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<b>Multivariate calibration and moisture control in yerba mate by near infrared spectroscopy<b>
摘要： This work describes the development of a multivariate model based on near infrared reflectance spectroscopy (NIR) and partial least squares regression for the prediction of the moisture content in yerba mate samples. The multivariate model based on derivatized and multiplicative sign correction (MSC) spectral signals (4000-8500 cm-1) was elaborated with 3 latent variables, allowing the fast evaluation of the moisture content with average prediction errors of about 2.5%. The minimal manipulation of the samples permits a high analytical speed that facilitates the implementation of quality control operations.
关键词： NIR,online analysis,PLSR
更新于2025-09-23 15:22:29
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Calibration-free quantitative analysis of D/H isotopes with a fs-laser filament
摘要： The analytical characteristics of D/H isotopes with a fs-laser filament are investigated via analyzing a set of D-enriched water samples with D concentrations ranging from 0.5 to 20%. The filament emission spectra feature a narrow peak width and near-zero continuum spectral component. The characteristics of Balmer lines (a, b and g) are evaluated, and the Balmer-a line is selected for isotope analysis. Isotopic information is extracted from filament emission spectra through four different approaches: spectral deconvolution least squares algorithm (SDA), partial least squares regression-internal validation (PLSR-IV), partial least squares regression-cross validation (PLSR-CV) and partial least squares regression-calibration free (PLSR-CF). A multivariate spectral fitting procedure is established in the SDA. Fine structure components (FSCs) of Ha and Da were integrated in the SDA, and it shows improved analytical performance compared to the conventional SDA which is carried out by fitting the experimental spectra with two Lorentzian or Voigt functions. It is also found that the SDA with FSCs gives more accurate results than PLSR-IV and PLSR-CV. Furthermore, the analytical performance is significantly improved by the use of PLSR-CF, in which the PLSR calibration matrix is constructed with a synthetic spectra set. The improvement of accuracy for the given sample set further allows a calibration curve exhibiting an R2 exceeding 0.998 and a slop of 1.009. In addition, the calibration procedure with isotopically enriched standard samples is not necessary in PLSR-CF, demonstrating its flexibility over classical chemometric approaches.
关键词： fs-laser filament,PLSR,spectral deconvolution,calibration-free analysis,Balmer lines,D/H isotopes
更新于2025-09-23 15:21:01
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The First Identification of the Uniqueness and Authentication of Maltese Extra Virgin Olive Oil Using 3D-Fluorescence Spectroscopy Coupled with Multi-Way Data Analysis
摘要： The potential application of multivariate three-way data analysis techniques, namely parallel factor analysis (PARAFAC) and discriminant multi-way partial least squares regression (DN-PLSR), on three-dimensional excitation emission matrix (3D-EEM) fluorescent data were used to identify the uniqueness and authenticity of Maltese extra virgin olive oil (EVOO). A non-negativity constrained PARAFAC model revealed that a four-component model provided the most appropriate solution. Examination of the extracted components in mode 2 and 3 showed that these belonged to different fluorophores present in extra virgin olive oil. Application of linear discriminate analysis (LDA) and binary logistic regression analysis on the concentration of the four extracted fluorophores, showed that it is possible to discriminate Maltese EVOOs from non-Maltese EVOOs. The application of DN-PLSR provided superior means for discrimination of Maltese EVOOs. Further inspection of the extracted latent variables and their variable importance plots (VIPs) provided strong proof of the existence of four types of fluorophores present in EVOOs and their potential application for the discrimination of Maltese EVOOs.
关键词： extra virgin olive oils,DN-PLSR,Maltese islands,PARAFAC,3D-fluorescence
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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Evaluation of Informative Bands Used in Different PLS Regressions for Estimating Leaf Biochemical Contents from Hyperspectral Reflectance
摘要： Partial least squares (PLS) regression models are widely applied in spectroscopy to estimate biochemical components through hyperspectral reflected information. To build PLS regression models based on informative spectral bands, rather than strongly collinear bands contained in the full spectrum, is essential for upholding the performance of models. Yet no consensus has ever been reached on how to select informative bands, even though many techniques have been proposed for estimating plant properties using the vast array of hyperspectral reflectance. In this study, we designed a series of virtual experiments by introducing a dummy variable (Cd) with convertible specific absorption coefficients (SAC) into the well-accepted leaf reflectance PROSPECT-4 model for evaluating popularly adopted informative bands selection techniques, including stepwise-PLS, genetic algorithms PLS (GA-PLS) and PLS with uninformative variable elimination (UVE-PLS). Such virtual experiments have clearly defined responsible wavelength regions related to the dummy input variable, providing objective criteria for model evaluation. Results indicated that although all three techniques examined may estimate leaf biochemical contents efficiently, in most cases the selected bands, unfortunately, did not exactly match known absorption features, casting doubts on their general applicability. The GA-PLS approach was comparatively more efficient at accurately locating the informative bands (with physical and biochemical mechanisms) for estimating leaf biochemical properties and is, therefore, recommended for further applications. Through this study, we have provided objective evaluations of the potential of PLS regressions, which should help to understand the pros and cons of PLS regression models for estimating vegetation biochemical parameters.
关键词： band selection,mechanism,PLSR,hyperspectral reflectance,PROSPECT
更新于2025-09-19 17:15:36
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Potato hierarchical clustering and doneness degree determination by near-infrared (NIR) and attenuated total reflectance mid-infrared (ATR-MIR) spectroscopy
摘要： Near-infrared (NIR) and attenuated total reflectance mid-infrared (ATR-MIR) spectroscopy were used to identify potato varieties and detect potato doneness degree. The varieties of potato tubers can be successfully classified by hierarchical cluster analysis (HCA). The partial least squares regression (PLSR) model exhibited good prediction result for the doneness degree evaluation. Principal component and first-derivative iteration algorithm (PCFIA) was introduced to select feature variables instead of using the full wavelength spectra for modelling. Based on two sets of feature variables selected from NIR and MIR regions, both NIR–PCFIA–HCA and MIR–PCFIA–HCA showed higher performances of hierarchical clustering. Moreover, NIR–PCFIA–PLSR and MIR–PCFIA–PLSR models were effectively used to predict tuber doneness degree, yielding the RP as high as 0.935 and the RMSEP as low as of 0.503. It is concluded that the PCFIA is an effective approach for feature variable selection, and both NIR and MIR spectroscopic techniques are capable of classifying potato varieties and determining potato doneness degree.
关键词： HCA,ATR-MIR,Potato,Variable selection,PLSR,NIR
更新于2025-09-19 17:15:36
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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
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Laser-induced fluorescence spectroscopy for early disease detection in grapefruit plants
摘要： Both biotic and abiotic stress causes considerable decrease in chlorophyll content in plant leaves which provide the means of early disease diagnosis. The emergence of disease affects the fluorescence of phenolic compounds and chlorophyll which have been appeared at 530, 686 and 735 nm. It has been found that the intensity of emission band of phenolic compounds at 530 nm increases and that of chlorophyll at 735 nm decreases with the onset of disease. Statistical analysis through principal component analysis (PCA) and partial least square regression (PLSR) has been performed which demonstrated the classification of apparently healthy leaf sites with diseased ones which provide the basis for the detection of disease at early stages. PLSR model was validated through the coefficient of determination (R2), standard error of prediction (SEP) and standard error of calibration (SEC) with the values 0.99, 0.394 and 0.401 which authenticated the model. The prediction accuracy of the model was evaluated through root mean square error in prediction (RMSEP) of 0.14 by predicting 22 unknown emission spectra of different leaf sites. Both PCA and PLSR models produced similar results and proved fluorescence spectroscopy as an excellent tool for early disease detection in plants.
关键词： Early disease diagnosis,Principal component analysis (PCA),Chlorophyll fluorescence,Partial least square regression (PLSR),Phenolic compounds
更新于2025-09-19 17:13:59
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Hydrogen isotopic analysis using molecular emission from laser-induced plasma on liquid and frozen water
摘要： The hydrogen isotopes in liquid and frozen water samples were analyzed by laser ablation molecular isotopic spectrometry (LAMIS), which employs the molecular emission from laser-induced plasma for isotope analysis. Molecular emission bands of OH and NH radicals were observed, and their spectral features were characterized in accordance with sample matrix and measurement conditions. The OH and NH bands exhibit different emission behavior, which was attributed to formation kinetics and thermodynamic factors. Under optimized condition, isotopic shift between hydrogenated (OH and NH) and deuterated (OD and ND) species was measured at individual rotational branches. By using these molecular bands, the partial least squares regression (PLSR) structure was established for quantitative analysis of hydrogen isotope and evaluated by cross-validation in terms of accuracy and precision. The PLSR result was good with root mean square error of prediction (RMSEP) of 0.3–0.7% for all species. Particularly, the case using OH/OD emission from liquid water revealed the most accurate result with RMSEP of 0.33%. It was affected by the quality and reproducibility of spectral data determined by utilized species. This study not only supports the behavioral understanding of molecular radicals in laser-induced plasma, but also identifies the feasibility of LAMIS for real-time application to quantitative analysis in various sample matrices.
关键词： Hydrogen isotope,LAMIS,Matrix effect,PLSR,Molecular emission band
更新于2025-09-12 10:27:22
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Determination of the superficial citral content on microparticles: An application of NIR spectroscopy coupled with chemometric tools
摘要： This work evaluates near-infrared (NIR) spectroscopy coupled with chemometric tools for determining the superficial content of citral (????????) on microparticles. To perform this evaluation, using spray drying, citral was encapsulated in a matrix of dextrin using twelve combinations of citral:dextrin ratios (CDR) and inlet air temperatures (IAT). From each treatment, six samples were extracted, and their ???????? and NIR absorption spectral profiles were measured. Then, the spectral profiles, pretreated and randomly divided into modeling and validation datasets, were used to build the following prediction models: principal component analysis-multilinear regression (PCA-MLR), principal component analysis-artificial neural network (PCA-ANN), partial least squares regression (PLSR) and an artificial neural network (ANN). During the validation stage, the models showed ??2 values from 0.73 to 0.96 and a root mean squared error (RMSE) range of [0.061–0.140]. Moreover, when the models were compared, the full and optimized ANN models showed the best fits. According to this study, NIR coupled with chemometric tools has the potential for application in determining ???????? on microparticles, particularly when using ANN models.
关键词： Food composition,Food science,Spectroscopy,Food chemistry,MLR,PCA,PLSR,Prediction,Chemometrics,Food analysis,ANN
更新于2025-09-12 10:27:22