IntroductionPoly(hydroxyalkanoate) (PHA) polymers are biologically synthesized polyester produced by the fermentation of renewable biomass, and are completely biodegradable under aerobic and anaerobic conditions. [1][2][3] Among PHA polymers, poly(3-hydroxybutyrate) (PHB), has been studied most extensively as environment-friendly polymers. To improve the physical properties of PHB for a wide range of applications, PHB-based copolymers, such as poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) (P(HB-co-HHx)) have recently been introduced. 4,5 The structure and the thermal behavior of PHB and P(HB-co-HHx) copolymer have been investigated by X-ray diffraction, FTIR spectroscopy, and differential scanning calorimetry. [6][7][8][9][10] It is well known that FTIR spectroscopy is a powerful technique for investigating polymers. [6][7][8]10,[11][12][13][14] However, the overlap of spectral features sometimes limits the utility of the technique. To overcome any shortcoming of conventional spectral analysis, two-dimensional (2D) correlation spectroscopy has been applied to FTIR spectra. [11][12][13][14][15] Generalized 2D correlation spectroscopy is a well-established analytical technique that provides considerable utility and benefit in various spectroscopic studies of polymers.16-18 Some of the notable features of generalized 2D correlation spectra are: the simplification of complex spectra consisting of many overlapped peaks; an enhancement of the spectral resolution by spreading peaks along the second dimension; the establishment of unambiguous assignments through the correlation of bands that are selectively coupled by various interaction mechanisms; and the determination of the sequence of the spectral peak emergence. The details of this technique were described previously. [16][17][18] We recently proposed a very powerful modification of generalized 2D correlation spectroscopy to improve the data quality for 2D correlation analysis, which involves the incorporation of multivariate chemometric techniques. [19][20][21][22][23][24] Principal component analysis-based 2D (PCA2D) correlation spectroscopy showed the great advantage of noise suppression for generalized 2D correlation spectroscopy. 20,24 We formulated the reconstructed data matrix A*, which no longer contains the residual (i.e., noise) contributions, from a few selected significant scores and loadings derived from PCA of the original set of perturbation-dependent spectra A.where W and V are a score matrix and a loading matrix, respectively. The notation V T stands for the transpose of V. The PCA-reconstructed data matrix, in place of the original data matrix, has been successfully utilized to calculate improved 2D correlation spectra. The 2D correlation analysis of this reconstructed data matrix can accentuate the most important features of synchronicity and asynchronicity without being hampered by noise. Furthermore, a radically new idea of an eigenvalue manipulating transformation (EMT) for generalized PCA2D correlation analysis was demonstrated. Principal co...