## 3 LCF Structure Analysis and Quality Control  
### Spectroscopic Data Processing Using Chemometric Methods for Biomass Analysis  
Modern literature on the use of machine learning methods in chemical analysis (chemometrics) is, in general, quite extensive and diverse. In recent years, a large number of reviews have been published on individual methods and analyzed objects [123; 124; 125; 126; 127]. However, the number of studies using chemometric methods, against the background of the total number of analytical works, is still extremely small. Furthermore, even less work has been done that utilizes chemometrics for studying LCF. [PERSON] and [PERSON] investigated the inter-annual variation in biomass yield and composition in a multi-genotype trial planted in southern Germany, focusing on climatic conditions (i.e., rainfall, temperature) and harvest dates [128]. Chemometric methods, such as multivariate regression analysis, were used to study correlations between harvesting time and rainfall. [PERSON] et al. combined Fourier-transform infrared spectroscopy (FTIR) and principal component analysis (PCA) for the classification of the botanical origin of lignins [129]. Regression models (e.g., partial least squares, PLS) resulted in the accurate determination of phenolic hydroxyl groups, which could then be correlated to antioxidant capacity. [PERSON] et al. used multivariate methods to process their experimental FTIR data obtained for various wood samples [130]. Results showed root-mean-square errors for all three LCF components, lignin, cellulose, and hemicellulose, of 1.51%, 0.96%, and 0.62%, respectively. Very recently, [PERSON] et al. reported a study on lignin 3D structure analysis using attenuated total reflection (ATR)-FTIR analysis combined with PCA and PLS modeling. In addition, the obtained quantitative results were comparable to gel-permeation chromatography (GPC) and 2D heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) methods [131]. Thus, only classical chemometric methods have been used for the modeling of predominately FTIR data, leaving open many interesting topics for research. For example, nothing is known about the application of calibration transfer methods in LCF analysis, or the application of novel algorithms, such as independent component analysis (ICA), to improve existing chemometric models. The same is applicable for the complementary vibrational Raman spectroscopy, which gives important insights into a polymer's structure and its characteristics. These data also require multivariate methods for the data interpretation, due to overlapping peaks of polymers present in the data that cannot be interpreted without machine learning techniques. Moreover, despite the obvious interest in multivariate modeling showed by some groups, there is no uniform methodology for applying machine learning methods in the analytical chemistry of LCF. It is also clear, however, that given the current level of automation, the amount of measured information, and throughput of analytical equipment, chemometrics should become an integral part of the analytical chemistry of natural polymers such as LCF. The implementation of chemometrics can be helpful in different aspects of polymer analytical science. For example, up to now the determination of the molecular weight (MW), corresponding distribution (MWD), and polydispersity (PD) of natural macromolecular structures is usually performed via GPC (gel permeation chromatography) or SEC (size exclusion chromatography) using polystyrene (PS) or polymethyl methacrylate (PMMA) standards. Due to the complex and unique 3D structure of natural polymers (particularly lignin), the hydrodynamic volume usually differs significantly between standards and analytes [132]. Therefore, universal calibration or additional methods (i.e., osmometry, light scattering) have to be used in order to determine MW and polydispersity.