- 标题
- 摘要
- 关键词
- 实验方案
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Potential of Near-infrared Spectroscopy to Detect Defects on the Surface of Solid Wood Boards
摘要： Defects on the surface of solid wood boards directly affect their mechanical properties and product grades. This study investigated the use of near-infrared spectroscopy (NIRS) to detect and classify defects on the surface of solid wood boards. Pinus koraiensis was selected as the raw material. The experiments focused on the ability to use the model to sort defects on the surface of wood into four types, namely live knots, dead knots, cracks, and defect-free. The test data consisted of 360 NIR absorption spectra of the defect samples using a portable NIR spectrometer, with the wavelength range of 900 to 1900 nm. Three pre-processing methods were used to compare the effects of noise elimination in the original absorption spectra. The NIR discrimination models were developed based on partial least squares and discriminant analysis (PLS-DA), least squares support vector machine (LS-SVM), and back-propagation neural network (BPNN) from 900 to approximately 1900 nm. The results demonstrated that the BPNN model exhibited the highest classification accuracy of 97.92% for the model calibration and 97.50% for the prediction set. These results suggest that there is potential for the NIR method to detect defects and differentiate between types of defects on the surface of solid wood boards.
关键词： Surface defects,BPNN,PLS - DA,LS-SVM,Near-infrared spectroscopy,Solid wood boards
更新于2025-09-23 15:22:29
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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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Authentication of Grappa (Italian grape marc spirit) by Mid and Near Infrared spectroscopies coupled with chemometrics
摘要： The aim of the present study is to authenticate Grappa spirits and to develop a non-destructive methodology which would allow detecting possible adulteration (by less valuable spirits) on this product. Grappa is an Italian alcoholic drink obtained by distillation of grape marks which has recently received the Geographical Indication (GI) label. As a high added-value product, it is relevant to develop methodologies which allow its authentication and detecting possible frauds (e.g., adulterations); and, whether feasible, it would be suitable to achieve these goals through non-destructive approaches (in order to minimize the economic loss). Mid Infrared (MIR) and Near Infrared (NIR) spectroscopies have been used for the authentication and the characterization of the spirits under investigation. The present work is conceptually divided into two parts: the first one, centered on the authentication of grappa spirits, focused on distinguishing them from other Italian distillates, and a second one aimed at developing an analytical methodology suitable to discern between pure and adulterated grappas. Both classification problems have been investigated by PLS-DA and by three multi-block strategies, i.e., Multi-Block Partial Least Squares (MB-PLS), Sequential and Orthogonalized Partial Least Squares (SO-PLS) and Sequential and Orthogonalized Covariance Selection (SO-CovSel) in order to test whether a data-fusion approach would lead to an improvements of the classification rates. The best results (in terms of predictions) were provided by multi-block strategies; in particular, they provided 100 % of correct classification when applied to discriminate pure and adulterated samples, suggesting these methodologies are definitely suitable for the proposed purpose.
关键词： Adulteration,IR,SO-CovSel,SO-PLS,Data fusion,PLS-DA
更新于2025-09-23 15:19:57
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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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Calibration of near infrared spectroscopy (NIRS) data of three Eucalyptus species with extractive contents determined by ASE extraction for rapid identification of species and high extractive contents
摘要： Plantations of naturally durable timber species could substitute unsustainably harvested wood from tropical forests or wood treated with toxic preservatives. The New Zealand Dryland Forests Initiative (NZDFI) has established a tree-breeding program to develop genetically improved planting stock for durable eucalyptus plantations. In this study the durable heartwood of Eucalyptus bosistoana, Eucalyptus globoidea and Eucalyptus argophloia was characterized by near infrared (NIR) spectroscopy and NIR data was calibrated with the extractives content (EC), determined by accelerated solvent extraction (ASE) extraction, by means of a partial least squares regression (PLSR) model. It was possible to predict the EC content in the range of 0.34–18.9% with a residual mean square error (RMSE) of 0.9%. Moreover, the three species could also be differentiated by NIR spectroscopy with 100% accuracy, i.e. NIR spectroscopy is able to segregate timbers from mixed species forest plantations.
关键词： variable selection (sMC),Eucalyptus argophloia,E. bosistoana,partial least squares regression (PLSR),E. globoidea,PLS-discriminant analysis (PLS-DA)
更新于2025-09-19 17:15:36
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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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Insight into Rapid DNA-Specific Identification of Animal Origin Based on FTIR Analysis: A Case Study
摘要： In this study, a methodology has been proposed to identify the origin of animal DNA, employing high throughput extension accessory Fourier transform infrared (HT-FTIR) spectroscopy coupled with chemometrics. Important discriminatory characteristics were identified in the FTIR spectral peaks of 51 standard DNA samples (25 from bovine and 26 from fish origins), including 1710, 1659, 1608, 1531, 1404, 1375, 1248, 1091, 1060, and 966 cm?1. In particular, the bands at 1708 and 1668 cm?1 were higher in fish DNA than in bovine DNA, while the reverse was true for the band at 1530 cm?1 was shown the opposite result. It was also found that the PO2? Vas/Vs ratio (1238/1094 cm?1) was significantly higher (p < 0.05) in bovine DNA than in fish DNA. These discriminatory characteristics were further revealed to be closely related to the base content and base sequences of different samples. Multivariate analyses, such as principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were conducted, and both the sensitivity and specificity values of PLS-DA model were one. This methodology has been further validated by 20 meat tissue samples (4 from bovine, 5 from ovine, 5 from porcine, and 6 from fish origins), and these were successfully differentiated. This case study demonstrated that FTIR spectroscopy coupled with PLS-DA discriminant model could provide a rapid, sensitive, and reliable approach for the identification of DNA of animal origin. This methodology could be widely applied in food, feed, forensic science, and archaeology studies.
关键词： animal origin,PLS-DA,DNA,rapid identification,FTIR spectroscopy
更新于2025-09-09 09:28:46