Yellow alkaline noodles (YAN) prepared by partial substitution of wheat flour with soy protein isolate and treated with microbial transglutaminase (MTG) and ribose were investigated during cooking. Cooking caused an increase in lightness but a decrease in redness and yellowness, pH, tensile strength and elasticity values of noodles. The extents of these changes were influenced by formulation and cross-linking treatments. The pH and lightness for YAN-ribose were lowest but the yellowness and redness were the highest whilst the tensile strength and elasticity values remained moderate. For YAN-MTG, the color and pH values were moderate, but tensile strength and elasticity values were the highest. YAN prepared with both cross-linking agents had physical values between YAN-ribose and YAN-MTG. Although certain sensory parameters showed differences in score, the overall acceptability of all 10-min-cooked YAN was similar. It is possible to employ cross-linking agents to improve physical properties of cooked YAN.
Response surface methodology (RSM) was carried out to study the effect of temperature, pH, and heating time as input variables on the yield and degree of esterification (DE) as the output (responses). The results showed that yield and DE of extracted pectin ranged from 2.27% to 9.35% (w/w, based on dry weight of durian rind) and 47.66% to 68.6%, respectively. The results also showed that a 2nd-order model adequately fitted the experimental data for the yield and DE. Optimum condition for maximum yield and DE was achieved at 85 degrees C, a time of either 4 or 1 h, and a pH of 2 or 2.5.
The addition of ribose to bovine or porcine gelatine solutions followed by heating at 95 °C yielded brown solutions with different pH, colour (CIE L(*) and b(*)) and absorbance (A(420*) values. These differences were used for gelatine powder identification, differentiation and quality control. Differentiation analysis of the Maillard reaction parameters was conducted using cluster analysis (CA) and confidence intervals (CI). The potential use of the method as a quality control procedure was evaluated by using statistical process control (SPC). CA revealed that the two types of gelatine could be classified into two different groups. CI (95% confidence) revealed that the absorbance and colour values could be used as indicators for differentiation between the two types of gelatine because the intervals between the Maillard reaction parameters of the samples were far apart. The methodology demonstrated good reproducibility because it behaved predictably based on the X¯-S charts generated from the SPC charts.
Candlenut oil was extracted using supercritical CO(2) (SC-CO(2)) with an optimization of parameters, by the response surface methodology. The ground candlenut samples were treated in 2 different ways, that is, dried in either a heat oven (sample moisture content of 2.91%) or dried in a vacuum oven (sample moisture content of 1.98%), before extraction. An untreated sample (moisture content of 4.87%) was used as a control. The maximum percentage of oil was extracted from the heat-oven-dried sample (77.27%), followed by the vacuum-oven-dried sample (74.32%), and the untreated sample (70.12%). At an SC-CO(2) pressure of 48.26 Mpa and 60 min of extraction time, the optimal temperatures for extraction were found to be 76.4 °C, 73.9 °C, and 70.6 °C for the untreated, heat-oven-dried, and vacuum-oven-dried samples, respectively. The heat-oven-dried sample contains the highest percentage of linoleic acid, followed by the untreated and vacuum-oven-dried samples. The antiradical activity of candlenut oil corresponded to an IC(50) value of 30.37 mg/mL.
Banana peel flour (BPF) prepared from green or ripe Cavendish and Dream banana fruits were assessed for their total starch (TS), digestible starch (DS), resistant starch (RS), total dietary fibre (TDF), soluble dietary fibre (SDF) and insoluble dietary fibre (IDF). Principal component analysis (PCA) identified that only 1 component was responsible for 93.74% of the total variance in the starch and dietary fibre components that differentiated ripe and green banana flours. Cluster analysis (CA) applied to similar data obtained two statistically significant clusters (green and ripe bananas) to indicate difference in behaviours according to the stages of ripeness based on starch and dietary fibre components. We concluded that the starch and dietary fibre components could be used to discriminate between flours prepared from peels obtained from fruits of different ripeness. The results were also suggestive of the potential of green and ripe BPF as functional ingredients in food.
Clinical solid waste (CSW) poses a challenge to health care facilities because of the presence of pathogenic microorganisms, leading to concerns in the effective sterilization of the CSW for safe handling and elimination of infectious disease transmission. In the present study, supercritical carbon dioxide (SC-CO2) was applied to inactivate gram-positive Staphylococcus aureus, Enterococcus faecalis, Bacillus subtilis, and gram-negative Escherichia coli in CSW. The effects of SC-CO2 sterilization parameters such as pressure, temperature, and time were investigated and optimized by response surface methodology (RSM). Results showed that the data were adequately fitted into the second-order polynomial model. The linear quadratic terms and interaction between pressure and temperature had significant effects on the inactivation of S. aureus, E. coli, E. faecalis, and B. subtilis in CSW. Optimum conditions for the complete inactivation of bacteria within the experimental range of the studied variables were 20 MPa, 60 °C, and 60 min. The SC-CO2-treated bacterial cells, observed under a scanning electron microscope, showed morphological changes, including cell breakage and dislodged cell walls, which could have caused the inactivation. This espouses the inference that SC-CO2 exerts strong inactivating effects on the bacteria present in CSW, and has the potential to be used in CSW management for the safe handling and recycling-reuse of CSW materials.
The performance of pectin in turbidity reduction and the optimum condition were determined using Response Surface Methodology (RSM). The effect of pH, cation's concentration, and pectin's dosage on flocculating activity and turbidity reduction was investigated at three levels and optimized by using Box-Behnken Design (BBD). Coagulation and flocculation process were assessed with a standard jar test procedure with rapid and slow mixing of a kaolin suspension (aluminium silicate), at 150 rpm and 30 rpm, respectively, in which a cation e.g. Al(3+), acts as coagulant, and pectin acts as the flocculant. In this research, all factors exhibited significant effect on flocculating activity and turbidity reduction. The experimental data and model predictions well agreed. From the 3D response surface graph, maximum flocculating activity and turbidity reduction are in the region of pH greater than 3, cation concentration greater than 0.5 mM, and pectin dosage greater than 20 mg/L, using synthetic turbid wastewater within the range. The flocculating activity for pectin and turbidity reduction in wastewater is at 99%.
Polyacrylamide (PAM), a commonly used organic synthetic flocculant, is known to have high reduction in turbidity treatment. However, PAM is not readily degradable. In this paper, pectin as a biopolymeric flocculant is used. The objectives are (i) to determine the characteristics of both flocculants (ii) to optimize the treatment processes of both flocculants in synthetic turbid waste water. The results obtained indicated that pectin has a lower average molecular weight at 1.63 x 10(5) and PAM at 6.00 x 10(7). However, the thermal degradation results showed that the onset temperature for pectin is at 165.58 degrees C, while the highest onset temperature obtained for PAM is at 235.39 degrees C. The optimum treatment conditions for the biopolymeric flocculant for flocculating activity was at pH 3, cation concentration at 0.55 mM, and pectin concentration at 3 mg/L. In contrast, PAM was at pH 4, cation concentration >0.05 mM and PAM concentration between 13 and 30 mg/L.
In view of green developments in water treatment, plant-based flocculants have become the focus due to their safety, degradation and renewable properties. In addition, cost and energy-saving processes are preferable. In this study, malva nut gum (MNG), a new plant-based flocculant, and its composite with Fe in water treatment using single mode mixing are demonstrated. The result presents a simplified extraction of the MNG process. MNG has a high molecular weight of 2.3 × 10⁵ kDa and a high negative charge of -58.7 mV. From the results, it is a strong anionic flocculant. Moreover, it is observed to have a branch-like surface structure. Therefore, it conforms to the surface of particles well and exhibits good performance in water treatment. In water treatment, the Fe-MNG composite treats water at pH 3.01 and requires a low concentration of Fe and MNG of 0.08 and 0.06 mg/L, respectively, when added to the system. It is concluded that for a single-stage flocculation process, physico-chemical properties such as molecular weight, charge of polymer, surface morphology, pH, concentration of cation and concentration of biopolymeric flocculant affect the flocculating performance.
D-optimal design was employed to optimize the mixture of cross-linking agents formulation: microbial transglutaminase (MTGase) and ribose, and the processing parameters (i.e. incubation and heating time) in the mixture in order to obtain combined-cross-linked bovine serum albumin gels that have high gel strength, pH close to neutral and yet medium in browning. Analysis of variance (ANOVA) showed that the contribution of quadratic term to the model over the linear was significant for pH and L* value, whereas linear model was significant for gel strength. Optimization study using response surface methodology (RSM) was performed to the mixture components and process variables and the optimum conditions obtained were: MTGase of 1.34-1.43 g/100 mL, ribose of 1.07-1.16 g/100 mL, incubation time of 5 h at 40 degrees C and heating time of 3 h at 90 degrees C.
The objectives of this work were to select suitable design parameters and optimize the operating parameters of a soybean oil-based bulk liquid membrane (BLM) for Cu(II) removal and recovery from aqueous solutions. The soybean oil-based BLM consists of an aqueous feed phase (Cu(II) source), an organic membrane phase (soybean oil (diluent), di-2-ethylhexylphosphoric acid (D2EHPA) (carrier) and tributylphosphate (phase modifier)) and an aqueous stripping phase (sulfuric acid solution (H(2)SO(4))). Effects of design parameters (stirring condition and stripping/membrane to feed/membrane interface area ratio) of soybean oil-based BLM on the Cu(II) removal and recovery from aqueous solutions were investigated and the suitable parameters were selected for further studies. Optimization of the operating parameters (D2EHPA concentration, H(2)SO(4) concentration, stirring speed, temperature and operating time) of soybean oil-based BLM for maximum percentage (%) recovery of Cu(II) was then conducted using Response Surface Methodology and the optimum parameters were determined. A regression model for % recovery was developed and its adequacy was evaluated. The experimental % recovery obtained under the optimum operating conditions was compared with the predicted one and they were found to agree satisfactorily with each other.
The ability of aluminum coagulant extracted from red earth to treat Terasil Red R (disperse) and Cibacron Red R (reactive) synthetic dye wastewater was studied. The effects of extractant concentration, soil-to-volume of extractant ratio, and the types of extracting agents (NaOH vs. KCl) on the concentration of aluminum extracted were also investigated. In addition, the efficiency of extracted aluminum was compared with aluminum sulfate, in terms of its capability to reduce the chemical oxygen demand (COD) and to remove synthetic color. Factorial design was applied to determine the effect of selected factors on the amount of aluminum extracted from red earth (i.e., pH, dose of coagulant, type of coagulant on COD reduction, and color removal). It was found that only selected factors exhibited a significant effect on the amount of aluminum extracted from red earth. It was also determined that all factors and their interactions exhibited a significant effect on COD reduction and color removal when applying the extracted aluminum in a standard coagulation process. The results were also compared to aluminum sulfate. Furthermore, NaOH was found to be a better extractant of aluminum in red earth than KCl. Therefore, the best extracting conditions for both extractants were as follows: 2 M NaOH and in a 1:5 (soil/volume of extractant) ratio; 1 M KCl and 1:5 ratio. In treating synthetic dye wastewater, the extracted coagulant showed comparable treatment efficiency to the commercial coagulant. The extracted coagulant was able to reduce the COD of the dispersed dye by 85% and to remove 99% of the color of the dispersed dye, whereas the commercial coagulant reduced 90% of the COD and removed 99% of the color of the dispersed dye. Additionally, the extracted coagulant was able to reduce the COD of the reactive dye by 73% and to remove 99% of the color of the reactive dye. However, the commercial coagulant managed to reduce the COD of the reactive dye by 94% and to remove 96% of the color for the reactive dye.
Statistical analysis of heavy metal concentrations in sediment was studied to understand the interrelationship between different parameters and also to identify probable source component in order to explain the pollution status of selected estuaries. Concentrations of heavy metals (Cu, Zn, Cd, Fe, Pb, Cr, Hg and Mn) were analyzed in sediments from Juru and Jejawi Estuaries in Malaysia with ten sampling points of each estuary. The results of multivariate statistical techniques showed that the two regions have different characteristics in terms of heavy metals selected and indicates that each region receives pollution from different sources. The results also showed that Fe, Mn, Cd, Hg, and Cu are responsible for large spatial variations explaining 51.15% of the total variance, whilst Zn and Pb explain only 18.93 of the total variance. This study illustrates the usefulness of multivariate statistical techniques for evaluation and interpretation of large complex data sets to get better information about the heavy metal concentrations and design of monitoring network.
Major (sodium, potassium, calcium, magnesium) and minor elements (iron, copper, zinc, manganese) and one heavy metal (lead) of Cavendish banana flour and Dream banana flour were determined, and data were analyzed using multivariate statistical techniques of factor analysis and discriminant analysis. Factor analysis yielded four factors explaining more than 81% of the total variance: the first factor explained 28.73%, comprising magnesium, sodium, and iron; the second factor explained 21.47%, comprising only manganese and copper; the third factor explained 15.66%, comprising zinc and lead; while the fourth factor explained 15.50%, comprising potassium. Discriminant analysis showed that magnesium and sodium exhibited a strong contribution in discriminating the two types of banana flour, affording 100% correct assignation. This study presents the usefulness of multivariate statistical techniques for analysis and interpretation of complex mineral content data from banana flour of different varieties.
Multivariate statistical techniques such as multivariate analysis of variance (MANOVA) and discriminant analysis (DA) were applied for analyzing the data obtained from two rivers in the Penang State of Malaysia for the concentration of heavy metal ions (As, Cr, Cd, Zn, Cu, Pb, and Hg) using a flame atomic absorption spectrometry (F-AAS) for Cr, Cd, Zn, Cu, Pb, As and cold vapor atomic absorption spectrometry (CV-AAS) for Hg. The two locations of interest with 20 sampling points of each location were Kuala Juru (Juru River) and Bukit Tambun (Jejawi River). MANOVA showed a strong significant difference between the two rivers in terms of heavy metal concentrations in water samples. DA gave the best result to identify the relative contribution for all parameters in discriminating (distinguishing) the two rivers. It provided an important data reduction as it used four parameters (Zn, Pb, Cd and Cr) affording 100% correct assignations. Results indicated that the two rivers were different in terms of heavy metals concentrations in water, and the major difference was due to the contribution of Zn. A negative correlation was found between discriminate functions (DF) and Cr and As, whereas positive correlation was exhibited with other heavy metals. Therefore, DA allowed a reduction in the dimensionality of the data set, delineating a few indicator parameters responsible for large variations in heavy metal concentrations. Correlation matrix between the parameters exhibited a strong evidence of mutual dependence of these metals.