Temperature and pH Influence on Citric Acid Production from Orange Peels Using Aspergillus nigerKefasHaruna MavakumbaAvsarCemreAbubakarAbdulhalim MusaSalehShamsiyya YakubuNIndianrajDepartment of Chemical Engineering, Modibbo Adama University, Adamawa State, Nigeria.Department of Chemical Engineering, Ankara University, 06100, Ankara, Turkey.Department of Chemical Engineering, Modibbo Adama University, Adamawa State, Nigeria.Department of Agronomy, Adhiyamaan College of Agriculture and Research, India.
Orange peels are undoubtedly abundant in nature, given the large volume of fruit juice produced daily across the globe from oranges coupled with ripe oranges consumed seasonally. Apart from juice industries, where they are obtained in cleaned form as raw materials for several products, including pectin and citric acid (CA), the alternative source is the uncleaned, often dry orange peels dumped in the surrounding environment. Arresting the environmental challenge posed by orange peels prompts its utilization in the grinded form to ferment and produce CA at different pH and temperature using 0.5 mL conidial suspension of Aspergillus niger in this study. At 5 days incubation period, Desing Expert 70.0 estimated 73.3% CA optimum yield at pH = 3 and optimal temperature of 70℃ and whose degree of substitution (DS) and reaction efficiency (RE) of 1.77 and 7.2%, respectively, implied that more citrate groups have been attached to each starch molecule. For increased emulsification, stability, and solubility, the CA produced at the highest DS and RE should be chosen to manufacture other products. Orange peel ripeness, moisture, micro or macro-nutrient contents, inoculum type, extraction method, and fermentation time (in the case of kinetic study) are influential factors to study during CA synthesis critically. Design of the solid-state fermentation technique used in this study at a laboratory scale is thus recommended.
Citric acidAspergillus nigerFermentationOrange peelDegree of substitutionIntroduction
Citric acid (CA, C6H8O7) or 2-hydroxy-1,2,3-propanetricarboxylic acid is a weak biodegradable organic tricarboxylic acid [1] consisting of 3 carboxyl (R-COOH) groups [2]. It is also a biochemical fermentation product of citrus fruits (or most plants) and animal tissues [3]. Extra pure anhydrous CA [4, 5] is very soluble and easily manufactured from molasses, carob pod extract, rape seed oil, corn-cobs, apple, lemon, orange, pine apple, plum, peas, peach, grape pomace, kiwi-fruit peel, mandarin orange, brewery wastes, animal bones, muscles and blood [6], and hence is contained in all fruit juices, soft drinks and syrups as it helps enhance their flavor and prevent color change by oxidation [7, 8]. Both laboratory and industrial manufacturing of CA followed the surface, submerged and solid-state fermentation production routes [1, 9] to recover CA for various industrial applications. Among them are fruits, dairy, cosmetic, food, chemical, beverage, textiles and pharmaceutical industries as a chelating agent, flavor enhancer, preservative, emulsifier, acidulant, antioxidant, preservative [6], pH regulator, stabilizer, digestion support, beverage sweetness balancing [9], synergistic agent, plasticizer [10], esterification agent for starch [11], buffering agent [12], toiletries, metal cleaners [13], organic coating material in the manufacture of nanoparticles [14] and for the modification of biomass for metal removal from wastewater [15].
These CA recovery fermentation techniques employ fungi, bacteria and yeast such as Saccharomycopsis lipolytica, Yarrowia lipolytica, Penicillium simplicissimum, Aspergillus foeitidus, Hansenula anamola, Candida tropicalis, Bacillus licheniformis, Arthrobacter paraffinens, Penicillium janthinelum, Mucor piriformis, Penicillium citrinum, Trichoderma viride, Ustulina vulgaris, Candida oleophila, Candida guilermondii, Bacillus subtilis, Brevibacterium flavum and Aspergillus niger [1, 6, 13, 16–18] by keeping track of one or more production-influencing factors shown in Table 1.
Findings on citric acid production using Aspergillus niger and other organisms
This table is shown in the PDF version of the article.
niger temperature, 11% sugar conc, 5% g/L concentration using date inoculum size & 7 days syrup incubation [25] Spoiled coconut + Optimum condition – pH = 3.5, Simple Laboratory 82.65, 80.68, 83.24 & 85.18 Aspergillus niger temperature = 30℃, inoculum Run g/L at the respective level = 5% & time conditions course = 192 h
Aspergillus niger is the most favored microorganism for CA production, as showcased in Table 1, where various yields from a different selection of production conditions were achieved. Long before those studies, CA has been first isolated and crystallized by Swedish Chemist, Carl Wilhelm Scheele [25]. During its syn-thesis, factors including trace elements concentration, microorganism morphology, pH, concentration of al-cohols, aeration, carbon, nitrogen and phosphorus sources are often examined [1, 13, 16]. CA's numerous applications warrant its large volume of manufacture on a very large progressive scale. In view of that, re-search efforts to optimize its production have been reported using design expert software (Table 1). For in-stance, world CA production was around 1.4 million tons in 2004, increasing to 1.6 million tons in 2007 with an estimated present global yearly manufacture of about 736, 000 tons [13, 18, 25, 26] against 400, 000 tons earlier reported by Ali et al. [20]. De Medeiros & De Medeiros [27] mentioned that, about 2 million tons of CA are produced by submerged fermentation while 92% of all manufacture uses Aspergillus niger. In 2023, the world CA market was projected to clock at $3.2 billion [9, 16].
In anticipation of increased patronage of the useful acid beyond the present record, this study focuses on exploring a new or additional biowaste from which CA could be extracted. Specifically, powdered orange peels are experimented with using Aspergillus niger for CA content. Normally, the fungus, Aspergillus niger, thrives in decaying vegetation and soils [28], being the most commonly used microorganism for the extraction of CA from plant wastes. Orange peels contain 42.5% pectin, 16.9% soluble sugars, 10.5% hemicellulose, and 9.21% cellulose [29], making them suitable carbon sources for CA production. The study aimed at being contributory, explicitly seeking to advance knowledge in former similar research conducted. Hence, Table 2 was deeply analyzed with the intention of addressing some identified drawbacks in those studies.
Previous work on CA fermentation using orange peels and Aspergillus niger fungus
This table is shown in the PDF version of the article.
Plainly, among the three main CA fermentation routes, surface fermentation is hardly used in the production of the acid using Aspergillus niger. Few studies have looked at the effect of the sucrose, glucose, and fructose contents as well as the moisture content of the orange peels on the end product (Table 2). Effects of other physicochemical properties such as metallic nutrient content, total solids, ash content and particle density are hardly examined. However, almost all studies emphasized the importance of using low particle-size peels. New methodological approaches observed are the use of autohydrolysis and ultrasonic-assisted extraction techniques [29, 33, 34]. Furthermore, only one out of multiple microbes capable of extracting CA from orange peels is used on a few occasions, such as in Zafar et al. [40], where co-culture has been experimented. Most often, optimal pH, incubation time, methanol concentration and temperature during the synthesis are reported with little attention paid to the presence of inhibitors. Altogether (so far), less than 30 studies have been conducted on CA production from orange peels based on the extent of this study's literature search, wherein yields of CA were reported in different dimensions.
As observed in Tables 1 and 2 of the literature review findings, RSM showcased an almost 'absence' from the records of concerned researchers on the subject matter. Hence, optimal values of influencing factors such as pH and temperature are analyzed using the Design Expert tool in this study, as the effect of higher temperature (> 65℃) is not tested on CA yield from orange peels. Several statistics based on specified input parameter ranges on the desired response, as well as its model and representative surface plots to determine the optimal parameters, are expected at the end of the study. Furthermore, the CA produced will be characterized by a degree of substitution and reaction efficiency to aid its choice for further application.
Methodology
Extracting CA from orange peel using Aspergillus niger involves a fermentation process. Step-by-step labo-ratory procedures followed to actualized the designed objectives were also detailed.
Materials and Equipment
A fermentation vessel, 50 mL conical flask, stirring equipment, centrifuge, CA assay kit, glassware (flask, & beakers), and autoclave for sterilization are some of the equipment needed for this study. Orange peels, Aspergillus niger culture, nutrient media (for Aspergillus niger growth) and sterile water were materials taken, which requires the use of one or more of the equipment listed.
Experimental ProcedureScreening of the fungal cultures
Aspergillus niger cultures were screened qualitatively for the production of CA, as described by Ali et al. [20]. Czapek-Dox agar medium (10 mL) was poured into individual sterile Petri plates and allowed to cool at room temperature. Approximately 0.5 mL of the conidial suspension of Aspergillus niger was transferred to each of the Petri plates. The plates were incubated at 50, 60 and 70℃ for 5 days. In the literature, the incubation period of 3-10 days at 23-65℃ had been practiced [10, 17, 19], with 30℃ being the most experimented temperature [6, 24, 25]. The plates were observed after incubation for yellow zones due to CA formation. Strains of Aspergillus niger with the widest yellow zone were used for further studies.
Fermentation media
Basal medium was prepared by introducing orange peels (30 g) into 200 mL Erlenmeyer flasks in accordance with Chukwuemeka et al. [22]. The medium was supplemented with glucose and sucrose at 5, 10 & 15% w/v. The effect of nitrogen supplements was not studied, though 0.5% ammonium nitrate and 0.3% ammonium phosphate were added to the basal medium and moistened to 55% moisture content. This is because nitrogen sources constitute a limiting factor in CA production [6, 20]. Subsequently, the flask was cotton plugged and autoclaved at 121°C for 15 min, slightly in accordance with Amenaghawon et al. [12]. After cooling at room temperature, each medium was inoculated with the Aspergillus niger (6.0 x 106) suspension and incubated at different temperatures (50, 60 & 70°C) in a rotary shaking incubator for 5 days. Methanol (0–5%) was added to the flasks before fermentation. After fermentation, the medium was diluted with distilled water (1:4 w/v).
Citric acid production
All equipment and media were sterilized to ensure aseptic conditions, which are crucial to preventing contamination. A polyethene bag size was used to collect orange peels obtained at Girei Market, Adamawa State, Nigeria. Before then, it was washed, dried, and ground using mortar and pestle. A prepared Aspergillus niger culture was prepared originally according to the instruction manual provided into a nutrient media. The grounded orange peels were added to a clean and sterilized fermentation vessel. Hang & Woodams [19] also ensured that their corn-cob biomass was cut into pieces before usage. Then, the Aspergillus niger culture was added to the vessel containing the orange peels and mixed thoroughly. Aspergillus niger produces CA during its growth, and this can take several days; hence the fermentation conditions (pH, temperature and agitation) must be maintained. This research employs the method followed by Hang & Woodams [19] in the production of CA using corn-cob by Aspergillus niger.
Optimization of Process Parameters
This work involves varying the pH (1, 2 & 3) and temperature (50, 60 & 70℃) for 13 separate runs in a vessel. Temperature and pH were taken as factors, while citric acid yield (CAY) was the response variable. In order to define their levels in Design Expert 70.0 for the purpose of optimization, a new response surface design (RSM) was created where the initial and final values or boundary values of the 2 factors were entered to check-in Central Composite Design (CCD). By default, the RSM tool takes 8 non-center points, 5 center points, rotatable alpha (k < 6) = 1.41421 and 1 replicate of factorial points for 13 runs shown in Table 3.
Experimental Design by RSM-CCD Tool.
This table is shown in the PDF version of the article.
The Design-Expert software gives the possible combinations of Factor 1 (temperature) and Factor 2 (pH) to be experimented with to obtain a corresponding response value of the product (left blank). Once the fermentation process is complete, the fermented mixture in the vessels is harvested after 7 days. The centrifuge was then used to separate the solid residue (orange peels and mycelium of Aspergillus niger) from the liquid portion (containing CA). The solvent extraction method was then employed to separate CA from the liquid portion. After that, the concentration of the CA was determined analytically using a titrimetric approach by using 0.1 NaOH and phenolphthalein as an indicator and calculated as % according to Equation 1 and the method followed by Odu et al. [3], Subramaniyan et al. [41] and Bakhiet & Al-mokhtar [2].
Normality × Volume of NaOH × Equiv. wt. of CA × Dilution Factor % Citric Acid = (1) Weight of Sample (g) × 10 mL
Purified CA was later stored in appropriate containers. The calculated yields were recorded appropriately and entered into the Response tab in Design-Expert to obtain the ANOVA as well as the surface plots.
CharacterizationDegree of substitution determination
The degree of substitution (DS) was determined or calculated using Equations 2 and 3 according to the method described in Ye et al. [11].
162A DS = (2) 100M − (M − 1)A (V0 − V1) × c × M × 100% A = (3)
Where A is the content of esterified carboxyl groups (%); M is the molar mass of the substituent (citrate: 175 g/mol); m is the mass of the samples (mg); c is the concentration of aqueous hydrochloric acid (HCl) solution (mol/L); V0 is the volume of aqueous HCl solution consumed by the blank (mL); and, V1 is the volume of aqueous HCl solution consumed by the esterified starch sample (mL).
Reaction efficiency determination
On the other hand, the reaction efficiency (RE) was calculated using Equations 4 and 5 given by Ye et al. [11].
DS E = ( ) × 100% (4) Theoretical DS C × 162 Theoretical DS = (5)
Here, 'C' is the mass of CA (g) divided by the mass of dry starch (g), and 175 is the relative molecular mass of citrate anhydride, whose chemical formula is Y(NO3)3.6H2O [5].
Results and DiscussionCitric Acid Yield
It is obvious that the CAY span from 40-79% over 45.9-74.1℃ and 0.59-3.41 pH, as shown in Table 4. The effect of pH and temperature was previously studied by Maharani et al. [25].
Citric Acid Percent Yield for RSM
This table is shown in the PDF version of the article.
Figure 1 now shows the variation of these two factors with the yield, in line with the Table 4 result. The highest CAY of 79% corresponds to a pH of 3.4, showing that the production of the acid using Aspergillus niger occurs at a less acidic pH. A low CAY of 55% was observed when the pH = 0.59 and 40% & 47% at pH = 1. The inability of Aspergillus niger to thrive at low pH during the early stages of fermentation may be the cause of the lower CA generation at pH 1, as exemplified by Alhadithy & Yasin [24]. As for temperature, the optimal value is 70℃ at 79% CAY. From these experimental deductions, it can be said that CA production is maximum at pH = 3.4 and temperature of 70℃. Future design of a CA manufacturing unit may consider the optimal conditions that will give a percentage of pH or greater. Fig. 1 (a & b) shows that both pH and temperature are important reacting conditions in the production of CA as both are respectively sensitive and should be maintained at their peak.
Effect of Varying Factors on CAY
Already observed is that the responses range from 40-79%, whose ratio of maximum to minimum is above 1.975. Normally, a ratio > 10 indicates a transformation is required. For ratios < 3, the power transforms have little effect. Since that is the case in this study, no transformation was selected for the purpose of this analysis.
Statistical Analysis Summary
As explained in the methodology, the ranges defined by Factors A and B for 13 runs of quadratic modelling Based on CCD in the RSM software, also studied by Amenaghawon et al. [12], the 'Linear model' was suggested as the best model based on the predicted R2 obtained in comparison with other model equations.
Entry and specification analysis summary.
This table is shown in the PDF version of the article.
Model Selection
At this juncture, a categoric warning was displayed by the Design-Expert software that the Cubic model is "Aliased" as shown in Table 6. Hence, it further instructed the selection of the highest-order polynomial where the additional terms are significant and the model is not aliased.
Sequential model sum of squares.
This table is shown in the PDF version of the article.
Source Squares df Square F-Value Prob > F
Mean vs Total 50806.253 1 50806.25 - - Linear vs Mean 1188.8938 2 594.4469 17.07478 0.0006 Suggested 2FI vs Linear 20.25 1 20.25 0.555821 0.4750 Quadratic vs 2FI 47.449923 2 23.72496 0.592187 0.5786 Cubic vs Quadratic 105.60621 2 52.8031 1.510067 0.3069 Aliased Residual 174.837 5 34.9674 - - Total 52343.29 13 4026.407 - -
Even though the criteria of selection are well stated by the design tool, it still went ahead and pinpointed the 'Linear vs Mean' comparison as the best model for this study observations. Consistently, the selected model shown in Table 7 must also have an insignificant lack of fit.
Lack of fit tests.
This table is shown in the PDF version of the article.
To this end, the best model remains the Linear Model as it has negligible lack-of-fit, as will be seen later. However, the most important statistical parameter RSM uses for model selection is the adjusted and predicted R2. Their values are shown in Table 8 for the different polynomial and factorial models under consideration.
Model summary statistics.
This table is shown in the PDF version of the article.
Therefore, the model maximizing the adjusted R2 and the predicted R2 was chosen, which is still the 'Linear' model. It has an adjusted R2 = 0.7282 and a predicted R2 = 0.5184, which is higher than its rival models, which are 2FI, quadratic and cubic models. Overwhelmingly, this study's observations (i.e., pH and temperature) can be set in a linear equation to predict the response or CAY equally.
ANOVA for Response Surface Linear Model
It consists of estimates of the sum of squares, degrees of freedom, mean square, F-value and the p-values of the model itself, parameters A & B, residual, lack of fit and the pure error obtainable, as showcased in Table The Model F-value of 17.07 implies the model is significant. There is only a 0.06% chance that a "Model F-value" this large could occur due to noise. Values of "Prob > F" less than 0.0500 indicate model terms are significant. In this case, B is a significant model term. Values greater than 0.1000 indicate the model terms are not significant. If there are many insignificant model terms (not counting those required to support hierarchy), model reduction may improve our model. The "Lack of Fit F-value" of 61.86 implies the Lack of Fit is significant. There is only a 0.07% chance that a "Lack of Fit F-value" this large could occur due to noise. A significant lack of fit is bad, as the fitted model is always desirable. The 3 R2 statistical parameters for the linear model are shown in Table 10.
Linear Model ANOVA
This table is shown in the PDF version of the article.
Selected model statistical parameters computed by the design tool.
This table is shown in the PDF version of the article.
The "Pred R2" of 0.5184 is not as close to the "Adj R2" of 0.7282 as one might normally expect. This may indicate a large block effect or a possible problem with the model and/or data. Things to consider are model reduction, response transformation, outliers, etc. Reports of Amenaghawon et al. [12] state that R2 should at least be 0.8 for a model to be considered to be a good fit with observed data. In addition, "Adeq Precision" measures the signal-to-noise ratio. A ratio greater than 4 is desirable. A ratio of 12.092 indicates an adequate signal. Such a model can be used to navigate the design space.
Modeled Equation and Validation
In terms of A and B or temperature and pH, the CAY or R1 can be written as given by Equation 6.
CAY = 46.27274 − 0.13321A + 12.11764B (6)
Equation 6 results in predicted R1 or CAY shown in Table 11.
Observed and predicted responses.
This table is shown in the PDF version of the article.
Some predicted CAYs are close enough to the empirical CAY, as evidenced in Table 11. However, despite the good statistical performance demonstrated by the linear model, considerable non-fit can be seen in Fig. 2.
Predicted CAY against actual CAY.
The slope of the fit is 0.7735, and the intercept is 14.16, which gives R2 = 0.773497. Normally, an R2 = 1 means a 100% fit, and a majority of studies try to keep the R2 as close to 1 as possible. In this study, 22.65% of the participants were non-fit, which shows the extent of the fit.
Citric Acid Optimization
Optimization of the CA production can be made to improve its manufacture using the microorganism selected. Figure 3 shows a surface plot of the highest desirability of the pH and temperature parameters/factors. It was obtained using Design Expert software, which is a statistical software package used for designing experiments, analyzing data, and optimizing processes. The surface plot in Figure 3 represents the relationship between pH and temperature and their effect on the response variable, which is the CAY. In a study conducted by Odu et al. [3], where the effect of pH & %methanol as well as pH & %nitrogen on CAY were examined using 3D surface plots, the representation reveals no interaction between nitrogen and pH but suggests that optimal CAY = 28 g/L occur at 2.6% methanol and 5.5 pH. Several other combinations of factors had been examined in previous works, especially in Amenaghawon et al. [12], where nitrogen sources in the form of peptone demonstrated a significant effect on CAY. But here (differently), the plot shows that the highest desirability (0.924) of the pH and temperature parameters/factors is achieved at a pH of around 3 and a temperature of around 70°C. This indicates that these conditions are optimal for maximizing the CAY.
Desirability of pH versus temperature optimization.
The surface plot also shows that the CAY decreases as the pH and temperature move away from the optimal values. This suggests that it is important to carefully control the pH and temperature during the fermentation process to achieve the highest possible CAY. Show et al. [1] reports that a previous study puts the pH value for CAY from Aspergillus niger at 2.5-3.5. By analyzing the surface plot in Fig. 4, this study could identify the optimal conditions for the fermentation process and make informed decisions about how to adjust the process parameters to achieve the highest possible CAY.
Optimal predicted response at optimal pH and temperature.
Under the same desirability value, the maximal prediction for the CAY is 73.3009%, as shown in Figure 4. Variations in colors in the surface plots represent the magnitude of the response variable, which is the CAY in this case. In other words, the colors indicate the level of CAY at different combinations of pH and temperature. Typically, the colors in a surface plot range from cool colors (such as blue and green) to warm colors (such as yellow and red). Cool colors represent lower values of the response variable, while warm colors represent higher values. In the case of the CAY, blue and green colors would indicate lower yields, while yellow and red colors would indicate higher yields. By analyzing the surface plot and the variations in colors, researchers can identify the optimal conditions for the fermentation process that result in the highest CAY. They can also identify the regions of the plot where the CAY is lower and make adjustments to the process parameters to improve the yield.
Citric Acid Characteristics
Where M = 175 g/mol, a mass of CA = 30g, a mass of dry starch = 3.75%×30g = 1.125g, c = 26.67 and theoretical DS = 24.69, DS and RE were computed using relevant equations to obtain the values in Table 11.
It is worthy of note that the main source of pectin is citrus peels, and pectin is classified through its degree of esterification (DE) or DS in order to define its gelling properties [42, 43]. In this case, Figure 5 was used to represent the DS and RE with the concentration of the acid measured experimentally.
Effect of CA Concentration on (a) DS and (b) RE.
DS is a parameter used to characterize the CA produced in the fermentation process. It is a measure of the degree to which the hydroxyl groups in the starch molecule have been replaced by citrate groups during the esterification process. In other words, DS is a measure of the average number of citrate groups that have been attached to each starch molecule. A higher DS value (e.g., 1.77 at 79% CA concentration) indicates that more citrate groups have been attached to the starch molecules, while a lower DS value (e.g., 0.41 at 40% CA concentration) indicates that fewer citrate groups have been attached. Citrate is used in the detergent industry as a replacement for phosphate in detergents to make them more ecologically friendly [9]. RE is another parameter used to characterize the CA produced in the fermentation process.
It is a measure of the efficiency of the esterification reaction, which is the process by which the citrate groups are attached to the starch molecules. RE provides information on the effectiveness of the esterification process and the extent to which the citrate groups have been attached to the starch molecules. A higher RE value (7.2%) indicates that more citrate groups have been attached to the starch molecules, while a lower RE value (1.7%) shows that the esterification process was less efficient. Therefore, a CA product with higher DS and RE values may be preferred for applications that require properties like increased solubility, improved emulsification, and increased stability, as well as for applications that require a high-yield and high-quality product.
Conclusion
There is growing application of orange peels in the extraction of CA using Aspergillus niger. This study successfully applied the SSF technique and varied temperature (50-70℃) and pH (1-3) to examine their influence on CAY, in addition to finding the optimal factors for the highest CAY in Design Expert 70.0. Findings in this study show that the acidic pH range (where pH = 3 is optimal) is the best for the grinded orange peels CA manufacture at a maximum approximate temperature of 70℃. Hence, optimal CAY was 79% observed and 73.3% predicted by the suggested linear model. Additionally, the produced CA may be suitable for high-yield product manufacture, especially at DS = 1.77 and RE = 7.2%, corresponding to the optimum pH and temperature. Adequate indication, both from the literature and the present study, emphasizes the effect of both pH and temperature in CA production from diverse materials, including orange peels. RSM 3D plot points to the leveled (equal) influence of the selected temperature range whose mean is 60℃, demonstrating that higher temperature is also favorable for the extraction. Since Aspergillus niger had been largely exploited, comparing the CAY from orange peels with several other microorganisms at specified or different inoculum sizes is encouraged.
Acknowledgments
We acknowledge the useful review comments and suggestions received through Optimality Journal. The review helped in aligning the article to the standards and guidelines designed by REA publishers.
Author Contributaion
Conceptualization, Haruna Mavakumba Kefas; Methodology, Shamsiyyah Yakubu Saleh; Software, Abdulhalim Musa Abubakar; Validation, Cemre Avsar and Haruna Mavakumba Kefas; Formal analysis, Indianraj N and Abdulhalim Musa Abubakar; Investigation, Shamsiyyah Yakubu Saleh; Resources, Indianraj N and Cemre Avsar; Data maintenance, Haruna Mavakumba Kefas and Shamsiyyah Yakubu Saleh; Writing-creating the initial design, Abdulhalim Musa Abubakar and Cemre Avsar; Writing-reviewing and editing, Haruna Mavakumba Kefas and Indianraj N; Visualization, Indianraj N and Cemre Avsar; Monitoring, Abdulhalim Musa Abubakar and Haruna Mavakumba Kefas; Project management, Shamsiyyah Yakubu Saleh; Funding procurement, Shamsiyyah Yakubu Saleh and Indianraj N. All authors have read and agreed to the published version of the manuscript.
Funding
There were no in-kind donations (e.g., materials used for experiments) or publishing charges for the project article.
Data Availability
All the data are contained in this text.
Conflicts of Interest
The authors declare no conflict of interest. There aren't any personal circumstances or interests that could be considered to have an inappropriate influence on the presentation or interpretation of the reported research
findings.ReferencesShow, P. L., Oladele, K. O., Siew, Q. Y., Zakry, F. A.-A., Lan, J. C.-W., & Ling, T. C. (2015). Overview of citric acid production from Aspergillus niger. Frontiers in life science, 8(3), 271–283. DOI: 10.1080/21553769.2015.1033653Bakhiet, S. E. A., & Al-Mokhtar, E. A. I. (2015). Production of citric acid by Aspergillus niger using sugarcane molasses as substrate. Jordan journal of biological sciences (jjbs), 8(3), 211–215.Odu, N. N., Uzah, G. A., & Akani, N. P. (2020). Optimization of citric acid production by Aspergillus niger and Candida tropicalis for solid state fermentation using banana peel substrate. Journal of life and bio-sciences research (jlbsr), 1(2), 51–60. DOI: 10.38094/jlbsr1214Lafontaine, A., Sanselme, M., Cartigny, Y., Cardinael, P., & Coquerel, G. (2013). Characterization of the transition between the monohydrate and the anhydrous citric acid. Journal of thermal analysis and calorimetry, 112(1), 307–315. DOI: 10.1007/s10973-012-2798-0Todorovsky, D. S., Dumanova, D. G., Todorovska, R. V, & Getsova, M. M. (2002). Preparation and characterization of yttrium-iron citric acid complexes. Croatica chemica acta (cca), 75(1), 155–164.Ramesh, T., & Kalaiselvam, M. (2011). An experimental study on citric acid production by Aspergillus niger using Gelidiella acerosa as a substrate. Indian journal of microbiology, 51(3), 289–293. DOI: 10.1007/s12088- 011-0066-9Fernandes, F. A., Heleno, S. A., Carocho, M., Pinela, J., Prieto Lage, M. A., Ferreira, I. C. F. R., & Barros, L. (2021). Recovering citric acid from orange juice production residues [presentation]. 5th international symposium on phytochemicals in medicine and food [5-ispmf august 25-september 01 2021, nanchang, china] (pp. 423– 426).Weikle, K. (2012). Determination of citric acid in fruit juices using HPLC. Concordia college journal of analytical chemistry, 3, 57–62.West, T. P. (2023). Citric acid production by Aspergillus niger using solid-state fermentation of agricultural processing coproducts. Applied biosciences, 2, 1–13. DOI: 10.3390/ applbiosci2010001Egbe, N. E., Ihediwa, L., Abdulsalami, M. S., Adebayo, A., & Onuh, K. (2022). Citric acid production from agricultural wastes using Aspergillus niger isolated from some locations within Kaduna metropolis, Nigeria. Journal of applied science and environmental management (jasem), 26(9), 1607–1614. DOI: 10.4314/jasem.v26i9.22Ye, J., Luo, S., Huang, A., Chen, J., Liu, C., & Mcclements, D. J. (2019). Synthesis and characterization of citric acid esterified rice starch by reactive extrusion: A new method of producing resistant starch, (413), 1– 37.Amenaghawon, A., Ebewele, E., Osakue, I., & Obaseki, R. (2017). Optimisation of media components using central composite design for increased citric acid production from yam peels using Aspergillus niger. FUOYE journal of engineering and technology, 2(2), 37-. DOI: 10.46792/fuoyejet.v2i2.124Vandenberghe, L. P. S., Soccol, C. R., Pandey, A., & Lebeault, J.-M. (1999). Microbial production of citric acid.Mikelashvili, V., Kekutia, S., Saneblidze, L., Maisuradze, N., Kriechbaum, M., & Almásy, L. (2023). Synthesis and characterization of citric acid-modified iron oxide nanoparticles prepared with electrohydraulic discharge treatment. Materials, 16(746), 1–12. DOI: 10.3390/ ma16020746Ajaelu, C. J., Dawodu, M. O., Faboro, E. O., & Ayanda, O. S. (2017). Copper biosorption by untreated and citric acid modified Senna alata leaf biomass in a batch system: Kinetics, equilibrium and thermodynamics studies. Physical chemistry, 7(2), 31–41. DOI: 10.5923/j.pc.20170702.02Behera, B. C., Mishra, R., & Mohapatra, S. (2021). Microbial citric acid: Production, properties, application, and future perspectives. Food frontiers, 2, 62–76. DOI: 10.1002/fft2.66Auta, H. S., Abidoye, K. T., Tahir, H., Ibrahim, A. D., & Aransiola, S. A. (2014). Citric acid production by Aspergillus niger cultivated on Parkia biglobosa fruit pulp. International scholarly research notices, 2014(762021), 1–9. DOI: 10.1155/2014/762021Uzah, G. A., Akani, N. P., & Odu, N. N. (2020). Screening of Aspergillus and Candida species with utmost potential to synthesize citric acid. Journal of advances in microbiology, 20(4), 10–18. DOI: 10.9734/JAMB/2020/v20i430232Hang, Y. D., & Woodams, E. E. (1998). Production of citric acid from corncobs by Aspergillus niger. Bioresource technology, 65(3), 251–253. DOI: 10.1016/S0960-8524(98)00015-7Ali, S., Ikram-ul-Haq, Qadeer, M. A., & Iqbal, J. (2002). Production of citric acid by Aspergillus niger using cane molasses in a stirred fermentor. Electronic journal of biotechnology, 5(3), 258–271.Massadeh, M. I., Fandi, K., Al-Abeid, H., Alsharafat, O., & Abu-Elteen, K. (2022). Production of citric acid by Aspergillus niger cultivated in olive mill wastewater using a two-stage packed column bioreactor. , 8 Fermentation (Vol. 8). Multidisciplinary Digital Publishing Institute (MDPI).Chukwuemeka, I. C., Ethel, O. C., Kalu, A. D., & Chigozie, N. C. (2019). Citric acid production by Aspergillus niger using banana and plantain peels. GSC biological and pharmaceutical sciences, 8(2), 15–21. DOI: 10.30574/gscbps.2019.8.2.0111Al-Shoaily, K., Al-Amri, M., Al-Rawahi, F., & Al-Sidrani, M. (2014). Multivariate study and analysis of the production of citric acid from dates by surface method. Journal of agricultural chemistry and environment, 3, 20–25. DOI: 10.4236/jacen.2014.32B004Alhadithy, D. A., & Yasin, S. R. (2023). Product concentration, yield percentage and productivity of citric acid formation using Aspergillus niger isolated from palm dates. Journal of ecological engineering (jee), 24(11), 1–13. DOI: 10.12911/22998993/169380Maharani, V., Reeta, D., Sundaramanickam, A., Vijayalakshmi, S., & Balasubramanian, T. (2014). Isolation and characterization of citric acid producing Aspergillus niger from spoiled coconut. International journal of current microbiology and applied sciences (ijcmas), 3(3), 700–705.Hamdy, H. S. (2013). Citric acid production by Aspergillus niger grown on orange peel medium fortified with cane molasses. Ann microbiology, 63, 267–278. DOI: 10.1007/s13213-012-0470-3de Medeiros, A. D. M., & de Medeiros, T. P. M. (2022). Citric acid production by Aspergillus spp. through submerged fermentation using different production mediums containing agroindustrial residues. Research, society and development, 11(6), 1–11. DOI: 10.33448/rsd-v11i6.28839Chatterjee, D., Datta, S. C., & Manjaiah, K. M. (2015). Characterization of citric acid induced transformation of short-range-order minerals in alfisol, inceptisol and vertisol of India. European journal of mineralogy, 27, 551–557. DOI: 10.1127/ejm/2015/0027-2446Rivas, B., Torrado, A., Torre, P., Converti, A., & Dominguez, J. M. (2008). Submerged citric acid fermentation on orange peel autohydrolysate. Journal of agriculture and food chemistry, 56(7), 2380–2387. DOI: 10.1021/jf073388rEmuka, E. (2021). Citric acid production from orange peels using Aspergillus niger. Michael Okpara University Of Agriculture Umudike (MOUAU) Online Repository.Aravantinos-Zafiris, G., Tzia, C., Oreopoulou, V., & Thomopoulus, C. D. (1994). Fermentation of orange processing wastes for citric acid production. Journal of the science of food and agriculture, 65(1), 117–120. DOI: 10.1002/jsfa.2740650117Vidya, P., Annapoorani, A. M., & Jalalugeen, H. (2018). Optimization and utilisation of various fruit peel as substrate for citric acid production by Aspergillus niger isolated from orange and carrot. The pharma innovation journal, 7(6), 141–146.Fernandes, F. A., Heleno, S. A., Carocho, M., Pinela, J., Prieto, M. A., Ferreira, I. C. F. R., & Barros, L. (2020). Optimization of citric acid extraction from citrus peels. 1st international electronic conference on food science and functional foods- session: food safety and sustainable development (Vol. 4, pp. 1–7). MDPI: Basel, Switzerland. DOI: 10.3390/foods_2020-07666Fernandes, F. A., Heleno, S. A., Pinela, M., Prieto, M. A., Ferreira, I. C. F. R., & Barros, L. (2022). Recovery of citric acid from citrus peels: Ultrasound-assisted extraction optimized by response surface methodology. Chemosensors, 10(257), 1–11. DOI: 10.3390/chemosensors10070257Iralapati, V., & Kummari, S. (2014). Production of citric acid from different fruit peels using Aspergillus niger. International journal of scientific engineering and research (ijser), 3(5), 129–130.Khati, I., Parande, S., Jagtap, A., & Bhosale, P. (2019). Citric acid production from different organic wastes by using Aspergillus niger. International conference on “empowering society with microbial technology” [icesmt|2019] held at tuljaram chaturchand college of arts, science and commerce, baramati-413 102 (Vol. 9, pp. 83– 90). International Journal of Pharmacy and Biological Sciences (JPBS). DOI: 10.21276/ijpbs.2019.9.sp2.10Dutta, A., Sahoo, S., Mishra, R. R., Pradhan, B., Das, A., & Behera, B. C. (2019). A comparative study of citric acid production from different agro-industrial wastes by Aspergillus niger isolated from mangrove forest soil. Environmental and experimental biology, 17(12), 115–122. DOI: 10.22364/eeb.17.12Abbas, N., Safdar, W., Ali, S., Choudhry, S., & Elahi, S. (2016). Citric acid production from Aspergillus niger using mango (Mangifera indica L.) and sweet orange (Citrus sinensis) peels as substrate. International journal of scientific & engineering research (ijser), 7(2), 868–872.Torrado, A. M., Cortes, S., Salgado, J. M., Max, B., Rodriguez, N., Bibbins, B. P., … Dominguez, J. M. (2011). Citric acid production from orange peel wastes by solid-state fermentation. Brazilian journal of microbiology, 42, 394–409. DOI: 10.1590/S1517-83822011000100049Zafar, M., Bano, H. S., & Anwar, Z. (2021). Orange peels valorization for citric acid production through single and co-culture fermentation. Jordan journal of biological sciences (jjbs), 14(2), 261–266. DOI: 10.54319/jjbs/140209Subramaniyan, S., Paramasivam, S., Kannaiyan, M., & Chinnaiyan, U. (2019). Utilization of fruit waste for the production of citrus acid by using Aspergillus niger. Journal of drug delivery and therapeutics (jddt), 9(4-A), 9–14. DOI: 10.22270/jddt.v9i4-A.3487Gimba, A. S. B., Zubairu, A., Abubakar, A. M., Salihu, A., & Nzerem, P. (2022). Extraction of pectin from waste orange peels: Influence of particle size and acid type. Journal of multidisciplinary engineering science and technology (jmest), 9(5), 15337–15340.Pérez, J., Gómez, K., & Vega, L. (2022). Optimization and preliminary physicochemical characterization of pectin extraction from watermelon rind (citrullus lanatus) with citric acid. , 2022 International Journal of Food Science (Vol. 2022). Hindawi.