The addition of fillers or additives to improve the mechanical properties of degradable plastics such as sago starch has gained the interest of researchers, scientists, and academicians. This research aims to investigate the addition of calcium silicate as an additive filler on the properties of a sago starch-based degradable plastic. The calcium silicate fillers used were 2, 4, 6, and 8% by weight starch, and the gelatinization process temperature used was 70, 80, and 90 ℃. The properties of these plastics were analyzed in terms of their strength, chemical composition, thermal stability, water absorption, and degradation rate. The optimum mechanical characteristics included a tensile strength of 28.04 MPa, 32.55 MPa of elongation at the break, and 70.02% of Young's modulus obtained with the addition of 8% calcium silicate and a gelatinization temperature of 90 ℃. Fourier Transform Infrared (FTIR) showed that there were O-H, C-H, and C = O groups that existed at wave numbers of 3795.91 cm-1, 2927.94 cm-1, and 1433.11–1616.35 cm-1; moreover, these groups are hydrophilic, which bind water, so they can be degraded by the microbial activity in the soil. Differential Scanning Calorimetry (DSC) showed that the degradable plastic had a thermogram peak at 271.38 ℃; additionally an endothermic peak occurred at 309.30 ℃. The maximum swelling value was 64.05% at 2% calcium silicate and a gelatinization temperature of 70 ℃. The addition of calcium silicate made the plastic more water-resistant. The degradation rate of the degradable plastic ranged from 12–15 days and conformed to the American Standard Testing and Materials (ASTM) D-20.96 (maximum 180 days of decomposition for degradable plastic).
Citation: Rozanna Dewi, Novi Sylvia, Zulnazri Zulnazri, Medyan Riza, Januar Parlaungan Siregar, Tezara Cionita. Use calcium silicate filler to improve the properties of sago starch based degradable plastic[J]. AIMS Environmental Science, 2025, 12(1): 1-15. doi: 10.3934/environsci.2025001
| [1] | Yeon Ju Lee, Jong Hyuk Kim, Ju Hyeon Ha, Ha Yeon Nam, Il Rae Rho . Effects of nitrogen topdressing fertilization on yield and quality in soybeans. AIMS Agriculture and Food, 2024, 9(4): 1004-1026. doi: 10.3934/agrfood.2024054 |
| [2] | Letisha Maistry, Abe Shegro Gerrano, John Mellem . Chemical and structural characterization of okra (Abelmoschus esculentus) seed protein isolate as a potential therapeutic agent. AIMS Agriculture and Food, 2025, 10(2): 353-370. doi: 10.3934/agrfood.2025018 |
| [3] | Sapta Raharja, Yogi Purna Rahardjo, Samsudin, Khaswar Syamsu . Aroma precursor enhancing in dried cocoa beans fermentation using enzyme and heat addition. AIMS Agriculture and Food, 2023, 8(2): 674-686. doi: 10.3934/agrfood.2023037 |
| [4] | Saranyu Khammuang, Kamonpan Sanachai, Methus Kittika, Kriengsak Lirdprapamongkol, Jisnuson Svasti, Rakrudee Sarnthima . Antioxidant potential of hydrolyzed proteins from Thai rice varieties and docking studies of novel peptides with free radicals. AIMS Agriculture and Food, 2025, 10(2): 371-389. doi: 10.3934/agrfood.2025019 |
| [5] | Eric Tzyy Jiann Chong, Lucky Poh Wah Goh, Mariam Abd. Latip, Zaleha Abdul Aziz, Noumie Surugau, Ping-Chin Lee . Genetic diversity of upland traditional rice varieties in Malaysian Borneo based on mitochondrial cytochrome c oxidase 3 gene analysis. AIMS Agriculture and Food, 2021, 6(1): 235-246. doi: 10.3934/agrfood.2021015 |
| [6] | Gullo Gregorio, Dattola Antonio, Zappia Rocco . Comparative study of some fruit quality characteristics of two of Annona cherimola Mill. grown in southern Italy. AIMS Agriculture and Food, 2019, 4(3): 658-671. doi: 10.3934/agrfood.2019.3.658 |
| [7] | Ilaria Marotti, Giovanni Dinelli, Valeria Bregola, Sara Bosi . Nutritional characterization of Italian common bean landraces (Phaseolus vulgaris L.): fatty acid profiles for “genotype-niche diversity” fingerprints. AIMS Agriculture and Food, 2020, 5(4): 543-562. doi: 10.3934/agrfood.2020.4.543 |
| [8] | Araya Ranok, Chanida Kupradit . Effect of whey protein and riceberry flour on quality and antioxidant activity under gastrointestinal transit of gluten-free cookies. AIMS Agriculture and Food, 2020, 5(3): 434-448. doi: 10.3934/agrfood.2020.3.434 |
| [9] | Dyah H Wardhani, Heri Cahyono, Hana N Ulya, Andri C Kumoro, Khairul Anam, José Antonio Vázquez . Spray-dryer feed preparation: Enzymatic degradation of glucomannan for iron nanoencapsulation. AIMS Agriculture and Food, 2022, 7(3): 683-703. doi: 10.3934/agrfood.2022042 |
| [10] | Badreddine Belhadi, Rachid Souilah, Redha Ould-Kiar, Mohamed Yousfi, Djaafar Djabali, Boubekeur Nadjemi . Effects of phenotype and wet milling procedures on the starch isolation from sorghum (Sorghum bicolor L. Moench) grains. AIMS Agriculture and Food, 2021, 6(2): 448-461. doi: 10.3934/agrfood.2021026 |
The addition of fillers or additives to improve the mechanical properties of degradable plastics such as sago starch has gained the interest of researchers, scientists, and academicians. This research aims to investigate the addition of calcium silicate as an additive filler on the properties of a sago starch-based degradable plastic. The calcium silicate fillers used were 2, 4, 6, and 8% by weight starch, and the gelatinization process temperature used was 70, 80, and 90 ℃. The properties of these plastics were analyzed in terms of their strength, chemical composition, thermal stability, water absorption, and degradation rate. The optimum mechanical characteristics included a tensile strength of 28.04 MPa, 32.55 MPa of elongation at the break, and 70.02% of Young's modulus obtained with the addition of 8% calcium silicate and a gelatinization temperature of 90 ℃. Fourier Transform Infrared (FTIR) showed that there were O-H, C-H, and C = O groups that existed at wave numbers of 3795.91 cm-1, 2927.94 cm-1, and 1433.11–1616.35 cm-1; moreover, these groups are hydrophilic, which bind water, so they can be degraded by the microbial activity in the soil. Differential Scanning Calorimetry (DSC) showed that the degradable plastic had a thermogram peak at 271.38 ℃; additionally an endothermic peak occurred at 309.30 ℃. The maximum swelling value was 64.05% at 2% calcium silicate and a gelatinization temperature of 70 ℃. The addition of calcium silicate made the plastic more water-resistant. The degradation rate of the degradable plastic ranged from 12–15 days and conformed to the American Standard Testing and Materials (ASTM) D-20.96 (maximum 180 days of decomposition for degradable plastic).
Protein hydrolyzates (PHs) can be obtained by thermal, chemical and/or enzymatic hydrolysis of raw materials of animal or vegetal origin. PHs are a well-known group of plant biostimulants [1] and their use in agriculture increased significantly during the last decade [2]. PHs are mainly used in order to improve the nutrition level of plants [3], stimulate their metabolism [4] and increase their resistance to abiotic stress [5]. PHs can be therefore considered products with a wide spectrum of activity.
Chemically, PHs are a mixture of free amino acids, oligopeptides and polypeptides. Due to different source materials and different hydrolytic processes used during production, PHs are a very complex and inhomogeneous category of products, a critical issue for both manufacturers and potential users of such products. Furthermore, the exact nature of the "active ingredient(s)" present in PHs is not clear, even though some amino acids (i.e. tryptophan, methionine, arginine, proline, etc.) are well known precursor of phytohormones or accumulate in cells following exposure to osmotic or oxidative stress. Some peptides can also influence plant metabolism in various ways [6]. We have therefore focused our attention on the amino acids and peptides concentration of PHs as the most useful indicators for their characterization.
Firstly, we have considered the total amino acids (TAA) concentration in PHs. TAA are generally strictly correlated to organic matter concentration, and are perhaps the only indicator of protein derived compounds in the end product. Nevertheless, the total concentration of major amino acids (i.e. proline and hydroxyproline for collagen-based products), or even better the molar ratio of all protein derived amino acids, are good indicators for the identification of the raw material used for the production of the PH. The presence or the absence of uncommon amino acids may also be used for the same purpose (i.e. the absence of tryptophan in collagen).
The concentration of free amino acids (FAA) in PHs is generally considered to be the most significant parameter. In fact, the hydrolytic processes used during PHs production are continuously implemented. These processes fall mainly in two groups: chemical (or better thermo-chemical) and enzymatic processes. However, it is clear that it is also possible to use a multi-step approach with chemical plus enzymatic hydrolysis.
Chemical hydrolysis is based on the use of concentrated solutions of strong bases (i.e. sodium hydroxide, calcium hydroxide) or acids (i.e. sulphuric acid, hydrochloric acid) under controlled temperature and pressure conditions. These processes are not selective and while they release FAA by breaking the peptide-bonds, they can also lead to degradation (i.e. tryptophan in acid hydrolysis) or transformation (i.e. arginine in citrulline) of several amino acids.
Another important factor to take into account is the racemization of the released amino acids, as after cleaving the peptide bonds the released amino acids are casually converted into the l-or d-form. However, amino acids are naturally present only in the l-form, while d-amino acids are probably less effective or even toxic to living organisms [7]. The racemization degree (RD) or the ratio between d-form and d-plus l-form is therefore another important indicator of PHs quality and identification of hydrolytic process.
Enzymatic hydrolysis, on the other hand, which is selective and can be conducted under milder conditions, does not yield d-amino acids during the cleavage of peptide bonds. However, one should always keep in mind that hydrolytic processes yield not only FAA, but also cut polypeptides into oligopeptides.
Hydrolysis of peptide bonds therefore increases the number of free amino groups (-NH2), released after the cleavage of peptide bonds. The ratio between the number of cleaved peptide bonds and the total number of peptide bonds defines the degree of hydrolysis (DH). The DH of a product is determined by calculating the ratio between free amino groups and total organic nitrogen. This indicator is very important, since it is a quantitative expression of the hydrolytic efficiency of the process. Nevertheless, the same DH value can be associated with samples containing widely different mixtures of peptides and FAA.
In order to define the size of the peptides present in a product, we must use a different approach. Considering that the PHs are a heterogeneous mixture, we can only determine the average molecular size (MS) of the peptides present in said mixture. Typically, the distribution of MS of peptides in PHs shows a unimodal distribution [4] that can be described by calculating the number-average (Mn) and weight-average molecular size (Mw). MS is an important indicator, since the major biostimulant activity seems to be linked to a specific MS fraction of peptides (i.e. peptides with low MS).
In order to characterize and describe PH biostimulants, we have therefore analyzed more than twenty PHs for (ⅰ) total amino acid concentration, (ⅱ) free amino acid concentration, (ⅲ) racemization degree, (ⅳ) degree of hydrolysis, and (ⅴ) apparent average molecular size.
The protein hydrolyzates (PHs) used in this study were purchased on the market. The main source materials, declared by manufacturers, were: animal epithelium (12 samples), meat meal (4 samples), seaweed (3 samples), and alfalfa (3 samples).
Moisture and ashes were determined by weight loss at 105 and 550 ℃, respectively; reaction (pH) in water (3/50, w/v); electrical conductivity (EC) in water (1/10, w/v); total organic carbon (TOC) by wet oxidation method with potassium dichromate and total nitrogen (TN) with the Kjeldahl method.
Total amino acids (TAA) were determined by chemical hydrolysis of samples in 6 M hydrochloric acid (HCl), and free amino acids (FAA) were extracted in 0.1 M HCl. TAA and FAA were determined by RP-HPLC after derivatization with 9-fluorenylmethoxycarbonyl chloride [8] using a 1525 binary HPLC (Waters) equipped with an Amino Tag TSK-Gel ODS 80-TM (Varian) 150 × 4.6 mm column.
The racemization degree (RD) was determined according to the capillary electrophoresis method proposed by Cavani et al. [9]. Briefly, free l-alanine (l-Ala) and d-alanine (d-Ala) were separated and measured by capillary electrophoresis after derivatization with dansyl chloride, and was calculated using the follow equation: RD (%) = 100 · d-Ala / (l-Ala + d-Ala).
The degree of hydrolysis (DH) was determined based on free α-amino N / total organic N ratio [10]. The free α-amino N was determined with the o-phthaldialdehyde plus N-acetylcysteine spectrophotometric assay [11].
The apparent molecular size was determined using size exclusion high performance chromatography [12]. Number-average molecular size (Mn) was calculated using the following equation: Mn = Σ (ni · Mi) / Σ ni; where Mi and ni are the molecular size and the height of each ith fraction eluted at the ith volume in the chromatogram, respectively [13].
Statistical analysis was carried out using the software R version 3.4.0 [14]: Pearson's product moment coefficient was determined using the command "correlation" from the package "agricolae", the Shapiro-Wilk test for normality using the command "shapiro.test" from the package "stats", the principal component analysis (PCA) using the command "prcomp" from the package "stats".
The major chemical characteristics of the 22 analysed PHs are summarized in Figures 1 and 2. The pH values of the tested PHs ranged from 4.1 to 8.2 (Figure 1), with an average value of 6.6 and a coefficient of variability (cv) of 17.9%, while electrical conductivity (EC) displayed a high variability and ranged from 3.9 to 27 dS m−1, with an average value of 10.2 dS m−1 (cv = 56.6%). Considering the distribution of the observed EC values and that the Shapiro-Wilk's test for the normal distribution was highly significant (P = 0.0075), the unusually high EC value (27 dS m−1) observed in sample #11, was most probably an outlier. A more realistic of EC range should therefore considered to be the one between 3.9 to 20 dS m−1.
Figure 1. Box plot showing the variation of pH and electrical conductivity (EC) of the PHs samples investigates.
Figure 2. Box plot showing the variation of water, ashes, total organic carbon (TOC/dw) and total nitrogen (TN/dw) concentrations of the PHs samples investigates (data for water and ashes are expressed on fresh weight; TOC and TN on dry weight).Figure 2 shows that water contents of PHs ranged from 16.7 to 88.8% (w/w) on fresh weight basis, with an average value of 52.3% (cv = 33.5%). This is not surprising, since the tested products were both in liquid and powder form. The same figure also shows that the content of ashes was also highly variable and ranged from 0.21 to 18.9% (w/w) on fresh weight basis, with an average value of 7.4 (cv = 65.1%), that total organic carbon (TOC) ranged from 33 to 58% (w/w) on dry weight basis with an average value of 42% (cv = 13.4%) and that total nitrogen (TN) concentration ranged from 4.1 to 17.7% (w/w) on dry weight basis, with an average value of 11.8% (cv = 34.3%).
The results of the analysis of total amino acid concentration (TAA), and free amino acid concentration (FAA) in the tested PHs are summarized in Figure 3. TAA concentration ranged from 5.33 to 52.5% (w/w) on fresh weight basis, with an average of 30.1 (cv = 44.6%). The variability decreases when TAA is reported on dry matter basis. In this case it ranges from 26.8 to 99.8%, with an average value of 61.8 (cv = 24.3%). FAA concentration of the tested PHs also displayed high variability, and ranged from 0.76 to 19.6% (w/w) on fresh weight basis, with an average of 6.7% (cv = 74.3%). This high variability and the absence of the not normal distribution of data was confirmed by the Shapiro-Wilk's test (P = 0.007) and is linked to the presence of two outliers (samples #2 and #14) with a FAA concentration near to 20%.
Figure 3. Box plot showing the variation of total amino acids (TAA) and free amino acids (FAA) of the PHs samples investigates: data are expressed on fresh weight (TAA and FAA) and dry weight (TAA/dw and FAA/dw).The RD of the tested PHs ranged from 4.5 to 50% (Figure 4), indicating that on the market it is possible to find PHs with both low and high racemization degree. The calculated average value of RD was 35.5% (cv = 38.6%). The lowest observed value (sample #2) could be considered an outlier.
Figure 4. Box plot showing the variation of racemization degree (RD), degree of hydrolysis (DH), and number-average molecular size (Mn) of the PHs samples investigates.The results of determination of the DH of the studied PHs samples are summarized in Figure 4. As already noticed in the case of other tested parameters, DH displayed a high level of variability of the obtained results. The lowest observed value was 10.2, while the highest observed value was 60.7%. The average DH value was 26.3% (cv = 46.7%).
The results of the determination of the Mn of the tested PHs are also summarized in Figure 4. The Mn ranged from 2.35 to 16.5 kDa with an average value of 8.91 kDa (cv = 41.0%).
The chemical characterization of the 22 tested PHs clearly indicates a substantial variability of this type of products. This is due both to the different origin of the start material (i.e. animal or vegetal) and the hydrolytic process used during the production process (i.e. chemical or enzymatic).
However, some considerations can be made based on the correlation between these parameters. The correlation matrix, which is reported in Table 1, indicates the presence of some significant correlations.
| Parameters | pH | EC | Water | Ashes | TOC | TN | TAA | FAA | RD | DH |
| pH | 1 | |||||||||
| EC | −0.12 | 1 | ||||||||
| Water | −0.01 | −0.51* | 1 | |||||||
| Ashes | −0.10 | 0.83*** | −0.42 | 1 | ||||||
| TOC | 0.06 | 0.37 | −0.94*** | 0.22 | 1 | |||||
| TN | 0.11 | 0.14 | −0.81*** | −0.07 | 0.93*** | 1 | ||||
| TAA | 0.07 | 0.19 | −0.87*** | 0.07 | 0.93*** | 0.91*** | 1 | |||
| FAA | 0.18 | −0.20 | −0.32 | −0.29 | 0.47* | 0.52* | 0.33 | 1 | ||
| RD | −0.39 | 0.30 | −0.22 | 0.37 | 0.11 | 0.07 | 0.04 | −0.05 | 1 | |
| DH | 0.30 | −0.26 | 0.48* | −0.16 | −0.53* | −0.53* | −0.48* | −0.01 | 0.04 | 1 |
| Mn | 0.31 | −0.03 | 0.18 | −0.21 | −0.34 | 0.42* | −0.30 | 0.42* | −0.28 | −0.19 |
| TOC = total organic carbon, TN = total nitrogen, TAA = total amino acids, FAA = free amino acids, RD = racemization degree, DH = degree of hydrolysis, Mn = number-average molecular size. * Correlation is significant at 0.05 level (two sided); ** correlation is significant at 0.01 level (two sided); *** correlation is significant at 0.001 level (two sided). |
||||||||||
DownLoad: CSVElectrical conductivity showed a negative correlation with water content (r = −0.51, P < 0.05) and a strong positive correlation (r = 0.83, P < 0.001) with ashes content. It appears therefore clear that a reduction in water content and an increase in salt concentration lead to an increase of EC. However, the increase of ashes (or salts) in some of the tested PHs is probably the consequence of the type of hydrolysis used during their production. The PHs with high ashes content or high EC are therefore most likely to have been obtained through chemical hydrolysis.
Since chemical hydrolysis leads to a complete racemization of free amino acids, we expected to find a close correlation between the RD and ashes content. Conversely, we have observed only a weak correlation (r = 0.37, P = 0.09). This lack of correlation could be attributed to the fact that hydrolysis can also be obtained through mixed hydrolysis (chemical + enzymatic) and that complete racemization can also be achieved by chemical hydrolysis with different concentrations of bases or acids.
TOC showed a high correlation both with TN (r = 0.93, P < 0.01) and TAA (r = 0.93, P < 0.01), indicating that these parameters are just different indicators of the organic matter content of the products. TOC, TN and TAA also correlated with FAA and DH (Tab. 1), but it is difficult to find an explanation for this behavior.
Unexpectedly, DH was not correlated with FAA (r = −0.01, P > 0.05). However, when FAA was expressed on dry matter basis in order to exclude the dilution effect, it was possible to observe a weak correlation (r = 0.39, P = 0.07). Finally, an even closer correlation could be observed (r = 0.68, P < 0.001) after the exclusion of two outliers. This indicates that DH is generally related to FAA concentration, even though several other possible exceptions should be taken into account. The correlation could also be attributable to the enzymatic phase of hydrolysis that allows the regulation of the release of amino acids or peptides by using specific exo-or endopeptidases.
The lack of correlation between parameters clearly indicates that it is not possible to characterize these products by using only one or even few parameters and that it is a necessary to utilize a multiparametric approach. This calls for the use of a wider array of different parameters in order to characterize the PHs present on the market.
TAA concentration, which was the first indicator we used, is an indicator of organic matter content of PHs, such as TOC and TN. More interesting is the (total) amino acid composition of PHs, since it is an indicator of the nature of the starting matrix used to produce the PHs [6]. Table 2 reports the value of Pearson's product moment coefficient between the amino acid composition of PHs and the amino acid composition of the natural compounds that are declared to be the source material used for PHs production. The results substantially confirmed that the amino acid composition of the final product mirrors the amino acid composition of the source material. For example, by applying the principal component analysis (PCA) to the amino acid composition of PHs it is possible to distinguish alfalfa and seaweed based products from each other (Figure 5). However, given the similarity between the two matrices, by using the same tools it is not possible to clearly discriminate between PHs originating from animal epithelium and those originating from meat meal. TAA composition could therefore be considered a suitable indicator for the recognition of the declared source material, even though it appears necessary to increase the knowledge about the exact amino acids composition of the matrices used for the production of PHs. In fact, we could use only data for the sixteen amino acids obtained during acid digestion, while natural matrices contain not only the 20 proteinogenic amino acids, but also others non-proteinogenic amino acids that could be also used as markers to recognize different starting matrices.
| Samples | Declared source | Natural compounds | |||
| Collagena | Alfalfa hayb | Meat mealc | Seaweedd | ||
| #01 | Animal epithelium | 0.943*** | 0.138 | 0.823*** | 0.220 |
| #02 | Animal epithelium | 0.990*** | 0.086 | 0.859*** | 0.206 |
| #03 | Animal epithelium | 0.893*** | 0.128 | 0.878*** | 0.397 |
| #04 | Animal epithelium | 0.991*** | 0.082 | 0.891*** | 0.277 |
| #05 | Meat meal | 0.887*** | 0.156 | 0.892*** | 0.437* |
| #06 | Animal epithelium | 0.991*** | 0.074 | 0.859*** | 0.214 |
| #07 | Animal epithelium | 0.992*** | 0.076 | 0.864*** | 0.212 |
| #08 | Meat meal | 0.859*** | 0.182 | 0.892*** | 0.437* |
| #09 | Animal epithelium | 0.992*** | 0.034 | 0.850*** | 0.180 |
| #10 | Meat meal | 0.974*** | 0.115 | 0.891*** | 0.277 |
| #11 | Animal epithelium | 0.995*** | 0.052 | 0.848*** | 0.173 |
| #12 | Animal epithelium | 0.872*** | 0.100 | 0.852*** | 0.385 |
| #13 | Animal epithelium | 0.755** | 0.006 | 0.568* | 0.085 |
| #14 | Animal epithelium | 0.868*** | 0.166 | 0.876*** | 0.405* |
| #15 | Alfalfa | 0.083 | 0.940*** | 0.346 | 0.537* |
| #16 | Seaweed | 0.074 | 0.633** | 0.517* | 0.908*** |
| #17 | Seaweed | 0.043 | 0.514* | 0.469* | 0.889*** |
| #18 | Seaweed | 0.086 | 0.582* | 0.548* | 0.900*** |
| #19 | Alfalfa | 0.044 | 0.775** | 0.438* | 0.676** |
| #20 | Animal epithelium | 0.991*** | 0.065 | 0.852*** | 0.200 |
| #21 | Alfalfa | 0.074 | 0.893*** | 0.426* | 0.752** |
| #22 | Meat meal | 0.985*** | 0.076 | 0.883*** | 0.269 |
| a Bovine collagen [6]; b Alfalfa hay [6]; c Beef meat meal [15]; d Laminaria sp. seaweed [16]. * Correlation is significant at 0.05 level (two sided); ** correlation is significant at 0.01 level (two sided); *** correlation is significant at 0.001 level (two sided). |
|||||
DownLoad: CSV
Figure 5. Biplot of principal component analysis (PCA) analysis of total amino acids concentration from the PHs samples investigated (PC1 and PC2 represent the first two components for explained variance).As well as for free amino acid concentration, PHs once again displayed a wide variability regardless the source material. This results confirm that the type of hydrolytic process plays a greater role than the source material in the final FAA concentration. Since the biological activity of D-amino acids is largely unknown [7], the RD of FAA is another important issue that should be taken into account. In fact, as it can be seen in Figure 6, most PHs are in the upper left-hand quadrant, where FAA < 30% and RD > 20%, meaning that the most of the samples have only a limited FAA concentration and a high RD. This indicates a prevalence of chemical hydrolysis during production, since it is during this process that RD reaches its maximum value (i.e. RD = 50%). Only one sample is present in the lower right-hand quadrant, where FAA > 30% and RD < 20%, indicating that it is not so easy to obtain PHs with high FAA concentration and a low RD. This is probably due to enzymatic hydrolysis being complex to manage and expensive. The few samples present in the other quadrants, two in the upper right corner and three in the lower left corner, show that it is possible to manage the hydrolytic process in order to obtain PHs with different characteristics.
Figure 6. Subdivision of the PHs samples investigated on the basis of free amino acids (FAA) concentration and racemization degree (RD).The histogram reported in Figure 7 shows that the observed degree of hydrolysis follows a pseudo-bimodal distribution, with a sharp peak at low DH (11–15%), and a wide pseudo-Gaussian peak ranging from 15 to 45%. Only one sample was out of this distribution (DH = 60–65%). These results indicate that the PHs present on the market are most probably obtained with two principal hydrolytic processes. The first is possibly a mild process that generates PHs with low DH. However, since DH is only a measure of the intensity of the hydrolytic process (number of peptide bonds cleaved) and not of the quality of the process, it is not possible to link DH with any particular kind of hydrolysis. The second one produces PHs with a wide DH range and an average value near to 30%. Considering this distribution of DH data (Figure 7), we can categorize PHs in three classes with low (<20%), medium (20–40%), and high (>40%) DH.
Figure 7. Histogram and proposed classification of the PHs samples investigates on the basis of the degree of hydrolysis (DH).Since PHs are heterogeneous mixture of peptides and amino acids, we could perhaps consider the polydispersity of the molecular sizes around an average value, such as the number-average molecular size (Mn). The histogram of this indicator presents a distribution similar to that observed for DH (Figure 8), with a sharp peak a low Mn (<5 kDa), and a wide band with Mn between 5 and 15 kDa. As discussed above, this indicator does not correlate with DH due to the fact that a high DH cannot reduce the Mn, because the Mn does not take into consideration the FAA. On the basis of results show in Figure 8 we can again categorize the PHs in three classes with low (<5 kDa), medium (5–15 kDa), and high (>15 kDa) Mn. Considering that peptides with low molecular size can play a role as auxin-like and/or gibberellin-like compounds and signaling molecules [4,17], it is noteworthy that 5 of the 22 PHs have a Mn lower than 5 kDa.
Figure 8. Histogram and proposed classification of the PHs samples investigates on the basis of the number-average of molecular size (Mn).Finally, even though each and every one of these indicators demonstrated to be important to characterize PHs, none of them, individually, was sufficient to describe this category of products. Not even by using more complex statistical techniques, such as the PCA (data not shown), it was not possible to discriminate among source materials and/or hydrolytic processes, probably because the number of different samples was not high enough for possible differences to stand out.
The chemical characterization of PHs demonstrated the heterogeneity of this class of products present on the market. The used indicators were able to discriminate between some aspects of PHs: (1) total amino acid composition was useful in order to recognize the source materials; (2) free amino acid concentration and racemization degree underlined the kind and the quality of the hydrolytic processes; (3) the degree of hydrolysis pointed out the intensity of the hydrolytic process; (4) the number-average of molecular size described the average size of the peptides and their polydispersity.
In conclusion, when we talk about PHs, we talk about a very inhomogeneous class of products, which can be adequately characterized only through a multi-analytical approach.
All authors declare no conflicts of interest in this paper.
| [1] |
Adhitama R, Setiawan JV, Sukweenadhi J, et al. (2023) Utilization of breadfruit (artocarpus altilis) peel waste and blood clam shell waste (anadara granosa) as raw materials for glycerol-plasticized degradable bioplastic production. IJOBB 7: 12–21. https://doi.org/10.47007/ijobb.v7i1.167 doi: 10.47007/ijobb.v7i1.167
|
| [2] |
Chia WY, Tang DY, Khoo KS, et al. (2020) Nature's fight against plastic pollution: Algae for plastic biodegradation and bioplastics production. Environ Sci Ecotech 4: 100065. https://doi.org/10.1016/j.ese.2020.100065 doi: 10.1016/j.ese.2020.100065
|
| [3] |
Tanjung DA, Jamarun N, Arief S, et al. (2022) Influence of LLDPE-g-MA on mechanical properties, degradation performance and water absorption of thermoplastic sago starch blends. Indones J Chem 22: 171. https://doi.org/10.22146/ijc.68558 doi: 10.22146/ijc.68558
|
| [4] |
Falua KJ, Pokharel A, Ghazvini AB, et al. (2022) Valorization of starch to biobased materials: A review. Polymers 14: 2215. https://doi.org/10.3390/polym14112215 doi: 10.3390/polym14112215
|
| [5] | Alebooyeh R, MohammadiNafchi A, Jokar M (2012) The effects of ZnOnanorodson the characteristics of sago starch biodegradable films. J Chem Health Risks 2: 13–16. |
| [6] | Ehara H, ToyodaY, Johnson DV (2018) Sago palm: Multiple contributions to food security and sustainable livelihoods, Springer Nature. https://doi.org/10.1007/978-981-10-5269-9 |
| [7] |
Ismail H, Zaaba NF (2014) Effect of unmodified and modified sago starch on properties of (sago starch)/silica/PVA plastic films. J Vinyl Addit Techn 20: 185–192. https://doi.org/10.1002/vnl.21344 doi: 10.1002/vnl.21344
|
| [8] | Nuriyah L, Saroja G, Rohmad J (2019) The effect of Calcium Carbonate addition to mechanical properties of bioplastic made from Cassava starch with Glycerol as plasticizer, IOP Conference Series: Materials Science and Engineering, IOP Publishing, 546: 042030. https://doi.org/10.1088/1757-899X/546/4/042030 |
| [9] |
Azevedo LCD, Rovani S, Santos JJ, et al. (2020) Biodegradable films derived from corn and potato starch and study of the effect of silicate extracted from sugarcane waste ash. ACS Appl Polym Mater 2: 2160–2169. https://doi.org/10.1021/acsapm.0c00124 doi: 10.1021/acsapm.0c00124
|
| [10] |
Post W, Kuijpers LJ, Zijlstra M, et al. (2021) Effect of mineral fillers on the mechanical properties of commercially available biodegradable polymers. Polymers 13: 394. https://doi.org/10.3390/polym13030394 doi: 10.3390/polym13030394
|
| [11] | Saleh ERM, Rakhman KA, Samad S (2022) Synthesis of biofoam from sago waste as a biodegradable food storage candidate. KnE Life Sci 162–169. |
| [12] |
Patil PD (2022) Production of Agar-Agar and sago based bioplastic. IJRASET 10: 1998–2005. https://doi.org/10.22214/ijraset.2022.42710 doi: 10.22214/ijraset.2022.42710
|
| [13] |
Kamali M, Ghahremaninezhad A (2018) A study of calcium-silicate-hydrate/polymer nanocomposites fabricated using the layer-by-layer method. Materials 11: 527. https://doi.org/10.3390/ma11040527 doi: 10.3390/ma11040527
|
| [14] |
Youness RA, El-Deen DMT, Taha MA (2023) A review on calcium silicate ceramics: Properties, limitations, and solutions for their use in biomedical applications. Silicon 15: 2493–2505. https://doi.org/10.1007/s12633-022-02207-3 doi: 10.1007/s12633-022-02207-3
|
| [15] |
Sasimowski E, Majewski Ł (2019) Effect of the intensive plasticizing zone design on the effectiveness of corotating twin-screw extrusion. Adv Polym Techn 2019: 1–11. https://doi.org/10.1155/2019/8518341 doi: 10.1155/2019/8518341
|
| [16] |
Sasimowski E, Majewski Ł, Grochowicz M (2019) Influence of the conditions of corotating twin-screw extrusion for Talc-filled polypropylene on selected properties of the extrudate. Polymers 11: 1460. https://doi.org/10.3390/polym11091460 doi: 10.3390/polym11091460
|
| [17] |
Melo PMA, Macêdo OB, Barbosa GP, et al. (2019) High-density polyethylene/mollusk shell-waste composites: Effects of particle size and coupling agent on morphology, mechanical and thermal properties. J Mater Res Technol 8: 1915–1925. https://doi.org/10.1016/j.jmrt.2019.01.008 doi: 10.1016/j.jmrt.2019.01.008
|
| [18] |
Martín LA, Silva DKG, Perilla JE, et al. (2023) Preliminary modeling study of a tape casting system for thermoplastic starch film forming. Biol Life Sci Forum 28: 4. https://doi.org/10.3390/blsf2023028004 doi: 10.3390/blsf2023028004
|
| [19] |
Du C, Jiang F, Jiang W, et al. (2020) Physicochemical and structural properties of sago starch. Int J Biol Macromol 164: 1785–1793. https://doi.org/10.1016/j.ijbiomac.2020.07.310 doi: 10.1016/j.ijbiomac.2020.07.310
|
| [20] |
Shanmathy M, Mohanta M, Thirugnanam A (2021) Development of biodegradable bioplastic films from Taro starch reinforced with bentonite. Carbohydr Polym Tech 2: 100173. https://doi.org/10.1016/j.carpta.2021.100173 doi: 10.1016/j.carpta.2021.100173
|
| [21] |
Zain AHM, Wahab MKA, Ismail H (2019) Effect of calcium carbonate incorporation on the properties of low linear density polyethylene/thermoplastic starch blends. J Eng Sci 15: 97–108. https://doi.org/10.21315/jes2019.15.2.7 doi: 10.21315/jes2019.15.2.7
|
| [22] |
Syafri E, Kasim A, Abral H, et al. (2017) Effect of precipitated calcium carbonate on physical, mechanical and thermal properties of cassava starch bioplastic composites. Int J Adv Sci Eng Inform Tech 7: 1950. https://doi.org/10.18517/ijaseit.7.5.1292 doi: 10.18517/ijaseit.7.5.1292
|
| [23] |
Boey JY, Lee CK, Tay GS (2022) Factors affecting mechanical properties of reinforced bioplastics: A review. Polymers 14: 3737. https://doi.org/10.3390/polym14183737 doi: 10.3390/polym14183737
|
| [24] | Maryam, Kasim A, Novelina, et al. (2022) Improvement on the bioplastic properties of Polyvinyl Alcohol (PVA) with the sago starch nanoparticle addition. Sylwan 1: 130. |
| [25] |
Tian J, Kong Y, Qian S, et al. (2024) Mechanically robust multifunctional starch films reinforced by surface-tailored nanofibrillated cellulose. Compos Part B-Eng 275: 111339. https://doi.org/10.1016/j.compositesb.2024.111339 doi: 10.1016/j.compositesb.2024.111339
|
| [26] |
Kong Y, Qian S, Zhang Z, et al. (2023) The impact of esterified nanofibrillated cellulose content on the properties of thermoplastic starch/PBAT biocomposite films through ball-milling. Int J Biol Macromol 253: 127462. https://doi.org/10.1016/j.ijbiomac.2023.127462 doi: 10.1016/j.ijbiomac.2023.127462
|
| [27] |
Sasimowski E, Majewski Ł, Grochowicz M (2020) Analysis of selected properties of biocomposites based on polyethylene with a natural origin filler. Materials 13: 4182. https://doi.org/10.3390/ma13184182 doi: 10.3390/ma13184182
|
| [28] | Dewi R, Sylvia N, Riza M, et al. (2024) Characterization of sago starch-based degradable plastic with agricultural waste cellulose fiber as filler. AIMS Environ 11: 304–323. https://doi.org/10.3934/environsci.2024014 |
| [29] |
Kubík Ľ, Zeman S (2014) Mechanical properties of polyethylene foils. J Cent Eur Agric 15: 138–145. https://doi.org/10.5513/JCEA01/15.1.1425 doi: 10.5513/JCEA01/15.1.1425
|
| [30] |
Tan SX, Andriyana A, Ong HC, et al. (2022) A comprehensive review on the emerging roles of nanofillers and plasticizers towards sustainable starch-based bioplastic fabrication. Polymers 14: 664. https://doi.org/10.3390/polym14040664 doi: 10.3390/polym14040664
|
| [31] |
Srihanam P, Thongsomboon W, Baimark Y (2023) Phase morphology, mechanical, and thermal properties of calcium carbonate-reinforced poly(L-lactide)-b-poly(ethylene glycol)-b-poly(L-lactide) bioplastics. Polymers 15: 301. https://doi.org/10.3390/polym15020301 doi: 10.3390/polym15020301
|
| [32] | Dewi R, Ibrahim N, Sylvia N (2017) Thermal behavior of modified thermoplastic starch (TPS) synthesized from sago (metroxylon sagu) with diphenylmethane diisocyanate and castor oil, In Proceedings of MICoMS 2017, Emerald Publishing Limited, 387–393. https://doi.org/10.1108/978-1-78756-793-1-00033 |
| [33] |
Lang H, Chen X, Tian J, et al. (2022) Effect of microcrystalline cellulose on the properties of PBAT/thermoplastic starch biodegradable film with chain extender. Polymers 14: 4517. https://doi.org/10.3390/polym14214517 doi: 10.3390/polym14214517
|
| [34] |
Gunawardene OHP, Gunathilake C, Amaraweera SM, et al. (2021) Compatibilization of starch/synthetic biodegradable polymer blends for packaging applications: A review. J Compos Sci 5: 300. https://doi.org/10.3390/jcs5110300 doi: 10.3390/jcs5110300
|
| [35] | Dewi R, Agusnar H, Wirjosentono B, et al. (2014) Synthesis of modified thermoplastic starch (TPS) using in-situ technique, Adv Environ Biol 8: 26–33. |
| [36] |
Nasir NN, Othman SA (2021) The physical and mechanical properties of Corn-based bioplastic films with different starch and glycerol content. J Phys Sci 32: 89–101. https://doi.org/10.21315/jps2021.32.3.7 doi: 10.21315/jps2021.32.3.7
|
| [37] | Abidin NDZ, Azhar NS, Sarip MN, et al. (2021) Production of bioplastic from cassava peel with different concentrations of glycerol and CaCO3 as filler, In AIP Conference Proceedings, AIP Publishing, 2332. https://doi.org/10.1063/5.0043482 |
| [38] | Pilla S (2011) Handbook of bioplastics & biocomposites engineering applications, John Wiley & Sons. https://doi.org/10.1002/9781118203699 |
| [39] |
Coppola G, Gaudio MT, Lopresto CG, et al. (2021) Bioplastic from renewable biomass: A facile solution for a greener environment. Earth Syst Environ 5: 231–251. https://doi.org/10.1007/s41748-021-00208-7 doi: 10.1007/s41748-021-00208-7
|
| [40] |
Elgharbawy AS, El Demerdash AGM, Sadik WA, et al. (2024) Synthetic degradable Polyvinyl Alcohol polymer and its blends with starch and cellulose—A comprehensive overview. Polymers 16: 1356. https://doi.org/10.3390/polym16101356 doi: 10.3390/polym16101356
|
| 1. | Efficient Chemo-Enzymatic Transformation of Animal Biomass Waste for Eco-Friendly Leather Production, 2019, 24, 1420-3049, 2979, 10.3390/molecules24162979 | |
| 2. | Beppe Benedetto Consentino, Giuseppe Virga, Gaetano Giuseppe La Placa, Leo Sabatino, Youssef Rouphael, Georgia Ntatsi, Giovanni Iapichino, Salvatore La Bella, Rosario Paolo Mauro, Fabio D’Anna, Teresa Tuttolomondo, Claudio De Pasquale, Celery (Apium graveolens L.) Performances as Subjected to Different Sources of Protein Hydrolysates, 2020, 9, 2223-7747, 1633, 10.3390/plants9121633 | |
| 3. | Zheng Wang, Rui Yang, Ying Liang, Shulan Zhang, Zejin Zhang, Chaohua Sun, Jing Li, Zhiyong Qi, Qichang Yang, Comparing Efficacy of Different Biostimulants for Hydroponically Grown Lettuce (Lactuca sativa L.), 2022, 12, 2073-4395, 786, 10.3390/agronomy12040786 | |
| 4. | Paola Critelli, Giovanna Pesante, Stefania Lupinelli, Michele Modesti, Silvia Zanatta, Federico Battista, David Bolzonella, Nicola Frison, Production and characterisation of PHAs by pure culture using protein hydrolysates as sole carbon source, 2022, 28, 23521864, 102919, 10.1016/j.eti.2022.102919 | |
| 5. | Francesco Cristofano, Christophe El-Nakhel, Youssef Rouphael, Biostimulant Substances for Sustainable Agriculture: Origin, Operating Mechanisms and Effects on Cucurbits, Leafy Greens, and Nightshade Vegetables Species, 2021, 11, 2218-273X, 1103, 10.3390/biom11081103 | |
| 6. | Marcin Sojka, Agnieszka Saeid, 2022, 9780128170366, 279, 10.1016/B978-0-12-817036-6.00001-7 | |
| 7. | Chiara Pituello, Stefano Ambrosini, Zeno Varanini, Tiziana Pandolfini, Anita Zamboni, Chiara Povolo, Fabio Agnolon, Eliana Franco, Manuela Cinzia Candido, Massimo Neresini, 2022, Chapter 5, 978-981-16-7079-4, 107, 10.1007/978-981-16-7080-0_5 | |
| 8. | Chiara Bastianelli, Giovanna Pesante, Stefano Ambrosini, Anita Zamboni, Nicola Frison, Upcycling of PHA-producing bacteria for biostimulants production and polyhydroxyalkanoates recovery, 2023, 888, 00489697, 164238, 10.1016/j.scitotenv.2023.164238 |
Figures(4) / Tables(1)
Rozanna Dewi, Novi Sylvia, Zulnazri Zulnazri, Medyan Riza, Januar Parlaungan Siregar, Tezara Cionita. Use calcium silicate filler to improve the properties of sago starch based degradable plastic[J]. AIMS Environmental Science, 2025, 12(1): 1-15. doi: 10.3934/environsci.2025001
| Parameters | pH | EC | Water | Ashes | TOC | TN | TAA | FAA | RD | DH |
| pH | 1 | |||||||||
| EC | −0.12 | 1 | ||||||||
| Water | −0.01 | −0.51* | 1 | |||||||
| Ashes | −0.10 | 0.83*** | −0.42 | 1 | ||||||
| TOC | 0.06 | 0.37 | −0.94*** | 0.22 | 1 | |||||
| TN | 0.11 | 0.14 | −0.81*** | −0.07 | 0.93*** | 1 | ||||
| TAA | 0.07 | 0.19 | −0.87*** | 0.07 | 0.93*** | 0.91*** | 1 | |||
| FAA | 0.18 | −0.20 | −0.32 | −0.29 | 0.47* | 0.52* | 0.33 | 1 | ||
| RD | −0.39 | 0.30 | −0.22 | 0.37 | 0.11 | 0.07 | 0.04 | −0.05 | 1 | |
| DH | 0.30 | −0.26 | 0.48* | −0.16 | −0.53* | −0.53* | −0.48* | −0.01 | 0.04 | 1 |
| Mn | 0.31 | −0.03 | 0.18 | −0.21 | −0.34 | 0.42* | −0.30 | 0.42* | −0.28 | −0.19 |
| TOC = total organic carbon, TN = total nitrogen, TAA = total amino acids, FAA = free amino acids, RD = racemization degree, DH = degree of hydrolysis, Mn = number-average molecular size. * Correlation is significant at 0.05 level (two sided); ** correlation is significant at 0.01 level (two sided); *** correlation is significant at 0.001 level (two sided). |
||||||||||
DownLoad: CSV| Samples | Declared source | Natural compounds | |||
| Collagena | Alfalfa hayb | Meat mealc | Seaweedd | ||
| #01 | Animal epithelium | 0.943*** | 0.138 | 0.823*** | 0.220 |
| #02 | Animal epithelium | 0.990*** | 0.086 | 0.859*** | 0.206 |
| #03 | Animal epithelium | 0.893*** | 0.128 | 0.878*** | 0.397 |
| #04 | Animal epithelium | 0.991*** | 0.082 | 0.891*** | 0.277 |
| #05 | Meat meal | 0.887*** | 0.156 | 0.892*** | 0.437* |
| #06 | Animal epithelium | 0.991*** | 0.074 | 0.859*** | 0.214 |
| #07 | Animal epithelium | 0.992*** | 0.076 | 0.864*** | 0.212 |
| #08 | Meat meal | 0.859*** | 0.182 | 0.892*** | 0.437* |
| #09 | Animal epithelium | 0.992*** | 0.034 | 0.850*** | 0.180 |
| #10 | Meat meal | 0.974*** | 0.115 | 0.891*** | 0.277 |
| #11 | Animal epithelium | 0.995*** | 0.052 | 0.848*** | 0.173 |
| #12 | Animal epithelium | 0.872*** | 0.100 | 0.852*** | 0.385 |
| #13 | Animal epithelium | 0.755** | 0.006 | 0.568* | 0.085 |
| #14 | Animal epithelium | 0.868*** | 0.166 | 0.876*** | 0.405* |
| #15 | Alfalfa | 0.083 | 0.940*** | 0.346 | 0.537* |
| #16 | Seaweed | 0.074 | 0.633** | 0.517* | 0.908*** |
| #17 | Seaweed | 0.043 | 0.514* | 0.469* | 0.889*** |
| #18 | Seaweed | 0.086 | 0.582* | 0.548* | 0.900*** |
| #19 | Alfalfa | 0.044 | 0.775** | 0.438* | 0.676** |
| #20 | Animal epithelium | 0.991*** | 0.065 | 0.852*** | 0.200 |
| #21 | Alfalfa | 0.074 | 0.893*** | 0.426* | 0.752** |
| #22 | Meat meal | 0.985*** | 0.076 | 0.883*** | 0.269 |
| a Bovine collagen [6]; b Alfalfa hay [6]; c Beef meat meal [15]; d Laminaria sp. seaweed [16]. * Correlation is significant at 0.05 level (two sided); ** correlation is significant at 0.01 level (two sided); *** correlation is significant at 0.001 level (two sided). |
|||||
DownLoad: CSV| Parameters | pH | EC | Water | Ashes | TOC | TN | TAA | FAA | RD | DH |
| pH | 1 | |||||||||
| EC | −0.12 | 1 | ||||||||
| Water | −0.01 | −0.51* | 1 | |||||||
| Ashes | −0.10 | 0.83*** | −0.42 | 1 | ||||||
| TOC | 0.06 | 0.37 | −0.94*** | 0.22 | 1 | |||||
| TN | 0.11 | 0.14 | −0.81*** | −0.07 | 0.93*** | 1 | ||||
| TAA | 0.07 | 0.19 | −0.87*** | 0.07 | 0.93*** | 0.91*** | 1 | |||
| FAA | 0.18 | −0.20 | −0.32 | −0.29 | 0.47* | 0.52* | 0.33 | 1 | ||
| RD | −0.39 | 0.30 | −0.22 | 0.37 | 0.11 | 0.07 | 0.04 | −0.05 | 1 | |
| DH | 0.30 | −0.26 | 0.48* | −0.16 | −0.53* | −0.53* | −0.48* | −0.01 | 0.04 | 1 |
| Mn | 0.31 | −0.03 | 0.18 | −0.21 | −0.34 | 0.42* | −0.30 | 0.42* | −0.28 | −0.19 |
| TOC = total organic carbon, TN = total nitrogen, TAA = total amino acids, FAA = free amino acids, RD = racemization degree, DH = degree of hydrolysis, Mn = number-average molecular size. * Correlation is significant at 0.05 level (two sided); ** correlation is significant at 0.01 level (two sided); *** correlation is significant at 0.001 level (two sided). |
||||||||||
| Samples | Declared source | Natural compounds | |||
| Collagena | Alfalfa hayb | Meat mealc | Seaweedd | ||
| #01 | Animal epithelium | 0.943*** | 0.138 | 0.823*** | 0.220 |
| #02 | Animal epithelium | 0.990*** | 0.086 | 0.859*** | 0.206 |
| #03 | Animal epithelium | 0.893*** | 0.128 | 0.878*** | 0.397 |
| #04 | Animal epithelium | 0.991*** | 0.082 | 0.891*** | 0.277 |
| #05 | Meat meal | 0.887*** | 0.156 | 0.892*** | 0.437* |
| #06 | Animal epithelium | 0.991*** | 0.074 | 0.859*** | 0.214 |
| #07 | Animal epithelium | 0.992*** | 0.076 | 0.864*** | 0.212 |
| #08 | Meat meal | 0.859*** | 0.182 | 0.892*** | 0.437* |
| #09 | Animal epithelium | 0.992*** | 0.034 | 0.850*** | 0.180 |
| #10 | Meat meal | 0.974*** | 0.115 | 0.891*** | 0.277 |
| #11 | Animal epithelium | 0.995*** | 0.052 | 0.848*** | 0.173 |
| #12 | Animal epithelium | 0.872*** | 0.100 | 0.852*** | 0.385 |
| #13 | Animal epithelium | 0.755** | 0.006 | 0.568* | 0.085 |
| #14 | Animal epithelium | 0.868*** | 0.166 | 0.876*** | 0.405* |
| #15 | Alfalfa | 0.083 | 0.940*** | 0.346 | 0.537* |
| #16 | Seaweed | 0.074 | 0.633** | 0.517* | 0.908*** |
| #17 | Seaweed | 0.043 | 0.514* | 0.469* | 0.889*** |
| #18 | Seaweed | 0.086 | 0.582* | 0.548* | 0.900*** |
| #19 | Alfalfa | 0.044 | 0.775** | 0.438* | 0.676** |
| #20 | Animal epithelium | 0.991*** | 0.065 | 0.852*** | 0.200 |
| #21 | Alfalfa | 0.074 | 0.893*** | 0.426* | 0.752** |
| #22 | Meat meal | 0.985*** | 0.076 | 0.883*** | 0.269 |
| a Bovine collagen [6]; b Alfalfa hay [6]; c Beef meat meal [15]; d Laminaria sp. seaweed [16]. * Correlation is significant at 0.05 level (two sided); ** correlation is significant at 0.01 level (two sided); *** correlation is significant at 0.001 level (two sided). |
|||||
