ABSTRACT
The utilization of UV-Vis spectroscopy with amino-functionalized carbon quantum dots (NCQD) as a positive fluorophore reagent for chloride sensing in oil marks a notable advancement in analytical spectroscopy chemistry. This approach streamlines the detection process by eliminating the need for lengthy procedures and pretreatment steps typically associated with chloride detection in edible oil. By incorporating NCQD in chloride detection within the oil matrix, the wavelength analysis transitions from the UV to the visible region. This shift eliminates interference from oil matrix interactions, ensuring more accurate results. Molecular analysis of NCQD reveals significant shifts in its Fourier Transformation Infrared and photoluminescence spectroscopy peaks due to interaction with chloride in edible oil. It has two impressive sensitivity ranges spanning from 0.1-1.0 to 1.0-8.0â¯ppm, with a value of -0.4656 au. ppm-1 (R2â¯=â¯0.998) and -0.0361 au. ppm-1 (R2â¯=â¯0.931), respectively, the technique meets regulatory standards while achieving a low limit of detection (LOD) of 0.1â¯ppm. This places it on par with conventional methods and commercial sensors. The NCQD-UV-Vis spectroscopy method not only enhances the efficiency and accuracy of chloride detection but also holds promise for various industrial applications requiring simple and precise monitoring of chloride levels in oil samples.
ABSTRACT
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.
Subject(s)
Algorithms , Nitrates , Bayes Theorem , Hydroponics , Machine Learning , Support Vector MachineABSTRACT
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.
ABSTRACT
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.
