170
A. Pérez-Gálvez and J. Fontecha
establish the threshold values for each filtering rule (mass error and isotopic pattern)
and the first list of potential candidates is obtained. The lower the threshold values,
the lower the number of potential candidates. The second dimension of data is then
applied to filter that list, and to obtain structural information of the compounds. This
is made by means of the tandem MS spectra that include product ions isolated from
the original protonated ion. The same filtering rules (mass error and isotopic pattern)
are applied to check the consistency of the product ions with the protonated ion,
thus increasing the reliability of the annotated tentative identification and reducing
the number of potential candidates initially included in the list, so that the second
list of filtered candidates is obtained. Indeed, the product ions yield information
regarding the structural features of the parent protonated ion, once their structure
has been predicted for each one of the potential candidates included in the list of
filtered candidates (Pérez-Gálvez et al. 2018). In this step, the use of predictive software has become a key tool to improve the accuracy of the formulations, and to
reduce the labor time in the analysis of data. Hence, the list of theoretical product
ions (qualifiers) predicted by the software for each of the filtered candidates included
in the second list is compared with the experimental product ions. Therefore, this
theoretical/experimental screening contributes to refine the number of filtered candidates included in the second list and to assign structure(s) for each protonated ion
measured in the initial MS spectra. The third dimension of physicochemical data
that should be applied to constrain the list of filtered candidates is that contained in
the UV-visible spectra and the chromatographic behavior, increasing the reliability
of the identification. Table 7.2 contains the common product ions observed for the
selected carotenoids that appear in the pigment profile of phytoplankton biomass.
The reader should consider that the occurrence of these product ions is not a sine qua
non condition, but helpful for the identification (some of the product ions may not
appear, other could constitute the base peak of the MS pattern, and even new product
ions not previously denoted may become as qualifiers). Indeed, the instrumental
configuration and conditions for acquisition of the MS spectra may produce changes
in the MS pattern. Therefore, the use of experimental data obtained from authentic
standards analyzed with the same MS conditions used for the sample, should be
considered a golden rule for reliability of the identification (Sumner et al. 2007).
Application of MS techniques to the analysis of carotenoids in phytoplankton
has enlarged the knowledge in carotenoid composition and metabolism for taxonomic screening (Frassanito et al. 2005), identification of novel carotenoids with
fucoxanthin-related structures (Airs and Llewellyn, 2005; Crupi et al. 2013),
screening the isomers and esters of astaxanthin (Holtin et al. 2009; Frassanito et al.
2008), monitoring of the bioavailability of carotenoids after microalgal biomass
ingestion in animal models (Rao et al. 2010), high-throughput paired identification
of chlorophylls and carotenoids species with the application of tandem MS (Fu et al.
2012; Juin et al. 2015; Zhang et al. 2016), or unraveled carotenoid esters (Maroneze
et al. 2019). The reader should note that different hardware configurations and experimental approaches are described in literature, but they succeed in achieving the
aim(s) proposed by the authors. This fact demonstrates the high plasticity of the MS
A. Pérez-Gálvez and J. Fontecha
establish the threshold values for each filtering rule (mass error and isotopic pattern)
and the first list of potential candidates is obtained. The lower the threshold values,
the lower the number of potential candidates. The second dimension of data is then
applied to filter that list, and to obtain structural information of the compounds. This
is made by means of the tandem MS spectra that include product ions isolated from
the original protonated ion. The same filtering rules (mass error and isotopic pattern)
are applied to check the consistency of the product ions with the protonated ion,
thus increasing the reliability of the annotated tentative identification and reducing
the number of potential candidates initially included in the list, so that the second
list of filtered candidates is obtained. Indeed, the product ions yield information
regarding the structural features of the parent protonated ion, once their structure
has been predicted for each one of the potential candidates included in the list of
filtered candidates (Pérez-Gálvez et al. 2018). In this step, the use of predictive software has become a key tool to improve the accuracy of the formulations, and to
reduce the labor time in the analysis of data. Hence, the list of theoretical product
ions (qualifiers) predicted by the software for each of the filtered candidates included
in the second list is compared with the experimental product ions. Therefore, this
theoretical/experimental screening contributes to refine the number of filtered candidates included in the second list and to assign structure(s) for each protonated ion
measured in the initial MS spectra. The third dimension of physicochemical data
that should be applied to constrain the list of filtered candidates is that contained in
the UV-visible spectra and the chromatographic behavior, increasing the reliability
of the identification. Table 7.2 contains the common product ions observed for the
selected carotenoids that appear in the pigment profile of phytoplankton biomass.
The reader should consider that the occurrence of these product ions is not a sine qua
non condition, but helpful for the identification (some of the product ions may not
appear, other could constitute the base peak of the MS pattern, and even new product
ions not previously denoted may become as qualifiers). Indeed, the instrumental
configuration and conditions for acquisition of the MS spectra may produce changes
in the MS pattern. Therefore, the use of experimental data obtained from authentic
standards analyzed with the same MS conditions used for the sample, should be
considered a golden rule for reliability of the identification (Sumner et al. 2007).
Application of MS techniques to the analysis of carotenoids in phytoplankton
has enlarged the knowledge in carotenoid composition and metabolism for taxonomic screening (Frassanito et al. 2005), identification of novel carotenoids with
fucoxanthin-related structures (Airs and Llewellyn, 2005; Crupi et al. 2013),
screening the isomers and esters of astaxanthin (Holtin et al. 2009; Frassanito et al.
2008), monitoring of the bioavailability of carotenoids after microalgal biomass
ingestion in animal models (Rao et al. 2010), high-throughput paired identification
of chlorophylls and carotenoids species with the application of tandem MS (Fu et al.
2012; Juin et al. 2015; Zhang et al. 2016), or unraveled carotenoid esters (Maroneze
et al. 2019). The reader should note that different hardware configurations and experimental approaches are described in literature, but they succeed in achieving the
aim(s) proposed by the authors. This fact demonstrates the high plasticity of the MS