## 3 LCF Structure Analysis and Quality Control
### Chemometrics Used for Ligocellulose Feedstock Specification  
Within the last five years, a tremendous number of LCF analysis studies have been reported, some of which include chemometric data processing (Table 3) [131; 134]. For example, in 2014 [PERSON] et al. reported an LCF cell-wall analysis study, including 25 _Miscanthus_ genotypes of different developmental stages separated into stem and leaf portions. In detail, the authors combined mid-infrared spectroscopy with PCA in order to quantify the differences in cell-wall composition of stem and leaf-derived _Miscanthus_ samples, which are in turn associated with different structural carbohydrates (Figure 7) [134]. [PERSON] et al. performed a study including a large number of _Miscanthus_, switchgrass, and reed canary grass samples, to investigate and compare the crop composition depending on origin and harvesting conditions [135]. In detail, ash, silicon, nitrogen, potassium, phosphorous, calcium, chloride, and sulfur content, and the heating value of the grasses were determined. Compared to switchgrass and reed canary grass, _Miscanthus_ genotypes showed significantly lower ash contents (1.6% to 4.0%, compared to 1.9% to 10.5% and 11.5%, respectively). [PERSON] and colleagues studied various moso bamboo samples, with regard to crop composition and ratio of cellulose versus hemicellulose and lignin, respectively. The samples (15 stalks of five ages) were collected from three different sites in China, including Jingning and Guangan counties, Sichuan Province. FTIR macro- and micro-spectroscopic imaging techniques, combined with chemometric  
Figure 7: Mean Fourier-transform infrared spectroscopy (FTIR) spectra of (**A**) leaf and (**B**) stem samples of 25 _Miscanthus_ genotypes, at three developmental stages in the range 1900–800 cm\({}^{-1}\). Plot of principal component one (PC1) and principal component two (PC2) scores for (**C**) all samples, (**D**) leaf samples, and (**E**) stem samples. PC1 loading plot for (**F**) all samples and (**G**) stem samples. Reproduced with permission from [134], Oxford University Press, 2019.  
processing (using partial least-squares regression (PLSR) and Monte Carlo sampling to identify abnormal data), have been applied for quantitative analysis of moso bamboo crop composition [136]. [PERSON] et al. investigated the cellulose and hemicellulose content (in particular alpha-cellulose and pentosan), as well as properties such as pulp viscosity, of dissolving jute pulp, using wet chemical analysis and various spectroscopic methods (i.e., FTIR, UV-Vis) combined with chemometric data processing. The authors were able to develop a fast and reliable procedure to quantify the abovementioned biomass parameters of dissolving pulp, with the help of simple and fast spectroscopic nondestructive methods combined with chemometric data processing [137]. [PERSON] and colleagues used Raman spectral imaging to specify the ratio of cellulose and lignin in surfaces of various trees (i.e., _Swietenia macrophylulla_ King, Mahogany/Eucalyptus hybrid, _E. urophyllla_\(\times\)_E. camaldensis_). They used a multivariate 'curve resolution' procedure to calculate the relative concentration maps and simulate the Raman spectra for cellulose and lignins (finding good correlations with literature data). For all samples, the lignin concentration varied between 20% and 45% for the Eucalyptus samples and some higher values for the Mahogany tree (depending on the local origin). The authors aimed to show that Raman image spectroscopy combined with chemometric data analysis (i.e., multivariate curve resolution-alternating least squares MCR-ALS) is an appropriate tool for final specification of the cellulose/lignin ratio in Mahogany and Eucalyptus hybrids [138]. [PERSON] et al.