## 3 LCF Structure Analysis and Quality Control
### Chemometrics Used for Ligocellulose Feedstock Specification
very recently reported the structural analysis to determine the ratio of cellulose, hemicellulose, and lignin of eight different greenhouse crop residues, namely _Cucurbita pepo, Cucumis sativus, Solanum melongena, Solanum lycopeneum, Phaseolus vulgaris, Capsicum annuum, Cirullus vulgaris Schrad._, and _Cucumis melo_, using chemometrics in NMR spectroscopy. In detail, the authors were able to specify correlations of metabolite profiles and cell wall composition using a PLS-DA (partial least square-discriminant analysis) and linear regression models (Figure 8) [139]. For reliability verification, composition analysis was also performed, according to the National Renewable Energy Laboratory (NREL) procedure, as control experiments. Woodchips and pellets of different plant species and origins have been studied by [PERSON] and colleagues in order to specify quantitative differences in their chemical composition. Methods used included wet chemical analysis and Vis-NIR (near infrared) spectroscopy, combined with chemometric data processing (i.e., PLS). The background for their study is the utilization of fast spectroscopy  
Figure 8: Partial least square-discriminant analysis (PLS-DA) scores plot derived from 80 \({}^{1}\)H nuclear magnetic resonance (NMR) spectra for the eight different crop residue plant species evaluated. Reproduced with permission from [139], Elsevier 2019.  
methods for biofuel combustion quality, i.e., moisture content, net calorific value, and ash, according to EN ISO 17225. Chemometric data processing of the near infrared region delivered the best results [140]. [PERSON] and colleagues investigated caro samples to specify their origin, using FTIR spectroscopy. With the help of PCA data processing, the authors were able to determine distinct groups, which could be assigned to the carob crop origin (Cyprus, Greece, Italy, Spain, Turkey, Jordan, and Palestine). In addition, chemometric methods, such as cluster analysis (CA), PLS, and Orthogonal Partial Least Square-Discriminant Analysis (OPLSDA), were applied, resulting in 95% confidence for origin specification (Figure 9) [141]. [PERSON] et al. studied the fractionation of different technical lignins using selective extraction in green solvents. Five samples were investigated, including two soda-derived lignins (wheat straw and a mixture consisting of Sarkanda grass/wheat from Greenvalue SA, Switzerland), one organosolv lignin (Alcell, obtained from maple/birch/poplar hard wood mixture, Repap Technologies Inc./USA), a pine-derived kraft lignin \"Indulin AT\" (MeadWestvaco/USA), and a wheat straw-based lignin from a mild alkaline process (Technical University Dresden/Germany). The chemical composition was determined via \({}^{31}\)P NMR and corresponding data were processed using PCA, showing high heterogeneity (Figure 10) [142]. The different extraction procedures resulted in distinct deviations in the functional group content. Structural information regarding the \(p\)-hydroxyphenyl (H), guajacol (G) and syringol unit (G/H/S) ratio and aliphatic OH content was obtained from PLS models based on FTIR data. [PERSON] et al. investigated lignins of different origins using infrared spectroscopy to classify the botanical source. IR data were processed using PCA and partial least squares (PLS) regression to specify phenol-derived hydroxy groups, in order to draw correlation to the antioxidant activity of the lignins [130]. Figure 9: PLS plot from analysis on principal component analyses (PCAs) of 1 st derivatives (2500–4000 cm\({}^{-1}\)). Reproduced with permission from [141], Elsevier 2019. Combining FTIR and multivariate data processing, [PERSON] and [PERSON] et al. examined a number of carob samples from seven different Mediterranean countries, using the first derivatives of the FTIR spectra, resulting in a confidence level of up to 95%. The contents of lignin, cellulose, and hemicellulose were determined.