Carlo G. Bertinetto

Automatic Baseline Recognition for the Correction of Large Sets of Spectra Using Continuous Wavelet Transform and Iterative Fitting

A new algorithm for the automatic recognition of peak and baseline regions in spectra is presented. It is part of a study to devise a baseline correction method that is particularly suitable for the simple and fast treatment of large amounts of data of the same type, such as those coming from high-throughput instruments, images, process monitoring, etc. This algorithm is based on the continuous wavelet transform, and its parameters are automatically determined using the criteria of Shannon entropy and the statistical distribution of noise, requiring virtually no user intervention. It was assessed on simulated spectra with different noise levels and baseline amplitudes, successfully recognizing the baseline points in all cases but for a few extremely weak and noisy signals. It can be combined with various fitting methods for baseline estimation and correction. In this work, it was used together with an iterative polynomial fitting to successfully process a real Raman image of 40 000 pixels in about 2.5 h.

Cellulose Elementary Fibrils Assemble into Helical Bundles in S1 Layer of Spruce Tracheid Wall

The ultrastructural organization of cellulose elementary fibrils (EFs) in wood cell wall is considered to be the prime factor regulating the material characteristics of wood in micro to macro levels and the conversion of delignified wood fibers into various products. Specifically, the complex assembly of EFs in wood cell wall limits its swellability, solubility, and reactivity, for example, in dissolution of cellulose for regeneration of textile fibers, fibril separation for the manufacture of nanocellulose, and enzymatic hydrolysis of cellulose into sugars for their subsequent fermentation to various products, like ethanol for future fossil fuels replacement. Here cryo-transmission electron tomography was applied on ultrathin spruce wood sections to reveal the EF assembly in S1 layer of the native cell wall. The resolution of these tomograms was then further enhanced by computational means. For the first time, cellulose in the intact cell wall was visualized to be assembled into helical bundles of several EFs, a structural feature that must have a significant impact on the swelling, accessibility, and solubility of woody biomass for its conversion into the aforementioned value added products.

Crystallite orientation maps in starch granules from polarized Raman spectroscopy (PRS) data

In this work, polarized Raman spectroscopy (PRS) was used to determine orientation maps of crystallites present in Phajus grandifolius starch granules based on the anisotropic response of the glycosidic Raman band at 865cm(-1). The response of this band was preliminarily evaluated using model A-amylose crystals as standard. The A-amylose crystals oriented in plane showed a maximal intensity ratio of ∼3.0 for bands 865/1343cm(-1) when the polarization of the laser was along the chain axis of the crystal, i.e., parallel to the axis of the amylose double helices, and a minimal intensity ratio of ∼0.25 when perpendicular. The orientation maps of Phajus grandifolius starch granules showed two distinct regions: one isotropic and the other with a highly anisotropic response. The origin of the difference might be changes in both organization/concentration and orientation of the crystallites across the starch granules.

Localization of Cereal Grain Components by Vibrational Microscopy and Chemometric Analysis

This chapter describes two microscopy techniques, Fourier transform infrared microspectroscopy and confocal Raman microspectroscopy (CRM), explaining how they can be employed to investigate the structure of cereal grains. An extensive overview on chemometric data analysis illustrates the most widely used methods for spectral preprocessing, unmixing, and resolution of different components in the sample. A few examples are provided, including a case study in which CRM is applied to analyze chemical and structural changes occurring in the endosperm of barley (Hordeum vulgare L.) during the malting process. This study shows that CRM combined with the employed computational processing can effectively distinguish and map several components even with strong spectral overlap.

Systematic comparison and potential combination between multivariate curve resolution-alternating least squares (MCR-ALS) and band-target entropy minimization (BTEM): Comparison and Combination between MCR-ALS and BTEM

This work does a systematic comparative evaluation of 2 methods originating from different fields, both dedicated to the problem of curve resolution/unmixing: multivariate curve resolution–alternating least squares (MCR‐ALS) and band‐target entropy minimization (BTEM). The MCR‐ALS factorizes the data matrix into spectral and concentration profiles that satisfy constraints expressing physicochemical knowledge on the analyzed system. The BTEM reconstructs the pure components spectral profiles as linear combinations of singular vectors that minimize the spectral entropy and contain specific peaks. Both methods were applied to 40 simulated and one real data set. The simulated data were generated from real spectral and concentration profiles that include different types of spectroscopy (mass spectrometry, Raman, and UV‐visible), data structures (random mixtures, images, and reaction system), and noise levels; the real data set was a Raman image of kidney calculus. For most data sets, both methods yielded accurate solutions, with a correlation between reference and resolved profiles >0.99. However, MCR‐ALS (here used with nonnegativity constraint only) was affected by rotational ambiguity in the recovery of spectral profiles coming from systems with high correlation or overlap in the concentration direction, whereas BTEM tended to distort UV‐visible spectra, a kind of measurement far in nature from low entropy conditions. MCR‐ALS solutions were more stable than BTEM to the increase in noise level. This work also explores the possibility of combining the 2 methods by performing them in sequence. The results show that this combination can significantly improve the outcome as compared to either method applied alone.