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- 实验方案
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The Inspection of CFRP Laminate with Subsurface Defects by Laser Arrays Scanning Thermography (LAsST)
摘要： Laser array scanning thermography (LAsST) was used to detect the subsurface defects of carbon fiber reinforced composite (CFRP). A series of bottom flat hole (BFHs) of CFRP were prepared for LAsST. Truncation pseudo-static matrix reconstruction (TC-PSMR) method was used to reconstruct the thermal response signal. Fast Fourier transform (FFT), principal component analysis (PCA) and partial least squares regression (PLSR) were used to process the thermal response signals, forming FFT image, PCA image, and PLSR image. The signal noise ratios (SNRs) of defects is calculated, and it is used to evaluate the defect detection ability of different post-processing algorithms. The experimental results show that the image based on FFT phase has a higher signal-to-noise ratio with PLSR image, and the FFT amplitude image and PLSR image can accurately represent the defect size.
关键词： FFT,Laser arrays scan thermography,Partial least squares regression,CFRP
更新于2025-09-23 15:19:57
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Coal Discrimination Analysis Using Tandem Laser-Induced Breakdown Spectroscopy and Laser Ablation Inductively Coupled Plasma Time-of-Flight Mass Spectrometry
摘要： The contribution and impact of combined laser ablation inductively coupled plasma time of flight mass spectrometry (LA-ICP-TOF-MS) and laser induced breakdown spectroscopy (LIBS) were evaluated for the discrimination analysis of different coal samples. This Tandem approach allows simultaneous determination of major and minor elements (C, H, Si, Ca, Al, Mg, etc), and trace elements (V, Ba, Pb, U, etc.) in the coal. The research focused on coal classification strategies based on principle component analysis (PCA) combined with K-means clustering, partial least squares discrimination analysis (PLS-DA), and support vector machine (SVM) for analytical performance. Correlation analyses performed from TOF mass and LIBS emission spectra from the coal samples showed that most major, minor, and trace elements emissions had negative correlation with the volatile content. Suitable variables for the classification models were determined from these data. The individual TOF data, LIBS data, and the combined data of TOF and LIBS, respectively, as the input for different models were analyzed and compared. In all cases, the results obtained with the combined TOF and LIBS data were found to be superior to those obtained with the individual TOF or LIBS data. The nonlinear SVM model combined with TOF and LIBS data provided the best coal classification performance, with a classification accuracy of up to 98%.
关键词： Principal component analysis,Support vector machine,Partial least squares discrimination analysis,Laser-induced breakdown spectroscopy,K-means clustering,Coal discrimination,Laser ablation inductively coupled plasma time of flight mass spectrometry
更新于2025-09-23 15:19:57
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Dual-emission CdTe/AgInS2 photoluminescence probe coupled to neural network data processing for the simultaneous determination of folic acid and iron (II)
摘要： This work focused on the combination of CdTe and AgInS2 quantum dots in a dual-emission nanoprobe for the simultaneous determination of folic acid and Fe(II) in pharmaceutical formulations. The surface chemistry of the used QDs was amended with suitable capping ligands to obtain appropriate reactivity in terms of selectivity and sensitivity towards the target analytes. The implementation of PL-based sensing schemes combining multiple QDs of different nature, excited at the same wavelength and emitting at different ones, allowed to obtain a specific analyte-response profile. The first-order fluorescence data obtained from the whole emission spectra of the CdTe/AgInS2 combined nanoprobe upon interaction with folic acid and Fe(II) were processed by using chemometric tools, namely partial least-squares (PLS) and artificial neural network (ANN). This enabled to circumvent the selectivity issues commonly associated with the use of QDs prone to indiscriminate interaction with multiple species, which impair reliable and accurate quantification in complex matrices samples. ANN demonstrated to be the most efficient chemometric model for the simultaneous determination of both analytes in binary mixtures and pharmaceutical formulations due to the non-linear relationship between analyte concentration and fluorescence data that it could handle. The R2P and SEP% obtained for both analytes quantification in pharmaceutical formulations through ANN modelling ranged from 0.92 to 0.99 and 5.7e9.1%, respectively. The obtained results revealed that the developed approach is able to quantify, with high reliability and accuracy, more than one analyte in complex mixtures and real samples with pharmaceutical interest.
关键词： CdTe/AgInS2 combined nanoprobe,Artificial neural network,Iron,Partial least-squares,Folic acid
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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Multiresolution Interval Partial Least Squares: A Framework for Waveband Selection and Resolution Optimization
摘要： Spectroscopic data generated by several PAT technologies is routinely used for the rapid assessment of quality properties in several industrial sectors, such as agrofood, beverages, pharmaceutics, chemicals, pulp & paper, etc. While spectra can easily provide hundreds of measurements across several wavelengths, only a fraction of the collected spectrum conveys relevant information to predict the property of interest. Therefore, the performance of current models is highly related with the ability to select key wavebands, for which the existence of prior knowledge cannot be always secured. Therefore, several feature selection procedures consisting of variants of interval partial least squares (iPLS) have been proposed. These methodologies are however solely focused on determining the most relevant wavebands and do not attempt to further enhance the prediction capabilities within each interval. On the other hand, standard full-spectrum models are often improved by reducing the spectral resolution, but this operation has not been yet synergistically integrated together with waveband selection. As spectral aggregation can effectively improve modelling performance, a multiresolution selection algorithm that simultaneously selects the most relevant wavebands and their optimal resolution is here proposed. By design, this methodology leads to prediction models that are at least as good as the full-spectrum models. The performance comparison made on simulated data and real NIR spectra of gasoline samples also shows that the proposed methodology outperforms iPLS and its variants based on forward and backward selection of intervals, in a statistically significant way.
关键词： Resolution selection,Partial least squares,Forward stepwise selection,Spectroscopy,Interval selection
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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Hyperspectral Imaging for Evaluating Impact Damage to Mango According to Changes in Quality Attributes
摘要： Evaluation of impact damage to mango (Mangifera indica Linn) as a result of dropping from three different heights, namely, 0.5, 1.0 and 1.5 m, was conducted by hyperspectral imaging (HSI). Reflectance spectra in the 900–1700 nm region were used to develop prediction models for pulp firmness (PF), total soluble solids (TSS), titratable acidity (TA) and chroma (?b*) by a partial least squares (PLS) regression algorithm. The results showed that the changes in the mangoes’ quality attributes, which were also reflected in the spectra, had a strong relationship with dropping height. The best predictive performance measured by coefficient of determination (R2) and root mean square errors of prediction (RMSEP) values were: 0.84 and 31.6 g for PF, 0.9 and 0.49 oBrix for TSS, 0.65 and 0.1% for TA, 0.94 and 0.96 for chroma, respectively. Classification of the degree of impact damage to mango achieved an accuracy of more than 77.8% according to ripening index (RPI). The results show the potential of HSI to evaluate impact damage to mango by combining with changes in quality attributes.
关键词： partial least squares regression,quality attributes,impact damage,mango,hyperspectral imaging
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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Quantitative Analysis of Organic Liquid Three-Component Systems: Near-Infrared Transmission versus Raman Spectroscopy, Partial Least Squares versus Classical Least Squares Regression Evaluation and Volume versus Weight Percent Concentration Units
摘要： The band shapes and band positions of near-infrared (NIR) and Raman spectra change depending on the concentrations of specific chemical functionalities in a multicomponent system. To elucidate these effects in more detail and clarify their impact on the analytical measurement techniques and evaluation procedures, NIR transmission spectra and Raman spectra of two organic liquid three-component systems with variable compositions were analyzed by two different multivariate calibration procedures, partial least squares (PLS) and classical least-squares (CLS) regression. Furthermore, the effect of applying different concentration units (volume percent (%V) and weight percent (%W) on the performance of the two calibration procedures have been tested. While the mixtures of benzene/cyclohexane/ethylbenzene (system 1) can be regarded as a blended system with comparatively low molecular interactions, hydrogen bonding plays a dominant role in the blends of ethyl acetate/1-heptanol/1,4-dioxane (system 2). Whereas system 1 yielded equally good calibrations by PLS and CLS regression, for system 2 acceptable results were only obtained by PLS regression. Additionally, for both sample systems, Raman spectra generally led to lower calibration performance than NIR spectra. Finally, volume and weight percent concentration units yielded comparable results for both chemometric evaluation procedures.
关键词： Raman spectroscopy,molecular interactions,organic liquid three-component mixtures,volume/weight percent concentration units,classical least squares (CLS) regression,near-infrared (NIR) spectroscopy,partial least squares (PLS) regression
更新于2025-09-19 17:13:59
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Improved measurement on quantitative analysis of coal properties using laser induced breakdown spectroscopy
摘要： It is of great significance to realize the rapid or online analysis of coal properties for combustion optimization of thermal power plants. In this work, a set of calibration schemes based on laser-induced breakdown spectroscopy (LIBS) was determined to improve the measurement on quantitative analysis of coal properties, including proximate analysis (calorific value, ash, volatile content) and ultimate analysis (carbon and hydrogen). Firstly, different normalization methods (channel normalization and normalization with the whole spectral area) combined with two regression algorithms (partial least-squares regression [PLSR] and support vector regression [SVR]) were compared to initially select the appropriate calibration method for each indicator. Then, the influence of de-noising by the wavelet threshold de-noising (WTD) on quantitative analysis was further studied, thereby the final analysis schemes for each indicator were determined. The results showed that WTD coupled SVR can be well estimated calorific value and ash, the root mean square error of prediction (RMSEP) were 0.80 MJ kg?1 and 0.60%. Coupling WTD and PLSR performed best for the measurement of volatile content, the RMSEP was 0.76%. For the quantitative analysis of carbon and hydrogen, normalization with the whole spectral area combined with SVR can get better measurement results, the RMSEP of the measurements were 1.08% and 0.21%, respectively. The corresponding average standard deviation (RSD) for calorific value, ash, volatile content, carbon and hydrogen of validation sets were 0.26 MJ kg?1, 0.57%, 0.79%, 0.47% and 0.08%, respectively. The results demonstrated that the selection of appropriate spectral pre-processing coupled with calibration strategies for each indicator can effectively improve the accuracy and precision of the measurement on coal properties.
关键词： partial least-squares regression (PLSR),quantitative analysis,normalization,Laser-induced breakdown spectroscopy (LIBS),coal properties,support vector regression (SVR),wavelet threshold de-noising (WTD)
更新于2025-09-19 17:13:59