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Surface-enhanced Raman scattering method for the identification of methicillin-resistant Staphylococcus aureus using positively charged silver nanoparticles
摘要： The article describes a SERS-based method for diagnosis of bacterial infections. Positively charged silver nanoparticles (AgNPs+) were employed for identification of methicillin-resistant Staphylococcus aureus (MRSA). It is found that AgNPs+ undergo self-assembly on the surface of bacteria via electrostatic aggregation. The assembled AgNPs+ are excellent SERS substrates. To prove the capability of SERS to differentiate between S. aureus and other microorganisms, six standard strains including S. aureus 29213, S. aureus 25923, C. albicans, B. cereus, E. coli, and P. aeruginosa were tested. To further demonstrate its applicability for the identification of MRSA in clinical samples, 52 methicillin-sensitive S. aureus (MSSA) isolates and 215 MRSA isolates were detected by SERS. The total measurement time (include incubation) is 45 min when using a 3 μL sample. The method gives a strongly enhanced Raman signal (at 730 cm?1 and 1325 cm?1) with good reproducibility and repeatability. It was successfully applied to the discrimination of the six strain microorganisms. The typical Raman peaks of S. aureus at 730, 1154, 1325, and 1457 cm?1 were observed, which were assigned to the bacterial cell wall components (730 cm?1- adenine, glycosidic ring mode, 1154 cm?1- unsaturated fatty acid, 1325 cm?1- adenine, polyadenine, and 1457 cm?1 for -COO- stretching). S. aureus was completely separated from other species by partial least squares discriminant analysis (PLS-DA). Moreover, 52 MSSA isolates and 215 MRSA isolates from clinical samples were identified by PLS-DA. The accuracy was almost 100% when compared to the standard broth microdilution method. A classification based on latent structure discriminant analysis provided spectral variability directly. Conceivably, the method offers a potent tool for the identification of bacteria and antibiotics resistance, and for studies on antibiotic-resistance in general.
关键词： S. aureus,Nanoparticles,Methicillin resistance,Antibiotics,Latent structure discriminant analysis classification (OPLS-DA),SERS,Partial least squares discriminant analysis (PLS-DA),AgNPs,Discriminant analysis,Raman spectroscopy
更新于2025-09-23 15:22:29
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The classification of plants by laser-induced breakdown spectroscopy based on two chemometric methods
摘要： The applications of laser-induced breakdown spectroscopy (LIBS) on classifying complex natural organics are relatively limited and their accuracies still needs to be improved. To study the methods on classification of complex organics, three kinds of fresh leaves were measured by LIBS in this work. 100 spectra from 100 samples of each kind of leaves were measured and then they were divided into training set and test set in a ratio of 7:3. Two algorithms of chemometric methods including the partial least squares discriminant analysis (PLS-DA) and principal component analysis Mahalanobis distance (PCA-MD) were used to identify these leaves. By using 23 lines from 16 elements or molecules as input data, these two methods can both classify these three kinds of leaves successfully. The classification accuracies of training set are both up to 100% by PCA-MD and PLS-DA, respectively. The classification accuracies of test set are 93.3% by PCA-MD and 97.8% by PLS-DA, respectively. It means that PLS-DA is better than PCA-MD in classifying plant leaves. Because the components in PLS-DA process are more suitable for classification than those in PCA-MD process. We think that this work can provide a reference for plant traceability using LIBS.
关键词： classification of complex organics,partial least squares discriminant analysis,principal component analysis Mahalanobis distance,laser-induced breakdown spectroscopy
更新于2025-09-23 15:21:01
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Rapid qualitative evaluation of velvet antler using laser-induced breakdown spectroscopy (LIBS)
摘要： The aim of this work is to study a rapid analysis method to evaluate velvet antler products qualitatively, using laser-induced breakdown spectroscopy (LIBS). We used principal component analysis to select feature lines of LIBS spectra of velvet antler, and built two partial least squares-discriminant analysis (PLS-DA) classification models to distinguish between inferior and good quality velvet antler by using the intensities of all lines and feature lines as input variables, respectively. The correct classification rates (CCRs) of these two models were both 100%. In order to test the robustness of the models, we used these two models to discriminate the samples not included in the training set to build the model, and the CCRs were 87.5% and 100%, respectively. The results demonstrated that combining LIBS with PLS-DA could evaluate the quality of velvet antler, and the robustness could be improved by using the intensities of feature lines as inputs.
关键词： qualitative evaluation,partial least squares-discriminant analysis (PLS-DA),velvet antler,principal component analysis (PCA),laser-induced breakdown spectroscopy (LIBS)
更新于2025-09-23 15:19:57
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EXPRESS: Use of Visible–Near-Infrared (Vis–NIR) Spectroscopy to Detect Aflatoxin B <sub/>1</sub> on Peanut Kernels
摘要： Current methods for detecting aflatoxin contamination of agricultural and food commodities are generally based on wet chemical analyses, which are time-consuming, destructive to test samples and require skilled personnel to perform, making them impossible for large-scale nondestructive screening and on-site detection. In this study, we utilized visible–near-infrared (Vis–NIR) spectroscopy over the spectral range of 400–2500 nm to detect contamination of commercial, shelled peanut kernels (runner type) with the predominant aflatoxin B1 (AFB1). The artificially contaminated samples were prepared by dropping known amounts of aflatoxin standard dissolved in methanol, onto peanut kernel surface to achieve different contamination levels. The partial least squares discriminant analysis (PLS-DA) models established using the full spectra over different ranges achieved good prediction results. The best overall accuracy of 88.57% and 92.86% were obtained using the full spectra when taking 20 and 100 parts per billion (ppb), respectively, as the classification threshold. The random frog (RF) algorithm was used to find the optimal characteristic wavelengths for identifying the surface AFB1-contamination of peanut kernels. Using the optimal spectral variables determined by the RF algorithm, the simplified RF-PLS-DA classification models were established. The better RF-PLS-DA models attained the overall accuracies of 90.00% and 94.29% with the 20 ppb and 100 ppb thresholds, respectively, which were improved compared to using the full spectral variables. Compared to using the full spectral variables, the employed spectral variables of the simplified RF-PLS-DA models were decreased by at least 94.82%. The present study demonstrated that the Vis–NIR spectroscopic technique combined with appropriate chemometric methods could be useful in identifying AFB1 contamination of peanut kernels.
关键词： Vis–NIR,PLS-DA,peanut kernel,visible–near-infrared spectroscopy,random frog,Aflatoxin,partial least squares discriminant analysis
更新于2025-09-19 17:15:36
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Robust Fourier transformed infrared spectroscopy coupled with multivariate methods for detection and quantification of urea adulteration in fresh milk samples
摘要： Urea is added as an adulterant to give milk whiteness and increase its consistency for improving the solid not fat percentage, but the excessive amount of urea in milk causes overburden and kidney damages. Here, an innovative sensitive methodology based on near‐infrared spectroscopy coupled with multivariate analysis has been proposed for the robust detection and quantification of urea adulteration in fresh milk samples. In this study, 162 fresh milk samples were used, those consisting 20 nonadulterated samples (without urea) and 142 with urea adulterant. Eight different percentage levels of urea adulterant, that is, 0.10%, 0.30%, 0.50%, 0.70%, 0.90%, 1.10%, 1.30%, and 1.70%, were prepared, each of them prepared in triplicates. A Frontier NIR spectrophotometer (BSEN60825‐1:2007) by Perkin Elmer was used for scanning the absorption of each sample in the wavenumber range of 10,000–4,000 cm-1, using 0.2 mm path length CaF2 sealed cell at resolution of 2 cm-1. Principal components analysis (PCA), partial least‐squares discriminant analysis (PLS‐DA), and partial least‐squares regressions (PLSR) methods were applied for the multivariate analysis of the NIR spectral data collected. PCA was used to reduce the dimensionality of the spectral data and to explore the similarities and differences among the fresh milk samples and the adulterated ones. PLS‐DA also showed the discrimination between the nonadulterated and adulterated milk samples. The R‐square and root mean square error (RMSE) values obtained for the PLS‐DA model were 0.9680 and 0.08%, respectively. Furthermore, PLSR model was also built using the training set of NIR spectral data to make a regression model. For this PLSR model, leave‐one‐out cross‐validation procedure was used as an internal cross‐validation criteria and the R‐square and the root mean square error (RMSE) values for the PLSR model were found as 0.9800 and 0.56%, respectively. The PLSR model was then externally validated using a test set. The root means square error of prediction (RMSEP) obtained was 0.48%. The present proposed study was intended to contribute toward the development of a robust, sensitive, and reproducible method to detect and determine the urea adulterant concentration in fresh milk samples.
关键词： urea,principal components analysis,partial least‐squares regressions,milk adulteration,NIR spectroscopy,partial least‐squares discriminant analysis
更新于2025-09-19 17:13:59
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Postmortem Diagnosis of Fatal Hypothermia by Fourier Transform Infrared Spectroscopic Analysis of Edema Fluid in Formalin‐Fixed, Paraffin‐Embedded Lung Tissues
摘要： The goal of this study was to investigate whether pulmonary edema could become a specific diagnostic marker for fatal hypothermia using Fourier transform infrared (FTIR) spectroscopy in combination with chemometrics. The spectral profile analysis indicated that hypothermia fatalities associated with pulmonary edema fluid contained more b-sheet protein conformational structures than the control causes of death, which included sudden cardiac death, brain injury, cerebrovascular disease, mechanical asphyxiation, intoxication, and drowning. Subsequently, the results of principal component analysis (PCA) further revealed that the content of b-sheet protein conformational structures in the pulmonary edema fluid was the main discriminatory marker between fatal hypothermia and the other causes of death. Ultimately, a robust postmortem diagnostic model for fatal hypothermia using a partial least-squares discriminant analysis (PLS-DA) algorithm was constructed. Pulmonary edema fluid spectra collected from eight new forensic autopsy cases that did not participate in the construction of the diagnostic model were predicted using the model. The results showed the causes of death of all these eight cases were correctly classified. In conclusion, this preliminary study demonstrates that FTIR spectroscopy in combination with chemometrics could be a promising approach for the postmortem diagnosis of fatal hypothermia.
关键词： postmortem diagnosis,fatal hypothermia,chemometrics,pulmonary edema fluid,Fourier transform infrared microspectroscopy,partial least-squares discriminant analysis
更新于2025-09-16 10:30:52
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Tracing the Geographical Origin of Lentils (Lens culinaris Medik.) by Infrared Spectroscopy and Chemometrics
摘要： The feasibility of applying the infrared spectroscopy for the geographical origin traceability of lentils from two different countries (Italy and Canada) was investigated. In particular, lentil samples were analyzed by Fourier transform near- and mid-infrared (FT-NIR and FT-MIR) spectroscopy and then discriminated by applying supervised models, i.e., linear discriminant analysis (LDA) and partial least squares discriminant analysis (PLS-DA). To avoid LDA overfitting, two variable strategies were adopted, i.e., a variable reduction by principal component analysis and a variable compression by wavelet packet transform algorithm. FT-MIR models were more discriminating compared to FT-NIR ones with prediction abilities ranging from 98 to 100% and from 91 to 100% for cross- and external validation, respectively. The combination of the FT-MIR and FT-NIR data did not improve the model performances. These findings demonstrated the suitability of the FT-MIR spectroscopy, in combination with supervised pattern recognition techniques, to successfully classify lentils according to their geographical origin.
关键词： Lentils,FT-NIR spectroscopy,FT-MIR spectroscopy,Partial least squares discriminant analysis,Geographical origin,Linear discriminant analysis
更新于2025-09-04 15:30:14
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Fast discrimination of bacteria using a filter paper–based SERS platform and PLS-DA with uncertainty estimation
摘要： Rapid and reliable identification of bacteria is an important issue in food, medical, forensic, and environmental sciences; however, conventional procedures are time-consuming and often require extensive financial and human resources. Herein, we present a label-free method for bacterial discrimination using surface-enhanced Raman spectroscopy (SERS) and partial least squares discriminant analysis (PLS-DA). Filter paper decorated with gold nanoparticles was fabricated by the dip-coating method and it was utilized as a flexible and highly efficient SERS substrate. Suspensions of bacterial samples from three genera and six species were directly deposited on the filter paper–based SERS substrates before measurements. PLS-DA was successfully employed as a multivariate supervised model to classify and identify bacteria with efficiency, sensitivity, and specificity rates of 100% for all test samples. Variable importance in projection was associated with the presence/absence of some purine metabolites, whereas confidence intervals for each sample in the PLS-DA model were calculated using a resampling bootstrap procedure. Additionally, a potential new species of bacteria was analyzed by the proposed method and the result was in agreement with that obtained via 16S rRNA gene sequence analysis, thereby indicating that the SERS/PLS-DA approach has the potential to be a valuable tool for the discovery of novel bacteria.
关键词： Chemometrics, partial least squares discriminant analysis,Surface-enhanced Raman spectroscopy,Reliability estimation,16S rRNA gene sequence analysis,Gold nanoparticles
更新于2025-09-04 15:30:14
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[American Society of Agricultural and Biological Engineers 2017 Spokane, Washington July 16 - July 19, 2017 - ()] 2017 Spokane, Washington July 16 - July 19, 2017 - <i>Variety classification of maize kernels using near infrared (NIR) hyperspectral imaging</i>
摘要： Variety classification of maize kernels was evaluated using near infrared (NIR) hyperspectral imaging in this work. Firstly, NIR hyperspectral images of kernels of four widely used maize varieties were acquired within effective spectral range of 1000-2500 nm. Spectral math was used to compensate for minor lighting differences, and band math combined with threshold method was used to remove the background from images. Minimum noise fraction (MNF) was adopted to reduce noise. Texture features (mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment, and correlation) as appearance character of each maize kernel were calculated and extracted to establish classification model combined with spectra data. Moving average smoothing and standard normal variate were applied on the raw spectra extracted from hyperspectral images. Four optimal wavelengths (1352.20 nm, 1615.50 nm, 1733.10 nm, and 2478.20 nm) were selected by competitive adaptive reweighted sampling (CARS) method. Partial least squares discriminant analysis (PLSDA) was employed to build varieties classification models, based on full wavelength data, the four wavelengths data, and combination of spectral and textural features at four wavelengths, respectively. Results demonstrated that PLSDA model based on combination of spectral and textural features had the best performance with accuracies of 0.89, 0.83 for calibration and prediction set, which indicated the hyperspectral imaging technique with combination of spectral and textural features had a potential of application for variety classification.
关键词： Variety classification,Maize kernel,NIR hyperspectral imaging,Partial least squares discriminant analysis (PLSDA),Competitive adaptive reweighted sampling (CARS) method
更新于2025-09-04 15:30:14