Holocellulose and α-cellulose content of the pretreated samples were determined using the Wise method (Wise et al., 1946). Hemicellulose content obtained by subtracting the holocellulose content with the α-cellulose content.

Lignin content determination was according to the NREL LAP standard method (Sluiter et al., 2012). The acid-insoluble lignin (AIL) content was determined based on a gravimetric method while the acid-soluble lignin (ASL) content was determined at 320 nm using Cary 8454 UV/Vis Diode Array Spectrophotometer (Agilent Technologies, Santa Clara, CA, United States). Absorptivity at lambda max value (ε) of 30 L/g cm was used to calculate the percentage of soluble lignin for all types of raw materials (Noureddini and Byun, 2010; Nomanbhay et al., 2013). The glucose content was determined using HPLC equipped with Bio-Rad Aminex HPX-87H column (Bio-Rad Laboratories, Hercules, CA, United States), Varian 356-LC refractive index detector (Varian, Inc., CA, United States), guard column and autosampler based on the following conditions: injection volume—10 μl; mobile phase—0.01 N HPLC grade sulfuric acid; flow rate—0.6 ml/min; column temperature—65°C; detector temperature—as close as possible the column temperature; detector—refractive index; run time—20 min. A set of sugar recovery standards (SRS) was prepared to determine degradation losses, which is used to compensate for degradation losses of the samples.

Equations 1–4 were used to calculate the percentage of lignin and glucose in the sample after hydrolysis, where AIR, acid-insoluble residue; ODW, oven dry weight; Wt._C, weight of crucible; Wt._P, weight of protein; V, volume; correction factor = 0.9. The protein content of each raw materials was assumed at 31.4% (Pedersen et al., 2014), 25.1% (Miron et al., 2001), 9.9% (Noureddini and Byun, 2010), and 5.3% (Khalil et al., 2012) for DDGS, CGF, CF, and OPF, respectively. %   A I L   =   ( W t . C + A I R ( g )   −   W t . C ( g ) )   −   ( W t . C + a s h ( g )   −   W t . C ( g ) )   −   W t . P ( g ) O D W s a m p l e ( g )   ×   100   (1) %   A S L   =   U V a b s   ×   V f i l t r a t e ( m L )   ×   D i l u t i o n ε   ( L / g . c m ) ×   O D W s a m p l e   ( g )   ×   P a t h l e n g t h   ( c m )   ×   100   (2) %   R e c o v e r y   s u g a r   = S u g a r   c o n c . ( a f t e r   h y d r o l y s i s ) ( m g / m L ) S u g a r   c o n c . ( b e f o r e   h y d r o l y s i s ) ( m g / m L )   ×   100   (3) %   G l u c o s e   = S u g a r   c o n c . ( m g / m L )     ×   C o r r e c t i o n   f a c t o r   ×   V f i l t r a t e   ( m L ) %   r e c o v e r y   s u g a r   ×   O D W s a m p l e ( g )   ×   100   (4)

Incubation time had a significant effect on DDGS compositions (Table 2). There was no significant difference in the compositions at the moisture contents tested, suggesting that working at lower MC will insignificantly affect sample compositions. The results (mean values) for the main effect (Table 3) and the interaction effect (Table 4) show that different incubation times resulted in significant differences in mean values of compositions between level UT and the others but not always significantly different between levels 24 and 72, suggesting that 24 h is a sufficient incubation time for DDGS.

The highest cellulose content recorded was 27.33 wt. % from 30% MC DDGS treated for 24 h (Table 4). Similarly, the same parameters resulted in no significant differences in the mean of hemicellulose and AIL, but not for ASL. It could be observed that cellulose in DDGS was slightly decreased as incubation time increased to 72 h, suggesting that a longer incubation period might enhance cellulose degradation, either partially into glucose or fully into the carbon unit. The latter is undesirable in the biochemical process because it would decrease the substrate available for the fermentation process. Results of glucan content (Figure 2) show that total glucan in the sample was not decreased, perhaps eliminating the possibility of occurrence for the latter scenario mentioned above.

In CGF, p-values of <0.05 were recorded with respect to a time factor, indicating that different times resulted in significantly different compositions. In spite of this, based on the results in Tables 3, 4, significant differences in compositions were recorded only between levels UT and 24, and levels UT and 72 but not always between levels 24 and 72. Varying the MC resulted in no significant difference in mean values of compositions (except for α-cellulose content). α-cellulose content in CGF was decreased after the pretreatment compared to that of untreated samples, possibly indicating that the pretreatment conditions applied were too much for CGF, causing degradation of α-cellulose.

Analysis of glucan content (Figure 2) shows a slight reduction of total glucan in 30% MC CGF, possibly indicating sugar degradation. This effect was not observed in 50% MC CGF. Pretreatment conducted on 50% MC CGF recorded a significantly less α-cellulose degradation than that of 30% MC CGF. However, the results between 24 and 72 h incubation times were not significantly different.

The effect of MC on CF compositions was more apparent than those of DDGS and CGF, with a significant difference in the results except for AIL content. Sample with 50% MC recorded significantly higher α-cellulose, lower hemicellulose, and lower ASL. Conversely, there was no statistically significant difference in mean values of compositions between levels 24 and 72 of the incubation time, suggesting that 24 h LMAA incubation time might be sufficient for CF. The α-cellulose content recorded for 50% MC CF treated for 24 h (38 wt. %) were not significantly different from those of 72 h (39.33 wt. %) (Table 4). Other compositions exhibited similar trends. There was a statistically significant interaction between all factors for hemicellulose and ASL content (Table 2).

For OPF, there was no significant difference of α-cellulose recorded for the different MC tested. This was also true for hemicellulose and AIL content, but not for ASL content. Different incubation time was mostly giving a significant difference of results at levels 0 and 24, and between levels 0 and 72, but not always between levels 24 and 72. Only the mean of ASL content has recorded a statistically significant interaction between all factors (Table 2). The cellulose content recorded for 50% MC OPF treated for 24 h (58 wt. %) was insignificantly different from those of 72 h (59.33 wt. %), possibly indicating that these parameters (50% MC and 24 h) are sufficient for LMAA pretreatment of OPF.

From the results, lower MC (30%) in the DDGS and OPF was observed to not giving a statistically significant difference in α-cellulose content after LMAA pretreatment from those for 50% MC biomass, suggesting its suitability for future use. For CGF and CF, significantly higher α-cellulose content was recorded at higher MC (50%). According to Yang and Rosentrater (2017), LMAA pretreatment performance was optimum at 50% MC for corn stover, in comparison to the same materials at 20 and 80% MC. In this regard, looking more detail into the structure and compositions of each material used in this work, OPF is more closely resembles that of corn stover, however, corn stover required a higher MC requirement might be due to slightly higher cellulose content in it (Mensah et al., 2021). In other aspect, this also proves the requirement of moisture in LMAA pretreatment, although it varies with the cellulose content of materials (Kim et al., 2016; Yang and Rosentrater, 2017; Stoklosa et al., 2021). The difference in MC requirement could also be attributed to the difference in water absorption behavior that somehow related to the compositional difference in materials, which will be described in the following paragraph. α-cellulose content was assumed to be the most critical component because it gives the most sugar (glucose) for the fermentation process and is a high purity material for bio-based structural as such nanocellulose development (Phanthong et al., 2018), this was used to decide the most effective parameters for the LMAA pretreatment process. Following α-cellulose, hemicellulose also potentially can supply sugar (xylose) to the system (Mahmud and Rosentrater, 2020). It was observed that hemicellulose contents at the selected MC (30% for DDGS and OPF, and 50% for CGF and CF) were slightly less than those of the other MC, although the reason for the trend is unclear.

In the LMAA pretreatment process, it was hypothesized that water molecules present in the biomass bind with NH₃ molecules during ammoniation and form ammonium ions (NH⁴⁺) and hydroxyl ions (OH⁻) that react with lignin (Yoo et al., 2011). Water molecules can also form hydrogen bonds with cellulose, which causes swelling of the cellulose crystalline structure, resulting in micro-cracks and later increasing accessibility of enzymes (Yoo et al., 2011; Célino et al., 2014). For CGF and CF, for which a significant amount of starch is part of their compositions, water might be absorbed into the starch in addition to that absorbed into the other cellulosic structure. Therefore, in total, more amount of water was absorbed as bound water, providing more sites for NH₃-H₂O reactions, justifying the requirement of higher MC by these materials. For OPF, although the presence of high cellulose might enhance water absorption and therefore potentially produce a higher α-cellulose content after the pretreatment, no significant differences were found between 50 and 30% MC samples. For DDGS, which consists of a low amount of starch and cellulose, only a small amount of water was retained in its structure to provide the pretreatment effect. The addition of more water to DDGS might result in its only being kept in the structure as free water and thus not contribute to any reaction with NH₃ (Yoo et al., 2011).

Several previous studies have reported that water absorption capacity was less in protein than in starch and cellulose, explaining the results of this study (Greer and Stewart, 1959; Wang et al., 2007). The approximate starch content in DDGS, CGF, and CF, are 6% (Pedersen et al., 2014), 20% (Schroeder, 2012), and 17.77% (Noureddini and Byun, 2010), respectively. For the incubation time factor, longer time (72 h) resulted in no significant overall effect on the materials, especially in terms of α-cellulose content, which was in contrast to the previous study (Yang and Rosentrater, 2017). This might be attributed to the higher NH₃ loading applied during ammoniation, which might be suitable for materials used in this work, speeding up the digestion reaction within the structural materials.

Other than increasing the α-cellulose available in the materials compared to that in untreated materials (except for CGF), the LMAA pretreatment decreased the hemicellulose and AIL content for all materials. While the ASL content for all materials after pretreatment was increased, this was not particularly valuable information in biochemical processing, because this fraction would be removed from the system either during filtration or evaporated by the drying process (a moisture reduction process that is required before the waste can be fed into the boiler).

The p-values from statistical analysis (Table 2) support the results described. p-values lower than 0.05 indicated no evidence that varying the associated factors would lead to a different mean value. Most interaction effects of the factors in all type of materials tested reflected insignificant results at α = 0.05, showing no evidence that effects of time differed with different materials MC, as reported by Cheng and Rosentrater (2016), Yang and Rosentrater (2017). Only several compositions for several types of materials, the mean of ASL content for CF and OPF, and the mean of hemicellulose content for CF, exhibited a significant interaction effect. It was rather weak supporting data for the selection of the best working conditions. Table 5 summarizes the comparison of materials compositions after the pretreatment process recorded in this study with the available results from others for DDGS, CF, and OPF. No previous research on CGF pretreatment was available for comparison. It has been proven that LMAA provides a competitive way of biomass pretreatment at a potentially lower cost than other pretreatment approaches (Yoo et al., 2011; Mahmud and Rosentrater, 2019).