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Thyroid dysfunction, comprising conditions such as hyperthyroidism and hypothyroidism, represents a substantial global health challenge, necessitating timely and precise diagnosis for effective therapeutic intervention and patient welfare. Conventional diagnostic modalities often involve invasive procedures, that could cause discomfort and inconvenience for individuals. The non-invasive techniques like diffuse reflectance spectroscopy (DRS) can offer a promising alternative. This study underscores the critical role of preprocessing methods in enhancing the accuracy of thyroid hormone functionality through a non-invasive approach. In the proposed study the spectral data acquired from the DRS setup are subjected to different preprocessing techniques to improve the efficacy of the prediction model. Thirty individuals with thyroid dysfunction were included in the study, and preprocessing methods such as baseline correction, multiplicative scatter correction (MSC), and standard normal variate (SNV), were systematically evaluated. The study highlights that SNV preprocessing outperformed other methods with a root mean square error (RMSE) of 0.005 and an R² of 0.99. In contrast, MSC resulted in an RMSE of 0.87 and an R² of 0.86, while baseline correction showed a RMSE of 0.84 and an unusual R² of 1.09, indicating potential issues. SNV proved to be the most effective technique. [ABSTRACT FROM AUTHOR]

A robust method for multiplicative scatter correction (MSC) in infrared spectroscopy is presented. Using quantile regression, the outlier wavelengths (concentration‐dependent wavelengths) that are irrelevant to the regression are identified and therefore excluded from the regression model. This new MCS method, which could be implemented in its simple or extended form, is much simpler than the recently proposed methods and has only one hyperparameter (the quantile value) to be adjusted. To achieve this, a scoring function based on residual analysis can automatically determine the correct quantile value. The method is first explained using simulation data sets and then its validation is explained by analysing some experimental data sets. It was found that our new method can perform well in the presence of strong outlying variables. On the other hand, when the data sets are not associated outlying wavelengths, this method behaves similarly to the conventional MSC method. [ABSTRACT FROM AUTHOR]

Angelica sinensis (Oliv.) diels is a perennial herbaceous plant. Its dried root, referred to as radix Angelicae sinensis (RAS), has been used for edible and medicinal purposes. This study is dedicated to spectroscopic characterization and quality detection of RAS powder material or aqueous solution using fluorescence excitation–emission matrix and near-infrared (NIR) spectroscopies. It is the first time that the fluorescence combined with net analyte signal–classical least-squares was able to accurately determine two active substances in 131 RAS batches from five geographical origins in Gansu, China. The concentrations of Z-ligustilide and L-tryptophan were 1.17 ± 0.43 mg g−1 and 0.59 ± 0.19 mg g−1, respectively. A satisfactory NIR-principal discriminant variate model was achieved, enabling the RAS batches to be discriminated by the location. The correct discrimination rates reached 100% for calibration and 87.50% for validation. The work provides an alternative and holistic strategy for reliable fingerprint authentication of RAS materials and helps promote its utility. [ABSTRACT FROM AUTHOR]

ABSTRACT Cowpea (Vigna unguiculata L. (Walp)) is a multipurpose legume, which has good nutritional properties. Nutritional parameters assessed conventionally can be labour intensive, costly and time taking for germplasm screening. Near‐infrared reflectance spectroscopy (NIRS) is a rapid and nondestructive method, which can facilitate high‐throughput germplasm screening. In our study, estimation of amylose and sugars has been done using NIRS. Two preprocessing methods, that is, SNV‐DT (standard normal variate with detrending) and MSC (multiplicative scatter correction), were performed for optimization of the original spectra. Subsequently, MPLS (modified partial least square) regression method was employed to construct the prediction models. In amylose, the best RSQexternal (coefficient of determination) (0.962) was found in SNV‐DT with mathematical treatment 3,8,8,2. The same result was shown in sugar where the best RSQexternal (0.914) was found in SNV‐DT with mathematical treatment 3,4,4,1. Overall, in the case of amylose and sugars, SNV‐DT was found to be a good preprocessing treatment than MSC. Paired t‐test values in all the treatments for both the preprocessing methods were > 0.05 indicating their reliability. High RSQexternal values for both the traits imply the applicability of the prediction models. Thus, these models can facilitate high‐throughput germplasm screening in different national and international crop improvement programmes focusing on quality traits.

[Display omitted] • Rapid and accurate identification of external water in rainwater pipelines. • A novel use of MSC combined with VMD for absorption spectroscopy analysis. • A new method for small samples identification of UV–Vis absorption spectroscopy. Ultraviolet–visible (UV–Vis) absorption spectroscopy, due to its high sensitivity and capability for real-time online monitoring, is one of the most promising tools for the rapid identification of external water in rainwater pipe networks. However, difficulties in obtaining actual samples lead to insufficient real samples, and the complex composition of wastewater can affect the accurate traceability analysis of external water in rainwater pipe networks. In this study, a new method for identifying external water in rainwater pipe networks with a small number of samples is proposed. In this method, the Generative Adversarial Network (GAN) algorithm was initially used to generate spectral data from the absorption spectra of water samples; subsequently, the multiplicative scatter correction (MSC) algorithm was applied to process the UV–Vis absorption spectra of different types of water samples; following this, the Variational Mode Decomposition (VMD) algorithm was employed to decompose and recombine the spectra after MSC; and finally, the long short-term memory (LSTM) algorithm was used to establish the identification model between the recombined spectra and the water source types, and to determine the optimal number of decomposed spectra K. The research results show that when the number of decomposed spectra K is 5, the identification accuracy for different sources of domestic sewage, surface water, and industrial wastewater is the highest, with an overall accuracy of 98.81%. Additionally, the performance of this method was validated by mixed water samples (combinations of rainwater and domestic sewage, rainwater and surface water, and rainwater and industrial wastewater). The results indicate that the accuracy of the proposed method in identifying the source of external water in rainwater reaches 98.99%, with detection time within 10 s. Therefore, the proposed method can become a potential approach for rapid identification and traceability analysis of external water in rainwater pipe networks. [ABSTRACT FROM AUTHOR]

The objective of this study was to confirm whether near-infrared spectroscopy could be used to discriminate the infected silkworm chrysalises. A total of 105 silkworm chrysalises — 65 infected and 40 uninfected — were collected at Beijing Shoucheng Agricultural Development Co., Ltd. Near-infrared spectra were acquired at the head, chest, abdomen, and posterior belly of each silkworm chrysalis (both uninfected and infected). Three spectral pre-processing methods and four discrimination models were used to identify the uninfected and infected silkworm chrysalises. Results indicated that the PLS–DA model based on the spectra processed by multiplicative scatter correction (MSC) had the best discrimination performance (the prediction accuracy of calibration set and prediction set were 100 and 97.5%, respectively), and the head portion was the best position for the discrimination of uninfected and infected silkworm chrysalises. The overall conclusion was that the uninfected and infected silkworm chrysalises could be successfully identified by using near-infrared spectroscopy technology in the cultivation of Cordyceps militaris. [ABSTRACT FROM AUTHOR]

The on-line detection of insulator pollution degree of transmission lines is important to the prevention and control of flashover. This paper proposed a non-contact detection method of insulator pollution degree based on hyperspectral technique. Firstly, hyperspectral images of the samples with different pollution degrees were obtained by hyper-spectrometer. Secondly, after original hyperspectral images were corrected by black-and-white correction and multiplicative scatter correction, hyperspectral curves from the region of interest (ROI) of corrected images were obtained. Finally, a multiclassification model of extreme learning machine (ELM) was built to realize the pollution degree classification of test samples. The results show that the absorption peak, the position of reflection peak, amplitude and the change trend of the hyperspectral curve obviously change with different kinds of pollution on the surface of silicone rubber, whereas only the amplitude obviously changes with same kind of pollution on the surface of silicone rubber; and the ELM-classification model can accurately and rapidly classify the pollution degree, with the pollution degree classification accuracy of NaCl, CaSO4 and mixed NaCl-CaSO4 respectively reaching 95%, 97.5% and 97.5%; and finally The ELM model based on hyperspectral curves data of the artificial pollution samples can classify the surface of insulator umbrellas with different pollution degrees, and the classification accuracy of CaSO4 and mixed NaCl-CaSO4 samples respectively are 87.5% and 90%. Consequently, the results of this study prove that hyperspectral technique has considerable potential for the non-contact detection of insulator pollution degree.

This study assessed the effect of offset normalizations on variability in kinematic data. The tarsal angles for 12 elderly horses, with mild lameness of the tarsal joint, were measured at the trot pre and post 2 weeks administration of a dietary supplement intended to promote joint health (Corta-Flx, Nature's Own, Aiken, SC). For five strides, pre-and postsupplement, the tarsal angles measured on the flexor side (full exten. = 180° were smoothed, normalized to 101 data points, and averaged. Four offset normalizations were applied: minus standing tarsal angle (Tarsal); minus impact angle (Impact); minus mean angle (Average); multiplicative scatter correction (MSC). For 11 angle variables across the stride there were no significant differences pre-and postsupplement, p > 0.05. Normalization had no effect on the timing of variables or magnitude of angles, but generally the variability in the angles was reduced. Least to greatest reduction occurred with the Tarsal, Impact, Average, then MSC normalizations. The Average and MSC techniques resulted in two and three variables, respectively, becoming significantly different. These differences were small, emphasizing that significant findings should be interpreted for meaningfulness. Normalizations based on the data gave the largest reductions in variability, but these may introduce biases into the data. Thus, normalization with respect to measurements external to data capture may be preferable, but their theoretical and statistical relationship to the kinematic variables should be confirmed. MSC altered the shape of the kinematic trace, which may be misleading. Offset normalizations should be used with care, but they can reduce variability in kinematic data to increase statistical power in biomechanical studies. [ABSTRACT FROM AUTHOR]

Near-infrared spectroscopy (NIRS) is an effective and efficient technique for evaluating coal quality. The original spectra might be contaminated by scattering interference and random noise. We propose a novel artifact removal framework to recover the buried information and to overcome limitations of currently available preprocessing techniques, such as the multiplicative scatter correction (MSC), as well as a smoothing process. The two-step framework is mainly constructed by MSC and Savitzky–Golay convolution (S-SGC). Moreover, a particle swarm optimization (PSO) algorithm is used to search the optimal parameters within the framework. In addition, the spectra are collected from coal samples with different particle sizes (i.e., 0.2 and 3 mm), which may carry different characteristics and interfering information. We have analyzed seven kinds of coal properties, such as moisture (%), ash (%), volatile matter (%), and heating value (MJ/kg) via partial least square regression (PLSR) models in order to verify the effectiveness of the proposed method. The results show that the proposed two-step method provides superior performances for zooming in the spectral characteristic peaks and filtering the random noise simultaneously, which mainly benefits from the appropriate combination of MSC and S-SGC. [ABSTRACT FROM AUTHOR]

Multiplicative scatter correction (MSC) is an overall linear correction method for near-infrared (NIR) diffuse reflectance spectroscopy. The overall correction of MSC is unreasonable for wide spectral wavebands and poor local correlation. Piecewise MSC (PMSC) is a piecewise linear correction method that achieves a significantly better prediction than MSC. However, the PMSC method still cannot achieve global optimal due to the complexity of its algorithm. The window optimisation method for PMSC on the basis of PLS regression was proposed in this study. A 2D traversal algorithm platform for correction parameters p and q was developed. The 2D hotspot distribution of three-level RMSEP M for parameters p and q was used for parameter adaptability analysis. NIR spectroscopy analyses of rice seed purity and soil organic matter were performed for verification. Results show that the optimal models of window optimisation PMSC–PLS of rice seed purity and soil organic matter were significantly better than traditional MSC-PLS models; the predicted errors (RMSEP V) decreased by 20% and 10%, respectively. In addition, moving-window correlation coefficient spectra were proposed to describe the local correlation between spectra. Excessive, insufficient and moderate spectral corrections were revealed. The discovery achieved in this work provides valuable strategies for the parameter selection and method applicability of NIR analysis of complex objects. The conducted experiments are the first to demonstrate the feasibility of using quick and reagent-free NIR spectroscopy in the analysis of the purity of multi-grain rice seeds. • Window Optimization method of PMSC. • Moving-window correlation coefficient spectra. • 2D traversal algorithm platform. • Obviously improved prediction performance. • Rapid purity analysis of multi-grain rice seeds. [ABSTRACT FROM AUTHOR]

Total volatile basic nitrogen (TVB-N) content is used to evaluate Pacific oyster (Crassostrea gigas) freshness. In this work, hyperspectral imaging (HSI; 400-1000 nm) was used to measure the TVB-N content in Pacific oysters. Accordingly, Pacific oyster samples stored in 15 °C water were assessed at intervals after 1, 3, 5, 7, or 9 days. Minimum noise separation processing of the hyperspectral images was performed before determining the region of interest for data dimension reduction. The effects of multiplicative scatter correction (MSC) on the obtained data were then investigated. To simplify the calibration model, 12 characteristic wavelengths were selected from the Pacific oyster hypercube using the correlation coefficient method. Finally, multiple linear regression (MLR) and back-propagation artificial neural network (BP-ANN) models were built from the selected wavelengths. The experimental results showed that the correlation coefficients between the corrected, predicted, and cross-validated datasets were lower in the MLR model than in the BP-ANN. However, the MLR model outperformed the BP-ANN in terms of the root-mean-square errors of correction, prediction, and interaction verification. Overall, both the MLR and BP-ANN models demonstrated that the combination of HSI with chemometric methods can be used to detect and accurately predict Pacific Oyster freshness during storage. [ABSTRACT FROM AUTHOR]

Soil texture is one of the most important indicators of soil physical properties, which has traditionally been measured through laborious procedures. Approaches utilizing visible near-infrared spectroscopy, with their advantages in efficiency, eco-friendliness and non-destruction, are emerging as potent alternatives. Nevertheless, these approaches often suffer from limitations in classification accuracy, and the substantial impact of spectral preprocessing, model integration, and sample matrix effect is commonly disregarded. Here a novel 11-class soil texture classification strategy that address this challenge by combining Multiplicative Scatter Correction (MSC) with Residual Network (ResNet) models was presented, resulting in exceptional classification accuracy. Utilizing the LUCAS dataset, collected by the Land Use and Cover Area frame Statistical Survey project, we thoroughly evaluated eight spectral preprocessing methods. Our findings underscored the superior performance of MSC in reducing spatial complexity within spectral data, showcasing its crucial role in enhancing model precision. Through comparisons of three 1D CNN models and two ResNet models integrated with MSC, we established the superior performance of the MSC-incorporated ResNet model, achieving an overall accuracy of 98.97 % and five soil textures even reached 100.00 %. The ResNet model demonstrated a marked superiority in classifying datasets with similar features, as observed by the confusion matrix analysis. Moreover, we investigated the potential benefit of pre-categorization based on land cover type of the soil samples in enhancing the accuracy of soil texture classification models, achieving overall classification accuracies exceeding 99.39 % for woodland, grassland, and farmland with the 2-layer ResNet model. The proposed work provides a pioneering and efficient strategy for rapid and precise soil texture identification via visible near-infrared spectroscopy, demonstrating unparalleled accuracy compared to existing methods, thus significantly enhancing the practical application prospects in soil, agricultural and environmental science. • A Vis-NIR based soil texture classification approach fusing MSC and ResNet. • Achieved an identification accuracy of 98.97 % across eleven soil textures. • Revealed the pivotal role of MSC in enhancing soil texture classification models. • Explored the promotional effect of pre-categorization strategy based on land cover. [ABSTRACT FROM AUTHOR]

Abstract Multiplicative light scattering has posed great challenge in near-infrared (NIR) quantitative analysis. When estimating the scattering parameters, uninformative variables for scattering effects may bias the estimation. Weighted least squares (WLS) can be used to avoid the influence of the uninformative variables. In this work, we proposed an improved weighted multiplicative scatter correction algorithm with the use of variable selection (WMSCVS). Baseline is removed first and then variable selection is used to obtain the optimal weights of WLS in estimating multiplicative parameters. The variable selection algorithm, which is designed based on model population analysis (MPA), implements an iterative optimization process. In each iteration, weighted bootstrap sampling (WBS) is used to generate variable subsets and exponentially decreasing function (EDF) is used to control the number of sampled variables. The interpretability and stability of the variable selection results as well as the predictive performance of the corrected spectra were investigated by using two NIR datasets. The experimental results showed that the proposed WMSCVS could give better predictive performance than the state-of-art correction methods. [ABSTRACT FROM AUTHOR]

Abstract The aim of this work is to apply and evaluate different chemometric approaches employing several machine learning techniques in order to characterize the moisture content in biomass from data obtained by Near Infrared (NIR) spectroscopy. The approaches include three main parts : a) data pre-processing, b) wavelength selection and c) development of a regression model enabling moisture content measurement. Standard Normal Variate (SNV), Multiplicative Scatter Correction and Savitzky-Golay first (SG 1) and second (SG 2) derivatives and its combinations were applied for data pre-processing. Genetic algorithm (GA) and iterative PLS (iPLS) were used for wavelength selection. Artificial Neural Network (ANN), Gaussian Process Regression (GPR), Support Vector Regression (SVR) and traditional Partial Least Squares (PLS) regression, were employed as machine learning regression methods. Results shows that SNV combined with SG1 first derivative performs the best in data pre-processing. The GA is the most effective methods for variable selection and GPR achieved a high accuracy in regression modeling while having low demands on computation time. Overall, the machine learning techniques demonstrate a great potential to be used in future NIR spectroscopy applications. [ABSTRACT FROM AUTHOR]

Recently, the use of Near Infrared (NIR) spectral sensor in agricultural process is getting much attention, particularly for fruit quality evaluation. The sensor requires a spectrometer to produce some sufficient information called spectrum as interaction between physical matters of the sample with the electromagnetic spectrum. In fact, the presence of experimental error or/and measurement error due to the heterogeneous particle size, moisture content variability, sample density, the instrument noise and pretreatment experience are often cannot be avoided. These would damage the spectra collected which results to decrease the performance in model selection, and increases the prediction error as the harmful influence of possible outlier and leverage points in dataset. To encounter these, a robust pretreatment of NIR spectral data is needed to correct the spectra before it is used for post-processing using any statistical method. In this paper, several different classical pretreatment methods were evaluated and a new robust Generalized Multiplicative Scatter Correction (GMSC) algorithm was proposed to correct the additive and/or multiplicative baseline effects in the spectral data. A dataset of NIR spectral on oil palm ( Elaeis guineensis Jacq.) fruit bunch was used in the simulation. In the simulation, a number of repetitions using the single and double cross validation with robust partial least square are also applied. The Desirability Indices as statistical measures are presented for evaluating the methods. [ABSTRACT FROM AUTHOR]

Water status controls plant physiology and is key to managing vineyard grape quality and yield. Water status is usually estimated by leaf water potential (LWP), which is measured using a pressure chamber; however, this method is difficult, time-consuming, and error-prone. While traditional spectral methods based on leaf reflectance are faster and non-destructive, most are based on vegetation indices derived from satellite imagery (and so only take into account discrete bandwidths) and do not take full advantage of modern hyperspectral sensors that capture spectral reflectance for thousands of wavelengths. We used partial least squares regression (PLSR) to predict LWP from reflectance values (wavelength 350−2500 nm) captured with a field spectroradiometer. We first identified wavelength ranges that minimized regression error. We then tested several common data pre-processing methods to analyze the impact on PLSR prediction precision, finding that derivative pre-processing increased the determination coefficients of our models and reduced root mean squared error (RMSE). The models fitted with raw data obtained their best results at around 1450 nm, while the models with derivative pre-processed achieved their best estimates at 826 nm and 1520 nm.

Spectral absorption index was proposed to extract the morphological features of the spectral curves in pork meat samples ( longissimus dorsi ) under the conditions including fresh, frozen–thawed, heated–dehydrated and brined–dehydrated. Savitzky–Golay (SG) smoothing and multiplicative scatter correction (MSC) were used for calibrating both the spectral reflectance and absorbance values. The absorption values were better than the reflectance values and the calibrated spectra by MSC were better than the raw and SG smoothing corrected spectra in building moisture content predictive models. The optimized partial least square regression (PLSR) model attained good results with the MSC calibrated spectral absorption values based on the spectral absorption index features ( R 2 P = 0.952, RMSEP = 1.396) and the optimal wavelengths selected by regression coefficients ( R 2 P = 0.966, RMSEP = 0.855), respectively. The models proved spectral absorption index was promising in spectral analysis to predict moisture content in pork samples using HSI techniques for the first time. [ABSTRACT FROM AUTHOR]

Data preprocessing and multivariate regression methods are two key factors influencing the model prediction ability of near-infrared (NIR) spectroscopy. The present paper evaluated the application of the combined stationary wavelet transform–support vector machine method for developing juice NIR models. The performance of this method has been compared with other methods, such as stand normal variate–partial least squares, stationary wavelet transform–partial least squares, and stand normal variate–stationary wavelet transform methods. The result showed that compared with other methods, the stationary wavelet transform–support vector machine method can provide good quantitative analysis on saccharose concentration in juice. [ABSTRACT FROM PUBLISHER]

Abstract: This paper investigates the nature of information contained in scatter correction parameters. The study had two objectives. The first objective was to examine the nature and extent of information contained in scatter correction parameters. The second objective is to examine whether this information can be effectively extracted by proposing a method to obtain particularly the mean particle diameter from the scatter correction parameters. By using a combination of experimental data and simulated data generated using fundamental light propagation theory, a deeper and more fundamental insight of what information is removed by the multiplicative scatter correction (MSC) method is obtained. It was found that the MSC parameters are strongly influenced not only by particle size but also by particle concentration as well as refractive index of the medium. The possibility of extracting particle size information in addition to particle concentration was considered by proposing a two-step method which was tested using a 2-component and 4-component data set. This method can in principle, be used in conjunction with any scatter correction technique provided that the scatter correction parameters exhibit a systematic dependence with respect to particle size and concentration. It was found that the approach which uses the MSC parameters gave a better estimate of the particle diameter compared to using partial least squares (PLS) regression for the 2-component data. For the 4 component data it was found that PLS regression gave better results but further examination indicated this was due to chance correlations of the particle diameter with the two of the absorbing species in the mixture. [Copyright &y& Elsevier]

Abstract: Pre-processing of near-infrared (NIR) spectral data has become an integral part of chemometrics modeling. The objective of the pre-processing is to remove physical phenomena in the spectra in order to improve the subsequent multivariate regression, classification model or exploratory analysis. The most widely used pre-processing techniques can be divided into two categories: scatter-correction methods and spectral derivatives. This review describes and compares the theoretical and algorithmic foundations of current pre-processing methods plus the qualitative and quantitative consequences of their application. The aim is to provide NIR users with better end-models through fundamental knowledge on spectral pre-processing. [Copyright &y& Elsevier]

Taking the air-dried samples of five soil types from middle and eastern China as test materials, the correlations of their organic matter content with the spectral reflectance of near-infrared (1000-2500 nm), and with the ratio index (RI), difference index (DI), and normalized difference index (ND) of the first derivative values of the reflectance between two bands were studied. Based on this, the key spectral indices and the quantitative models for estimating soil organic matter (SOM) content were developed. After corrected with Multiplicative Scatter Correction (MSC) and Savitzky-Golay (SG) smoothing methods, the spectral reflectance of near-infrared had an obviously high correlation with SOM, compared with the original spectral reflectance, while the corrected spectral indices of the first derivative values of the reflectance between two bands took the intermediate position. The correlation of the spectral indices with SOM was in the order of was DI > RI > ND, regardless the composition of the original spectral reflectance or the first derivative spectra. The DI of the reflectance of near-infrared between 1883 and 2065 nm corrected with MSC and SG smoothing methods [DI(CR1883, CR2065)] had the best linear correlations with SOM. The test of the monitoring model based on DI(CR1883, CR2065) with the independent datasets of SOM showed that the R2 and RMSE validation values were 0.837 and 4.06, respectively. Comparing with the results from the Partial Least Square (PLS) method, the monitoring model based on DI (CR1883, CR2065) was somewhat inferior. However, the DI(CR1883, CR2065) only needed two reflectance bands, and the monitoring model was simpler, being able to provide more available information for developing portable instruments, and a good spectral index for estimating SOM content. [ABSTRACT FROM AUTHOR]

Abstract: The spectral pre-treatments known as standard normal variate (SNV) and multiplicative scatter correction (MSC) often give very similar results, and are widely regarded as exchangeable. However their geometry in spectral space is not the same, and the results of this are sometimes apparent. SNV has the capacity to induce curved structures in score plots derived from the treated spectra, whilst MSC has a tendency to produce outliers in these plots. These phenomena are illustrated with an example from a near infrared imaging camera, and explained geometrically. [Copyright &y& Elsevier]

Abstract: A fast and simple method for the quantitative analysis of monoclinic (form I) and orthorhombic (form II) paracetamol was developed, based on FT-Raman spectroscopy and PLS regression. Three different preprocessing algorithms, namely orthogonal signal correction (OSC), standard normal variate transformation (SNV) and multiplicative scatter correction (MSC), were applied in order to eliminate effects caused by sample preparation and sample inhomogeneities. Subsequently, PLS regression models were fitted and their predictive performance was evaluated on the basis of the root mean squared error of cross-validation (RMSECV) over the complete data set. Furthermore, the data were split into two equal-sized training and test subsets by the Kennard-Stone design and the errors of calibration (RMSEC) and prediction (RMSEP) were calculated. It was found that the OSC preprocessing contributes to a significant increase in the predictive performance of the PLS regression model (RMSECV=0.500%, RMSEC=0.842% and RMSEP=0.538%) in the overall concentration range of form I, compared to the SNV (RMSECV=2.398%, RMSEC=0.911% and RMSEP=7.177%) and MSC (RMSECV=2.7648%, RMSEC=1.572% and RMSEP=4.838%). In addition, the model developed on OSC preprocessed data is more parsimonious, requiring a single latent variable, compared to three latent variables required by the models fitted to the SNV and MSC preprocessed data. The proposed multivariate calibration presents a significant improvement over existing methods for the quantitation of paracetamol polymorphs. [Copyright &y& Elsevier]

Abstract: Near infrared spectroscopic (NIR; 1100–2500nm), chemical and genetic data were combined to study the pleiotropic secondary effects of mutant genes on milled samples in a barley seed model. NIR and chemical data were both effective in classifying gene and gene combinations by Principal Component Analysis (PCA). Risø mutants R-13, R-29 high (1→3, 1→4)-β-glucan, low starch and R-1508 (high lysine, reduced starch), near isogeneic controls and normal lines and recombinants were studied. Based on proteome analysis results, six anti-microbial proteins were followed during endosperm development revealing pleiotropic gene effects in expression timing that supporting the gene classification. To verify that NIR spectroscopy data represents a physio–chemical fingerprint of the barley seed, physical and chemical spectral components were partially separated by Multiple Scatter Correction and their genetic classification ability verified. Wavelength bands with known water binding and (1→3, 1→4)-β-glucan assignments were successfully predicted by partial least squares regression giving insight into how NIR-data works in classification. Highly reproducible gene-specific, covariate, pleiotropic classification patterns from NIR and chemical data were demonstrated in PCAs and by visual inspection of NIR spectra. Thus PCA classification of NIR-data gives the classical genetic concept, ‘pleiotropy’, a new operational definition as a fingerprint from a spectroscopic representation of the phenome carrying genetic, physical and chemical information. It is concluded that barley seed phenotyping by NIR and chemometrics is a new, reliable tool for characterising the pleiotropic effects of mutant gene combinations and other genotypes in selecting barley for quality in plant breeding. [Copyright &y& Elsevier]

• Chia seeds have different contents of fat and FA depending on the country of origin. • 912, 1600–1800 nm is the wavelength for discriminating the origin of chia seeds. • 912 nm and 1600–1800 nm includes information on fats and FA. • Hyperspectral imaging analysis can be used to discriminate the origin of chia seeds. In this study, hyperspectral imaging was used to study chia seeds grown in Argentina, Paraguay, and Bolivia to determine their origin. Multiplicative scatter correction was the most effective data preprocessing method, with a discrimination prediction accuracy of 0.9111. Discriminative wavelengths obtained from beta coefficients were 912, 1270, 1405, 1611, and 1700 nm, associated O H bonds in water and C H bonds of fat and fatty acids). Crude lipid content and fatty acid composition differed significantly by origin. Palmitic, linoleic and α-linolenic acid values were predicted with R2 values of 0.9735, 0.9729 and 0.9526, respectively, with a partial least squares regression model. Thus, the origins of chia seeds can be used to discriminate using hyperspectral imaging analysis. [ABSTRACT FROM AUTHOR]

The NH 3 -TPD analysis is a costly and tedious method to determine zeolites acidity. Thus, to do so, FTIR spectroscopy was quantitatively used as a fast and cost-effectively method. Back-propagation artificial neural network (BP-ANN) was used for the analysis of multivariate base on the characteristic absorbance of 11 zeolite samples after metal substitution in the ~3612 cm-1 region. The successive projection algorithm (SPA) was conducted for the uninformative variable elimination and feature selection strategies. The effect of pre-processing methods (e.g. MC and MSC) was examined. It is observed after using MSC for minimizing the light scattering effect and signal-to-noise correction, the minimum mean squared error (MSE) value of the testing set data reduced from 5.36 × 10-2 to 2.19 × 10-4 and R tot increases from 0.91 to 0.99. Also, the results of nonparametric Wilcoxon t-test and Sign test methods also confirmed that there is no clear difference between the zeolite acidity obtained by two conventional method and the proposed method. Image 1 • The zeolites FTIR vibration intensity in ~3612 cm-1 after metal loading is reduced. • Back-propagation artificial neural network (BP-ANN) as a multivariate analysis was applied for prediction of zeolites acidity. • Mean centering (MC) and Multiplicative scatter correction (MSC) were used as the preprocessing method. • After MSC, results of BP-ANN (R and MSE) were improved. • The results of nonparametric methods confirmed that there is no clear difference between the zeolite acidity obtained by two methods. [ABSTRACT FROM AUTHOR]

• NIR spectroscopy was used to determine pure cashmere content without drying. • Spectral reconstruction method was proposed to retrive valuable physical information in NIR spectra. • Moisture elimination combing spectral reconstruction method improves prediction accuracy. • The RMSEP achieved 5.18% for moisture-containing scoured cashmere. Pure cashmere content (PCC) is a key parameter for assessing the quality of scoured cashmere. The traditional manual analytical method is quite laborious and time-consuming. A novel method has been developed by using near-infrared (NIR) spectroscopy combined with chemometrics and other mathematical methods. The accuracy of PCC calibration model, which is of great importance in practical application, remains a challenge due to the fact that the NIR diffuse reflectance spectra of scoured cashmere are badly influenced by absorbed moisture and light scattering. To improve the prediction accuracy, moisture elimination was first adopted to decrease the adverse effect of moisture on the spectra by subtracting the spectral contribution of water from the raw spectra. Spectral reconstruction was subsequently designed to take full advantage of the information contained in the residual values obtained after multiplicative scatter correction (MSC) pretreatment to retrieve the light scatter signal correlated with the physical properties of pure cashmere. Both the above two methods can improve the prediction performance of the partial least-squares regression (PLSR) model, and their combination can achieve the optimal model with root mean of square error of prediction (RMSEP) of 5.18 %, which satisfy the accuracy requirement for PCC measurement. The proposed method is rapid, low-cost, and environment-friendly for evaluating the quality of scoured cashmere fibers and shed light on the NIR-based analysis of the complex systems with valuable physical information. [ABSTRACT FROM AUTHOR]

Water status controls plant physiology and is key to managing vineyard grape quality and yield. Water status is usually estimated by leaf water potential (LWP), which is measured using a pressure chamber; however, this method is difficult, time-consuming, and error-prone. While traditional spectral methods based on leaf reflectance are faster and non-destructive, most are based on vegetation indices derived from satellite imagery (and so only take into account discrete bandwidths) and do not take full advantage of modern hyperspectral sensors that capture spectral reflectance for thousands of wavelengths. We used partial least squares regression (PLSR) to predict LWP from reflectance values (wavelength 350–2500 nm) captured with a field spectroradiometer. We first identified wavelength ranges that minimized regression error. We then tested several common data pre-processing methods to analyze the impact on PLSR prediction precision, finding that derivative pre-processing increased the determination coefficients of our models and reduced root mean squared error (RMSE). The models fitted with raw data obtained their best results at around 1450 nm, while the models with derivative pre-processed achieved their best estimates at 826 nm and 1520 nm. [ABSTRACT FROM AUTHOR]

Hyperspectral images of intact grapes during ripening were recorded using a near infrared hyperspectral imaging system (900-1700 nm). Spectral data have been correlated with grape skin total phenolic concentration, sugar concentration, titratable acidity and pH by modified partial least squares regression (MPLS) using a number of spectral pre-treatments and different sets of calibration. The obtained results (RSQ and SEP, respectively) for the global model of red and white grape samples were: 0.89 and 1.23 mg g(-1) of grape skin for total phenolic concentration, 0.99 and 1.37 °Brix for sugar concentration, 0.98 and 3.88 g L(-1) for titratable acidity and for pH 0.94 and 0.12. Moreover, separate calibration models for red and white grape samples were also developed. The obtained results present a good potential for a fast and reasonably inexpensive screening of these parameters in intact grapes and therefore, for a fast control of technological and phenolic maturity., (Copyright © 2013 Elsevier Ltd. All rights reserved.)