Vegetable impurities in harvested sugarcane impair juice quality and reduce sugar yield: implications for processing and payment systemsABSTRACT
Despite the numerous economic, social, and environmental benefits of green cane, there is a significant increase in the accumulation of plant impurities in the raw material, particularly when harvesters are not operated properly. This study assessed the impact of adding measured quantities of sugarcane vegetable impurities (green leaves, straw, and tops) on the quality of both cane and juice during the harvest seasons from 2015/2016 to 2019/2020. The impurity levels ranged from 0 % to 36 %, with equal and varying proportions of tops, straw, and green leaves added across different varieties, ratoons, and harvest periods. More fibrous varieties showed lower sucrose recovery during juice extraction, suggesting a need for adjustments in processing. The current total recoverable sugars (TRS) decreased by nearly 1 % across varieties, depending on the volume of cane trash added. Furthermore, an increase in vegetable impurities adversely affected the quality of the chopped cane and reduced the farmers’ profits. The results also suggest that Tanimoto fiber analysis may provide a more accurate assessment than the weight of wet cake (WWC) methodology for TRS calculations and payment system. These findings are crucial for farmers, sugarcane agroindustry, harvesters, manufacturers, and training programs for machine operators to improve sugarcane quality.
Keywords:
Saccharum spp.; green cane; straw; tops; green leaves
Introduction
Sugarcane harvesting can be carried out with or without crop burning. The mechanized method (without burning) is the most widely practiced in Brazil (Toniêto et al., 2016) and in various other countries, including Thailand (Pongpat et al., 2017), Mauritius (Cheong and Teeluck, 2016), and the United States (Eggleston et al., 2012).
In the Brazilian sugarcane harvesting system, a specialized harvesting attachment is used, with harvesters cutting the stalks and separating plant impurities, such as green leaves, straw, and plant tops. Once cut, the stalks are chopped into lengths of 15-20 cm, while the impurities are returned to the field (Sordi and Manechini, 2013). The resulting raw material, consisting of both stalks and any unseparated impurities, is then sent for industrial processing (Rein, 2017).
While green cane offers numerous environmental advantages, inadequate harvesting operations can lead to greater accumulation of plant impurities in the raw material (Eggleston et al., 2010). The process of mechanical harvesting utilizes primary cane top cutters (primary) and secondary extractors (Sordi and Manechini, 2013).
Malfunctions or interruptions of the harvester's secondary extractor for straw and leaves can increase the amounts of impurities in the raw material. Reports indicate that turning off the secondary extractor may improve the fiber content delivered to the sugar mill (Sordi and Manechini, 2013). This practice aims to aid in impurity separation in mills that use this waste for energy cogeneration.
Despite continuous advancements by specialized agricultural machinery companies, the efficiency of separating plant impurities still lags behind traditional burning and manual harvesting practices. In Brazil, mechanized harvesting allows up to 6 % vegetable impurities (Lavanholi, 2008).
The adoption of green, mechanized cane harvesting has significantly increased the need to understand how high impurity levels affect sugarcane quality. Therefore, this study aimed to examine how adding specific quantities of plant impurities – such as green leaves, straw, and tops – impacts both cane and juice quality.
Materials and Methods
Over four years, studies were conducted by adding various quantities and proportions of impurities to sugarcane from the 2015/2016 to 2019/2020 harvest seasons, covering a wide range of varieties, harvest times, and sugarcane ages (Table 1).
Dates, varieties, ratoon, vegetable, and mineral impurities at harvest, and expected variety maturation from 2015/2016 to 2019/2020.
The experimental design for Experiment 1 was a 6 × 5 factorial scheme with randomized blocks and four replications. The first factor involved the sugarcane varieties harvested during the 2015/2016 and 2016/2017 harvest seasons (Table 1). Experiment 2 included adding specific amounts of plant impurities during the hydraulic press extraction process. This comprised a control (clean stalks), and treatments with 3 %, 6 %, 9 %, and 12 % impurities, consisting of equal parts green leaves, tops, and straw, each representing 1/3 of the total sample (Table 2).
This study used a range of 3-6 % to represent the maximum allowable level of vegetative impurities for mechanized harvesting, as specified by Lavanholi (2008). Additionally, a combined impurity level of 10 % was set, based on reports from Usina Santa Fé indicating that 98 % of its raw sugarcane fields were mechanically harvested during the experimental years of 2015 and 2016. The 12 % treatment was considered extreme, as it doubled the impurity level reported by Lavanholi (2008).
The sugarcane varieties were sourced from the Usina Santa Fé area in Nova Europa, São Paulo State (21°49’05.3" S, 48°36’40.4" W, altitude 488 m). The sugarcane was mechanically harvested from 10 meter wide plots using a John Deere 3520® harvester. After harvesting, the extractors discharged the material to the side of the machine, where it was sorted into chopped stalks, green leaves, tops, and straw. Each type of material was then separately conditioned, processed for disintegration, and quickly assembled for treatment, including impurity level assessment and juice extraction.
The experimental design employed in Experiment 2 was a split-plot design (10 × 5) with four replications. Treatment 1 was the harvest timing across ten varieties, considering ratoon and maturation (Table 1). Treatment 2 involved adding specific quantities of vegetative impurities during hydraulic press extraction: control (clean stalks), as well as 9 %, 18 %, 27 %, and 36 % impurities with green leaves, tops, and straw, respectively (Table 2). The 36 % treatment was simulated to reflect reports from farmers who disengaged the secondary extractors of their harvesters.
The raw material was harvested between May and Oct of the 2017/2018 season in Guariba, São Paulo State (21°21’36" S, 48°13’42" W, altitude 618 m). The procedures for harvesting and sample preparation were the same as those used in Experiment 1 (Table 2).
In conjunction with Experiment 2, another study was conducted to measure the vegetable impurities in the truck compartments. This study included two scenarios: one where the cane was harvested with the secondary extractor engaged, and another where the extractor was disengaged. The vegetable impurities were categorized into green leaves, straw, and straw tops. Subsequently, each component was weighed, and its proportions were recorded (Table 3).
The quantity and proportion of vegetable impurities, Straw (S), Leaf (L), and Tops (T), found in material harvested by the John Deere 3520® harvester with a secondary extractor on or off in the compartment of truck transportation (35 t) sampled with the oblique probe during the 2017/2018 season.
The experimental design in Experiment 3 employed a randomized complete block design with two replications (Table 2). A large number of repetitions was initially planned. However, as time went on, the polarimeter failed, which hindered the analysis of the remaining samples and prevented completing all required calculations.
The main treatment involved intentionally adding specific quantities of vegetative impurities during hydraulic press extraction process. These included a control (clean stalks) and groups with 9 %, 18 %, 27 %, and 36 % impurities that comprised green leaves, tops, and straw in proportions determined from the previous year's data. This setup was used to evaluate the level of impurities found when the harvester's exhaust fans running versus when they were turned off during the 2017/2018 season (Table 3).
Sugarcane was sourced from the same Guariba region in São Paulo State, at the same coordinates in Experiment 2. During the 2017/2018 season, two studies were conducted. In Study (a), the amount of added top increased in line with rising impurity levels, reflecting a natural increase in top availability. Conversely, Study (b) kept the same mass of the plant's apical part without any increase (Table 3). Additionally, two control treatments (0 %) were implemented to simulate a scenario without the tops, along with another treatment using 0 % top levels applied to the loads with connected extractors, which included 14.55 g (6.96 %) of tops.
The individual components were fed into the disintegrator for all treatments, and 500 g was allocated for juice extraction using a hydraulic press. In the control treatment, only disintegrated stalks were used. Impurities were added according to the proportions outlined in Table 3. In the experiment comparing the extractors’ operations, with 6.96 % impurities observed alongside the working extractors, the ratio for straw, green leaves, and straw tops were 3.04:1:2.90, respectively (Table 3). This ratio was used to evaluate the impurity levels at 0 % and 9 %. At the 27 % and 36 % points, when the secondary extractors were removed, the ratio of straw, green leaves, and tops changed to 2.75:1.22:1, representing an average of 24.61 % of the total impurities (Table 3). The average impurity levels (from 6.96 % to 24.61 %) at the 18 % addition point matched findings from a previous study conducted during the 2017/2018 season (Table 3).
The approach was the same as that employed for the enhanced tops; however, in this case, the simulation was conducted with a constant mass of tops (Table 2). The details regarding the varieties and the harvest conditions applied in both studies are presented in Table 1.
The experimental design for Experiment 4 was a randomized complete block with ten replications. The main treatment involved adding specific amounts of plant impurities during the extraction process using a hydraulic press. Treatments included a control (clean stalks), and variations with 3 %, 6 %, 9 %, 12 %, 15 %, 18 %, 21 %, 24 %, and 27 % impurities consisting of green leaves, tops, and straw (Table 2). The sugarcane was sourced from the Guariba region in São Paulo State, and the varieties and harvest conditions are detailed in Table 1. The methodology followed was consistent with that of the previous experiments, maintaining the cane tops while varying the levels of added impurities.
The characteristics of sugarcane and its juice were assessed using multiple attributes: soluble solids (°Brix) content was measured by refractometry at 20 °C, along with Pol juice (Scheneider, 1979); juice pH was determined directly with a digital pH meter; weight of wet cake (WWC fiber) of the sugarcane and Tanimoto fiber were analyzed (Tanimoto, 1964); juice purity and the calculation of reducing sugars (RS) were conducted following the methodology outlined by CONSECANA (2006); moisture content was evaluated by drying the disintegrated sugarcane in an oven at 65 °C for 48 h, according to (CTC, 2011). Additionally, ash, starch, International Commission for Uniform Methods of Sugar Analysis (ICUMSA) color (from Experiment 1), and total acidity were measured (CTC, 2011). The volume of extracted juice during the pressing process was collected in a 500 mL beaker (Experiment 1).
The results were analyzed using a variance analysis with the F test and Tukey's test (p < 0.05). Subsequently, polynomial regression analysis was conducted on the quantitative data, following the methodology outlined by Barbosa and Maldonado Júnior (2015).
Results
The following section presents the findings of our regression analysis and discusses the evolving understanding of how impurities affect the quality of raw materials. As impurity levels increased to 12 %, notable differences emerged in the technological analyses concerning RS (F = 4.83; p < 0.01), WWC fiber (F = 34.93; p < 0.01), Tanimoto fiber (F = 28.41; p < 0.01), extracted juice volume (F = 19.30; p < 0.01), Pol cane (F = 2.79; p < 0.05), total recoverable sugars (TRS) (F = 2.93; p < 0.05), pH (F = 2.79; p < 0.05), and acidity (F = 7.81; p < 0.01).
Regression analysis indicated increases in RS, WWC fiber, and Tanimoto fiber (Figure 1A-C), along with acidity and ash content as impurity levels increased (Figure 1G-H). Notably, reductions of 6.5 %, 7.7 %, and 6.8 % were observed in the extracted juice volume, Pol cane (sucrose), and TRS treatments, respectively, when comparing the 0 % and 12 % treatments based on the polynomial equation (Figure 1D-F).
Polynomial regression for vegetable impurities up to 12 % of the 2015/2016 and 2016/2017 seasons. A) Juice reducing sugar (RS); B) WWC fiber (Weight of wet cake); C) Tanimoto fiber; D) Juice volume; E) Pol cane (sucrose); F) Total recoverable sugars (TRS); G) Acidity and H) Ash. p < 0.01 was considered to indicate statistical significance. R2 = coefficient of determination; **1 % significant according to polynomial analysis; Bars = standard error.
The polynomial regression analysis revealed that for every 1 % increase in impurities in the raw material, there was a corresponding increase of 0.03 times in RS, 0.15 in WWC fiber, and 0.18 in Tanimoto fiber (Figure 1A-C). Additionally, there was an increase of 0.02 in acidity and 0.004 times in ash content (Figure 1G-H), while TRS decreased by 0.64 times (Figure 1F).
The quality of the raw material for Experiment 2 exhibited significant variations due to an increase in plant impurities, which reached 36 %. Notable results include soluble solids (F = 151.39; p < 0.01), apparent sucrose content (F = 482.30; p < 0.01), purity (F = 89.30; p < 0.01), WWC RS (F = 87.06; p < 0.01), RS (F = 29.72; p < 0.01), WWC fiber (F = 841.18; p < 0.01), Tanimoto fiber (F = 1107.53; p < 0.01), moisture (F = 12.16; p < 0.01), Pol cane (F =1 782.18; p < 0.01), Cane RS (F = 13.40; p < 0.01), TRS (F = 1681.88; p < 0.01), acidity (F = 5.75; p < 0.01) and ash (F = 11.50; p < 0.01).
Each unit of additional trash impurity resulted in a reduction of sugar per ton of cane by 0.98 times (Figure 2A). In the control treatment, the average TRS was 152.77 kg t−1. In contrast, for the treatment with 10 % added impurities, the average, calculated using the polynomial equation, was 142.95 kg t−1, reflecting a 7 % decrease in this metric.
Polynomial regression for vegetable impurities for each sugarcane variety up to 36 % (2017/2018 season). A) Total recoverable sugars (TRS); B) Ash. p < 0.01 was considered to indicate statistical significance. Bold line is the average of the varieties. R2 = coefficient of determination; **1 % significant, *5 % significant, and ns nonsignificant according to polynomial analysis; Bars = standard error.
The polynomial analysis for plant impurities (°Brix, Pol, TRS, and others) revealed higher R2 values (Figure 3A-G) compared to those observed in the previous experiment (Figure 1A-H). This analysis also indicated significant interactions between the added impurities and the different varieties. The details of these interaction effects were elaborated upon various harvesting conditions (Table 1, Figures 2A-B and 4A-C).
Polynomial regression for vegetable impurities up to 36 % of the 2017/2018 season. A) Soluble solids; B) Pol juice (sucrose); C) Juice purity; D) Determined reducing sugar (RS); E) Calculated RS; F) Cane RS; G) Acidity. p < 0.01 was considered to indicate statistical significance. R2 = coefficient of determination; **1 % significant according to polynomial analysis; Bars = standard error.
Polynomial regression for vegetable impurities for each sugarcane variety up to 36 % (2017/2018 season). A) Weight of wet cake - WWC fiber; B) Tanimoto fiber; C) Pol cane (sucrose). p < 0.01 was considered to indicate statistical significance. Bold line is the average of the varieties. R2 = coefficient of determination; **1 % significant according to polynomial analysis; Bars = standard error.
The WWC fibers of the RB87-365, SP80-3280, and RB867515 varieties were found to be superior to those of the CTC 2 variety (Figure 4A). CTC 2 was harvested twice during its premature maturation stages in May and June. However, when harvested again in October, once it reached the correct maturation stage, the fiber percentage was nearly at the medium level observed across all varieties (Figure 4A). In the control treatment, the TRS for this variety was recorded at 160.72 kg t−1 (Figure 2A). In contrast, the TRS values for the May and June harvests, when the variety was collected at improper maturation stages, were significantly lower, at 128.50 and 124.75 kg t−1, respectively (Figure 2A). Consequently, sucrose loss was more pronounced in the fibrous varieties (Figures 2A and 4A).
In Experiment 3, which involved varying proportions of trash, the analysis of TRS (Figure 5A-B) revealed a higher R2 value when the sugarcane tops were kept constant. In the absence of impurities, the TRS value with a constant top and 0 % impurities was 145.17 kg t−1, while an increase in the top region raised it to 150.67 kg t−1. The trend of TRS loss described by the polynomial equation is more pronounced for constant tops (Figure 5A-B). Additionally, it was observed that the WWC methodology produced lower fiber quantities than the Tanimoto methodology (Figure 5C-F).
Polynomial regression for vegetable impurities up to 36 % with different top quantities added (2017/2018 season) for total recoverable sugars (TRS), WWC (Weight of wet cake), and Tanimoto fiber. A) TRS with tops increasing; B) TRS with constant tops added; C) WWC fiber with tops increasing; D) WWC fiber with constant tops; E) Tanimoto fiber with tops increasing; F) Tanimoto fiber with constant tops added in vegetable impurities; R2 = coefficient of determination; Bars = standard error.
During the 2019/2020 season, the impact of raw material attributes was lower than in previous years, when no significant impurity levels (up to 27 %) were present. This included factors such as acidity (F = 151.39; p < 0.01), WWC fiber (F = 41.87; p < 0.01), Tanimoto fiber (F = 237.99; p < 0.01), cane moisture (F = 7.81; p < 0.01), Pol cane (F = 151.39; p < 0.01), ash content (F = 4.64; p < 0.01), and TRS (F = 151.39; p < 0.01). According to the polynomial equation, the sugar loss per % of vegetable impurities added to the raw material was 1.08 times higher (Figure 6D).
Polynomial regression for vegetable impurities up to 27 % with the same quantity of straw and different proportions of straw and leaves. A) Pol cane (sucrose); B) WWC fiber (Weight of wet cake); C) Acidity; D) Total recoverable sugars (TRS); E) Ash; F) Cane moisture; and G) Tanimoto fiber (p < 0.01). R2 = coefficient of determination; **1 % significant according to polynomial analysis; Bars = standard error.
Discussion
This study evaluated 28 harvesting times across the 2015/2016 to 2019/2020 seasons, with some varieties appearing in different seasons, ratoons, and maturation stages (Table 1). Notably, there were significant levels of vegetable impurities during harvesting in Experiments 2 to 4 (Table 1). On average, the samples in these experiments contained 21.78 % vegetable impurities and 0.48 % mineral impurities (Table 1).
The acceptable level of vegetable impurities in mechanical harvesting is approximately 6 % (Lavanholi, 2008). However, in this study, the observed vegetable impurities exceeded this standard, ranging from 9 % to 34.94 % (Table 1). In practical observations shared by Usina Santa Fé, an average of 10 % was observed when the secondary harvester extractors were operating. In fields in Florida (USA), the raw material showed vegetable impurity levels of 12.1 %, 18.9 %, and 22.7 %, depending on the speed of the secondary extractor, measured at 110, 89, and 68.1 rad s−1, respectively (Eggleston et al., 2012).
Almost all situations, including varieties harvested at different maturation stages, were examined, revealing natural variations for maturation stages across seasons.
The first and the second studies used the same proportions of straw, green leaves, and tops. However, the impurity levels varied under natural harvesting conditions. Therefore, a separate study was conducted to determine the impurity levels within the total cane load (Table 3). In the 2017/2018 season, during Experiment 2, harvesting was carried out with the harvester's secondary extractors both on and off, and the mass of each impurity was measured.
The secondary extractors of the harvester, both on and off, played a key role in determining the impurity levels. During the 2018/2019 season, the study faced limitations in the number of repetitions performed. Nevertheless, the results obtained were essential for stablishing the methodology used in Experiment 4 of the 2019/2020 season.
The impurities were added, leading to an increase in the amounts of green leaves and straw, while maintaining a constant mass of the sugarcane tops. To remove the apical bud, the stalk is cut with a saw near the breakpoint of the sugarcane (Eggleston et al., 2010). Simultaneously, the remaining sugarcane trash is fed into the machine. The secondary extractor separates the trash, and the machine expels the vegetable impurities back into the field while chopping the stalks for transshipment vehicles (Faria et al., 2019).
An increase in the level of vegetable impurities to 12 % or 36 % adversely affected the quality of sugarcane. Both studies indicated increases in reducing sugars (RS), fiber, acidity, and ash content, while Pol cane and total recoverable sugars (TRS) decreased. The sucrose, purity, and soluble solids content were reduced for the higher level of added impurities.
During the 2019/2020 season, the levels of total impurities in sugarcane tops decreased; however, analyses of sucrose and related components showed no impacts on the raw material quality. The tops, or growing-point regions around the plant's apical bud, are typically discarded during harvesting due to their high levels of organic compounds, such as aconitic acid, and their reduced sucrose content (Eggleston et al., 2010).
Interestingly, adding straw and green leaves in Experiment 4 increased fiber content along with related parameters, including moisture levels in sugarcane, Pol cane, and TRS. Moreover, it is noteworthy that acidity and ash contents increased across all seasons, which could negatively affect the quality of both ethanol and sugar processing.
Acidity and ash content can impact sugar production, as they tend to hinder crystallization and adversely affect sugar color (Rein, 2017). Conversely, high acidity can compromise yeast cell viability in ethanol production, hindering the reuse of yeast in subsequent fermentations. Additionally, increased acidity diminishes the ethanol yield during fermentation (Ravaneli et al., 2011).
Regrettably, the volume of extracted juice was measured only in Experiment 1, revealing a reduction of 6.6 % compared to the 0 % and 12 % in vegetable impurities. The impact becomes more pronounced at 36 % or even 27 %, with the addition of nonproportional trash. In Experiments 2 and 4, vegetable impurities contributed to a decrease in cane moisture, and the increase in fiber content further intensified this reduction in extracted juice. Straw or brow leaves, often classified as "dead" leaves, can act as a "sponge" during extraction, reducing the volume of juice extracted in mills or diffusers (Sordi and Manechini, 2013).
The analyses of fibers and related components proved to be the most significant factors when assessing trash impurities across all experiments. Two methodologies – WWC and Tanimoto – were employed to determine fiber content and evaluate this critical parameter. Although the Tanimoto method is considered more accurate, Brazilian mills traditionally rely on WWC fiber as the parameter for their daily analyses (CONSECANA, 2006).
According to the Tanimoto methodology, the wet bagasse cake must be dried in an oven after hydraulic pressing. Subsequently, the dry mass was measured (CTC, 2011). However, the drying time is impractical for many daily samples, which often exceeds 100 samples per shift.
The same rationale applies to the determination of reducing sugars (RS) in juice, known as WWC RS. Sugar mills in Brazil use this calculation to estimate the reducing sugar content in juice and cane, utilizing values of soluble solids (°Brix) and sucrose (Pol). While chemical analysis methods may be unconventional, they offer greater accuracy (Rein, 2017).
WWC fiber was designed to predict up to 11 % of the fiber amount in burned cane in Brazil (CONSECANA, 2006). However, applying WWC calculations to green cane is not advisable. According to the regression analysis (Figure 4A), the addition of 1.50 % vegetable impurities resulted in the detection of more than 11 % WWC fibers in the samples. Furthermore, the increase in fiber content for Tanimoto fibers was 23 % greater compared to WWC fibers (Figure 4A-B). Traditionally, fiber content estimation has been accomplished by lowering the precision of sugar production estimates using TRS.
Fibers contribute to reducing TRS, and an increase in fiber content can lead to greater penalties for farmers. Some sugar mills utilize fiber for cogeneration of energy (Palacios-Bereche et al., 2022); furthermore, sugarcane with a higher fiber content has been explored for cellulosic ethanol production (Prado et al., 2024). Currently, two enterprises in Brazil produce ethanol from sugarcane bagasse (Teixeira et al., 2024).
In South Africa, the payment system is undergoing revisions. The current cane payment structure incentivizes growers to supply sugarcane with high sucrose yield per hectare. Consequently, they are less inclined to produce energy cane cultivars that yield high fiber but lower sucrose (Mafunga et al., 2023). The introduction of a composite fiber price is expected not only to the acreage dedicated to energy cane but also to alter enterprise and cultivar selection, enhance biomass availability, and revitalize the biofuel sector.
The payment system may vary depending on the type of sugar mill. For instance, it could involve only ethanol, a combination of ethanol and sugar, or a more comprehensive model that includes ethanol, sugar, and additional products derived from sugarcane residue, such as electrical energy, biogas, vapor generation, and cellulosic ethanol. In the present study, however, WWC fiber was consistently used for TRS calculations across all seasons to facilitate comparison. It is also essential to further elaborate on the specific application of TRS calculations using Tanimoto fiber.
The impact of impurities on TRS was measured at −0.64 for an additional 1 % of impurities when the trash content was at 12 %, and −0.98 when it reached 36 %, as evidenced by the proportional contributions from straw, green leaves, and tops (Figures 1F and 2A). Regarding sugarcane varieties, each percentage increase in impurities corresponded to a 0.816-fold reduction in sugarcane quality (Figure 2A). Among the different varieties, CTC 2 (harvested in June) exhibited the smallest reduction in TRS at 0.769, while SP80-3280 showed the most significant decrease at 1.2182. It is important to note that sugar recovery is influenced by fiber content, which in turn is determined by the specific sugarcane variety and its maturation stage.
A comparison of increasing sugarcane tops (–0.88) and constant plant tops (–0.91) indicates that fiber quantity, driven by the addition of straw and green leaves, contributes to a higher loss of TRS. In experiments where impurities remained consistent up to 27 %, each additional 1 % of impurity in the raw material resulted in a 1.08-fold decrease in TRS content. Furthermore, whether TRS is calculated using Tanimoto fiber, the loss is expected to be even greater. Utilizing the TRS curve from WWC fiber, it becomes possible to simulate the impact of adding impurities up to 27 % of a truckload.
Farmers or producers should assess the raw material quality in the truck trailer, identify the impurity level, and input this value into the regression equation to estimate the expected TRS loss (Figure 6D). For instance, if a sample collected from a truckload reveals 15 % vegetable impurities, the anticipated TRS would be 126.81 kg t−1, representing a 12.74 % reduction compared to a sample with zero trash.
In addition to TRS, the quality of the raw materials can be predicted by analyzing the plant impurities. Another application for these vegetable impurities is to forecast and mitigate potential factory issues caused by the quality of the raw materials. The equations developed should be applied in real-world scenarios to assess the impact of impurities on the sugar and ethanol production process. However, it is essential to acknowledge the current limitations in measuring and evaluating these impurities.
In post-studies (data not shown), we compared the same percentage of vegetable impurities from the equation in Figure 6D with TRS obtained from the field samples. Various TRS values were recorded with the same quantity of impurities in a range from 0 % to 27 %.
The TRS values of the field samples are influenced not only by the percentage of added impurities but also by factors such as the sugarcane maturation stage, the variety type, the proportions of impurities added, the seasonal period, and the year (whether dry or wet). This observation underscores the need for further investigation to refine the equation presented in Figure 6D, which elucidates the interactions among these variables, rather than focusing solely on individual impurity concentrations. However, with certain limitations, the potential impacts of vegetable impurities on the industry should be verified in the Figure 6A-G.
In the sugar and ethanol production, reducing vegetable impurities in raw sugarcane is essential for increasing farmers’ profits and the industrial process quality. Each 1 % increase in vegetable impurities results in nearly 1 % decrease in TRS, with the impact varying by the fiber quantity of the sugarcane variety.
The impacts of vegetable impurities are likely to be more pronounced in the industry, as sugar mills utilize imbibition with hot water during the second milling process. Conversely, other facilities that use diffusers enhance sucrose extraction while removing other undesirable molecules. In contrast, laboratory experiments have limitations in extracting various biomolecules from the juice, as the hydraulic press only simulates the first mill and does not incorporate imbibition.
Industrial losses may be increased by the indeterminable losses associated with sugarcane trash, which negatively impact sucrose extraction, juice treatment, and mud filtration efficiency. While optimizing factory unit processes to incorporate other products derived from sugar and ethanol residues can help mitigate issues related to excess vegetable impurity, it is essential to identify and implement economic strategies to reduce the processing of green and brown leaves (Eggleston et al., 2010).
This study shows that our knowledge of vegetable impurities has not been fully elucidated and needs to be further developed. Several aspects need improvements to enhance the payment system and ensure the quality of raw materials. The remuneration system for farmers should correspond with the various biorefinery products generated by sugar mills, including sugar, ethanol, bioelectricity, biogas, and others.
Acknowledgments
This work was supported and funded by the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP - Grant number 2015/05567-4 Bioen Program), Usina Santa Fé, and Associação dos Fornecedores de Cana de Guariba (Socicana).
Data availability statement
All data supporting the findings of this study are available from the first author upon reasonable request via e-mail.
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Edited by
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Edited by:
Leonardo Oliveira Medici https://orcid.org/0000-0002-4411-1082
