Open access peer-reviewed article

Statistical Modeling and Optimization of Bioplastic Synthesis from Waste Corn Using Polynomial Regression Analysis

Festus Adeyemo

Olawale Theophilus Ogunwumi

Kamilu Oyedeko

Olusola Solomon Amodu

This Article is part of Environmental Engineering/Green Technologies Section

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Article Type: Research Paper

Date of acceptance: October 2024

Date of publication: November 2024

DoI: 10.5772/geet.20240012

copyright: ©2024 The Author(s), Licensee IntechOpen, License: CC BY 4.0

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Table of contents


Graphical Abstract
Highlights
Introduction
Materials and methods
Results and discussion
Conclusion
Author’s contribution
Funding
Ethical statement
Data availability statement
Conflict of interest

Abstract

Bioplastics are of great importance and are viable in domestic and industrial applications. The eco-friendly polymers derived from agricultural wastes mitigate and substitute the use of their petro-plastic counterparts because they are safe, inexpensive to produce, and biodegradable. This work focused on the synthesis and optimization of bioplastic from waste corn. The experimental design is a requisite to the main experiment in order to reduce the number of experimental runs while minimizing errors. The experiment was designed using the Box–Behnken response surface methodology of central composite design of Minitab 19.0, in which low and high bounds are set for the process variables with 6 centered points and 46 runs. The statistical analysis gave R2 values of 99.01%, 98.62%, 85.53%, and 83.60% with composite desirability of 0.7103, showing good model strength of energy consumed (kJ), weight of bioplastic (g), tensile strength (MPa), and elongation (%), respectively. The optimal energy consumed, weight of bioplastic, tensile strength, and elongation were found to be 289.64 kJ, 44.15 g, 1.44 MPa, and 12.02%, respectively, at the optimal predicting mass of starch, volume of glycerin, volume of vinegar, volume of water, and time of 17.27 g, 2.0 ml, 3.0 ml, 50.81 ml, and 10.25 min, respectively. The work revealed that waste maize can be turned into bioplastic for electrical insulation and packaging.

Keywords

  • bioplastics

  • eco-friendly synthesis

  • response surface methodology

  • optimal conditions

  • biodegradable polymer

Author information

Graphical Abstract

Highlights

  • Eco-friendly polymers from plants substitute their petro-plastic counterparts.

  • Corn is selected due to its starch abundance and apt amylose/amylopectin proportion.

  • The statistical analysis shows good model strength of the response variables.

  • Optimal energy consumed, the weight of bioplastic, tensile strength, and elongation are obtained.

  • Waste corn can be used as bioplastic for electrical insulation and packaging.

Introduction

A staggering estimate of 400 million metric tons of plastic waste is generated around the world annually, most of which are non-biodegradable [15]. This represents a 45% increase when compared to the estimate presented in 2018 by the United States Environmental Protection Agency [6]. Despite reported environmental concerns, non-biodegradable plastics are widely used for packaging due to their high barrier characteristics, stiffness, tensile strength, and tear strength. The continued use of these plastics has resulted in a growing proportion of waste piles constituting plastic waste. In addition to non-biodegradability, petroleum-based plastics have a low water vapor transmission rate owing to their large carbon footprint and thus recalcitrant in the environment [7]. Apart from environmental considerations, biodegradable polymers improve resource use, landfill reduction, and material recovery [810].

Petro-plastic waste contamination can cause devastating effects on both land and aquatic animals, thereby destabilizing natural ecosystems. The possibility of chemical leaching from plastic products, which can be absorbed from the soil into plants, and injection of microplastics could transfer toxins to wildlife and humans, leading to hormonal disorders, cancer, infertility, autism, and pathogenic infections [11, 12]. One of the ways to circumvent the challenges associated with non-biodegradable plastics is the application of bioplastics, which have the tendency to biodegrade in the environment. However, such bio-based plastic needs to possess the characteristics of stiffness, tensile strength, and tear strength to effectively serve as an alternative.

Bio-based plastics are derived naturally from plants while some microorganisms can transform biomass into plastics. For example, lignin, proteins, lipids, and polysaccharides (e.g., starch, chitin, and cellulose) are natural materials that can be directly used to make bioplastics [13, 14]. Adding synthetic or other materials to a polymer matrix changes the mechanical and physical properties of polymer base composites [1519]. Due to the availability of suitable precursors from agricultural and/or agro-industrial wastes, it was anticipated that the global market for biodegradable polymers would increase by 12.6% on an annualized basis, reaching 206 million pounds by 2020 and 675 million pounds by 2030 [20, 21]. Cassava starch has been reported as a suitable precursor for the synthesis of plastics with the required properties [22, 23]. In addition, Viana et al. [24] attempted to create bioplastic from banana starch, but the challenges are the abundance of this source and the compromising properties of the resultant plastic such as low tensile strength and heavy weights at moderate heat treatment. However, biomass extracts have been supplemented with xanthan gum, glycerol, and nano-SiO2 to improve the functional properties of bioplastics. Since bioplastic from corn gives brittle material, Kharb and Saharan [25] attempted to create bioplastic from cucumber and corn at a ratio of 1:2 to obtain a material with improved handling and mechanical properties. Celletti et al. [26] made bioplastic from corn starch and planted basil using 2.5 wt% of this bioplastic buried for 35 days in soil, showing stunted growth. Further examination might suggest whether this will impair the plant ecosystem. This stipulates that not all bioplastics are compostable for planting; the composition of the raw material is vital.

Bioplastics are generally manufactured in three types on a commercial scale: biodegradable plastics made from fossil carbon sources, biodegradable plastics made from biomass-derived polymers, and non-biodegradable biomass-derived polymers. Traditional biotechnological approaches have only had limited success converting biomass to industrially useful polymers, indicating that complex networks acting in synergy are needed [27]. Notably, in the European Union, recycling containers like compostable plastics from naturally regenerating materials like crops have gained attention [8, 28, 29]. This advancement has necessitated research efforts to seek more bioplastic precursors in waste crops, in support of plastic circularity.

West Africa, especially Nigeria, has historically grown crops such as corn, which is predominantly cellulose and starch. Corn starch has been found to contain 28 wt% amylose and 72 wt% amylopectin; these two molecules, branching amylopectin and linear and helical amylose, are necessary for bioplastic synthesis [14]. Moreover, the production of next-generation materials and processes is driven by renewable biocomposites owing to their industrial, environmental, and ecological sustainability, and the green chemistry involved [30, 31]. Hence, the aim of this work was to statistically model and optimize the production of bioplastic from waste corn. Furthermore, the physical and mechanical properties of the synthesized bioplastics such as weight, tensile strength, and elongation were optimized using polynomial regression analysis.

As depicted in Figure 1, bioplastics will solve the problems of littering, biofouling, chemical injection, sedimentation, and so on, as characterized by the unchecked disposal of petro-plastics. These environmental challenges are gaining attention globally.

Figure 1.

Effects of waste plastics on the environment.

This research contributes to the field of sustainable materials and waste management by proposing a biodegradable alternative product for packaging while cleaning the environment of waste.

Materials and methods

Materials and sample preparation

In the course of these experiments and the processing of waste corn into bioplastic, the following apparatus and instruments were used: glass flasks (250 ml and 100 ml); magnetic hot plate and stirrer (Jiangsu J. Instrument Tech, China); thermometers (−10 to 110 °C and −10 to 360 °C); stopwatch (Accusplit, China); digital mass balance (Scientific Enterprises, Kochi, India); glass spoons; foil papers; graduated cylinders (10 ml and 100 ml); dumbbell mold (16 cm × 2 cm × 0.5 cm); heating and drying oven (Genlab Limited, UK).

The following materials were also used: starch extracted from waste corn (95% purity, 5% moisture content; Chemical and Polymer Laboratory, Lagos State University, Epe, Lagos, Nigeria); glycerin (99% purity; Veeclare Chemicals, Ojota, Lagos, Nigeria); vinegar (90% purity; Veeclare Chemicals, Ojota, Lagos, Nigeria); distilled water.

Waste corn, a starchy agricultural crop waste, used as the precursor for bioplastic synthesis, was obtained from a food processing industry. The waste corn was soaked in pure water for 48 h and thereafter milled, washed, and screened through a fine screen of 120 US mesh to remove the starch. The resulting thin slurry was transferred into a cloth sieve of 500 US mesh under pressure to remove water content. The obtained starch was sun-dried for 4 days.

Response variables such as energy consumed (computed from the amount of heat absorbed/consumed by each sample), elongation, tensile strength (obtained by analyzing samples on the Instron tensile machine), and weight were recorded.

Production of bioplastic from starch and degradability test

According to [14]; 15.0 g of the starch obtained was dissolved in 50 ml of distilled water in a 250 ml beaker. The mixture was stirred on a hot plate at 105 °C and then 3.0 ml of glycerin was added [32], followed by the addition of 2.5 ml of vinegar while stirring continued for 5 min. Thereafter, the flask was removed from the hot plate and transferred into an oil-greased dumbbell wooden mold with the aid of aluminum foil paper for curing to take place. The product formed (bioplastic) took the shape of the mold at this point. The mold was baked at above 90 °C for 4 h, and then the bioplastic was extracted from the mold. Subsequently, the procedure was repeated by varying the weight of starch, volumes of glycerin, vinegar, and water, and mixing time.

To test for biodegradability or bioplastic decay, random samples of bioplastic formed were buried in humus soil for 15 days, about 20 cm deep, with an average soil temperature of 23.5 °C [4] and 1.80% moisture [33].

Compression and tensile strength measurements

In a compression test, the specimen is placed between two plates, and a force is applied by closing the crossheads. The specimen is compressed, and the resulting deformation is measured and plotted against the applied force.

Procedure for compressive and tensile strength

Using the same Instron testing machine (model 3369), compressive testing is carried out similarly to tensile strength testing. Anvils take the role of gripping jaws, and rather than pulling away, the crosshead moves in the direction of the stationary grasp. In contrast to dogbone specimens, compressive testing of the plastic film sample is carried out on typically thick pads under far less force. For plastics and rubbers, this kind of test arrangement is usually appropriate. The specimen is pushed inward by opposing uniaxial forces acting on the plastic film sample from opposite sides. According to ASTM E9-19, compressive testing is carried out with loading forces that are the opposite of those in conventional tensile strength tests.

Experimental design

The experimental design for the bioplastic synthesis experiment was implemented with the Box–Behnken Design (BBD) of response surface methodology (RSM) as presented in Table 1. The BBD is considered to be one of the most proficient and powerful designs among others such as central composite design, Doehlert design, and full factorial design. Typically, for the five factors considered, the BBD involves a total of 46 experimental runs with 21 coefficients in the quadratic model.

Factor typeNameUnitsLowHigh
A [Numeric]Mass of starch, MSg1020
B [Numeric]Volume of glycerin, VGml23
C [Numeric]Volume of vinegar, VVml23
D [Numeric]Volume of water, VWml5060
E [Numeric]Time, Tmin515
Response variables
W [Numeric]Energy consumedkJ
X [Numeric]Weight of bioplasticg
Y [Numeric]Tensile strengthMPa
Z [Numeric]Elongation%

Table 1

Experimental design.

The design equation is of the following form:

where MS is the mass of starch (g), VG is the volume of glycerin (ml), VV is the volume of vinegar (ml), VW is the volume of water (ml), and T is time (min) while a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, and u are coefficients of the model.

Results and discussion

Effects of input variables on bioplastic characteristics

The experimental results for the production of bioplastic from waste corn, presented in Table 2, show the effects of the independent variables—mass of starch, volume of glycerin, volume of vinegar, volume of water, and time—on the response variables.

FactorsResponses
RunA: Mass of starch (g)B: Volume of glycerin (ml)C: Volume of vinegar (ml)D: Volume of water (ml)E: Time (min)W: Energy consumed (kJ)X: Weight of bioplastic (g)Y : Tensile strength (MPa)Z: Elongation (%)
1153.02.550.01024034.500.41739.80
2202.53.055.01036051.750.43509.20
3152.52.555.01027039.001.69166.90
4152.03.055.01027039.001.543311.30
5202.52.555.01539048.750.74006.80
6152.52.550.0521036.000.54007.30
7102.52.560.01021032.251.113514.30
8153.02.560.01027044.251.345012.40
9152.02.555.01530038.100.574013.40
10152.52.560.0524041.250.301011.80
11102.52.555.0518030.750.547012.70
12153.03.055.01027040.501.0038.43
13102.52.055.01021030.000.415914.74
14202.52.555.0533046.500.75007.10
15152.52.050.01024034.500.96780.85
16202.52.055.01036047.250.35649.20
17152.52.560.01530041.250.34238.80
18152.52.550.01527038.250.16586.43
19102.52.555.01524030.750.89505.70
20152.52.055.0524038.250.95608.40
21153.02.555.0524038.251.256011.90
22152.52.060.01027041.000.44897.00
23152.52.555.01027039.001.66006.70
24152.02.560.01027040.500.17647.40
25202.52.560.01036050.251.45608.50
26152.02.555.0524038.251.56407.70
27102.52.550.01021028.500.31406.67
28152.52.555.01027039.001.682013.31
29152.53.055.01530040.501.432014.87
30203.02.555.01036049.500.790513.41
31102.02.555.01021028.500.42926.51
32153.02.055.01027039.001.39157.50
33152.52.055.01530038.250.89659.30
34153.02.555.01530038.250.444614.50
35152.02.550.01024034.501.249813.40
36152.52.555.01027039.001.760010.38
37103.02.555.01021031.500.19308.91
38152.53.055.0524041.250.87596.60
39152.53.050.01024040.500.85326.43
40202.02.555.01036047.251.25005.00
41152.53.060.01027043.651.763015.67
42152.52.555.01027039.001.81009.40
43202.52.550.01036047.101.950212.80
44102.53.055.01021032.250.49137.20
45152.02.055.01027036.000.56173.02
46152.52.555.01027039.001.699012.35

Table 2

Experimental results of bioplastic produced.

Figure 2 shows the six-centered point samples synthesized for the BBD of response surface methodology, indicating the uniformity in the appearance of the products. Table 2 also shows that these six-centered point samples have close responses and are therefore reproducible.

Figure 2.

Six-centered point samples for the RSM.

Effect of water on the bioplastic

The effect of water on the produced bioplastic composition can be observed vividly when comparing runs 1 and 8 having 50.0 ml and 60.0 ml of water, respectively, but with other parameters held constant: run 8 has 12.50% more energy consumed, 28.26% higher weight of bioplastic, 222.31% more tensile strength, and 26.53% better elongation. Expectedly, the increase in water proportion resulted in a higher weight of bioplastic except that the mold is baked at above 90 °C to allow excess water to evaporate. The presence of water has been found to increase water vapor permeability and elongation of the bioplastic [34, 35]. Although the addition of water during the mixing stage aids the plasticization, injection molding can be affected by excess water as voids are created in the mold cavity when the water evaporates, leading to the production of stiffer but brittle and less elastic biopolymers. In addition, moisture content has been shown to increase the biodegradability of bioplastics. The starch bioplastic produced by Shafqat et al. [36] recorded a degradability of 12.05% with a moisture content of 5.58% but rose to 69.96% when the moisture content increased to 21.34%.

Effect of glycerol on the bioplastic

The effect of glycerol (plasticizer) on the produced bioplastic composition can be seen in runs 8 and 24 having 3.0 ml and 2.0 ml of glycerol with other parameters held constant, respectively. The trend shows that run 8 has 9.26% higher weight of bioplastic, 622.47% more tensile strength, and 67.56% better elongation; a similar trend is depicted in runs 7 and 27. Glycerol is hydrophilic and thus can form water molecules through its hydroxyl group, thereby increasing the size of the biopolymer. Moreover, plasticizers are vital in impacting the mechanical properties of the bioplastic produced. Glycerol acted as a plasticizer, yielding increased film thickness and creating a slippage of starch chains over each other for flexibility. As observed in this study, a 50% increase in glycerol proportion led to a 622.47% increase in tensile strength. According to Tarique et al. [44], the percentage of break elongation increased from 2.41 to 57.33. This increase is caused by an increase in the amount of glycerol in the composition. Similarly, as the glycerol ratio rises, the bioplastic created by Samer et al. [13] softens and can withstand compression only up to 0.5 MPa, but as it falls, the bioplastic becomes rigid and can withstand compression up to 1.1 MPa. Shafqat et al. [36] showed that plasticizers had a similar impact on different bioplastics, with glycerol-containing samples exhibiting the weakest tensile strength. Agusman et al. [37] found that the best treatment was (agar:glycerol:water = 5:3:2) bioplastics with a tensile strength of 16.19 MPa and an elongation at break of 102.56%. Moreover, in this work, more glycerol and less water yield better elongation and tensile strength.

Effect of vinegar on the bioplastic

The effect of vinegar on the produced bioplastic composition can be seen in runs 17 and 22 having 2.5 ml and 2.0 ml of vinegar with other parameters held constant, respectively. Run 22 has 11.11% less energy consumed, 0.61% lesser weight of bioplastic, 31.14% more tensile strength, and 25.71% lesser elongation, as excess vinegar content leads to less cohesion between the starch chains. Vinegar imparts elongation and flexibility to the plastic film.

Effect of mass of starch on the bioplastic

The effect of the mass of starch on the produced bioplastic composition can be seen in runs 7 and 25 having 10 g and 20 g of starch with other parameters held constant, respectively. Run 7 consumed 71.43% less energy, 55.81% lesser weight of bioplastic, 30.76% lesser tensile strength, and 68.24% better elongation. This showed that an optimal mixing time is required. Furthermore, the greater the mass of starch, the harder the bioplastic [13]. A similar attribute is reproduced in runs 27 and 43.

Effect of mixing time on the bioplastic

Bioplastics are made by physically combining biomass into a single component [38]. The effect of the mixing time on the produced bioplastic composition can be seen in runs 10 and 17, having 5 min and 15 min mixing time with other parameters held constant, respectively. Run 17 with more mixing time consumed 25.00% more energy, no weight difference, 13.72% more tensile strength, and 34.09% less elongation. Samer et al. [13] found that processing at 5.5 min consumed 165 kJ and at 10.0 min required 300 kJ, which is 81.81% more energy. It was also found that the bioplastic manufactured by Amri et al. [39] using 15% graphene oxide and 50 min of mixing time demonstrated tensile strength 4.018 MPa and elongation 16.13%. The greater the time for mixing, the better the smoothness of the bioplastic; however, the lesser the mechanical merit.

Graphical representation of experimental results

The interactive effect of the mass of starch (g), the volume of glycerin (ml), the volume of vinegar (ml), the volume of water (ml), and time (min) on the response of the system was assessed by plotting three-dimensional curves of the response against the predicting variables as shown in Figures 36. The response distribution in this experimental work with respect to the variation of the independent variables shows that for a higher R2 value of 99.01% for the energy consumed, the weight of bioplastic is 98.62% and the tensile strength is 85.53% as depicted in Table 5.

Figure 3.

Surface plot for energy consumed.

Figure 4.

Surface plot for the weight of bioplastic.

Figure 5.

Surface plot for tensile strength of bioplastic.

Figure 6.

Surface plot for elongation of bioplastic.

Within the limit of statistical optimization as depicted by this contour plot, the energy consumed, the weight of bioplastic, tensile strength, and elongation are found to be 289.64 kJ, 44.15 g, 1.44 MPa, and 12.02%, respectively, when the optimum predicting mass of starch, the volume of glycerin, volume of vinegar, volume of water, and time are 17.27 g, 2.0 ml, 3.0 ml, 50.8 ml, and 10.25 min, respectively, as depicted in Figure 7.

Figure 7.

Optimal point from the numerical optimization.

Statistical analysis and modeling

Statistical analysis and modeling for energy consumed

The analysis of variance (ANOVA) for each of the response variables was performed at a 95% confidence level, indicating a 5% level of significance (𝛼 = 0.05). The model results for the ANOVA for the quadratic model for energy consumed are presented in Table 3. The p-value < 0.05 in all cases indicates a good fit for the model. The coefficient estimate term revealing the values of each factor term described in the statistical model at a 95% confidence level is presented in Table 4 for energy consumed (kJ), which is also analogous to the weight of bioplastic (g), tensile strength (MPa), and elongation. Based on the model strength, the ANOVA of the second-order regression model for the energy consumed (kJ), the weight of bioplastic (g), tensile strength (MPa), and elongation revealed the significant levels of the model at 99.01%, 98.62%, 85.53%, and 83.60% for energy consumed (kJ), the weight of bioplastic (g), and tensile strength (MPa) as presented in Table 5. Variance analysis revealed that the amount of starch, glycerin, vinegar, water, and mixing time had a significant effect on the amount of energy used, bioplastic weight, tensile strength, and film elongation. The statistical results with low p-values in the tables also show how the model fits the simulation results. This highly infers that the total variance in the response could be explicated using this model.

Source Sum of squaresDfMean squareF-valuep-value
Model 1.122E+0520.00005609.8400124.6600<0.0001
A: Mass of starch 90000.00001.000090000.00002000.0000<0.0001
B: Volume of glycerin 0.00001.00000.00000.00001.0000
C: Volume of vinegar 0.00001.00000.00000.00001.0000
D: Volume of water 2025.00001.00002025.000045.0000<0.0001
E: Time1.4400E+041.00001.4400E+040.3200E+03<0.0001
AB 0.00001.00000.00000.00001.0000
AC 0.00001.00000.00000.00001.0000
AD 0.00001.00000.00000.00001.0000
AE 0.00001.00000.00000.00001.0000
BC 0.00001.00000.00000.00001.0000
BD 0.00001.00000.00000.00001.0000
BE 0.00001.00000.00000.00001.0000
CD 0.00001.00000.00000.00001.0000
CE 0.00001.00000.00000.00001.0000
DE 0.00001.00000.00000.00001.0000
A2 3068.18001.00003068.180068.1800<0.0001
B2 13.64001.000013.64000.30300.5869
C2 13.64001.000013.64000.30300.5869
D2 1104.55001.00001104.550024.5500<0.0001
E2 13.64001.000013.64000.30300.5869
Residual 1125.000025.000045.0000
Lack of fit 1125.000020.000056.2500
Pure error 0.00005.00000.0000
Cor total 1.133E+0545.0000

Table 3

ANOVA for quadratic model for energy consumed.

Factor Coefficient estimateDfStandard error95% CI low95% CI highVIF
BE 0.00001.00003.3500−6.91006.91001.0000
CD 0.00001.00003.3500−6.91006.91001.0000
CE 0.00001.00003.3500−6.91006.91001.0000
DE 0.00001.00003.3500−6.91006.91001.0000
A2 18.75001.00002.270014.070023.43001.2000
B2−1.25001.00002.2700−5.93003.43001.2000
C2−1.25001.00002.2700−5.93003.43001.2000
D2−11.25001.00002.2700−15.9300−6.57001.2000
E2−1.25001.00002.2700−5.93003.43001.2000

Table 4

Coefficient estimate for energy consumed.

WeightTensile strengthElongationEnergy consumed
Std. dev.0.91300.41403.45006.7100
Mean39.24000.96509.4400271.300
C.V. %2.330042.960036.6002.4700
R20.98600.85500.83600.9900
Adjusted R20.97500.78000.72800.9820
Predicted R20.94500.67900.64000.9600
Adeq precision37.83305.46603.889046.3300

Table 5

Fit statistics for energy consumed, elongation, weight, and tensile strength of bioplastic.

Table 6 summarizes the lack of fit analyses for additional response variables that are not included in Table 3. Specifically, lack of fit was statistically evaluated for the weight of bioplastic, tensile strength, and elongation to assess deviations from model fitness. The results indicate that the lack of fit values were negligible, suggesting an excellent fit of the data with minimal error, thereby confirming the model's suitability for these response variables.

ResponsesSourceDFAdj SSAdj MSF-valuep-value
Weight of bioplastic, XError2522.720.91
Lack of fit2022.721.14**
Pure error500
Total451496.12
Tensile strength, YError254.73510.1894
Lack of fit204.71870.2359472.220
Pure error50.01630.00327
Total4512.9745
Elongation, ZError25294.70211.7881
Lack of fit20257.37212.86861.720.285
Pure error537.3297.4659
Total45510.711

Table 6

Summary of lack of fit analyses for other responses.

The statistical correlation is often shown by regression results and significance between the independent variables and the dependent variables. The correlation coefficient (R2) is the percentage of responses (%) (energy consumed (kJ), weight of bioplastic (g), tensile strength (MPa), and elongation) variation that is described by their relationship with the mass of starch (g), the volume of glycerin (ml), the volume of vinegar (ml), the volume of water (ml), and times (min). Therefore, the adjusted correlation coefficient is the percentage of responses (%) variation that is explained by its relationship with energy consumed, the weight of bioplastic, and tensile strength variation that are described by their relationship with the mass of starch, volume of glycerin, volume of vinegar, volume of water, and times, adjusted for the number of independent variables (predictors) in the model. This adjustment is important because the correlation coefficient (R2) for this model increases when a new predicting variable is added [40].

Hence, for evaluating and analyzing the explanatory strength of models with different numbers of independent variables, the adjusted R2 is a useful tool. The significance of the p-value for each coefficient is to test the null hypothesis that the coefficient has zero effect [41]. Hence, good model analysis is represented by this statistical analysis with minimal standard error of each term. With values of R2 being 99.00%, 98.60%, 85.50%, and 83.60%, the anticipated results were quite close to matching the experimental data for energy consumed, the weight of bioplastic, tensile strength, and elongation, respectively [42].

Final equation for energy consumed (W) in terms of actual factors

The predictive models are as follows:

Similar model types were generated for the weight of bioplastic, tensile strength (MPa), and elongation (%):

Tables 7 and 8 present the optimized response predictions and validation analyses, respectively, for the specified process variables. These tables support the findings by comparing the predicted outcomes with the experimentally validated results, thereby verifying the reliability and accuracy of the optimization approach. The data underscores the effectiveness of the optimized conditions in enhancing the desired response variables.

VariableSetting
A: Mass of starch (g)17.2727
B: Volume of glycerin (ml)2
C: Volume of vinegar (ml)3
D: Volume of water (ml)50.8081
E: Time (min)10.2525

ResponseFitSE fit95% CI95% PI

Z: Elongation (%)12.023.57(4.66, 19.38)(1.81, 22.22)
Y: Tensile strength (MPa)1.4390.453(0.507, 2.372)(0.146, 2.733)
X: Weight of bioplastic (g)44.1500.992(42.107, 46.193)(41.316, 46.983)
W: Energy consumed (kJ)289.646.98(275.26, 304.01)(269.70, 309.58)

Table 7

Optimized response prediction.

Process variablesSynthesizedStatisticDeviation% errorStd. dev.
Energy (kJ)290.500000289.637500−0.862500−0.2977860.609879
Weight of film (g)44.20000044.149900−0.050100−0.1134770.035426
Tensile strength (MPa)1.4502001.439300−0.010900−0.7573130.007707
Elongation (%)11.95000012.0194000.0694000.5773990.049073

Table 8

Validation analysis using the optimal value obtained from the RSM model.

Moisture and biodegradability test

The moisture contents of the randomly selected samples are determined as follows:

where w0 is the weight of the sample while w1 is the weight of the bone-dry sample.

The % moisture of the selected samples shown in Figure 8  above are 5%, 7%, and 10%, respectively.

The biodegradation test showed that the selected bioplastic samples of masses 11.5 g, 8.0 g, and 10.0 g, respectively, before burial are depleted by mass after 15 days of burial, resulting in masses of 6.0 g, 5.0 g, and 4.5 g, respectively, at 47.83%, 37.5%, and 55% mass depletion rates. This proved that the produced bioplastic meets the material circularity or sustainability requirement. Comparable biodegradability results have been reported by researchers. For instance, Marichelvam et al. [14] obtained a biodegradability of 48.73% after 15 days of bioplastic burial in the soil, at a depth of 3 cm, while Accinelli et al. [43] recorded 37% loss of sample weight in soil and 43% lost in compost after 90 days. Furthermore, Shafqat et al. [36] observed degradability results of 12.05%, 31.94%, and 49.48% in 5 days of burial.

Figure 8.

Degradation test of bioplastics produced. A—samples before burial: A1 (11.5 g), A2 (8.0 g), and A3 (10.0 g); B—corresponding mass after being buried in the soil for 15 days at an average soil temperature of 23.5 °C and 20.0 cm depth: B1 (6.0 g), B2 (5.0 g), and B3 (4.5 g). That is, A1 → B1, A2 → B2, and A3 → B3.

Conclusion

The study designed, statistically analyzed, and optimized bioplastic synthesis from waste corn.

The correlation coefficient, R2 values of 99.01%, 98.62%, 85.53%, and 83.60% showed good model strength of energy consumed (kJ), bioplastic weight (g), tensile strength (MPa), and elongation (%), respectively. At optimal predicting mass of starch, the volume of glycerin, volume of vinegar, volume of water, and time of 17.27 g, 2.0 ml, 3.0 ml, 50.81 ml, and 10.25 min, respectively, the optimal value of energy consumed, bioplastic weight, tensile strength, and elongation are 289.64 kJ, 44.15 g, 1.44 MPa, and 12.02%, respectively. The synthesized optimal sample compared well with statistical data, which confirms that waste corn starch produces bioplastic. Electrical insulators and packaging are possible with bioplastic. Due to the preservation of the food chain, this study was limited to the extraction of starch from waste corn and the production of bioplastic from this starch. Eco-friendly polymers derived from agricultural waste help to promote material circularity by completely avoiding waste, and rather these agricultural wastes undergo processing to form biodegradable plastics. After use, they are also passed into the earth to decompose and provide nutrients to the soil for more agricultural products. Pursuant to the net-zero initiative, process industries can replace petro-plastics with bioplastics.

Recommendation

Biofillers like eggshell, chitosan, bagasse, or any suitable polysaccharides and proteins can be added to the bioplastic at the optimal conditions to improve its tensile strength and elongation, thermal resistance, moisture, or gas barriers, stiffness, flexibility, and durability, making it useful in more applications.

Author’s contribution

Adeyemo, Festus:  Conceptualization, Methodology, Writing -- original draft preparation, Writing -- review & editing, Visualization; Ogunwumi, Olawale Theophilus: Data analysis; Oyedeko, Kamilu:  Writing -- review & editing, Supervision; Amodu, Olusola Solomon: Writing -- review & editing, Visualization, Supervision.

Funding

This research did not receive external funding from any agencies.

Ethical statement

Not Applicable.

Data availability statement

Source data sharing is not applicable.

Conflict of interest

The authors declare no conflict of interest.

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Written by

Festus Adeyemo, Olawale Theophilus Ogunwumi, Kamilu Oyedeko and Olusola Solomon Amodu

Article Type: Research Paper

Date of acceptance: October 2024

Date of publication: November 2024

DOI: 10.5772/geet.20240012

Copyright: The Author(s), Licensee IntechOpen, License: CC BY 4.0

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© The Author(s) 2024. Licensee IntechOpen. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.


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