Standard Normal Variate Transformation Research Articles

Determination and Visualization of Different Levels of Deoxynivalenol in Bulk Wheat Kernels by Hyperspectral Imaging

A hyperspectral imaging system is proposed as a method to rapidly and nondestructively predict mycotoxin deoxynivalenol (DON) levels in FHB-infected wheat kernels. Standard normal variate transformation and multiplicative scatter correction (MSC) were used in spectral preprocessing. The successive projections algorithm (SPA) and random frog algorithm were used to select the optical wavelengths. Finally, the support vector machine (SVM) technique and partial least squares discriminant analysis were applied to establish different models for determining DON levels. Based on a comparison of the results, the MSC–SPA–SVM model, with the highest classification accuracy (100.00% for the training test and 97.92% for the testing set), gave the best performance, and a visualization map of the DON content level based on this model was created.

Dynamic Localized SNV, Peak SNV, and Partial Peak SNV: Novel Standardization Methods for Preprocessing of Spectroscopic Data Used in Predictive Modeling

An essential part of multivariate analysis in spectroscopic context is preprocessing. The aim of preprocessing is to remove scattering phenomena or disturbances in the spectra due to measurement geometry in order to improve subsequent predictive models. Especially in vibrational spectroscopy, the Standard Normal Variate (SNV) transformation has become very popular and is widely used in many practical applications, but standardization is not always ideal when performed across the full spectrum. Herein, three different new standardization techniques are presented that apply SNV to defined regions rather than to the full spectrum: Dynamic Localized SNV (DLSNV), Peak SNV (PSNV) and Partial Peak SNV (PPSNV). DLSNV is an extension of the Localized SNV (LSNV), which allows a dynamic starting point of the localized windows on which the SNV is executed individually. Peak and Partial Peak SNV are based on picking regions from the spectra with a high correlation to the target value and perform SNV on these essential regions to ensure optimal scatter correction. All proposed methods are able to significantly improve the model performance in cross validation and robustness tests compared to SNV. The prediction errors could be reduced by up to 16% and 29% compared with LSNV for two regression models.

A study on prediction performance of the mechanical properties of rubber using Fourier-transform near infrared spectroscopy

Near infrared spectroscopy is a spectroscopic method used for quality and quantity analysis of agriculture products and industry materials. Rubber is a mostly raw material of any products. NIR spectroscopy had been using to analyze the mechanical properties of rubber and polymer materials. Prediction models were built from the correlation between the NIR spectra and mechanical strength values (hardness and tensile strength). Raw data were pretreated to improve the prediction models, where the prediction models were based on partial least squares regression and support vector regression. In the case of hardness prediction, the raw dataset was pretreated with standard normal variate transformation or a combination of Savitzky–Golay smoothing and multiplicative scatter correction, following which orthogonal signal correction and uninformative variable elimination were used for feature selection, and partial least squares regression and support vector regression were applied for the prediction model. For tensile strength prediction, the pretreatments were multiplicative scatter correction or combination of Savitzky–Golay smoothing and multiplicative scatter correction, following which orthogonal signal correction and uninformative variable elimination were used for feature selection, and partial least squares regression and support vector regression were applied for the prediction model. From these processes, the r2 values were greater than 0.9, the bias values were among ±0.5, and the RMSEP values were lower than 5.

Differentiation of bitumen according to the refinery and ageing state based on FTIR spectroscopy and multivariate analysis methods

The aim of this work was to determine models for the characterisation of bitumen based on the Fourier transform infrared (FTIR) spectroscopy and a chemometrical evaluation of the spectra. For this, 90 bitumen samples were investigated using the attenuated total reflexion technology with multiple reflexions. The gained spectra were divided into two partial spectra with the relevant peaks and pre-processed with a Standard Normal Variate transformation and the first derivative. For a comprehensive evaluation, multivariate analysis methods in terms of the factor analysis and the discriminant analysis were used. Based on the pre-processed spectra and the evaluation methods, models could be determined allowing the differentiation of bitumen samples according to the refinery and the ageing state. Concerning the long-term aged states, a simultaneous consideration of samples from different refineries is possible while, for the differentiation of the unaged and the short-term aged states, the separate consideration of the different refineries is necessary. Thus, the FTIR spectroscopy as a fast investigation method enables the first characterisation of bitumen samples according to the refinery and the ageing state.

Bitumen Characterization with Fourier Transform Infrared Spectroscopy and Multivariate Evaluation: Prediction of Various Physical and Chemical Parameters

As the aim of this work, the characterization of bitumen based on Fourier transform infrared spectroscopy combined with multivariate evaluation was investigated. For this, 90 bitumen samples were analyzed using the attenuated total reflection technology with multiple reflections. The gained spectra were divided into two partial spectra with the relevant peaks and pre-processed with a standard normal variate transformation and the first derivative. For a comprehensive evaluation, a multivariate analysis method in terms of the partial least square regression was used. On the basis of this procedure, models could be determined allowing for the description and prediction of different bitumen parameters, including the penetration, the complex shear modulus, the phase angle, and the asphaltene content. Further, the softening point can be estimated roughly. In contrast, the description of the flexural stiffness a well as the m value capturing the low-temperature behavior and the description of the content of the...

Qualitative and quantitative analysis of chlorpyrifos residues in tea by surface-enhanced Raman spectroscopy (SERS) combined with chemometric models

Surface-enhanced Raman spectroscopy (SERS) combined with chemometric models were employed to develop a rapid, low-cost, and sensitive method for qualitative and quantitative analysis of chlorpyrifos residues in tea. Au@Ag nanoparticles (NPs) with high enhancement factor were synthesized and coupled with chemometric algorithms for SERS measurements. K-nearest neighbors (KNN) classification models gave the best performance model with high classification rates (90.84–100.00%) achieved. For the quantification models for predicting chlorpyrifos contents, the genetic algorithm-partial least squares (GA-PLS) models and synergy interval partial least squares-genetic algorithm (siPLS-GA) models applied to standard normal variate transformation (SNV) preprocessed training and validation data set showed better prediction performances with excellent regression quality (slope = 0.98–1.00), higher correlation coefficient of determination (r2 = 0.96–0.98), and lower root-mean-square error of prediction (RMSEP = 0.29, 0.31) than other quantification models. Paired sample t-test exhibited no statistically significant difference between the reference values determined by GC-MS and the predicted values in most quantification models. The proposed method would be a more effective and powerful tool for classification and determination of chlorpyrifos (CPS) residues in tea samples.

Rapid qualitative and quantitative analysis of methamphetamine, ketamine, heroin, and cocaine by near-infrared spectroscopy

Rapid and nondestructive near infrared spectroscopy (NIR) methods have been developed for simultaneous qualitative and quantitative analysis of methamphetamine, ketamine, heroin, and cocaine in seized samples. This is the first systematic report regarding a qualitative and quantitative procedure of applying NIR for drug analysis. A total of 282 calibration samples and 836 prediction samples were used for the building and validating of qualitative and quantitative models. Two qualitative analysis modeling methods for soft independent modeling by class analogy (SIMCA) and supporting vector machine (SVM) were compared. From its excellent performance in rejecting false positive results, SIMCA was chosen. The drug concentrations in the calibration and validation sample sets were analyzed using high-performance liquid chromatography. Based on the use of first-order derivative spectral data after standard normal variate (SNV) transformation correction, in the wavelength range from 10,000 to 4000cm−1, four partial least squares quantitative-analysis models were built. The coefficients of determination for all calibration models were >99.3, and the RMSEC, RMSECV, and RMSEP were all less than 1.6, 2.9, and 3.6%, respectively. The results obtained here indicated that NIR with chemometric methods was accurate for qualitative and quantitative analysis of drug samples. This methodology provided a potentially useful alternative to time-consuming gas chromatography–mass spectroscopy and high-performance liquid chromatography methods.

Authenticity identification and classification of Rhodiola species in traditional Tibetan medicine based on Fourier transform near-infrared spectroscopy and chemometrics analysis

Rhodiola is an increasingly widely used traditional Tibetan medicine and traditional Chinese medicine in China. The composition profiles of bioactive compounds are somewhat jagged according to different species, which makes it crucial to identify authentic Rhodiola species accurately so as to ensure clinical application of Rhodiola. In this paper, a nondestructive, rapid, and efficient method in classification of Rhodiola was developed by Fourier transform near-infrared (FT-NIR) spectroscopy combined with chemometrics analysis. A total of 160 batches of raw spectra were obtained from four different species of Rhodiola by FT-NIR, such as Rhodiola crenulata, Rhodiola fastigiata, Rhodiola kirilowii, and Rhodiola brevipetiolata. After excluding the outliers, different performances of 3 sample dividing methods, 12 spectral preprocessing methods, 2 wavelength selection methods, and 2 modeling evaluation methods were compared. The results indicated that this combination was superior than others in the authenticity identification analysis, which was FT-NIR combined with sample set partitioning based on joint x-y distances (SPXY), standard normal variate transformation (SNV) + Norris-Williams (NW) + 2nd derivative, competitive adaptive reweighted sampling (CARS), and kernel extreme learning machine (KELM). The accuracy (ACCU), sensitivity (SENS), and specificity (SPEC) of the optimal model were all 1, which showed that this combination of FT-NIR and chemometrics methods had the optimal authenticity identification performance. The classification performance of the partial least squares discriminant analysis (PLS-DA) model was slightly lower than KELM model, and PLS-DA model results were ACCU = 0.97, SENS = 0.93, and SPEC = 0.98, respectively. It can be concluded that FT-NIR combined with chemometrics analysis has great potential in authenticity identification and classification of Rhodiola, which can provide a valuable reference for the safety and effectiveness of clinical application of Rhodiola.

Potential of hyperspectral imaging for rapid identification of true and false honeysuckle tea leaves

Honeysuckle (Lonicerae japonicae Flos, LJF) tea is a favorite cool tea in China and Southeast Asia. However, some unscrupulous traders usually use Lonicera Flos (LF) as LJF to sell for earning high profit. In order to identify true and false honeysuckle tea leaves rapidly and precisely, hyperspectral imaging technology was applied to develop a nondestructive identification model for LJF and LF. Firstly, the original spectral data were analyzed by three pretreatment methods including Savitzky–Golay (SG) convolution smoothing, multiple scatter correct and standard normal variate transformation (SNV). Then, a full-band analysis model was established by using the partial least squares-discriminant analysis method. And after the selection of characteristic wavelengths by regression coefficients algorithm, the identification analysis models based on the back-propagation neural network and extreme learning machine (ELM) discriminant were established. The results showed that the BP neural network and ELM discriminant analysis model based on SNV denoising at 9 characteristic wavelengths could achieve the best identification results. The recognition rates of both modeling sets and forecasting sets could reach 100%. Therefore, the application of hyperspectral imaging technology can identify LJF and LF effectively and nondestructively, and has potential in the identification of true and false honeysuckle tea leaves.

Nondestructive determination of the modulus of elasticity of Fraxinus mandschurica using near-infrared spectroscopy

A nondestructive methodology is proposed to determine the modulus of elasticity (MOE) of Fraxinus mandschurica samples by using near-infrared (NIR) spectroscopy. The test data consisted of 150 NIR absorption spectra of the wood samples obtained using an NIR spectrometer, with the wavelength range of 900 to 1900 nm. To eliminate the high-frequency noise and the systematic variations on the baseline, Savitzky–Golay convolution combined with standard normal variate and detrending transformation was applied as data pretreated methods. The uninformative variable elimination (UVE), improved by the evolutionary Monte Carlo (EMC) algorithm and successive projections algorithm (SPA) selected three characteristic variables from full 117 variables. The predictive ability of the models was evaluated concerning the root-mean-square error of prediction (RMSEP) and coefficient of determination (Rp2) in the prediction set. In comparison with the predicted results of all the models established in the experiments, UVE-EMC-SPA-LS-SVM presented the best results with the smallest RMSEP of 0.652 and the highest Rp2 of 0.887. Thus, it is feasible to determine the MOE of F. mandschurica using NIR spectroscopy accurately.

Hyperspectral imaging as an effective tool for prediction the moisture content and textural characteristics of roasted pistachio kernels

The objective of this study was to develop calibration models for prediction of moisture content and textural characteristics (fracture force, hardness, apparent modulus of elasticity and compressive energy) of pistachio kernels roasted in different conditions (temperatures 90, 120 and 150 °C; times 20, 35 and 50 min and air velocities 0.5, 1.5 and 2.5 m/s) using Vis/NIR hyperspectral imaging and multivariate analysis. The effects of different pre-processing methods and spectral treatments such as normalization [multiplicative scatter correction (MSC), standard normal variate transformation (SNV)], smoothing (median filter, Savitzky–Golay and Wavelet) and differentiation (first derivative, D1 and second derivative, D2) on the obtained data were investigated. The prediction models were developed by partial least square regression (PLSR) and artificial neural network (ANN). The results indicated that ANN models have higher potential to predict moisture content and textural characteristics of roasted pistachio kernels comparing to PLSR models. High correlation was observed between reflectance data and fracture force (R2 = 0.957 and RMSEP = 3.386) using MSC, Savitzky–Golay and D1, compressive energy (R2 = 0.907 and RMSEP = 15.757) using the combination of MSC, Wavelet and D1, moisture content (R2 = 0.907 and RMSEP = 0.179) and apparent modulus of elasticity (R2 = 0.921 and RMSEP = 2.366) employing combination of SNV, Wavelet and D1, respectively. Moreover, Vis–NIR data correlated well with hardness (R2 = 0.876 and RMSEP = 5.216) using SNV, Wavelet and D2. These results showed the capability of Vis/NIR hyperspectral imaging and the central role of multivariate analysis in developing accurate models for prediction of moisture content and textural properties of roasted pistachio kernels.

Chemometric methods applied in the image plane to correct striping noise in hyperspectral chemical images of biomaterials

AbstractArray detectors improve data collection speed in hyperspectral chemical imaging, yet are prone to striping noise patterns in the image plane, which is difficult to remove. This type of noise affects spectral features and disturbs visual impression of the scene. We found that this type of noise depends on the material composition and setting parameters, ie, pixel size, and it also varies in accordance with the signal intensity of the observed wavelength. To address this, we proposed a new correction method on the basis of the application of chemometric techniques in the image plane of each wavelength. To verify the effectiveness of this method, infrared transmission images of the 2″ × 2″ positive, 1951 USAF Hi‐Resolution Target and biomaterial samples were obtained with a 16‐element (8 × 2) pixel array detector. Point detector images of some samples were also acquired and used as reference images. The proposed correction method produced substantial improvements in the visual impression of intensity images. Principal components analysis was performed to inspect spectral changes after preprocessing, and the results suggested that the major spectral features were not altered while the stripes on intensity images were removed. Spectral profiles and principal components analysis loadings inspection confirmed the smoothing ability of this correction method. As traditional preprocessing techniques, standard normal variate and derivative transformation were not able to remove line artifacts, especially on the biomaterial images. Overall, the proposed method was effective for removing striping noise patterns from infrared images with a minimal alteration of the valuable hyperspectral image information.

Quantitative Analysis of Calorific Value of Coal Based on Spectral Preprocessing by Laser-Induced Breakdown Spectroscopy (LIBS)

Calorific value is an essential fuel parameter during the process of energy utilization. Forty-four coal samples with different calorific values were quantitatively analyzed by laser-induced breakdown spectroscopy (LIBS) in this paper. The influences of different spectral preprocessing methods such as smoothing, standard normal variate transformation (SNV), multiplicative scatter correction (MSC), mean centering (MC), and derivation by convolution (Savitzky–Golay) on the quantitative model have been analyzed and compared. The results showed that the quantitative model obtained the best comprehensive performance with the application of the 11 points smoothing combined with the second-order derivation. The correlation coefficient of calibration set, correlation coefficient of validation set, RMSECV, and RMSEP were 0.9909, 0.9972, 0.467, and 0.276 MJ/kg, respectively, which were optimized compared with the traditional partial least-squares (PLS) model by 15.9%, 37.7%, 75.1%, and 88.3%, respectively. It indic...

Study on bruising degree classification of apples using hyperspectral imaging and GS-SVM

Bruising degree can affect the end use and sales price of apples directly. In order to identify the bruising degrees of apples quickly and accurately, using hyperspectral imaging technology, this study proposed a method combining successive projections algorithm (SPA) with support vector machine based on grid search parameter optimization (GS-SVM) to classify and identify apple samples with different degrees of bruising. In the process of research, firstly, random forest method was used to extract spectral data of bruised areas of apples with a high accuracy, then Kennard-Stone algorithm was performed to partition sample set reasonably to improve performance of the model. After comparing different pretreatment methods, standard normal variate (SNV) transformation method with the best performance was selected to process the spectral data. Finally, in order to reduce the time required to build the model, the GS-SVM models were set up based on the characteristic variables selected by SPA and competitive adaptive reweighted sampling (CARS), and the classification results were compared with the results of the model constructed with the full spectra. The experimental results showed that the SNV-SPA-GS-SVM model had the best prediction effect, and the prediction accuracy of apples with four kinds of bruising degrees was 95%.

Predicting the moisture content and textural characteristics of roasted pistachio kernels using Vis/NIR reflectance spectroscopy and PLSR analysis

The objective of this study is to develop calibration models in order to predict the moisture content and textural characteristics (including fracture force, hardness, apparent modulus of elasticity, and compressive energy) of pistachio kernels roasted at different temperatures (90, 120 and 150 °C), times (20, 35 and 50 min), and air velocities (0.5, 1.5 and 2.5 m/s) by using visible/near infrared spectroscopy and PLSR. The effects of different pre-processing methods and spectra treatments such as normalization, multiplicative scatter correction (MSC), standard normal variate transformation, median filter, Savitzky–Golay, wavelet, and differentiation (first derivative (D1) and second derivative (D2)) were analyzed. High correlation between Vis/NIR measurements with fracture force and apparent modulus of elasticity were obtained with the combination of SNV, wavelet, and D1. NIR measurements correlated well with hardness and compressive energy. Meanwhile, moisture content had correlation with SNV, median filter, D1; SNV, Savitzky–Golay, D1 and MSC, median filter, D1 respectively.

Determination of Magnesium Oxide Content in Mineral Medicine Talcum Using Near-Infrared Spectroscopy Integrated with Support Vector Machine.

In this research paper, a fast, quantitative, analytical model for magnesium oxide (MgO) content in medicinal mineral talcum was explored based on near-infrared (NIR) spectroscopy. MgO content in each sample was determined by ethylenediaminetetraacetic acid (EDTA) titration and taken as reference value of NIR spectroscopy, and then a variety of processing methods of spectra data were compared to establish a good NIR spectroscopy model. To start, 50 batches of talcum samples were categorized into training set and test set using the Kennard-Stone (K-S) algorithm. In a partial least squares regression (PLSR) model, both leave-one-out cross-validation (LOOCV) and training set validation (TSV) were used to screen spectrum preprocessing methods from multiplicative scatter correction (MSC), and finally the standard normal variate transformation (SNV) was chosen as the optimal pretreatment method. The modeling spectrum bands and ranks were optimized using PLSR method, and the characteristic spectrum ranges were determined as 11995-10664, 7991-6661, and 4326-3999 cm-1, with four optimal ranks. In the support vector machine (SVM) model, the radical basis function (RBF) kernel function was used. Moreover, the full spectrum data of samples pretreated with SNV, the characteristic spectrum data screened using synergy interval partial least squares (SiPLS), and the scoring data of the first four ranks obtained by a partial least squares (PLS) dimension reduction of characteristic spectrum were taken as input variables of SVM, and the MgO content reference values of various sample were taken as output values. In addition, the SVM model internal parameters were optimized using the grid optimization method (GRID), particle swarm optimization (PSO), and genetic algorithm (GA) so that the optimal C and g-values were determined and the validation model was established. By comprehensively comparing the validation effects of different models, it can be concluded that the scoring data of the first four ranks obtained by PLS dimension reduction of characteristic spectrum were taken as input variables of SVM, and the PLS-SVM regression model established using GRID was the optimal NIR spectroscopy quantitative model of talc. This PLS-SVM regression model (rank = 4) measured that the MgO content of talcum was in the range of 17.42-33.22%, with root mean square error of cross validation (RMSECV) of 2.2127%, root mean square error of calibration (RMSEC) of 0.6057%, and root mean square error of prediction (RMSEP) of 1.2901%. This model showed high accuracy and strong prediction capacity, which can be used for rapid prediction of MgO content in talcum.

Potential of visible and near-infrared reflectance spectroscopy for the determination of rare earth elements in soil

The simple and rapid determination of rare earth elements (REE) in soil is essential for the assessment of their environmental and health risks. Inductively coupled plasma-mass spectrometry (ICP-MS) and -emission spectrometry (ICP-ES) are most commonly used for the determination of REE. However, both require complex sample preparation. In this study, we investigated the potential of visible and near-infrared (Vis-NIR) reflectance spectroscopy for determining REE in soil. We collected 130 soil samples near REE mines. Their Vis-NIR spectra, which range from 350 to 2500nm, were measured after air-drying and grinding in the laboratory. Partial least squares regression (PLSR) was selected to calibrate the spectra and REE contents measured by ICP-MS. The results showed that the absorption features of REE were not distinct in the soil spectra, such as for Nd at 798nm, which were masked by soil components, particularly by iron oxides because they have overlapping spectral features with REE. However, when using the spectra in the 400–1000nm range, good or reasonable accuracy [R2>0.5 and residual prediction deviation (RPD)>1.4] was observed for La, Pr, Nd, Sm, and total light REE (∑La–Eu excluding Pm) because their spectral features could be identified by PLSR calibration. The spectral pretreatments of the first derivative, standard normal variate transformation, and multiplicative scatter correction did not improve the prediction accuracy of REE compared with Savitzky-Golay smoothing. This study provides a simple and rapid method for determining REE in soil, particularly for La, Pr, Nd, Sm, and total light REE.

Non-destructive quality assessment of hens’ eggs using hyperspectral images

Freshness of hens’ eggs is important for consumers and the food processing industry and the Haugh unit (HU) is a commonly used index for freshness. Measurement of HU is destructive and also assumes that the sample that is tested accurately reflects the batch of eggs being processed. This research tests the use reflectance near infrared hyperspectral imaging in the wavelength range of 900–1700 nm for nondestructive prediction of eggs freshness and compared these measurements to HU. To achieve this fresh eggs were stored at 25 °C and were measured after storage for 0, 4, 7, 10, 14, 18 and 21 days by hyperspectral imaging technique and compared to HU for each egg. Hyperspectral imaging technique combines between conventional imaging and NIR spectroscopy to achieve spatial and spectral information from eggs. The acquired near infrared hyperspectral imaging data from samples in the calibration set were analyzed in order to develop a calibration model for HU using partial least squares regression (PLSR) and then crossvalidated. The standard normal variate transformation (SNV) spectral pretreatment gave the optimum conditions for establishing the calibration model with a coefficient of determination (R2) of 0.91 and root mean square error of calibration (RMSEC) of 4.58. Distribution maps of HU were generated from the acquired calibration model by interpretation of predicted HU to different colors using image processing algorithms. Displayed colors of acquired image of eggs were different correspond to the freshness of the eggs based on HU. The results show that the near infrared hyperspectral imaging technique can be possible to use for presenting the images of egg related to HU in order to nondestructively evaluate hens’ eggs freshness.

Temperature compensation for determination of moisture and reducing sugar of longan honey by near infrared spectroscopy

Near infrared spectra of honeys are affected by sample temperature variation, mainly due to a change in hydrogen bonding of water. The aim of this study was to develop robust and powerful calibration models which can compensate for a variation of sample temperature for the determination of moisture and reducing sugar content in honey using near infrared spectroscopy. Partial least squares regression with the aid of standard normal variate transformation was used to develop three calibration models at constant temperature (25, 35 and 45℃) and a robust calibration model with temperature compensation. All the developed models for moisture and reducing sugar content showed high performance of prediction with coefficient of determination ( r2) and residual prediction deviation values greater than 0.95 and 3.8, respectively. The results show that the temperature compensation model can be considered as a robust calibration model for near infrared determination of moisture and reducing sugar in the honey when sample temperature is varied.

Detection of unexpected frauds: Screening and quantification of maleic acid in cassava starch by Fourier transform near-infrared spectroscopy.

Fourier transform near-infrared (FT-NIR) spectroscopy and chemometrics were adopted for the rapid analysis of a toxic additive, maleic acid (MA), which has emerged as a new extraneous adulterant in cassava starch (CS). After developing an untargeted screening method for MA detection in CS using one-class partial least squares (OCPLS), multivariate calibration models were subsequently developed using least squares support vector machine (LS-SVM) to quantitatively analyze MA. As a result, the OCPLS model using the second-order derivative (D2) spectra detected 0.6%(w/w) adulterated MA in CS, with a sensitivity of 0.954 and specificity of 0.956. The root mean squared error of prediction (RMSEP) was 0.192(w/w, %) by using the standard normal variate (SNV) transformation LS-SVM. In conclusion, the potential of FT-NIR spectroscopy and chemometrics was demonstrated for application in rapid screening and quantitative analysis of MA in CS, which also implies that they have other promising applications for untargeted analysis.