Presently, the quality assurance of agarwood oil is performed by sensory panels which has significant drawbacks in terms of objectivity and repeatability. In this paper, it is shown how an electronic nose (e-nose) may be successfully utilised for the classification of agarwood oil. Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA), were used to classify different types of oil. The HCA produced a dendrogram showing the separation of e-nose data into three different groups of oils. The PCA scatter plot revealed a distinct separation between the three groups. An Artificial Neural Network (ANN) was used for a better prediction of unknown samples.
Oil palm shell, a waste from palm oil industry, was cleaned and utilized as adsorbent. Its particle size distribution gave the uniformity coefficient of approximately two indicating that it can be used as filter bed media for continuous operation without resting. Its measured pH(pzc) of 4.1 is below the common pH of constructed wetland water body suggesting positive adsorption for heavy metal. The effect of various parameters on its adsorption was studied via batch experiments. The adsorption of Cu(II) and Pb(II) ions by oil palm shell showed a slightly better fit with the Freundlich compared to Langmuir. Its monolayer adsorption capacities were found to be 1.756 and 3.390mg/g for Cu(II) and Pb(II), respectively. High correlation coefficient of over 0.99 given by the pseudo-second-order model suggests that the rate limiting factor may be chemisorption. These findings suggest its potential application as constructed wetland media for the removal of heavy metal.
A biosensor for measuring the antidiabetic potential of medicinal plants was developed by covalent immobilization of α-glucosidase (AG) enzyme onto amine-functionalized multi-walled carbon nanotubes (MWCNTs-NH2). The immobilized enzyme was entrapped in freeze-thawed polyvinyl alcohol (PVA) together with p-nitrophenyl-α-D-glucopyranoside (PNPG) on the screen-printed carbon electrode at low pH to prevent the premature reaction between PNPG and AG enzyme. The enzymatic reaction within the biosensor is inhibited by bioactive compounds in the medicinal plant extracts. The capability of medicinal plants to inhibit the AG enzyme on the electrode correlates to the potential of the medicinal plants to inhibit the production of glucose from the carbohydrate in the human body. Thus, the inhibition indicates the antidiabetic potential of the medicinal plants. The performance of the biosensor was evaluated to measure the antidiabetic potential of three medicinal plants such as Tebengau (Ehretis laevis), Cemumar (Micromelum pubescens), and Kedondong (Spondias dulcis) and acarbose (commercial antidiabetic drug) via cyclic voltammetry, amperometry, and spectrophotometry. The cyclic voltammetry (CV) response for the inhibition of the AG enzyme activity by Tebengau plant extracts showed a linear relation in the range from 0.423-8.29 μA, and the inhibition detection limit was 0.253 μA. The biosensor exhibited good sensitivity (0.422 μA/mg Tebengau plant extracts) and rapid response (22 s). The biosensor retains approximately 82.16 % of its initial activity even after 30 days of storage at 4 °C.
Many reports have revealed that the abundance of microalgae in shrimp ponds vary with changes in environmental factors such as light, temperature, pH, salinity and nutrient level throughout a shrimp culture period. In this study, shrimp cultivation period was divided into three stages (initial = week 0-5, mid = week 6-10 and final = week 11-15). Physical and chemical parameters throughout the cultivation period were studied and species composition of microalgae was monitored. Physical parameters were found to fluctuate widely with light intensity ranging between 182.23-1278 μmol photon m(-2)s(-1), temperature between 29.56°C -31.59°C, dissolved oxygen (DO) between 4.56-8.21 mg/l, pH between 7.65-8.49 and salinity between 20‰-30‰. Ammonium (NH4 (+)-N), nitrite (NO2 (-)-N), nitrate (NO3 (-)-N), and orthophosphate (PO4 (3-)-P) concentrations in the pond at all cultivation stages ranged from 0.017 to 0.38 mg/l, 0.24 to 2.12 mg/l, 0.06 to 0.98 mg/l and 0.16 to 1.93 mg/l respectively. Statistical test (ANOVA) showed that there were no significant difference (p<0.05) in nutrients concentrations among the cultivation stages. All nutrients concentrations however were still in the tolerable level and safe for shrimp culture. The chlorophyll a contents were found to range from 5.03±2.17 to 32.61±0.35 μg/l throughout the cultivation period. A total of 19 microalgae species were found in the shrimp pond, with diatoms contributing up to 72% of the species followed by Chlorophyta (11%) and Cyanophyta (11%). However, weekly species abundance varied through the study period. At the initial stage, when there were no shrimps in the pond, Anabaena spp. and Oscillatoria spp. (Cyanophyta) were the dominant species, followed by Chlorella sp. and Dunaliella sp. (Chlorophyta). When shrimps were introduced into the pond, Amphora sp., Navicula sp. Gyrosigma sp. and Nitzschia sp. (diatoms) started to exist. At the middle and towards the final stage of the shrimp culture period diatoms were the dominant species. The Chlorophyta (Chlorella sp.) domination took place only twice, which was at week 2 and 13. The absence of some of the coastal water microalgae species in the shrimp pond was most likely due to the fact that they could not tolerate the physicochemical factors of harsh environment. In this study, Cylindrotheca closterium was regarded as the most tolerant species among the microalgae due to its ability to exist for 6 weeks out of the 15 weeks of cultivation.
The discharge of industrial effluents, such as phenol, into aquatic and soil environments is a global problem due to its serious negative impacts on human health and aquatic ecosystems. In this study, the ability of polyvinylpolypyrrolidone (PVPP) to remove phenol from an aqueous medium was investigated. The results showed that a significant proportion of phenol (up to 74.91%) was removed using PVPP at pH 6.5. Isotherm adsorption experiments of phenol on PVPP indicated that the best-fit adsorption was obtained using Langmuir models. The response peaks of the hydroxyl groups of phenol (OH) and the carboxyl groups (i.e., C=O) of PVPP were altered, indicating the formation of a hydrogen bond between the PVPP and phenol during phenol removal, as characterized using 1D and 2D IR spectroscopy. The resulting complexes were successfully characterized based on their thermodynamic properties, Mulliken charge, and electronic transition using the DFT approach. To clarify the types of interactions taking place in the complex systems, quantum theory of atoms in molecules (QTAIM) analysis, reduced density gradient noncovalent interaction (RDG-NCI) approach, and conductor-like screening model for real solvents (COSMO-RS) approach were also successfully calculated. The results showed that the interactions that occurred in the process of removing phenol by PVPP were through hydrogen bonding (based on RDG-NCI and COSMO-RS), which was identified as an intermediate type (∇2ρ(r) > 0 and H < 0, QTAIM). To gain a deeper understanding of how these interactions occurred, further characterization was performed based on adsorption mechanisms using molecular electrostatic potential, global reactivity, and local reactivity descriptors. The results showed that during hydrogen bond formation, PVPP acts as a nucleophile, whereas phenol acts as an electrophile and the O9 atom (i.e., donor electron) reacts with the H22 atom (i.e., acceptor electron).
Tyrosinase is a key enzyme in enzymatic browning, causing quality losses in food through the oxidation process. Thus, the discovery of an effective and natural tyrosinase inhibitor via green technology is of great interest to the global food market due to food security and climate change issues. In this study, Syzygium aqueum (S. aqueum) leaves, which are known to be rich in phenolic compounds (PC), were chosen as a natural source of tyrosinase inhibitor, and the effect of the sustainable, supercritical fluid extraction (SFE) process was evaluated. Response surface methodology-assisted supercritical fluid extraction (RSM-assisted SFE) was utilized to optimize the PCs extracted from S. aqueum. The highest amount of PC was obtained at the optimum conditions (55 °C, 3350 psi, and 70 min). The IC50 (661.815 μg/mL) of the optimized extract was evaluated, and its antioxidant activity (96.8 %) was determined. Gas chromatography-mass spectrometry (GC-MS) results reveal that 2',6'-dihydroxy-4'-methoxychalcone (2,6-D4MC) (82.65 %) was the major PC in S. aqueum. Chemometric analysis indicated that 2,6-D4MC has similar chemical properties to the tyrosinase inhibitor control (kaempferol). The toxicity and physiochemical properties of the novel 2,6-D4MC from S. aqueum revealed that the 2,6-D4MC is safer than kaempferol as predicted via absorption, distribution, metabolism, and excretion (ADME) evaluation. Enzyme kinetic analysis shows that the type of inhibition of the optimized extract is non-competitive inhibition with Km = 1.55 mM and Vmax = 0.017 μM/s. High-performance liquid chromatography (HPLC) analysis shows the effectiveness of S. aqueum as a tyrosinase inhibitor. The mechanistic insight of the tyrosinase inhibition using 2,6-D4MC was successfully calculated using density functional theory (DFT) and molecular docking approaches. The findings could have a significant impact on food security development by devising a sustainable and effective tyrosinase inhibitor from waste by-products that is aligned with the United Nation's SDG 2, zero hunger.
Enzymatic browning is of concern as it can affect food safety and quality. In this study, an effective and safe tyrosinase inhibitor and anti-browning agent, methyl 4-pyridyl ketone thiosemicarbazone (4-PT), was synthesised and characterised using Fourier-transform infrared (FTIR) spectroscopy, CHNS elemental analysis, and proton (1H) and carbon-13 (13C) nuclear magnetic resonance (NMR) spectroscopy. The vibrational frequencies of 4-PT were studied theoretically using vibrational energy distribution analysis (VEDA). Density functional theory (DFT) was applied to elucidate its chemical properties, including the Mulliken atomic charges, molecular electrostatic potential (MEP), quantum theory of atoms in molecules (QTAIM) and reduced density gradient non-covalent interactions (RDG-NCIs). Moreover, 4-PT was compared with kojic acid in terms of its effectiveness as a tyrosinase inhibitor and anti-browning agent. The toxicity and physicochemical properties of 4-PT were predicted via ADME evaluation, which proved that 4-PT is safer than kojic acid. Experimentally, 4-PT (IC50 = 5.82 μM, browning index (10 days) = 0.292 ± 0.002) was proven to be an effective tyrosinase inhibitor and anti-browning agent compared to kojic acid (IC50 = 128.17 μM, browning index (10 days) = 0.332 ± 0.002). Furthermore, kinetic analyses indicated that the type of tyrosinase inhibition is a mixed inhibition, with Km and Vmax values of 0.85 mM and 2.78 E-09 μM/s, respectively. Finally, the mechanism of 4-PT for tyrosinase inhibition was proven by 1D, second derivative and 2D IR spectroscopy, molecular docking and molecular dynamic simulation approaches.
The Gram-negative pathogenic spirochetal bacteria Leptospira spp. cause leptospirosis in humans and livestock animals. Leptospira kmetyi strain LS 001/16 was isolated from a soil sample associated with a leptospirosis patient in Kelantan, which is among the states in Malaysia with a high reported number of disease cases. Here, we report the complete genome sequence of Leptospira kmetyi strain LS 001/16.
Virtual reality (VR) is one of the state-of-the-art technological applications in the healthcare domain. One major aspect of VR applications in this domain includes virtual reality-based training (VRT), which simplifies the complicated visualization process of diagnosis, treatment, disease analysis, and prevention. However, not much is known on how well the domain knowledge is shared and considered in the development of VRT applications. A pertinent mechanism, known as ontology, has acted as an enabler toward making the domain knowledge more explicit. Hence, this paper presents an overview to reveal the basic concepts and explores the extent to which ontologies are used in VRT development for medical education and training in the healthcare domain. From this overview, a base of knowledge for VRT development is proposed to initiate a comprehensive strategy in creating an effective ontology design for VRT applications in the healthcare domain.
Abstract: Meat tenderness is the most important criterion in food quality because it strongly influences the consumer's satisfaction. Tenderness generally depends on connective tissue and sarcomere length of muscle. One of the effective methods for meat tenderizing is protease treatment. In this study, Manihot esculenta root was chosen as a protease source due to its skin blistering effect, suggesting the presence of strong proteolytic activity. The extraction of the crude protease was optimized by using response surface methodology (RSM) with four independent variables, which were pH (X1), CaCl2 (X2), Triton X-100 (X3) and 2-mercaptoethanol (X4). Based on the RSM model, all the independent variables were significant and the optimum extraction conditions were pH 9, 3.24 mM CaCl2, 4.12% Triton X-100 and 6.32 mM 2-mercaptoethanol. Tukey's test results showed that the difference between the expected and experimental protease activity value was 0.05%. A reduction of meat firmness was observed when samples treated with enzyme were compared with a control by using a texture analyser. Electrophoretic patterns also showed extensive proteolysis and a reduction of intensity and number of the protein bands in the treated sample. SEM clearly revealed the degradation of muscle fibres and connective tissue of meat treated with crude protease.
The major compounds in honey are carbohydrates such as monosaccharides and disaccharides. The same compounds are found in cane-sugar concentrates. Unfortunately when sugar concentrate is added to honey, laboratory assessments are found to be ineffective in detecting this adulteration. Unlike tracing heavy metals in honey, sugar adulterated honey is much trickier and harder to detect, and traditionally it has been very challenging to come up with a suitable method to prove the presence of adulterants in honey products. This paper proposes a combination of array sensing and multi-modality sensor fusion that can effectively discriminate the samples not only based on the compounds present in the sample but also mimic the way humans perceive flavours and aromas. Conversely, analytical instruments are based on chemical separations which may alter the properties of the volatiles or flavours of a particular honey. The present work is focused on classifying 18 samples of different honeys, sugar syrups and adulterated samples using data fusion of electronic nose (e-nose) and electronic tongue (e-tongue) measurements. Each group of samples was evaluated separately by the e-nose and e-tongue. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to separately discriminate monofloral honey from sugar syrup, and polyfloral honey from sugar and adulterated samples using the e-nose and e-tongue. The e-nose was observed to give better separation compared to e-tongue assessment, particularly when LDA was applied. However, when all samples were combined in one classification analysis, neither PCA nor LDA were able to discriminate between honeys of different floral origins, sugar syrup and adulterated samples. By applying a sensor fusion technique, the classification for the 18 different samples was improved. Significant improvement was observed using PCA, while LDA not only improved the discrimination but also gave better classification. An improvement in performance was also observed using a Probabilistic Neural Network classifier when the e-nose and e-tongue data were fused.
Three main types of PVC solvent polymeric membrane ion-selective electrodes for chloroquine are described. They are based on three ion-pairing agents namely dipicrylamine (DPA), tetraphenylborate (TPB) or tetrakis(4-chlorophenyl)borate (TCPB) with either dioctylphenyl phosphonate (DOPP) or trioctyl phosphate (TOP) solvent mediator. All electrodes exhibit Nernstian responses, fast dynamic response times and a wide useful pH range. The best all-round electrode is based on TPB and TOP plasticizing solvent mediators with a limit of detection of 7.1 x 10(-6)M and was utilized for the assay of chloroquine in tablets. Direct potentiometric determinations with either the analyte addition method or the normal calibration method gave results comparable to the official method.
In recent years, there have been a number of reported studies on the use of non-destructive techniques to evaluate and determine mango maturity and ripeness levels. However, most of these reported works were conducted using single-modality sensing systems, either using an electronic nose, acoustics or other non-destructive measurements. This paper presents the work on the classification of mangoes (Magnifera Indica cv. Harumanis) maturity and ripeness levels using fusion of the data of an electronic nose and an acoustic sensor. Three groups of samples each from two different harvesting times (week 7 and week 8) were evaluated by the e-nose and then followed by the acoustic sensor. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were able to discriminate the mango harvested at week 7 and week 8 based solely on the aroma and volatile gases released from the mangoes. However, when six different groups of different maturity and ripeness levels were combined in one classification analysis, both PCA and LDA were unable to discriminate the age difference of the Harumanis mangoes. Instead of six different groups, only four were observed using the LDA, while PCA showed only two distinct groups. By applying a low level data fusion technique on the e-nose and acoustic data, the classification for maturity and ripeness levels using LDA was improved. However, no significant improvement was observed using PCA with data fusion technique. Further work using a hybrid LDA-Competitive Learning Neural Network was performed to validate the fusion technique and classify the samples. It was found that the LDA-CLNN was also improved significantly when data fusion was applied.
An improved classification of Orthosiphon stamineus using a data fusion technique is presented. Five different commercial sources along with freshly prepared samples were discriminated using an electronic nose (e-nose) and an electronic tongue (e-tongue). Samples from the different commercial brands were evaluated by the e-tongue and then followed by the e-nose. Applying Principal Component Analysis (PCA) separately on the respective e-tongue and e-nose data, only five distinct groups were projected. However, by employing a low level data fusion technique, six distinct groupings were achieved. Hence, this technique can enhance the ability of PCA to analyze the complex samples of Orthosiphon stamineus. Linear Discriminant Analysis (LDA) was then used to further validate and classify the samples. It was found that the LDA performance was also improved when the responses from the e-nose and e-tongue were fused together.
Lipid oxidation and microbial contamination are the major factors contributing to food deterioration. Food additives like antioxidants and antibacterials can prevent food spoilage by delaying oxidation and preventing the growth of bacteria. Artocarpus altilis leaves exhibited biological properties that suggested its use as a new source of natural antioxidant and antimicrobial. Supercritical fluid extraction (SFE) was used to optimize the extraction of bioactive compounds from the leaves using response surface methodology (yield and antioxidant activity). The optimum SFE conditions were 50.5 °C temperature, 3784 psi pressure and 52 min extraction time. Verification test results (Tukey's test) showed that no significant difference between the expected and experimental DPPH activity and yield value (99%) were found. Gas-chromatography -mass spectrometry (GC-MS) analysis revealed three major bioactive compounds existed in A. altilis extract. The extract demonstrated antioxidant and antibacterial properties with 2,3-diphenyl-1-picrylhydrazyl (DPPH) scavenging activity, ferric reducing ability of plasma (FRAP), hydroxyl radical scavenging activity, tyrosinase mushrrom inhibition of 41.5%, 8.15 ± 1.31 (µg of ascorbic acid equivalents), 32%, 37% and inhibition zone diameter of 0.766 ± 0.06 cm (B. cereus) and 1.27 ± 0.12 cm (E. coli). Conductor like screening model for real solvents (COSMO RS) was performed to explain the extraction mechanism of the major bioactive compounds during SFE. Molecular electrostatic potential (MEP) shows the probability site of nucleophilic and electrophilic attack during bacterial inhibition. Based on molecular docking study, non-covalent interactions are the main interaction occurring between the major bioactive compounds and bacteria (antibacterial inhibition).
Meat tenderness is one of the most important organoleptic properties in determining consumer acceptance in meat product marketability. Therefore, an effective meat tenderization method is sought after by exploring plant-derived proteolytic enzymes as meat tenderizer. In this study, a novel protease from Cashew was identified as a new alternative halal meat tenderizer. The extraction of cashew protease was optimized using response surface methodology (R2 = 0.9803) by varying pH, CaCl2 concentration, mixing time, and mass. pH 6.34, 7.92 mM CaCl2 concentration, 5.51 min mixing time, and 19.24 g sample mass were the optimal extraction conditions. There was no significant difference (n = 3; p
Fused deposition modelling (FDM) is a filament-based rapid prototyping technology that allows new composite materials to be introduced into the FDM process as long as they can be manufactured in feedstock filament form. The purpose of this research was to analyze the rheological behavior of oil palm fiber-reinforced acrylonitrile butadiene styrene (ABS) composites when used as a feedstock material, as well as to determine the best processing conditions for FDM. The composite's shear thinning behavior was observed, and scanning electron microscopy was used to reveal its composition. The morphological result found that there was a good fiber/matrix adhesion with a 3 wt% fiber loading, as no fiber pullouts or gaps developed between the oil palm fiber and ABS. However, some pores and fiber pullouts were found with a 5 and 7 wt% fiber loading. Next, the rheological results showed that the increment of fiber content (wt%) increased the viscosity. This discovery can definitely be used in the extrusion process for making wire filament for FDM. The shear thinning effect was increased by adding 3, 5, or 7 wt% of oil palm fiber. The non-Newtonian index (n) of the composites increased as the number of shear rates increased, indicating that the fiber loading had a significant impact on the rheological behavior. As the fiber loading increased, the viscosity and shear stress values increased as well. As a result, oil fiber reinforced polymer composites can be used as a feedstock filament for FDM.
Today, additive manufacturing (AM) is the most recent technology used to produce detailed and complexly built parts for a variety of applications. The most emphasis has been given to fused deposition modeling (FDM) in the development and manufacturing fields. Natural fibers have received attention in the area of 3D printing to be employed as bio-filters with thermoplastics, which have prompted an effort for more ecologically acceptable methods of manufacturing. The development of natural fiber composite filaments for FDM requires meticulous methods and in-depth knowledge of the properties of natural fibers and their matrices. Thus, this paper reviews natural fiber-based 3D printing filaments. It covers the fabrication method and characterization of thermoplastic materials blended with natural fiber-produced wire filament. The characterization of wire filament includes the mechanical properties, dimension stability, morphological study, and surface quality. There is also a discussion of the difficulties in developing a natural fiber composite filament. Last but not least, the prospects of natural fiber-based filaments for FDM 3D printing are also discussed. It is hoped that, after reading this article, readers will have enough knowledge regarding how natural fiber composite filament for FDM is created.
Two hundred ten samples of selected vegetables (okra, pumpkin, tomato, potato, eggplant, spinach, and cabbage) from Faisalabad, Pakistan, were analyzed for the analysis of heavy metals: cadmium (Cd), lead (Pb), arsenic (As), and mercury (Hg). Inductively coupled plasma optical emission spectrometry was used for the analysis of heavy metals. The mean levels of Cd, Pb, As, and Hg were 0.24, 2.23, 0.58, and 7.98 mg/kg, respectively. The samples with Cd (27%), Pb (50%), and Hg (63%) exceeded the maximum residual levels set by the European Commission. The mean levels of heavy metals found in the current study are high and may pose significant health concerns for consumers. Furthermore, considerable attention should be paid to implement comprehensive monitoring and regulations.