Hyperspectral confocal fluorescence imaging provides the opportunity to obtain individual fluorescence emission spectra in small (Ϸ0.03-m 3 ) volumes. Using multivariate curve resolution, individual fluorescence components can be resolved, and their intensities can be calculated. Here we localize, in vivo, photosynthesis-related pigments (chlorophylls, phycobilins, and carotenoids) in wild-type and mutant cells of the cyanobacterium Synechocystis sp. PCC 6803. Cells were excited at 488 nm, exciting primarily phycobilins and carotenoids. Fluorescence from phycocyanin, allophycocyanin, allophycocyanin-B/terminal emitter, and chlorophyll a was resolved. Moreover, resonance-enhanced Raman signals and very weak fluorescence from carotenoids were observed. Phycobilin emission was most intense along the periphery of the cell whereas chlorophyll fluorescence was distributed more evenly throughout the cell, suggesting that fluorescing phycobilisomes are more prevalent along the outer thylakoids. Carotenoids were prevalent in the cell wall and also were present in thylakoids. Two chlorophyll fluorescence components were resolved: the shortwavelength component originates primarily from photosystem II and is most intense near the periphery of the cell; and the long-wavelength component that is attributed to photosystem I because it disappears in mutants lacking this photosystem is of higher relative intensity toward the inner rings of the thylakoids. Together, the results suggest compositional heterogeneity between thylakoid rings, with the inner thylakoids enriched in photosystem I. In cells depleted in chlorophyll, the amount of both chlorophyll emission components was decreased, confirming the accuracy of the spectral assignments. These results show that hyperspectral fluorescence imaging can provide unique information regarding pigment organization and localization in the cell.cyanobacteria ͉ photosynthetic pigments ͉ multivariate curve resolution C yanobacteria convert light energy to chemical energy by means of photosynthesis, using water as a source of electrons for CO 2 reduction and O 2 production. A key part of the photosynthesis process is light absorption (harvesting) by pigments, followed by excitation transfer to reaction center chlorophyll (Chl) a of photosystems (PS) II and I (1). These processes take place in thylakoid membranes that in cyanobacteria generally form an extensive internal membrane complex of several layers along the periphery of the cytoplasm, with thylakoids found less frequently toward the center of the cell (2).The pigments associated with the photosynthetic apparatus are bound to thylakoid proteins, modifying their spectral properties and providing a spatial distribution that aids in the efficiency of light harvesting and energy transfer to reaction center Chls. Pigments bound to integral membrane proteins in reaction center complexes in thylakoids of cyanobacteria include Chl a [Ϸ40 per PS II (3) and Ϸ100 per PS I (4)] and carotenoids; the latter act in photoprotection and 3 Chl quenchin...

prediction ability is corrected and predictions become comparable to that of a CLS calibration that contains all analyte concentrations. It is also demonstrated that constanttemperature CLS models can be used to predict variable-temperature data by employing the PACLS method augmented by the spectral shape of a temperature change of the water solvent. In this case, PACLS can also be used to predict sample temperature-with a

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Hyperspectral confocal fluorescence microscopy, when combined with multivariate curve resolution (MCR), provides a powerful new tool for improved quantitative imaging of multi-fluorophore samples. Generally, fully non-negatively constrained models are used in the constrained alternating least squares MCR analyses of hyperspectral images since real emission components are expected to have non-negative pure emission spectra and concentrations. However, in this paper, we demonstrate four separate cases in which partially constrained models are preferred over the fully constrained MCR models. These partially constrained MCR models can sometimes be preferred when system artifacts are present in the data or where small perturbations of the major emission components are present due to environmental effects or small geometric changes in the fluorescing species. Here we demonstrate that in the cases of hyperspectral images obtained from multicomponent spherical beads, autofluorescence from fixed lung epithelial cells, fluorescence of quantum dots in aqueous solutions, and images of mercurochrome-stained endosperm portions of a wild-type corn seed, these alternative, partially constrained MCR analyses provide improved interpretability of the MCR solutions. Often the system artifacts or environmental effects are more readily described as first and/or second derivatives of the main emission components in these alternative MCR solutions since they indicate spectral shifts and/or spectral broadening or narrowing of the emission bands, respectively. Thus, this paper serves to demonstrate the need to test alternative partially constrained models when analyzing hyperspectral images with MCR methods.

A significant extension to the classical least-squares (CLS) algorithm called concentration residual augmented CLS (CRACLS) has been developed. Previously, unmodeled sources of spectral variation have rendered CLS models ineffective for most types of problems, but with the new CRACLS algorithm, CLS-type models can be applied to a significantly wider range of applications. This new quantitative multivariate spectral analysis algorithm iteratively augments the calibration matrix of reference concentrations with concentration residuals estimated during CLS prediction. Because these residuals represent linear combinations of the unmodeled spectrally active component concentrations, the effects of these components are removed from the calibration of the analytes of interest. This iterative process allows the development of a CLS-type calibration model comparable in prediction ability to implicit multivariate calibration methods such as partial least squares (PLS) even when unmodeled spectrally active components are present in the calibration sample spectra. In addition, CRACLS retains the improved qualitative spectral information of the CLS algorithm relative to PLS. More importantly, CRACLS provides a model compatible with the recently presented prediction-augmented CLS (PACLS) method. The CRACLS/PACLS combination generates an adaptable model that can achieve excellent prediction ability for samples of unknown composition that contain unmodeled sources of spectral variation. The CRACLS algorithm is demonstrated with both simulated and real data derived from a system of dilute aqueous solutions containing glucose, ethanol, and urea. The simulated data demonstrate the effectiveness of the new algorithm and help elucidate the principles behind the method. Using experimental data, we compare the prediction abilities of CRACLS and PLS during cross-validated calibration. In combination with PACLS, the CRACLS predictions are comparable to PLS for the prediction of the glucose, ethanol, and urea components for validation samples collected when significant instrument drift was present. However, the PLS predictions required recalibration using nonstandard cross-validated rotations while CRACLS/PACLS was rapidly updated during prediction without the need for time-consuming cross-validated recalibration. The CRACLS/PACLS algorithm provides a more general approach to removing the detrimental effects of unmodeled components.