In this paper, a comprehensive study has been made on the detection of free fatty acids (FFAs) in palm oil via an optical technique based on enzymatic aminolysis reactions. FFAs in crude palm oil (CPO) were converted into fatty hydroxamic acids (FHAs) in a biphasic lipid/aqueous medium in the presence of immobilized lipase. The colored compound formed after complexation between FHA and vanadium (V) ion solution was proportional to the FFA content in the CPO samples and was analyzed using a spectrophotometric method. In order to develop a rapid detection system, the parameters involved in the aminolysis process were studied. The utilization of immobilized lipase as catalyst during the aminolysis process offers simplicity in the product isolation and the possibility of conducting the process under extreme reaction conditions. A good agreement was found between the developed method using immobilized Thermomyces lanuginose lipase as catalyst for the aminolysis process and the Malaysian Palm Oil Board (MPOB) standard titration method (R2 = 0.9453).
Nutrient solution plays an essential role in providing macronutrients to hydroponic plants. Determining nitrogen in the form of nitrate is crucial, as either a deficient or excessive supply of nitrate ions may reduce the plant yield or lead to environmental pollution. This work aims to evaluate the performance of feature reduction techniques and conventional machine learning (ML) algorithms in determining nitrate concentration levels. Two features reduction techniques, linear discriminant analysis (LDA) and principal component analysis (PCA), and seven ML algorithms, for example, k-nearest neighbors (KNN), support vector machine, decision trees, naïve bayes, random forest (RF), gradient boosting, and extreme gradient boosting, were evaluated using a high-dimensional spectroscopic dataset containing measured nitrate-nitrite mixed solution absorbance data. Despite the limited and uneven number of samples per class, this study demonstrated that PCA outperformed LDA on the high-dimensional spectroscopic dataset. The classification accuracy of ML algorithms combined with PCA ranged from 92.7% to 99.8%, whereas the classification accuracy of ML algorithms combined with LDA ranged from 80.7% to 87.6%. The PCA with the RF algorithm exhibited the best performance with 99.8% accuracy.
The detection of Pb(II) ions in a river using the surface plasmon resonance (SPR)-based silver (Ag) thin film technique was successfully developed. Chitosan-graphene oxide (CS-GO) was coated on top of the Ag thin film surface and acted as the active sensing layer for Pb(II) ion detection. CS-GO was synthesized and characterized, and the physicochemical properties of this material were studied prior to integration with the SPR. In X-ray photoelectron spectroscopy (XPS), the appearance of the C=O, C-O, and O-H functional groups at 531.2 eV and 532.5 eV, respectively, confirms the success of CS-GO nanocomposite synthesis. A higher surface roughness of 31.04 nm was observed under atomic force microscopy (AFM) analysis for Ag/CS-GO thin film. The enhancement in thin film roughness indicates that more adsorption sites are available for Pb(II) ion binding. The SPR performance shows a good sensor sensitivity for Ag/CS-GO with 1.38° ppm-1 ranging from 0.01 to 5.00 ppm of standard Pb(II) solutions. At lower concentrations, a better detection accuracy was shown by SPR using Ag/CS-GO thin film compared to Ag/CS thin film. The SPR performance using Ag/CS-GO thin film was further evaluated with real water samples collected from rivers. The results are in agreement with those of standard Pb(II) ion solution, which were obtained at incidence angles of 80.00° and 81.11° for local and foreign rivers, respectively.
Accurate, label-free, and rapid methods for measuring phosphorus concentrations are essential in a hydroponic system, as excessive or insufficient phosphorus levels can adversely affect plant growth, human health, and environmental sustainability. In this study, we demonstrate the advantages of hybrid machine learning models compared to single machine learning models in predicting phosphorus concentration based on the absorbance dataset. Three machine learning classifiers- Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN)- were employed as bases for single and hybrid machine learning models. Three ensemble techniques (voting, bagging, and stacking) were used to hybridize the classifiers. Among the single models, KNN demonstrated the fastest computational time of 18.07 s, while SVM achieved the highest accuracy of 99.6%. The hybrid SVM/KNN model using a voting classifier showed a significant increase in accuracy for KNN with only a slight increase in computational time. Bagging techniques increased the accuracy but at a longer computational time. The stacking technique, which combined SVM, KNN, and RF, achieved the highest accuracy of 99.73% with a short computational time of 36.18 s compared to the bagging and voting technique. This study demonstrates that the machine learning method can effectively distinguish phosphorus concentrations. In contrast, hybrid machine learning techniques can improve accuracy for predicting phosphorus without using labels, despite requiring longer computational time.
This research demonstrates a one-step modification process of biopolymer carrageenan active sites through functional group substitution in κ-carrageenan structures. The modification process improves the electronegative properties of κ-carrageenan derivatives, leading to enhancement of the material's performance. Synthesized succinyl κ-carrageenan with a high degree of substitution provides more active sites for interaction with analytes. The FTIR analysis of succinyl κ-carrageenan showed the presence of new peaks at 1068 cm-1, 1218 cm-1, and 1626 cm-1 that corresponded to the vibrations of C-O and C=O from the carbonyl group. A new peak at 2.86 ppm in 1H NMR represented the methyl proton neighboring with C=O. The appearance of new peaks at 177.05 and 177.15 ppm in 13C NMR proves the substitution of the succinyl group in the κ-carrageenan structure. The elemental analysis was carried out to calculate the degree of substitution with the highest value of 1.78 at 24 h of reaction. The XRD diffractogram of derivatives exhibited a higher degree of crystallinity compared to pristine κ-carrageenan at 23.8% and 9.2%, respectively. Modification of κ-carrageenan with a succinyl group improved its interaction with ions and the conductivity of the salt solution compared to its pristine form. This work has a high potential to be applied in various applications such as sensors, drug delivery, and polymer electrolytes.
The resistive switching (RS) mechanism is resulted from the formation and dissolution of a conductive filament due to the electrochemical redox-reactions and can be identified with a pinched hysteresis loop on the I-V characteristic curve. In this work, the RS behaviour was demonstrated using a screen-printed electrode (SPE) and was utilized for creatinine sensing application. The working electrode (WE) of the SPE has been modified with a novel small organic molecule, 1,4-bis[2-(5-thiophene-2-yl)-1-benzothiopene]-2,5-dioctyloxybenzene (BOBzBT2). Its stability at room temperature and the presence of thiophene monomers were exploited to facilitate the cation transport and thus, affecting the high resistive state (HRS) and low resistive state (LRS) of the electrochemical cell. The sensor works based on the interference imposed by the interaction between the creatinine molecule and the radical cation of BOBzBT2 to the conductive filament during the Cyclic Voltammetry (CV) measurement. Different concentrations of BOBzBT2 dilution were evaluated using various concentrations of non-clinical creatinine samples to identify the optimised setup of the sensor. Enhanced sensitivity of the sensor was observed at a high concentration of BOBzBT2 over creatinine concentration between 0.4 and 1.6 mg dL-1-corresponding to the normal range of a healthy individual.
High sensitivity and capturing ratio are strongly demanded for surface plasmon resonance (SPR) sensors when applied in detection of small molecules. Herein, an SPR sensor is combined with a novel smart material, namely, MoS2 nanoflowers (MNFs), to demonstrate programmable adsorption/desorption of small bipolar molecules, i.e., amino acids. The MNFs overcoated on the plasmonic gold layer increase the sensitivity by 25% compared to an unmodified SPR sensor, because of the electric field enhancement at the gold surface. Furthermore, as the MNFs have rich edge sites and negatively charged surfaces, the MNF-SPR sensors exhibit not only much higher bipolar-molecule adsorption capability, but also efficient desorption of these molecules. It is demonstrated that the MNF-SPR sensors enable controllable detection of amino acids by adjusting solution pH according to their isoelectric points. In addition, the MNFs decorated on the plasmonic interface can be as nanostructure frameworks and modified with antibody, which allows for specific detection of proteins. This novel SPR sensor provides a new simple strategy for pre-screening of amino acid disorders in blood plasma and a universal high-sensitive platform for immunoassay.
This paper demonstrates carbon quantum dots (CQDs) with triangular silver nanoparticles (AgNPs) as the sensing materials of localized surface plasmon resonance (LSPR) sensors for chlorophyll detection. The CQDs and AgNPs were prepared by a one-step hydrothermal process and a direct chemical reduction process, respectively. FTIR analysis shows that a CQD consists of NH2, OH, and COOH functional groups. The appearance of C=O and NH2 at 399.5 eV and 529.6 eV in XPS analysis indicates that functional groups are available for adsorption sites for chlorophyll interaction. A AgNP-CQD composite was coated on the glass slide surface using (3-aminopropyl) triethoxysilane (APTES) as a coupling agent and acted as the active sensing layer for chlorophyll detection. In LSPR sensing, the linear response detection for AgNP-CQD demonstrates R2 = 0.9581 and a sensitivity of 0.80 nm ppm-1, with a detection limit of 4.71 ppm ranging from 0.2 to 10.0 ppm. Meanwhile, a AgNP shows a linear response of R2 = 0.1541 and a sensitivity of 0.25 nm ppm-1, with the detection limit of 52.76 ppm upon exposure to chlorophyll. Based on these results, the AgNP-CQD composite shows a better linearity response and a higher sensitivity than bare AgNPs when exposed to chlorophyll, highlighting the potential of AgNP-CQD as a sensing material in this study.
This research investigates the physicochemical properties of biopolymer succinyl-κ-carrageenan as a potential sensing material for NH4+ Localized Surface Plasmon Resonance (LSPR) sensor. Succinyl-κ-carrageenan was synthesised by reacting κ-carrageenan with succinic anhydride. FESEM analysis shows succinyl-κ-carrageenan has an even and featureless topology compared to its pristine form. Succinyl-κ-carrageenan was composited with silver nanoparticles (AgNP) as LSPR sensing material. AFM analysis shows that AgNP-Succinyl-κ-carrageenan was rougher than AgNP-Succinyl-κ-carrageenan, indicating an increase in density of electronegative atom from oxygen compared to pristine κ-carrageenan. The sensitivity of AgNP-Succinyl-κ-carrageenan LSPR is higher than AgNP-κ-carrageenan LSPR. The reported LOD and LOQ of AgNP-Succinyl-κ-carrageenan LSPR are 0.5964 and 2.7192 ppm, respectively. Thus, AgNP-Succinyl-κ-carrageenan LSPR has a higher performance than AgNP-κ-carrageenan LSPR, broader detection range than the conventional method and high selectivity toward NH4+. Interaction mechanism studies show the adsorption of NH4+ on κ-carrageenan and succinyl-κ-carrageenan were through multilayer and chemisorption process that follows Freundlich and pseudo-second-order kinetic model.