**2.6 ABTS assay**

The Trolox equivalent antioxidant capacity (TEAC) was determined using the method of Re et al. [26]. The results obtained were reported as mmol Trolox equivalents per g extract and 100 g of seeds.


*Article* **Effects of a Diet of** *Phragmites australis* **instead of** *Triticum aestivum* **L. on Immune Performance and Liver Tissue Structure of** *Ctenopharyngodon idellus*

**Ronghua Wang 1,2,3, Chaobo Lei 1, Zhenyu Li 1, Yanju Lei 1,2,3, Congqiang Luo 1,2,3, Liye Shao 1,2,3,\*, Chunhong Huang 1,2,3 and Pinhong Yang 1,2,3,\***

**Abstract:** This experiment aimed to study the effects on liver tissue structure and immune performance of grass carp *Ctenopharyngodon* idellus when the common reed *Phragmites australis* is in its diet, instead of wheat *Triticum aestivum* L. Seventy-two healthy grass carps (145.52 ± 2.56 g) were randomly divided into three groups according to their body weight. Fishes in each group were fed an essential diet with 0% (control group), 50% and 100% (test group) common reed, instead of wheat, respectively. After feeding for 41 days, the changes of serum biochemical indices, liver tissue structure and immune related indices of grass carp were detected. The results showed that, compared with the control group, the activities of serum alanine transaminase (ALT) and aspartate transaminase (AST) in the substitution groups were significantly increased (*p* < 0.05), but still at a normal level. The contents of total protein, albumin and globulin did not change significantly (*p* > 0.05). Compared with the control group, the liver cells of grass carp in the substitution groups had clear boundaries, tight arrangement and less vacuolation. The contents of serum interleukin-1 (IL-1) and complement 3 (C3) in the 100% substitution group were significantly higher than those in the control group (*p* < 0.05), and the contents of liver IL-1 and total complement (CH50) in the 100% substitution group were significantly higher than those in the control group (*p* < 0.05). The contents of IL-1, C3 in serum and IL-1, CH50 in liver in the 50% substitution group were significantly higher than those in the control group (*p* < 0.05). The mRNA relative expression levels of C3, IL-1, MHC-I and interferon (IFN) in the head-kidney, kidney, liver and spleen of grass carp were significantly affected by feeding the grass carp with different common reed substitution ratios (*p* < 0.05). In summary, common reed, instead of wheat, in feed can improve the liver tissue structure, and increase the non-specific immune response level, of grass carp.

**Keywords:** *Ctenopharyngodon idellus*; *Phragmites australis*; serum biochemistry; immune-related factors; immune gene

#### **1. Introduction**

The common reed *Phragmites australis* is one of the most widely distributed and abundant wetland plant genera in the world [1] and found on every continent except Antarctica. The common reed plays an important role in regulating climate, inhibiting algae growth, accumulating heavy metals, preventing floods, strengthening dikes and maintaining biodiversity, with high ecological and socio-economic value [2–5]. The common reed has been used throughout history to produce non-food commodities, such as paper pulp, roofing and building materials or litter material, as well as for heating and as forage feed [6].

**Citation:** Wang, R.; Lei, C.; Li, Z.; Lei, Y.; Luo, C.; Shao, L.; Huang, C.; Yang, P. Effects of a Diet of *Phragmites australis* instead of *Triticum aestivum* L. on Immune Performance and Liver Tissue Structure of *Ctenopharyngodon idellus*. *Fishes* **2022**, *7*, 378. https:// doi.org/10.3390/fishes7060378

Academic Editors: Marina Paolucci and Shunsuke Koshio

Received: 1 November 2022 Accepted: 6 December 2022 Published: 8 December 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

The Dongting Lake is one of the main producing areas of common reed in China, with a planting area of 666 km2 and an annual output of more than 900,000 tons, accounting for about 30% of the total output in China [7]. In recent years, the main utilization mode of the common reed in the Dongting Lake area is as papermaking raw materials. However, due to environmental pollution, the paper industry has completely withdrawn from the Dongting Lake area, and the utilization rate of the common reed in the lake area has significantly reduced. A mass of common reeds rotted and accumulated, thus causing eutrophication in local areas of the lake, as well as great resource waste and ecological pollution [8]. With the continuous growth of the aquaculture industry in China, the demand for feed raw materials has increased greatly. Limited by the output of agricultural products and resource allocation, the gap of conventional feed raw materials is getting bigger and bigger.

Common reed is an excellent forage grass of Gramineae for horses, cattle, and goats, and research on its forage mainly focuses on its use as roughage or silage for breeding herbivores, in which good results have been achieved [9,10]. However, there is no research on high-value and high-efficiency utilization of common reed processed into pellet feed. The Grass carp *Ctenopharyngodon* idellus is the main freshwater breeding fish in China. Since 2013, the output of grass carp culture in China has reached more than 5 million tons. In this experiment, grass carp was taken as the research object, and the effects of different proportions of common reed instead of wheat *Triticum aestivum* L. on liver tissue structure and immune performance index of grass carp were evaluated. The results can provide some theoretical basis for the development and utilization of common reed resources in Dongting Lake.

#### **2. Materials and Methods**

#### *2.1. Experimental Feed*

Common reeds were collected in the Changde area of Dongting Lake, chopped with a 220 V straw hay cutter, dried at 56 ◦C and ground into powder. The moisture, crude ash, crude protein, crude fiber and crude fat contents of common reed, wheat and the diets were determined as described by AOAC (2003). The gross energy of common reed, wheat and the diets were detected by an Oxygen bomb heat meter (Calorimeter, Parr instrument Company Moline, Illinois, USA). The main effective components of common reed and wheat were shown in Table 1.

**Table 1.** Main active ingredients of common reed and wheat medium residue (air-dry basis%).

The basic feed raw materials came from Alpha Feed, and the feed raw materials were crushed and screened, with a pore size of 0.43 mm. According to the nutrient content of common reed powder and wheat flour measured in this experiment, the grass carp compound feed in the control group was designed with VF123 feed formula software. In the experimental group, 50% and 100% common reed flour were used, instead of wheat flour, in the basic feed. See Table 2 for the composition and nutritional level of the experimental feed.

**Table 2.** Composition and nutrient levels of experimental diets (dry matter %).

1. Vitamin premix (mg/kg diet): vitamin A, 120,000 IU, vitamin B1, 200 mg, vitamin B2, 280 mg, vitamin B8, 240 mg, vitamin B12, 0.6 mg, vitamin D3, 40,000 IU, vitamin E 480 mg, vitamin K3, 200 mg, biotin 1.2 mg, folic acid, 60 mg, calcium pantothenic, 720 mg, nicotinic acid, 1000 mg, vitamin C phosphate, 6850 mg, inositol, 3200 mg; 2. Mineral premix (mg/kg diet): Cu (as copper sulfate) l60 mg, Fe (as ferrous sulfate) 4800 mg, Mn (as manganese sulfate) 800 mg, Zn (as zinc sulfate) 2000 mg, I (as potassium iodide) 40 mg, Se (as sodium selenite )4 mg, Mg (as magnesium sulfate) 400 mg, Co (as cobalt dichloride)12 mg; 3. Nutrient levels were measured values. Nutrient contents analyzed according to AOAC (1995) protocols.

#### *2.2. Feeding Management*

Grass carp was purchased from a grass carp farm in Changde city, Hunan, China. The grass carp were acclimatized for two weeks in fiberglass tanks (diameter: 1.06 m, the volume of aquaculture water was 626L) with a flow-through system (source of water: Baima Lake in Changde; water flow velocity: 1300 mL/min) before the main culture experiment. Then, 72 healthy grass carps with an average body weight of 145.52 ± 2.56 g were randomly divided into three groups according to body weight, with no significant difference (*p* > 0.05). Each group had three replicates with eight fishes in each replicate. The feeding amount was about 3.0~5.0% of the fish body weight and adjusted according to the intake of the diet during the feeding trial, and feeding occurred at 08:00 and 15:00 every day. The feces were removed by siphoning before each feeding. The feeding status and health status of each group of experimental fish were recorded accurately every day, and the water quality was detected every week during the experimental culture. During the culture period, the water temperature was 20~26 ◦C, the dissolved oxygen was above 6.0 mg/L, pH was 7.5~7.8, and ammonia nitrogen under 0.5 mg/L.

#### *2.3. Sample Collection and Processing*

After culturing for 41 days and stopping feeding for one day, three fishes were randomly selected from each culturing bucket. All experimental fish were anesthetized with MS-222 (150 mg/L) (Sigma-Aldrich, Darmstadt, Germany) before sampling. Blood was taken from the tail vein, stood at 4 ◦C for 1 h, centrifuged for 15 min at 4000 r/min, and the serum was taken for biochemical parameters and immune factors content detection. After dissection, 0.2 g liver, spleen, kidney and head kidney were taken in each fish, frozen in liquid nitrogen and stored in a refrigerator at −80 ◦C for detection of immune gene expression level. One gram liver in each fish was rinsed with ice-cold PBS, and then homogenized in 9 mL PBS used JXFSTPRP-24L (Shanghai JingXin, Shanghai, China), centrifuged for

5 min at 5000 r/min, and the supernatant were stored at −80 ◦C for detecting the content of immune factors in liver. The liver tissues of 0.5 cm × 0.5 cm × 0.5 cm were fixed in 10% formaldehyde to observe the liver structure.

#### *2.4. Detection of Serum Biochemical Parameters*

Serum biochemical indices were tested in Liyuan Medical Testing Center of Changde City. Total protein (TP) was measured by the biuret method, albumin (ALB) was measured with bromocresol green (BCG) method, and globulin (GLB) was measured by the calculation method. Blood glucose (GLU) was measured by the hexokinase method, and Aspartate aminotransferase (AST) and Alanine aminotransferase (ALT) activities were measured by the IFCC rate method.

#### *2.5. Observation of Liver Tissue Structure*

The liver tissue was fixed in 10% formaldehyde for 24 h, then dehydrated by 30~100% ethanol gradient, made transparent with xylene and embedded in wax. Paraffin sections with a thickness of 4~5 μm were prepared, stained by means of the HE routine, sealed with neutral gum, and observed with a Leica Advanced Microscope System (DM3000). The area ration and number of hepatocyte vacuolization was visualized and quantified using ImageJ version 1.5.3 software. three areas of each liver section were quantified. By selecting ranges of pixel values in color images the pixels associated with black could be distinguished. The number of selected pixels was then quantified using a particle analysis operation and by counting the area of all bright objects (in pixels).

#### *2.6. Detection of Immune Factor Activity and Content*

Immunoglobulin M (IgM), Interleukin 1 (IL-1), Complement 3 (C3), Total Complement (50% Haemolytic Complement, CH50) and Cortisol in serum and in the liver were detected using an ELISA kit (Shanghai Enzyme Linked Biotechnology Co., Ltd., Shanghai, China).

#### *2.7. Detection and Analysis of Immune Gene mRNA Expression Level* 2.7.1. RNA Extraction and cDNA First Strand Synthesis

Total RNA was extracted from the liver, spleen, kidney and head kidney tissues, and stored at −80 ◦C using TRIzol reagent (Invitrogen, Carlsbad, CA, USA). The method referred to the instructions of TRIzol reagent. The integrity of the extracted RNA was detected by 1% agarose electrophoresis, and the total RNA, with good integrity, was used for the next experiment. A quantity of 2 μg total RNA was taken, and the first strand of cDNA was synthesized by using a RevertAid™ First Strand cDNA Synthesis reverse transcription kit (ThermoFisher, Carlsbad, CA, USA). The reverse transcription product was diluted 20 times and used for the analysis of immune gene mRNA expression level.

#### 2.7.2. Fluorescence Quantitative PCR Analysis

According to the related literatures [11,12], immune genes Heat Shock Protein 70 (Hsp70), Lysozyme, Major histocompatibility complex I (MHC-I), Interferon (IFN), C3, IgM and IL-1 were screened out and detected these genes relative expression level after culture experiment. Primer synthesis was completed by Sangon Biotech Co., Ltd. (Shanghai, China), and primers sequence information is shown in Table 3. The fluorescence quantitative PCR reaction system involved the following: 2 × ChamQ Universal SYBR qPCR Master Mix (Vazyme, Nanjing, China) 10 μL, 10 μmol/L primer 0.4 μL, diluted cDNA template 9.2 μL. The reaction procedure was as follows: 95 ◦C for 3 min: 95 ◦C for 5 s, 60 ◦C for 10 s, 72 ◦C for 15 s. The melting curve ranged from 65 ◦C to 97 ◦C and increased by 0.1 ◦C per second. The relative expression level of genes was calculated by the 2−ΔΔCt method. LightCycler 480 software release 1.5. 0 and Excel 2021 software were used for data processing. The β-actin gene was used as a reference gene, and the relative expression level of gene mRNA in the control group was analyzed by homogenization.

**Table 3.** The primers used in this study.

#### *2.8. Data Analysis*

SPSS 17.0 software was used for one-way ANOVA, and the Duncan method was used for significance analysis between groups. The significance level was *p* < 0.05. The results were plotted by mean ± SD, the activity and content of immune factors, and the expression level of immune-related genes used graph Pad Prism 6 software.

#### **3. Results**

#### *3.1. Serum Biochemical Indicators*

It can be seen from Table 4 that the contents of TP, ALB and GLB did not change significantly among the three groups (*p* > 0.05). The blood glucose concentration of grass carp in the substitution group was lower than that in the control group, especially in the 50% substitution group (*p* < 0.05). Except for AST activity in the 100% substitution group, serum ALT and AST activities of grass carp in the substitution group were significantly higher than those in the control group (*p* < 0.05).

Different superscript letters of peer data indicate significant difference between the two groups (*p* < 0.05), while the same or no superscript letters indicate no significant difference between the two groups (*p* > 0.05).

#### *3.2. Histological Structure of Liver*

In the control group, hepatic cell boundaries were unclear and hepatocyte vacuolation was severe, accompanied by a small amount of lymphocyte infiltration (Figure 1a,b). In the 50% and 100% substitution groups, the boundaries of hepatocytes in grass carp liver were clear, the nuclei were clearly colored and basically located in the center, and the cells were arranged neatly and were of uniform size (Figure 1c,d,e,f). The area ratio and number of hepatocyte vacuolization in the substitution group was significantly lower than that in the control group (*p* < 0.05) (Table 5).

**Figure 1.** Effect of common reed on liver histology of grass carp (200 times). (**a**,**b**): 0% replacement group; (**c**,**d**): 50% replacement group; (**e**,**f**): 100% replacement group. The triangle in the figure indicates lymphocyte cells, and the arrow indicates vacuolar degeneration of cells.

1 The area ratio of hepatocyte vacuolization (%) = The area of hepatocyte vacuolization (pixels)/The area of image (pixels) × 100% . Different superscript letters of peer data indicate significant difference between the two groups (*p* < 0.05), while the same or no superscript letters indicate no significant difference between the two groups (*p* > 0.05).

#### *3.3. Activity and Contents of Immune Factors*

Compared with the control group, the contents of serum IL-1 and C3 in the 100% common reed substitution group were significantly higher (*p* < 0.05), and the activity of CH50 in the 50% and 100% common reed substitution groups were significantly lower than in the control group (*p* < 0.05). The contents of IL-1 and CH50 in the liver of the 100% common reed substitution group were significantly higher than those in the control group (*p* < 0.05), but the content of C3 was significantly lower than that in the control group (*p* < 0.05). Compared with the control group, the contents of IL-1, C3, IgM and cortisol in the serum and liver of grass carp in the 50% common reed substitution group were not significantly different (*p* > 0.05) (Figure 2).

#### *3.4. The mRNA Expression Level of Immune Genes*

Compared with the control group, the expression levels of Hsp70, IL-1, MHC-I and lysozyme mRNA in the head and kidney of grass carp in the 100% common reed substitution group were significantly up-regulated (*p* < 0.05), while the expression levels of C3 and mRNA were significantly down-regulated. The mRNA expression levels of C3, Hsp70, lysozyme, MHC-I and other genes in the 50% common reed substitution group were significantly down-regulated in the head kidney (*p* < 0.05) (Figure 3A). Compared with the control group, the mRNA expression levels of IL-1 and lysozyme in the liver of grass carp in the substitution group were significantly down-regulated (*p* < 0.05), while the mRNA expression levels of C3, MHC-I and IgM genes were not significantly different (*p* > 0.05) (Figure 3B). The mRNA expression of C3 and Hsp70 in the spleen of the 50% common reed substitution group was significantly higher than that in the control group, while the expression of MHC-I was significantly lower than that in the control group

(*p* < 0.05); The mRNA expression levels of other detected genes, except the IgM gene, in grass carp spleen of the 100% common reed substitution group were significantly lower than those in the control group (Figure 3C). The mRNA expression levels of Hsp70, MHC-I and IgM genes in the kidneys of the 100% common reed substitution group were significantly higher than those of the control group (*p* < 0.05), while the mRNA expression levels of other detected genes, except C3, in the kidneys of the 50% common reed substitution group were not significantly different (*p* > 0.05) (Figure 3D).

**Figure 2.** Effect of common reed on content and activity of immune factors in serum (**A**) and liver (**B**) of grass carp (N = 3). The units of IgM, IL-1, C3, CH50 and Cortisol were μg/mL, pg/mL, μg/mL, U/mL and ng/mL respectively. Different small letters mean significant difference among groups (*p* < 0.05), the same letters or no letters mean no significant difference (*p* > 0.05).

**Figure 3.** Effect of common reed on expression of immune genes in head kidney (**A**), liver (**B**), spleen (**C**) and kidney (**D**) of grass carp (N = 3). Different letters mean significant difference among groups (*p* < 0.05), the same letters or no letters mean no significant difference (*p* > 0.05).

#### **4. Discussion**

The liver is the most important metabolic center of fish. When fish ingest feed with unreasonable nutritional structure, it causes nutritional stress to the liver, and then damages the normal structure and functions of the liver [13,14]. In this study, we found that the cellular structure of the liver tissue of grass carp in the common reed substitution group was closely arranged, while, in the control group, lymphocytes had infiltrated, cells were vacuolated and had obvious inflammatory reaction and cell vacuolization. Therefore, from the analysis of the results of the tissue sections, adding common reed to feed was helpful to improve the health status of the liver tissue.

Fish serum biochemical indicators are widely used to evaluate the health status, nutritional status and adaptation to the environment of fish, and are good physiological, pathological and toxicological indicators. TP content in serum can reflect the metabolism of protein in the body, and increase of TP content can accelerate the deposition of protein and promote the growth of the body. ALB and GLB mainly maintain the balance of cell nutrition and blood osmotic pressure in vivo [15]. In this experiment, the TP content of the substitution group and the control group were at the normal level of total protein content for teleost fish (30–50 g/L) [16]. The TP content of the substitution group was slightly higher than that in the control group, but the difference was not significant (*p* > 0.05), indicating that the nutritional status of the substitution group was better. Blood glucose is an important index reflecting the glucose metabolism of fish, the functional state of tissues and cells and endocrine function of the whole body [17]. The normal content of blood glucose in grass carp is 2.78–12.72 mmol/L [18]. In this study, the blood glucose concentration of grass carp in the substitution group was lower than that in the control group, and the 50% substitution group was significantly lower than that in the control group, but all of them were in the normal range. Transaminase is involved in protein metabolism and synthesis [19], and the activities of ALT and AST directly reflect the protein metabolism level [20], and are affected by the quantity and quality of dietary protein [21]. In this study, the serum ALT and AST activities of grass carp in the substitution group were significantly higher than those in the control group (*p* < 0.05), but still in the normal range, though lower than those detected by Cheng et al. [22] and Chen et al. [23]. In this study, the activity of ALT and AST in the serum of the grass carp in the substitution group might be related to the amino acid composition and high cellulose content of common reed. High cellulose content affects protein absorption of grass carp.

As a lower vertebrate, the fish has a relatively perfect acquired immune response, which can resist pathogen invasion through nonspecific immune response and specific immune response [24]. Complements and lysozymes are important non-specific immune system components of the body, which play the role of dissolving bacteria and eliminating cells (bacteria) in resisting the invasion of pathogenic microorganisms [25–27]. IL-1 and IFN are important cytokines in the body, which play an important role in inducing and activating inflammatory response and regulating immune response [28]. MHC-I and IgM are important factors in the activation and effects of the specific immune system, and play an important role in antigen binding and inhibition of pathogen activity [29]. In this study, the activity and content of immune factors, such as C3, CH50, IL-1, lysozymes, IgM, and MHC-I in the serum and liver of grass carp, and the mRNA expression level of corresponding genes in different tissues, were detected to judge the effect of a common reed diet, instead of a wheat diet, on the immune performance of grass carp. The activity and content of immune factors showed that the contents of IL-1, C3 and cortisol in the serum, IL-1 and CH50 in the liver of grass carp in the substitution group increased with increase of the common reed replacement ratio. The changes of non-specific immune indices of grass carp in the 100% common reed substitution group were significantly different from those in the control group (*p* < 0.05), but there was no significant difference in IgM content (*p* > 0.05). From the change results of immune factor content, adding the proper amount of common reed to feed could enhance the nonspecific immune response of grass carp, but had no significant effect on the specific immune response. The results were consistent with

the research results of Chen et al. [30], who found that rutin, an active ingredient in the common reed, could significantly improve the nonspecific immunity of grass carp. At the same time, the results of gene expression level showed that the mRNA expression level of the immune gene of grass carp in the 50% common reed substitution group was downregulated compared with that in the control group, while the expression of Hsp70, IL-1, lysozymes, MHC-I, and IgM in head kidney and kidney of grass carp in the 100% common reed substitution group were up-regulated to varying degrees. This might be related to the abundant bioactive components in the roots, stems and leaves of the common reed, such as flavonoids, reed polysaccharides and rutin. Studies have shown that flavonoids and rutin have significant antibacterial and antioxidant properties, and increase immune response [31,32]. Therefore, combined with the changes of immune factor activity and content and gene mRNA expression level, adding a certain amount of common reed in feed can improve the nonspecific immune response level of grass carp.

#### **5. Conclusions**

In conclusion, this study indicated that the replacement of wheat with common reed could improve the liver structure and function of grass carp, and, at the same time, improve the level of non-specific immune response of grass carp to a certain extent. However, the most suitable amount of common reed in grass carp feed remains to be further studied.

**Author Contributions:** Conceptualization, R.W., Y.L. and C.L. (Congqiang Luo); Data curation, C.H. and L.S.; Formal analysis, R.W.; Funding acquisition, P.Y.; Methodology, R.W., C.L. (Chaobo Lei) and Z.L.; Project administration, C.L. (Chaobo Lei), Z.L., Y.L. and L.S.; Resources, L.S.; Software, Z.L.; Supervision, C.L. (Congqiang Luo); Writing—original draft, R.W.; Writing—review & editing, R.W., C.H. and P.Y. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by The Natural Science Foundation of Hunan Province (Grant no. 2021JJ30468), The Investigation of Aquaculture Germplasm Resources in Hunan Province (Grant no. 2130122), The Research Project of Education Department of Hunan Province (Grant no. 22B0701 and 20A339), Innovation Platform project of Education Department of Hunan Province (Grant No. 19K064) and Hunan University of Arts and Science Doctoral Foundation project (Grant no. 18BSQD20).

**Institutional Review Board Statement:** The study was conducted in accordance with the Declaration of Helsinki, and all procedures used in this experiment were approved by the Hunan University of Arts and Science Institutional Animal Care and Use Committee and were performed in accordance with approved protocols, protocol code: JSDX-2021-025 and date of approval: 15 March 2021.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** We would like to acknowledge the contribution of the college students (Zhiwei Zhang, Fengling He and Kang Huang) at Hunan University of Arts and Science for their roles in fish husbandry and for their assistance with sampling.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

#### *Article*


*3.2. Raman and SERS Analysis*

Preliminary Raman analyses were performed directly on the samples focusing on dye spots or particles. However, the fluorescence background did not allow acquiring meaningful spectra. The collection of spectra at the highest intensity of the laser was attempted, but this only resulted in degradation of the sample with the formation of amorphous carbon or in the individuation of signals of the cellulose from the stubbons (Figure S6, Supplementary Materials).

With respect to the SERS spectra obtained for sample D816-4, it was possible to distinguish some common characteristic peaks (Figure 4, top). In particular, in most spectra, two broad bands around 461 and 477 cm−1 are observable, while more defined peaks appear at 622, 680, and 735 cm−1. At higher wavenumbers, two peaks are observable at 1004 and 1049 cm−1, and other bands are distinguishable at 1213, 1255, 1301, 1329, 1364, 1436, 1461, 1484, 1509, 1565, 1577, 1582, 1601, 1623, and 1646 cm−1. These signals change in intensity and width in the different spectra.

All the spectra of the D816-9 sample (Figure S7, Supplementary Materials) present peaks at 452 (shoulder), 479, 680, 1002, 1049, 1207, 1447, 1485, 1578, and 1623 cm−1. It is interesting to notice that several bands observed for the D816-9 sample are also present in the spectra of the D816-4 sample.

In the case of sample D816-7, instead, the acquired spectra (Figure 5, top) present high reproducibility: there are peaks at 436, 457, 479, 530, 620, 644, 677, 693, 732, 761, 804, 827, and 918 cm−1, and defined signals at around 1030, 1176, 1221, 1298, 1364, 1399, 1429, 1476 (shoulder), 1530, 1586, and 1621 cm−1 are clearly observable. In two spectra, further sharp bands are distinguishable at 546, 601, 1488, and 1576 cm−1.

**Figure 4.** Comparison of SERS spectra obtained for the D816-4 (top) and D817-2 (bottom) samples. Some peak wavenumbers are evidenced for clarity. The spectrum of sample D817-2 presents a clear correspondence with Rhodamine B SERS spectrum [50] (literature spectrum not reported in the image).

**Figure 5.** Comparison of SERS spectra obtained for the D816-7 (top) and D817-4 (bottom) samples. Some peak wavenumbers are evidenced for clarity. The spectra of samples presents a clear correspondence with the SERS spectra of Malachite Green [44,46] (D816-7) and Crystal Violet [44,46,54] (present in both the samples, see Discussion; literature spectra not reported in the image).

The SERS spectra obtained for D816-5 sample present a good reproducibility, even if their relative intensities can change in the different spectra. In most of them, peaks at 479, 656, 691, 733, 1139, 1225, 1300, 1340, 1443, 1458, 1585, and 1628 cm−1 are present. In some spectra, further peaks can be identified: one spectrum presents bands at 434, 518, 570, 716, 810, 1001, 1207, and 1315 cm−1, which result generally sharper than the above-mentioned ones (Figure 6, top), while in another spectrum, intense signals at 798, 1404, and 1539 cm−1 are observable. For the D816-15 sample, the spectra (Figure 6, bottom) present a general reproducibility, even if, also in this case, change in relative intensities are observed; the main spectral features are at 422, 440, 479, 519, 562, 593, 679, 717, 813, 857, 914, 1003, 1033, 1047, 1130, 1183, 1209, 1252, 1323, 1335, 1445, 1458 (shoulder), 1531, 1586, and 1627 cm−1. Some similarities with the spectra of sample D816-5 are evident.

**Figure 6.** Comparison of SERS spectra obtained for the D816-5 (top) and D816-15 (bottom) samples.

About the samples from S¯ıta puppet, the SERS spectra acquired show a great repro- ¯ ducibility. For the D817-2 sample, all the spectra present distinguishable peaks at 589, 622, 634 (shoulder), 661, 726, 737, 768, 1050, 1129, 1185 (shoulder), 1197, 1279, 1345 (shoulder), 1362, 1437, 1471, 1510, 1530, 1578, 1598, and 1649 cm−1 (Figure 4, bottom). The SERS spectra of the D817-4 sample (Figure 5, bottom), instead, present reproducible bands at 423, 443, 530, 570, 622, 636, 665, 732, 745 (shoulder), 760, 802, 834 (shoulder), 914, 944, 977, 1124, 1181, 1217, 1298, 1345 (shoulder), 1376, 1444, 1473, 1524, 1533, 1565, 1589, and 1621 cm−1. The general pattern of this spectra presents high similarity with the spectra obtained for the D816-7 sample.


*Review* **Recent Advances of Anderson-Type Polyoxometalates as Catalysts Largely for Oxidative Transformations of Organic Molecules**

**Zheyu Wei 1, Jingjing Wang 2, Han Yu 1,2,\*, Sheng Han 2,\* and Yongge Wei 1,\***

**Abstract:** Anderson-type ([XM6O24] n−) polyoxometalates (POMs) are a class of polymetallic-oxygen cluster inorganic compounds with special structures and properties. They have been paid extensive attention by researchers now, due to their chemical modification and designability, which have been widely applied in the fields of materials, catalysis and medicine. In contemporary years, the application of Anderson-type POMs in catalytic organic oxidation reaction has gradually shown great significance for the research of green catalytic process. In this paper, we investigate the application of Anderson-type POMs in organic synthesis reaction, and these works are summarized according to the different structure of POMs. This will provide a new strategy for further investigation of the catalytic application of Anderson-type POMs and the study of green catalysis.

**Keywords:** polyoxometalate; Anderson structure; organic synthesis; molecular catalysis; green chemistry


**Author details**

Upasana Mahila College Dalmianagar, Dehri – on – Sone, Rohtas, Veer Kunwar Singh University, Ara, Bihar, India

\*Address all correspondence to<EMAIL_ADDRESS>
© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


**4. Verification and validation of modeling results**

#### **4.1 Grid independence study**

To ensure optimum numbers of cells are used in the final simulations, several grid independence tests using flat plate smooth wall (SSS) base cases are conducted. For each case, the overall friction coefficient *CF* is calculated using the increasing number of cells in the simulation. The number of cells in the latter simulation is approximately twice than that in the former. The overall friction coefficient *CF* is defined in Eq. (9), where *D* is the drag per unit width, *τw* is the wall shear stress, *ρ* is the fluid density, *U*∞ is the free stream velocity, *L* is the plate length, and *x* is the distance downstream from the leading edge of the plate. Furthermore, a percent error *en*þ1,*n* between the lower and higher cell numbers is defined in Eq. (10) [40].

*CF* ¼ *D ρU*∞2 *L=*2 ¼ Ð *L* 0 *τwdx ρU*∞2 *L=*2 (9)

$$\varepsilon\_{n+1,n} = \frac{C\_F(n+1) - C\_F(n)}{C\_F(n)} \times 100\,\% \tag{10}$$

The results of the grid independence study are summarized in **Table 1**. The number of cells is varied from 750,000 to 6,127,550. **Table 1** shows that the *CF* value increases monotonically with the increasing number of cells, which is expected to reach an asymptotic value at a very large number of cells. The value of *en*þ1,*n* as listed in **Table 1** decreases with the increasing number of cells used in the simulation. The results show that the error is very low (in the range 0.0206– 0.131%), well below the recommended 2% from the literature [41]. Based on this grid independence test, N = 3,000,000 is chosen as an optimum number of cells for all the cases (including homogeneous and inhomogeneous roughness).

#### **4.2 Verification and validation**

In addition to the grid independence tests, further analyses are carried out for varied viscous-scaled wall-normal distance *y*þ ranges of the first cell center above the wall. For the smooth plate SSS case, the calculation result is verified using the well-known Schoenherr's friction coefficient and the 1957 ITTC (International Towing Tank Conference) ship-model correlation line. Schoenherr's friction coefficient *CF* is given in Eq. (11). It was adopted by ATTC (American Towing Tank Conference) as a standard for the clean hull skin friction resistance in 1947, and it is often referred to as the 1947 ATTC line. The second correlation is the 1957 ITTC ship-model correlation line, which is given in Eq. (12). A percent error is defined

#### **Table 1.**

*Friction coefficients CF calculated using increasing number of cells in the simulations for the smooth plate (SSS).*

#### **Table 2.**

*Overall friction coefficients for the plate segments CF*; 1, *CF*; 2, *CF*; 3 *and the entire plate CF, and the percent errors* e*r*; *s*, e*i*; *q and* e*i*,*h.*

#### **Table 3.**

*Overall friction coefficient CF calculated using different ranges of y*þ *value for the first cell center above the wall compared with 1947 ATTC (Eq. (11)) and 1957 ITTC (Eq. (12)) lines for the smooth SSS case.*

between the CFD result and the 1947 ATTC line and, in a similar manner, between the CFD result and the 1957 ITTC ship-model correlation line to quantify the accuracy of the CFD results. The percent error *e* in the latter case is calculated using Eq. (13).

$$\frac{0.242}{\sqrt{\mathbf{C}\_F}} = \log\_{10}(\text{Re}\,\mathbf{C}\_F) \tag{11}$$

$$C\_F = \frac{0.075}{\left[\log\_{10}(\text{Re}) - 2\right]^2} \tag{12}$$

$$e = \frac{C\_{F; \text{CFD}} - C\_{F; \text{ITTC 1957}}}{C\_{F; \text{ITTC 1957}}} \times 100\text{\%} \tag{13}$$

The results are summarized in **Table 3**, showing *CF* values calculated using different *y*þ ranges for the first cell center above the wall targeted in the simulations. **Table 3** shows that for the SSS case, using a *y*þ range between 155 and 254 would result in the closest *CF* value to the 1947 ATTC and 1957 ITTC lines with percent errors of *e* = 0.620% and *e* = 0.814%, respectively. A smaller *y*þ range will result in larger differences, but the percentage differences do not exceed 1.76%, which is relatively small. Despite these anomalies, overall, the CFD and the 1947 ATTC or the 1957 ATTC line are small. Despite some discrepancies, for the smooth surface cases considered in this study, any *y*þ range between 64 and 254 will result in an acceptable *CF* value with a maximum magnitude of percent errors of 1.57%

*The Phenomenon of Friction Resistance Due to Streamwise Heterogeneous Roughness with… DOI: http://dx.doi.org/10.5772/intechopen.99137*

when compared with the 1947 ATTC line and 1.76% when compared with the 1957 ITTC line. This result is following the recommended *y*þ range in the literature for smooth flat plate CFD simulations between 50 and 300 [42].

To model the roughness effects correctly, the *y*þ value for the first cell center above the wall denoted as (Δ*y*þ)1, must be larger than the local equivalent sand grain roughness Reynolds number *k*þ *s* , i.e., (Δ*y*þ)1> *k*þ *s* . However, when one employs a fine mesh near the wall, the (Δ*y*þ)1value may have a smaller value than the *k*þ *s* value, i.e., (Δ*y*þ)1< *k*þ *s* . If such a case happens, ANSYS Fluent applies a virtual shift of the wall by increasing the value of (Δ*y*þ)1 with an amount *k*þ *s* /2, such that (Δ*y*þ)1> *k*þ *s* .

To gain more insight into the CFD results for rough conditions, they are verified using the empirical calculation, Granville's similarity law scaling method [43]. The simplified Granville similarity scaling can be calculated using Eqs. 14, 15, and 16. Where: *CFR* is the coefficient of frictional resistance for rough condition, where the empirical formula as the foundation is taken from the approximated Kármán-Schoenherr formula [44]; *Re r* is the Reynolds number for calculate the *CFR* using the empirical formula, which equal of the Reynolds number for smooth condition ( *Res*) that is shifted as described in Eq. (15). Then, *κ* is the von Kármán constant; *ks* þ is roughness Reynolds number; *ν* is kinematic viscosity; *Uτ* is friction velocity defined as ffiffiffiffiffiffiffiffiffiffi *τw=ρ* p or approached by *U*∞ð Þ *CF=*2 1*=*2 ; *τw* is the shear stress magnitude, where to get it is necessary to do iterative calculations against *CFR* . The roughness function *ΔU*þ is from Cebeci and Bradshaw [39] in Eq. (7), according to this study.

The verification result using the similarity scaling from Granville [43] can be seen in **Table 4**. The calculation uses *eC*; *G*, as described in Eq. (17). From the results of the calculation of *eC*; *G*, it can be concluded that CFD modeling for homogeneous roughness can be accepted with the difference in error against the empirical is not exceed 1.8%.

$$C\_{F\_R} = \frac{0.0795}{\left(\log\_{10}{Re\_r} - 1.729\right)^2} \tag{14}$$

$$Re\_r = Re\_t - \mathbf{10}^{\left(\frac{4U+\kappa}{\ln\left(10\right)}\right)} \tag{15}$$

$$
\Delta U^{+} = f\left(k\_{\varepsilon}^{+}\right) = f\left(\frac{k\_{\varepsilon}U\_{\tau}}{\nu}\right) \tag{16}
$$

$$e\_{C;G} = \frac{C\_{F;GFD} - C\_{F;Gannville}}{C\_{F;Gannville}} \times 100\text{\%} \tag{17}$$

$$k\_s^+ = \left(\frac{k\_s}{L}\right) \left(\frac{\text{Re C}\_{FS}}{2}\right) \left(\sqrt{\frac{2}{C\_{FR}}}\right) \left[1 - \frac{1}{\kappa} \left(\sqrt{\frac{2}{C\_{FR}}}\right) + \frac{1}{\kappa} \left(\frac{3}{2\kappa} - \Delta U^{+'}\right) \left(\frac{C\_{FR}}{2}\right)\right] \tag{18}$$

**Table 4.**

*Overall friction coefficient CF of homogeneous rough condition calculated compared with the similarity law scaling procedure from Granville [43].*

**Figure 3.**

Δ*U*þ *and k*þ *s for homogeneous cases that calculated using Granville [45] compared with the used roughness function, and other roughness functions, Colebrook-type roughness function [46], and Schultz and Flack [47].*

$$
\Delta U^{+} = \left(\sqrt{\frac{2}{\mathbf{C}\_{F}}}\right) - \left(\sqrt{\frac{2}{\mathbf{C}\_{FR}}}\right) - \mathbf{19.7} \left[\left(\sqrt{\frac{\mathbf{C}\_{FS}}{2}}\right) - \left(\sqrt{\frac{\mathbf{C}\_{FR}}{2}}\right)\right] - \frac{\mathbf{1}}{\kappa} \Delta U^{+\prime} \left(\sqrt{\frac{\mathbf{C}\_{FR}}{2}}\right) \tag{19}
$$

The homogeneous roughness simulation results were also verified using other literature from Granville [45], as written in Eqs. 18 and 19. This method can predict the characteristic roughness function, *ΔU*þ ¼ *f ks* þ � �, by plotting the predicted value of *ΔU*þ (Eq. (19)) against *ks* þ (Eq. (18)) with the difference in the overall drag results from the rough conditions (*CFR*) to the smooth conditions (*CFS*). Where, *L* is the plate length, *Re* is the Reynolds number, *ks* is the roughness height, and Δ*U*þ0 is the roughness function slope, which is the slope of Δ*U*þ as a function of ln *ks* þ � �. The verification results are plotted in **Figure 3** and the simulation results successfully approached the planned roughness function model, namely from Cebeci and Bradshaw [39], with *Cs* = 0.253. Verification of the simulation results using Granville [45] method described in **Figure 3**. The results collapse on the roughness function used (Cebeci and Bradshaw [39]).

### **5. Results and discussion**

A systematic analysis of the results from the inhomogeneous rough surface cases is given in this section. To study the roughness effects, the local (*cf* ) and overall (*CF*) skin friction coefficients are calculated for both the homogeneous and inhomogeneous roughness cases. The effects from the roughness height and the roughness sequence in the streamwise direction are studied by analyzing the plots of local skin friction coefficient as a function of the length and plotting the mean velocity profile for the step up and step-down phenomenon and by comparing its integral values (*CF*) for the different cases. We also calculate the skin friction coefficient percentage differences between rough surfaces (both homogeneous and inhomogeneous) and the smooth wall reference case and between inhomogeneous roughness cases (combination of PQR) and the homogeneous roughness reference case (i.e., QQQ). Lastly, we carried out the prediction of how the single *ks* value of the homogeneous case that equal to the three different *ks* that composed the inhomogeneous case.

*The Phenomenon of Friction Resistance Due to Streamwise Heterogeneous Roughness with… DOI: http://dx.doi.org/10.5772/intechopen.99137*


2. Influence of wort composition on fermentation performance

#### 2.1. The chemical nature of sugarcane substrates and its by-products

In short, fermentation is initiated by the addition of the wort (also referred to as must), containing 18–25% (w/v) sugars, to a high-density yeast cell suspension, which represents 1/3 of the vat volume. Due to its large volumes, the feeding takes 4–6 h, and the fermentation is completed within 10 h. At the end of the fermentation, ethanol titres between 8 and 12% (v/v) are obtained, with a final cell density of 10–14% (w/v). Yeasts cells are then separated from the wine by centrifugation, which goes for distillation. The yeast slurry is diluted with equal volume of water and treated with sulfuric acid to reduce bacterial contamination, and reused in a subsequent fermentation cycle (Figure 1). This process configuration, using high cell densities and operating with cell recycling, is quite peculiar and allows two fermentation rounds per day during the harvest season that spans for almost 250 days. The reuse of cells reduces the need for yeast propagation, therefore diverting less sugar to yeast growth and

Figure 1. Simplified process flow diagram of the Brazilian ethanol production process (Courtesy of Jens C. F. Nielsen).

saving it to ethanol formation.

24 Fuel Ethanol Production from Sugarcane

As mentioned above, ethanol can be produced via direct fermentation of sugarcane juice, a mixture of juice and molasses, or molasses diluted in water [2, 3]. After shredding, cane is crushed in a milling tandem, constantly mixed with water, resulting in juices containing ca. 10– 15% sucrose content [3]. This process results in two types of juice: the primary – which leaves the first set of miller and is richer in nutrients – and the secondary – coming from the subsequent millers. The primary juice is commonly used for sugar production, whereas the secondary juice can either be used for sugar or ethanol production [3].

Sugarcane juice is passed through clarification (reducing impurities to less than 2%), decantation, and concentration (up to 18–25% total sugars) steps before sugar or ethanol production. These steps help reducing wild yeast and bacterial contaminations during the subsequent fermentation step, allowing for higher ethanol titre and yield [2].

During sugar production, the juice is clarified with lime, and concentrated through repetitive steps of evaporation and centrifugation. The concentrated juice later passes through a crystallization step, which catalyzes the formation of sucrose crystals that are later removed via centrifugation. The remaining sugar is left in the spent, dark and viscous liquid called molasses [4]. Molasses can be further recycled back into the sugar production process, resulting in several other types of molasses (i.e. A, B, C). The higher the number of recycles molasses is subjected to, the poorer its quality as a fermentation substrate [2].

Sugarcane juice and molasses are a complex mixture of carbohydrates, proteins, inorganic salts and organic acids [5], and wort prepared with either juice, molasses mixed with juice, or molasses diluted with water will have different nutrient composition, which will ultimately impact fermentation performance [1]. A comparison between sugarcane juice and molasses composition is shown below (Table 1).

Asparagine, glutamine and aspartic acid are the most abundant amino acids in sugarcanebased worts, while the disaccharide sucrose is by far the major sugar. Minor amounts of the monosaccharides, glucose and fructose, and the oligosaccharide, kestose, are also found [5]. Lipids are represented by a mixture of n-alkanes and ethyl and methyl esters of fatty acids (palmitate and oleate are the predominant), as well as of phytosterols (stigmasterol, β-sitosterol, and campesterol) [6, 7].

After fermentation cells are separated from the fermented wort (wine) by centrifugation, and this stream is sent for distillation. The distillation of ethanol generates a considerable amount of a wastewater stream named vinasse (stillage) [8].

Vinasse is currently applied in soil as a fertilizer (fertirrigation), due to its high potassium titre [4, 9]. However, this high organic matter load shows deleterious impacts on soil, water and groundwater [10]. A possible solution to this environmental issue is the anaerobic digestion of vinasse, reducing the organic matter of vinasse for later fertirrigation use, and generating a new stream of revenue (biogas) for the sugarcane ethanol plant [11].

Table 1. Composition of sugarcane juice and molasses.

Wort preparation influences final vinasse composition, which will have an impact on the performance of anaerobic digesters, and should be taken in consideration when designing such system. Vinasse can be defined as a mixture of various compounds, including organic acids, mainly fermentation-derived, such as succinic and malic acids, as well as lactic and acetic acids, derived from bacterial contamination. Glycerol is a substantial compound in vinasse, and non-distilled minor amounts of ethanol are also found [11, 12]. A general characterization of vinasse originated from different substrates is depicted in Table 2.

#### 2.2. Substrate related stresses on ethanol fermentation

Even being successfully used as substrate for ethanol production for decades, sugarcane based-worts present many challenging conditions for the fermenting yeast S. cerevisiae [1]. Besides nutrients, industrial worts used in the fermentation process, also carry inhibitors which can be both feedstock- or process-related [13]. During heating steps of juice pretreatment, some fermentation inhibitors are produced, from sugar degradation (e.g. furfural) and Maillard melanoidins [14].

Furfural, for instance, has been shown to reduce the specific growth rate, the biomass yield on ATP, and both ethanol yield and productivity [15], and is lethal to cells in concentrations above 84 nmol/gDW [16]. During fermentation, furfural is reduced to furfuryl by NADH-dependent alcohol dehydrogenase (ADH) [17, 18], in a NAD+ regenerative manner, resulting in lower glycerol formation, and higher ethanol titres, when furfural concentration is kept under 29 nmol/l [16].

Tauer et al. (2004) have investigated the effect of Maillard derived products during fermentation of different beverages (i.e. Tequila, Mezcal, whiskey and beer). In their study they observed a reduction in the formation of ethanol of up to 80%. Also, it was observed that the inhibition of these Maillard products is pH dependent, showing little inhibition at pH 4, and increasing at higher pH values.

Wort

composition

 Parameters

CODa

> Sugarcane juice

Molasses

Mix Based on data compiled by [11].

aChemical oxygen demand.

bBiochemical

cTotal solids. dVolatile solids. Table 2. Vinasse

composition

 from different

sugarcane-based

 worts.

Clash of Kingdoms: How Do Bacterial Contaminants Thrive in and Interact with Yeasts during Ethanol Production?

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27

 oxygen demand.

 27.7

69

37.8

 5.8

 15.7

 4.4

 0.5

 0.03

 0.04

 2.3

 0.9

 1.4

 0.3

 105

 52.5

 8.4

 4.7

 0.04

 1.1

 0.5

 1.1

 0.1

 24.7

 28.6

 13.8

 0.9

 0.13

 0.21

 4.1

 2.3

 1.9

 0.6

 82

 0.2

 1.4

 1.9

 2.4

 0.6

 7.3

 14.7

 4.9

 0.5

 0.11

 0.1

 1.5

 0.7

 0.4

 0.2

 22.5

 1.5

 17.9

60

 1.8

 0.1

 0.4

 1.0

 0.8

 0.2

BODb

N

P

K

SO4

2

Ca

Mg

TSc

VSd

 (in g/L)

Wort preparation influences final vinasse composition, which will have an impact on the performance of anaerobic digesters, and should be taken in consideration when designing such system. Vinasse can be defined as a mixture of various compounds, including organic acids, mainly fermentation-derived, such as succinic and malic acids, as well as lactic and acetic acids, derived from bacterial contamination. Glycerol is a substantial compound in vinasse, and non-distilled minor amounts of ethanol are also found [11, 12]. A general charac-

Composition Sugarcane juice (g/L) Sugarcane molasses (g/kg)

Total solids 140–190 735–875 Total sugars 105–175 447–587 Sucrose 98–167 157–469 Reducing sugars 6–11 97–399 Nitrogen 0.08–0.3 0.25–1.5 Phosphorous 0.02–0.1 0.3–0.7 Potassium 0.7–1.5 19–54 Calcium 0.1–0.5 6–12 Magnesium 0.1–0.5 4–11

Even being successfully used as substrate for ethanol production for decades, sugarcane based-worts present many challenging conditions for the fermenting yeast S. cerevisiae [1]. Besides nutrients, industrial worts used in the fermentation process, also carry inhibitors which can be both feedstock- or process-related [13]. During heating steps of juice pretreatment, some fermentation inhibitors are produced, from sugar degradation (e.g. furfural)

Furfural, for instance, has been shown to reduce the specific growth rate, the biomass yield on ATP, and both ethanol yield and productivity [15], and is lethal to cells in concentrations above 84 nmol/gDW [16]. During fermentation, furfural is reduced to furfuryl by NADH-dependent alcohol dehydrogenase (ADH) [17, 18], in a NAD+ regenerative manner, resulting in lower glycerol formation, and higher ethanol titres, when furfural concentration is kept under

Tauer et al. (2004) have investigated the effect of Maillard derived products during fermentation of different beverages (i.e. Tequila, Mezcal, whiskey and beer). In their study they observed a reduction in the formation of ethanol of up to 80%. Also, it was observed that the inhibition of these Maillard products is pH dependent, showing little inhibition at pH 4, and

terization of vinasse originated from different substrates is depicted in Table 2.

2.2. Substrate related stresses on ethanol fermentation

and Maillard melanoidins [14].

Based on data compiled by [2].

26 Fuel Ethanol Production from Sugarcane

Table 1. Composition of sugarcane juice and molasses.

increasing at higher pH values.

29 nmol/l [16].

Table 2. Vinasse composition from different sugarcane-based worts.

Sugarcane juice also presents potentially toxic metal ions on its composition. Walford (1996) presented a compilation of mineral values from sugarcane juices (Table 3).

(900–1800 ppm/Brix) acids. Other acids found in lower concentrations are succinic, oxalic, tartaric and glycolic acids [5]. During fermentation, lactic and acetic acids are also formed via

Clash of Kingdoms: How Do Bacterial Contaminants Thrive in and Interact with Yeasts during Ethanol Production?

http://dx.doi.org/10.5772/intechopen.78413

29

Undissociated organic acids are lipid permeable and thus enter yeast cells. Due to the higher intracellular pH as compared to the environment, dissociation into the corresponding anion, leads to intracellular acidification and ATP expenditure, a mechanism known as weak acid uncoupling [22], which is described in more details in the following sections. Moreover, the anion accumulated

inside the cells may reach toxic concentrations that impair essential metabolic functions.

nation is a concurrent problem in industrial fermentations.

yield, yeast cell flocculation, and low yeast viability [24–27].

plants found that Lactobacillus was the most abundant genus [28].

3.1. Homo- and heterofermentative lactic acid bacteria

have shown to include both types [31].

3. The interplay between yeast and bacteria in the fermentation process

Fuel ethanol fermentation performed in Brazil is one of the largest industrial biotechnological processes in the world, with the most favorable energy balance as compared to other similar processes for ethanol production [13, 23]. However, in view of the nature of the process and the large volumes processed, aseptic conditions are never achieved. Therefore, bacterial contami-

This is regarded as a major drawback that deviates sugars away from ethanol formation and lead to detrimental effects upon yeast fermentative performance, such as reduced ethanol

Bacterial contaminants found present in the fermentation step of ethanol production comprise mainly lactic acid bacteria (LAB) [28], probably because of their higher tolerance towards acidic pH and ethanol titres when compared to other microorganisms [29, 30]. Studies that investigated the identity of these contaminants during yeast fermentation in Brazilian ethanol

Contaminating lactic acid bacteria are traditionally classified in two major metabolic subgroups according to the pathway used to metabolize hexose sugars: homo- and heterofermentative [29]. In general, bacteria isolates from industrial fermented sugarcane substrates

Homofermentative bacteria catabolize hexoses via the so-called Embden-Meyerhof-Parnas (EMP) pathway, in which 1 mol of hexose results in the formation of 2 mol of lactic acid and 2 mol of ATP. In comparison, in heterofermentative bacteria another pathway is active, 6 phosphogluconate/phosphoketolase (6-PG/PK) pathway [29], and hexoses are converted to equimolar amounts of lactic acid, ethanol or acetate, and carbon dioxide, yielding 1 mol of ATP per mol of hexose fermented [32, 33]. With the conversion of acetyl phosphate to acetate instead of ethanol, an additional ATP can be produced. Then, regeneration of surplus NAD+ must be achieved by an alternative electron acceptor. Under aerobic conditions, oxygen may serve as the electron acceptor [34], but under anaerobic or even oxygen-limited conditions, fructose is reduced to mannitol, serving as an electron sink [35]. There is a third classification

contaminating bacteria.

Aluminum shows high toxicity under acidic conditions (being present in Al3+ form) and is particularly deleterious to yeast cells, reducing cell viability, trehalose content, and ethanol yield [1]. Its deleterious effects can be alleviated by magnesium concentration in the broth, or completely abolished by mixing sugarcane juice with molasses. This might be related to some chelating property of molasses [2].

Other metal ions also play an important role in yeast fermentation inhibition. Sugarcane molasses showed inhibitory effect towards invertase activity of a laboratory S. cerevisiae strain. This inhibition could be further replicated in laboratory media when copper ions (CuCl2) where added in the media at a concentration of 0.04 M [19].

Another important factor is the osmotic stress that is caused by elevated concentration of salts. Cations such as Ca2+, Mg2+, K+ , Na+ , and anions, like Cl and SO4 2, can all have severe effects on yeast growth and ethanol production [20].

Sugarcane juice and molasses have a complex composition of organic acids. Even though they do not respond to a major fraction of the composition of these raw materials, they are responsible for their pH values (ca. 5–6) and their buffering capacity [5]. The composition, and concentration, of such acids depend on several factors, such as the maturity stage and variety of the plant, weather, soil and health state [21]. The most common organic acids found in sugarcane are trans-aconitic (5000–8000 ppm/Brix), malic (1200–1800 ppm/Brix) and citric

Table 3. Inorganic composition of sugarcane juice.

(900–1800 ppm/Brix) acids. Other acids found in lower concentrations are succinic, oxalic, tartaric and glycolic acids [5]. During fermentation, lactic and acetic acids are also formed via contaminating bacteria.

Undissociated organic acids are lipid permeable and thus enter yeast cells. Due to the higher intracellular pH as compared to the environment, dissociation into the corresponding anion, leads to intracellular acidification and ATP expenditure, a mechanism known as weak acid uncoupling [22], which is described in more details in the following sections. Moreover, the anion accumulated inside the cells may reach toxic concentrations that impair essential metabolic functions.


*3.3. Overall Comparison*

Overall, all the three herds were same in terms of resource-based indicators. A slight difference among the three herds was present in terms of animal-based indicators. The herd at CU1 (43.64%) showed aggression and some animals (34.78%) were observed showing signs of ophthalmic discharge. The second herd showed an aggression of 37.75%, which was comparatively lower than that observed in the other two herds, but no diseased animal was found. The herd at CU3 showed the highest aggression (66.59%), but no animal was diseased. Comparing the results obtained for all the three groups, we assumed the herd at CU2 was at maximum welfare, followed by CU3 and CU1.