from evolving systems using factor analysis. 4. Fluorescence spectra of hematoporphyrin IX.. https://doi.org/10.1016/0301-4622(94)00152-a

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Brief fear of negative evaluation scale-revised.

Rodebaugh et al. [2004: Psychol Assess 2:169-181] recently performed a confirmatory factor analysis (CFA) on the Brief Fear of Negative Evaluation scale (BFNE; Leary, 1983: Psychol Bull 9:371-375]. Their study resulted in the emergence of a two-factor solution comprising straightforwardly worded items and reverse-worded items. They concluded by recommending use of only the straightforwardly worded items in the BFNE. Our intent in this study was to evaluate this recommendation through replication and extension. Participants included 385 undergraduates from the Universities of Regina and Houston, who provided responses to a questionnaire battery including either the BFNE or a revision utilizing straightforwardly worded versions of the reverse-worded items (BFNE-II). A CFA of the BFNE, using the two-factor model proposed by Rodebaugh et al., supported their conclusion that the reverse-worded items comprise a separate, methodologically based factor. However, CFA of the BFNE-II resulted in an acceptable unitary model that conforms to the theoretical basis for the BFNE, without risking loss of sensitivity from item removal. Additional analyses suggest use of the BFNE-II rather than a shortened form.

Factor Analysis, Statistical↗

Factorial validation of a French short-form of the Working Alliance Inventory.

Evaluation of the therapeutic alliance is crucial for understanding the therapeutic process and its results. However, few instruments are available in French. This article aims to validate a French short form of the Working Alliance Inventory (WAI). Unlike other questionnaires, the WAI is the most widely used in psychotherapy research as well as in social psychiatry. Confirmatory factor analyses were carried out on a sample of 150 client-case manager dyads in order to determine the validity of this short-form instrument. The results of these confirmatory factor analyses allowed us to answer different authors' questions (Horvath and Greenberg, 1989; Tracey and Kokotovic, 1989) regarding the factorial structure of the WAI. The results also indicated a unidimensional solution as being the most valid for the two samples. We suggest that, in future studies, only one score be considered for the evaluation of the WAI. We also suggest modifying two statements in the English and French versions in order to render a faithful comparison between the therapist and client versions.

Factor Analysis, Statistical↗

Random intercept item factor analysis.

The common factor model assumes that the linear coefficients (intercepts and factor loadings) linking the observed variables to the latent factors are fixed coefficients (i.e., common for all participants). When the observed variables are participants' observed responses to stimuli, such as their responses to the items of a questionnaire, the assumption of common linear coefficients may be too restrictive. For instance, this may occur if participants consistently use the response scale idiosyncratically. To account for this phenomenon, the authors partially relax the fixed coefficients assumption by allowing the intercepts in the factor model to change across participants. The model is attractive when m factors are expected on the basis of substantive theory but m + 1 factors are needed in practice to adequately reproduce the data. Also, this model for single-level data can be fitted with conventional software for structural equation modeling. The authors demonstrate the use of this model with an empirical data set on optimism in which they compare it with competing models such as the bifactor and the correlated trait-correlated method minus 1 models.

Factor Analysis, Statistical↗