The proliferation of pathogenic fungi in sugarcane crops poses a significant threat to agricultural productivity and economic sustainability. Early identification and management of sugarcane diseases are therefore crucial to mitigate the adverse impacts of these pathogens. In this study, visible and near-infrared spectroscopy (380-1400 nm) combined with a novel wavelength selection method, referred to as modified flower pollination algorithm (MFPA), was utilized for sugarcane disease recognition. The selected wavelengths were incorporated into machine learning models, including Naïve Bayes, random forest, and support vector machine (SVM). The developed simplified SVM model, which utilized the MFPA wavelength selection method yielded the best performances, achieving a precision value of 0.9753, a sensitivity value of 0.9259, a specificity value of 0.9524, and an accuracy of 0.9487. These results outperformed those obtained by other wavelength selection approaches, including the selectivity ratio, variable importance in projection, and the baseline method of the flower pollination algorithm.
Consumption of agricultural products with pesticide residue is risky and can negatively affect health. This study proposed a nondestructive method of detecting pesticide residues in chili pepper based on the combination of visible and near-infrared (VIS/NIR) spectroscopy (400-2498 nm) and deep learning modeling. The obtained spectra of chili peppers with two types of pesticide residues (acetamiprid and imidacloprid) were analyzed using a one-dimensional convolutional neural network (1D-CNN). Compared with the commonly used partial least squares regression model, the 1D-CNN approach yielded higher prediction accuracy, with a root mean square error of calibration of 0.23 and 0.28 mg/kg and a root mean square error of prediction of 0.55 and 0.49 mg/kg for the acetamiprid and imidacloprid data sets, respectively. Overall, the results indicate that the combination of the 1D-CNN model and VIS/NIR spectroscopy is a promising nondestructive method of identifying pesticide residues in chili pepper.
Dioscorea oppositifolia is an important crop and functional food. D. oppositifolia tuber is often adulterated with D. persimilis, D. alata, and D. fordii tuber in the commercial market. This study proposed an integrated Fourier transform infrared spectroscopy (FT-IR) with chemometric approach to differentiate these four Dioscorea species. A total of 107 Dioscorea spp. tuber samples were collected from different locations in China. Principal Component Analysis (PCA), PCA-Class, and Orthogonal Partial Least Square Discriminant Analysis (OPLS-DA) were utilised to classify the FT-IR spectra. In this PCA is unable to differentiate the Dioscorea spp. tuber effectively. However, PCA-Class and OPLS-DA can distinguish spp. these 4 species Dioscorea tuber with high accuracy, sensitivity, and specificity. Additionally, the RMSEE, RMSEP and RMSECV values for OPLS-DA model were low, showing that it is a good model. The combination of FT-IR with the PCA-Class and OPLS-DA is practical in discriminating Dioscorea spp. tubers.
A new thiosemicarbazone derivative, N-(2-hydroxyphenyl)-2-[1-(pyridin-4-yl)ethylidene]hydrazinecarbothioamide (HPEH), has been synthesized, characterized, and further developed as a highly selective and sensitive colorimetric chemosensor for Hg2+ recognition in environmental water samples. Structural conformers of HPEH were successfully identified using a combination of the potential energy surface (PES) and time-dependent density functional theory (TD-DFT) methods. The synthesized HPEH was successfully characterized further and analyzed based on its harmonic vibrational frequencies, NMR spectra, and electronic transitions using the DFT approach. Sigma profiles were generated using the COSMO-RS approach to identify a compatible medium for HPEH to act as a chemosensor. The conditions for the highly sensitive and selective detection of Hg2+ by HPEH were successfully optimized using the statistical response surface methodology approach. The optimum sensing of HPEH occurred in an 8:2 v/v DMSO/pH 7.8 solution at a 20:60 μM HPEH/Hg2+ concentration and after a reaction time of 18 min, with statistically significant independent variables (p
Experiments demonstrated that visible and near-infrared (Vis-NIR) spectroscopy is a highly reliable tool for determining the nutritional status of plants. Although numerous studies on various kinds of plants have been conducted, there are only a few summaries of the research findings regarding the absorbance bands in the visible and near-infrared region and how they relate to the nutritional status of plants. This article will discuss the application of Vis-NIR spectroscopy for monitoring the nutrient conditions of plants, with a particular emphasis on three major components required by plants, namely nitrogen (N), phosphorus (P), and potassium (K), or NPK. Each section discussed different topics, for instance, the essential nutrients needed by plants, the application of Vis-NIR spectroscopy in nutrient status analysis, chemometrics tools, and absorbance bands related to the nutrient status, respectively. Deduction made concluded that factors affecting the plant's structure are contributed by several circumstances like the age of leaves, concentration of pigments, and water content. These factors are intertwined, strongly correlated, and can be observed in the visible and near-infrared regions. While the visible region is commonly utilised for nutritional analysis in plants, the literature review performed in this paper shows that the near-infrared region as well contains valuable information about the plant's nutritional status. A few wavelengths related to the direct estimation of nutrients in this review explained that information on nutrients can be linked with chlorophyll and water absorption bands such that N and P are the components of chlorophyll and protein; on the other hand, K exists in the form of cationic carbohydrates which are sensitive to water region.
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.
Spectroscopy in the visible and near-infrared region (Vis-NIR) region has proven to be an effective technique for quantifying the chlorophyll contents of plants, which serves as an important indicator of their photosynthetic rate and health status. However, the Vis-NIR spectroscopy analysis confronts a significant challenge concerning the existence of spectral variations and interferences induced by diverse factors. Hence, the selection of characteristic wavelengths plays a crucial role in Vis-NIR spectroscopy analysis. In this study, a novel wavelength selection approach known as the modified regression coefficient (MRC) selection method was introduced to enhance the diagnostic accuracy of chlorophyll content in sugarcane leaves. Experimental data comprising spectral reflectance measurements (220-1400 nm) were collected from sugarcane leaf samples at different growth stages, including seedling, tillering, and jointing, and the corresponding chlorophyll contents were measured. The proposed MRC method was employed to select optimal wavelengths for analysis, and subsequent partial least squares regression (PLSR) and Gaussian process regression (GPR) models were developed to establish the relationship between the selected wavelengths and the measured chlorophyll contents. In comparison to full-spectrum modelling and other commonly employed wavelength selection techniques, the proposed simplified MRC-GPR model, utilizing a subset of 291 selected wavelengths, demonstrated superior performance. The MRC-GPR model achieved higher coefficient of determination of 0.9665 and 0.8659, and lower root mean squared error of 1.7624 and 3.2029, for calibration set and prediction set, respectively. Results showed that the GPR model, a nonlinear regression approach, outperformed the PLSR model.
The fabrication of molecular electronics from non-toxic functional materials which eventually would potentially able to degrade or being breaking down into safe by-products have attracted much interests in recent years. Hence, in this study, the introduction of mixed highly functional substructures of chalcone (-CO-CH=CH-) and ethynylated (C≡C) as building blocks has shown ideal performance as solution-processed thin film candidatures. Two types of derivatives, (MM-3a) and (MM-3b) repectively, showed a substantial Stokes shifts at 75 nm and 116 nm, in which such emission exhibits an intramolecular charge transfer (ICT) state and fluoresce characteristics. The density functional theory (DFT) simulation shows that MM-3a and MM-3b exhibit low energy gaps of 3.70 eV and 2.81 eV, respectively. TD-DFT computations for molecular electrostatic potential (MEP) and frontier molecular orbitals (FMO) were also used to emphasise the structure-property relationship. A solution-processed thin film with a single layer of ITO/PEDOT:PSS/MM-3a-MM-3b/Au exhibited electroluminescence behaviour with orange and purple emissions when supplied with direct current (DC) voltages. To promote the safer application of the derivatives formed, ethynylated chalcone materials underwent toxicity studies toward Acanthamoeba sp. to determine their suitability as non-toxic molecules prior to the determination as safer materials in optical limiting interests. From the preliminary test, no IC50 value was obtained for both compounds via 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay analysis and molecular docking analysis between MM-3a and MM-3b, with profilin protein exhibited weak bond interactions and attaining huge interaction distances.
Trace and minor elements play crucial roles in a variety of biological processes, including amyloid fibrils formation. Mechanisms include activation or inhibition of enzymatic reactions, competition between elements and metal proteins for binding positions, also changes to the permeability of cellular membranes. These may influence carcinogenic processes, with trace and minor element concentrations in normal and amyloid tissues potentially aiding in cancer diagnosis and etiology. With the analytical capability of the spectroscopic technique X-ray fluorescence (XRF), this can be used to detect and quantify the presence of elements in amyloid characterization, two of the trace elements known to be associated with amyloid fibrils. In present work, involving samples from a total of 22 subjects, samples of normal and amyloid-containing tissues of heart, kidney, thyroid, and other tissue organs were obtained, analyzed via energy-dispersive X-ray fluorescence (EDXRF). The elemental distribution of potassium (K), calcium (Ca), arsenic (As), and iron (Fe) was examined in both normal and amyloidogenic tissues using perpetual thin slices. In amyloidogenic tissues the levels of K, Ca, and Fe were found to be less than in corresponding normal tissues. Moreover, the presence of As was only observed in amyloidogenic samples; in a few cases in which there was an absence of As, amyloid samples were found to contain Fe. Analysis of arsenic in amyloid plaques has previously been difficult, often producing contradictory results. Using the present EDXRF facility we could distinguish between amyloidogenic and normal samples, with potential correlations in respect of the presence or concentration of specific elements.
The binding mode of antineoplastic antimetabolite, floxuridine (FUDR), with human serum albumin (HSA), the leading carrier in blood circulation, was ascertained using multi-spectroscopic, microscopic, and computational techniques. A static fluorescence quenching was established due to decreased Ksv values with rising temperatures, suggesting FUDR-HSA complexation. UV-vis absorption spectral results also supported this conclusion. The binding constant, Ka values, were found within 9.7-7.9 × 103 M-1 at 290, 300, and 310 K, demonstrating a moderate binding affinity for the FUDR-HSA system. Thermodynamic data (ΔS = +46.35 J.mol-1.K-1 and ΔH = -8.77 kJ.mol-1) predicted the nature of stabilizing forces (hydrogen-bonds, hydrophobic, and van der Waals interactions) for the FUDR-HSA complex. Circular dichroism spectra displayed a minor disruption in the protein's 2° and 3° structures. At the same time, atomic force microscopy images proved variations in the FUDR-HSA surface morphology, confirming its complex formation. The protein's microenvironment around Trp/Tyr residues was also modified, as judged by 3-D fluorescence spectra. FUDR-bound HSA showed better resistance against thermal stress. As disclosed from ligand displacement studies, the FUDR binding site was placed in subdomain IIA (Site I). Further, the molecular docking analysis corroborated the competing displacement studies. Molecular dynamics evaluations revealed that the complex achieved equilibrium during simulations, confirming the FUDR-HSA complex's stability.
Fish epidermal mucus is an important reservoir of antipathogenic compounds which serves as the first line of the immune defence. Despite its significant role in the physiology and health of fish, detailed profiling of fish epidermal mucus has yet to be explored. Therefore, this study investigates a label-free colloidal surface-enhanced Raman spectroscopic (SERS) method for profiling grouper mucus. Gold nanoparticles were first synthesised using the standard citrate reduction and characterised using ultraviolet-visible spectroscopy, transmission electron microscopy and dynamic light scattering. The influence of acidified sodium sulphate (Na2SO4) at pH 3 as the aggregating agent on the enhancement of the SERS spectrum of different analyte samples including rhodamine 6G (R6G) dye, lysozyme solution and hybrid grouper (Epinephelus fuscoguttatus × Epinephelus lanceolatus) mucus was observed. Based on the results, an optimal Na2SO4 concentration of 1 M was recorded to achieve the highest enhancement of the SERS signal for R6G and grouper mucus, while the optimal concentration for lysozyme was 0.1 M. The results indicated a higher degree of aggregation induced by lysozyme than R6G and grouper mucus. A few overlapping peaks of the SERS spectra of lysozyme and grouper mucus made it possible to confirm the presence of lysozyme as potential biomarkers.
A rapid, simple, sensitive, and selective point-of-care diagnosis tool kit is vital for detecting the coronavirus disease (COVID-19) based on the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strain. Currently, the reverse transcriptase-polymerase chain reaction (RT-PCR) is the best technique to detect the disease. Although a good sensitivity has been observed in RT-PCR, the isolation and screening process for high sample volume is limited due to the time-consuming and laborious work. This study introduced a nucleic acid-based surface-enhanced Raman scattering (SERS) sensor to detect the nucleocapsid gene (N-gene) of SARS-CoV-2. The Raman scattering signal was amplified using gold nanoparticles (AuNPs) possessing a rod-like morphology to improve the SERS effect, which was approximately 12-15 nm in diameter and 40-50 nm in length. These nanoparticles were functionalised with the single-stranded deoxyribonucleic acid (ssDNA) complemented with the N-gene. Furthermore, the study demonstrates method selectivity by strategically testing the same virus genome at different locations. This focused approach showcases the method's capability to discern specific genetic variations, ensuring accuracy in viral detection. A multivariate statistical analysis technique was then applied to analyse the raw SERS spectra data using the principal component analysis (PCA). An acceptable variance amount was demonstrated by the overall variance (82.4 %) for PC1 and PC2, which exceeded the desired value of 80 %. These results successfully revealed the hidden information in the raw SERS spectra data. The outcome suggested a more significant thymine base detection than other nitrogenous bases at wavenumbers 613, 779, 1219, 1345, and 1382 cm-1. Adenine was also less observed at 734 cm-1, and ssDNA-RNA hybridisations were presented in the ketone with amino base SERS bands in 1746, 1815, 1871, and 1971 cm-1 of the fingerprint. Overall, the N-gene could be detected as low as 0.1 nM within 10 mins of incubation time. This approach could be developed as an alternative point-of-care diagnosis tool kit to detect and monitor the COVID-19 disease.
Dental caries has high prevalence among kids and adults thus it has become one of the global health concerns. The current modern dentistry focused on the preventives measures to reduce the number of dental caries cases. The employment of machine learning coupled with UV spectroscopy plays a crucial role to detect the early stage of caries. Artificial neural network with hyperparameter tuning was employed to train spectral data for the classification based on the International Caries Detection and Assesment System (ICDAS). Spectra preprocessing namely mean center (MC), autoscale (AS) and Savitzky Golay smoothing (SG) were applied on the data for spectra correction. The best performance of ANN model obtained has accuracy of 0.85 with precision of 1.00. Convolutional neural network (CNN) combined with Savitzky Golay smoothing performed on the spectral data has accuracy, precision, sensitivity and specificity for validation data of 1.00 respectively. The result obtained shows that the application of ANN and CNN capable to produce robust model to be used as an early screening of dental caries.