Comparison of calibrations for the determination of soluble solids content and pH of rice vinegars using visible and short-wave near infrared spectroscopy

Visible and short-wave near infrared (Vis/SWNIR) spectroscopy combined with chemometrics was investigated for the fast determination of soluble solids content (SSC) and pH values of rice vinegars. Two hundred and twenty-five samples (45 for each variety) were selected randomly for the calibration set, whereas, 75 samples (15 for each variety) for the validation set, and the remaining 25 samples for the independent set. After some preprocessing, partial least squares (PLS) analysis was implemented for calibration models with different wavelength bands including visible, SWNIR and Vis/SWNIR regions. The best PLS models were achieved with Vis/SWNIR (550–1000 nm) region. Furthermore, different latent variables (5, 6, 7, 8 LVs) were used as inputs of least squares-support vector machine (LS-SVM) to develop the LV-LS-SVM models with grid search technique and RBF kernel. The optimal models were obtained with 6 LVs and they outperformed PLS models. Moreover, effective wavelengths (EWs) were selected according to regression coefficients. The EW-LS-SVM models were developed and an excellent prediction precision was achieved, and the effectiveness of the EWs was also validated. The correlation coefficient (r), root mean square error of prediction (RMSEP) and bias for the best prediction by EW-LS-SVM were 0.999, 0.189 and 0.051 for SSC, whereas 0.999, 0.008 and −1.7 × 10⁻³ for pH, respectively. The overall results indicated that Vis/SWNIR spectroscopy combined with LS-SVM could be applied as a high precision and fast way for the determination of SSC and pH values of rice vinegars.     

Variable selection in visible/near infrared spectra for linear and nonlinear calibrations: A case study to determine soluble solids content of beer

Three effective wavelength (EW) selection methods combined with visible/near infrared (Vis/NIR) spectroscopy were investigated to determine the soluble solids content (SSC) of beer, including successive projections algorithm (SPA), regression coefficient analysis (RCA) and independent component analysis (ICA). A total of 360 samples were prepared for the calibration (n = 180), validation (n = 90) and prediction (n = 90) sets. The performance of different preprocessing was compared. Three calibrations using EWs selected by SPA, RCA and ICA were developed, including linear regression of partial least squares analysis (PLS) and multiple linear regression (MLR), and nonlinear regression of least squares-support vector machine (LS-SVM). Ten EWs selected by SPA achieved the optimal linear SPA-MLR model compared with SPA-PLS, RCA-MLR, RCA-PLS, ICA-MLR and ICA-PLS. The correlation coefficient (r) and root mean square error of prediction (RMSEP) by SPA-MLR were 0.9762 and 0.1808, respectively. Moreover, the newly proposed SPA-LS-SVM model obtained almost the same excellent performance with RCA-LS-SVM and ICA-LS-SVM models, and the r value and RMSEP were 0.9818 and 0.1628, respectively. The nonlinear model SPA-LS-SVM outperformed SPA-MLR model. The overall results indicated that SPA was a powerful way for the selection of EWs, and Vis/NIR spectroscopy incorporated to SPA-LS-SVM was successful for the accurate determination of SSC of beer.     

Discrimination of Rhizoma Corydalis from two sources by near-infrared spectroscopy supported by the wavelet transform and least-squares support vector machine methods

Near-infrared spectroscopy (NIRS) was applied for direct and rapid collection of characteristic spectra from Rhizoma Corydalis, a common traditional Chinese medicine (TCM), with the aim of developing a method for the classification of such substances according to their geographical origin. The powdered form of the TCM was collected from two such different sources, and their NIR spectra were pretreated by the wavelet transform (WT) method. A training set of such Rhizoma Corydalis spectral objects was modeled with the use of the least-squares support vector machines (LS-SVM), radial basis function artificial neural networks (RBF-ANN), partial least-squares discriminant analysis (PLS-DA) and K-nearest neighbors (KNN) methods. All the four chemometrics models performed reasonably on the basis of spectral recognition and prediction criteria, and the LS-SVM method performed best with over 95% success on both criteria. Generally, there are no statistically significant differences in all these four methods. Thus, the NIR spectroscopic method supported by all the four chemometrics models, especially the LS-SVM, are recommended for application to classify TCM, Rhizoma Corydalis, samples according to their geographical origin.     

Determination of α-linolenic acid and linoleic acid in edible oils using near-infrared spectroscopy improved by wavelet transform and uninformative variable elimination

This paper proposes an analytical method for simultaneous near-infrared (NIR) spectrometric determination of α-linolenic and linoleic acid in eight types of edible vegetable oils and their blending. For this purpose, a combination of spectral wavelength selection by wavelet transform (WT) and elimination of uninformative variables (UVE) was proposed to obtain simple partial least square (PLS) models based on a small subset of wavelengths. WT was firstly utilized to compress full NIR spectra which contain 1413 redundant variables, and 42 wavelet approximate coefficients were obtained. UVE was then carried out to further select the informative variables. Finally, 27 and 19 wavelet approximate coefficients were selected by UVE for α-linolenic and linoleic acid, respectively. The selected variables were used as inputs of PLS model. Due to original spectra were compressed, and irrelevant variables were eliminated, more parsimonious and efficient model based on WT-UVE was obtained compared with the conventional PLS model with full spectra data. The coefficient of determination (r²) and root mean square error prediction set (RMSEP) for prediction set were 0.9345 and 0.0123 for α-linolenic acid prediction by WT-UVE-PLS model. The r² and RMSEP were 0.9054, 0.0437 for linoleic acid prediction. The good performance showed a potential application using WT-UVE to select NIR effective variables. WT-UVE can both speed up the calculation and improve the predicted results. The results indicated that it was feasible to fast determine α-linolenic acid and linoleic acid content in edible oils using NIR spectroscopy.     

An evaluation of orthogonal signal correction methods for the characterisation of arabica and robusta coffee varieties by NIRS

Two orthogonal signal correction methods (OSC and DOSC) were applied on a set of 83 roasted coffee NIR spectra from varied origins and varieties in order to remove information unrelated to a specific chemical response (caffeine), which was selected due to its high discriminant ability to differentiate between arabica and robusta coffee varieties. These corrected NIR spectra, as well as raw NIR spectra and three chemical quantities (caffeine, chlorogenic acids and total acidity), were used to develop separate classification models accordingly using the potential functions method as a class-modelling technique in order to evaluate their respective capacities to discriminate between coffee varieties and the influence of these pre-processing methods on the classification of the coffee samples into their corresponding variety class. The transformation of roasted coffee NIR spectra by means of an orthogonal signal correction method, taking into account in this correction a chemical response closely related to the sample origin, prompted a notable improvement in the specificity of the constructed classification models.     

Identification of metabolomic markers of lavender and lavandin essential oils using mid-infrared spectroscopy

Lavender (Lavandula angustifolia) and lavandin (sterile hybrid of L. angustifolia P. Mill. × Lavandula latifolia (L.f.) Medikus) are widely cultivated in the Mediterranean area for produce essential oils. In this study, 80 lavandin and 55 lavender essential oil samples from various varieties were analyzed. Firstly, a chemometric treatment of mid-infrared spectra was used to evaluate the capacity of Partial Least Squares Discriminant Analysis (PLS-DA) regression to discriminate French lavandin and lavender essential oil (EO) samples and their varieties (Abrial, Fine, Grosso, Maillette, Matherone, Sumian and Super), and secondly, to quantify the main compounds such as linalyl acetate, linalool, eucalyptol and camphor by PLS regression using reference data from gas chromatography. The examination of PLS and PLS-DA regression coefficients allowed the identification of metabolomic markers. The lavender/lavandin EOs and their varieties were very well classified (100% for lavender/lavandin EOs and between 98 and 100% for varieties). The calibration models obtained by PLS regression for the determination of the main compound contents revealed good correlation (≥0.86) between the predicted and reference values. This method can be used to control the authenticity and traceability of lavender/lavandin and their varieties. Finally, mid-infrared and Raman spectroscopy results were compared.     

Prediction of wood property in Chinese Fir based on visible/near-infrared spectroscopy and least square-support vector machine

A method for the quantification of density of Chinese Fir samples based on visible/near-infrared (vis–NIR) spectrometry and least squares-support vector machine (LS-SVM) was proposed. Sample set partitioning based on joint x–y distances (SPXY) algorithm was used for dividing calibration and prediction samples, it is of value for prediction of property involving complex matrices. A stepwise procedure is employed to select samples according to their differences in both x (instrumental responses) and y (predicted parameter) spaces. For comparison, the models were also constructed by Kennard–Stone method, as well as by using the duplex and random sampling methods for subset partitioning. The results revealed that the SPXY algorithm may be an advantageous alternative to the other three strategies. To validate the reliability of LS-SVM, comparisons were made among other modeling methods such as support vector machine (SVM) and partial least squares (PLS) regression. Satisfactory models were built using LS-SVM, with lower prediction errors and superior performance in relation to SVM and PLS. These results showed possibility of building robust models to quantify the density of Chinese Fir using near-infrared spectroscopy and LS-SVM combined SPXY algorithm as a nonlinear multivariate calibration procedure.     

Screening of humic and fulvic acids in estuarine sediments by near-infrared spectrometry

Diffuse reflectance near-infrared spectroscopy (NIR) combined with partial least squares (PLS) data treatment has been employed for the rapid and nondestructive determination of sedimentary humic substances. Forty one samples of surface estuarine sediments, taken during distinct seasonal periods from different locations across Ria de Arousa (northwest of Spain), were scanned at wavelengths from 833 to 2,976 nm (12,000 to 3,360 cm⁻¹). Twenty four samples were randomly selected, from previous hierarchical cluster analysis of their NIR spectra, for the calibration set, and the 17 remaining samples were assigned to the validation set. NIR spectra of calibration samples were correlated to measured values of humic acids (HAs) and fulvic acids (FAs), which ranged from 1.53 to 28.17 mg/g and from 0.37 to 2.45 mg/g, respectively, using PLS regression and multiplicative scattering correction on the raw and first-derivative NIR spectra, respectively. Low root mean square error of prediction values of 4.3 mg HA/g sediment and 0.25 mg FA/g sediment were obtained. Good residual prediction deviation values of 1.16 and 1.2 were obtained for HA and FA, respectively, allowing the PLS models built to be considered as appropriate tools for screening purposes. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Simultaneous chemiluminescence determination of thebaine and noscapine using support vector machine regression

In this work, a batch chemiluminescence (CL) method has been proposed for the simultaneous determination of two structurally similar alkaloids, noscapine and thebaine. The method is based on the kinetic distinction of the CL reactions of noscapine and thebaine with Ru(bipy)₃²⁺ and Ce(IV) system in a sulfuric acid medium. The least squared support vector machine (LS-SVM) regression was applied for relating the concentrations of both compounds to their CL profiles. The parameters of the model consisting of σ² and γ were optimized by constructing LS-SVM models with all possible combinations of these two parameters to select the model with the minimum root mean squared error of cross validation (RMSECV) as the best. The parameters of this model were then selected as optimized values. Under the optimized experimental conditions for both compounds, the detection limits obtained using the LS-SVM regression were 0.08 and 0.1 μmol L^?1 for noscapine and thebaine, respectively. The proposed method was utilized for the simultaneous determination of the compounds in pharmaceutical formulations and plasma samples with satisfactory results.     

最小二乘支持向量机算法与紫外光谱法用于鉴别清开灵注射液四混中间体

采用一阶导数数据预处理,最小二乘支持向量机(LS-SVM)紫外可见光谱建模,对清开灵注射液四混中间体进行质量评价。以二次网格法和十折交叉验证法优化建模参数,预测集的总正确率和接受器工作特性曲线(ROC)下面积分别可达98.0%和0.983。结果表明,与经典的支持向量机相比,LSSVM鉴别准确率更高,模型的泛化能力更强。可用于清开灵注射液生产过程中质量控制,为中药注射液生产过程的质量控制提供了一条有效的途径。     

Classification and quantification analysis of Radix scutellariae from different origins with near infrared diffuse reflection spectroscopy

A rapid near infrared spectroscopy analysis method was developed for the geographical origin discrimination and content determination of Radix scutellariae, a kind of Traditional Chinese Medicine (TCM). 81 R. scutellariae samples from six different origins were analyzed with HPLC-UV as reference method. The NIR spectra were collected in integrating-sphere diffused reflection mode and processed with different spectra pretreated methods. Discriminant analysis (DA) and discriminant partial least squares (DPLS) were applied to classify the geographical origins of those samples, and the latter had a better predictive ability with 100% accuracy after two exceptional samples eliminated from the calibration set. For the quantitative calibration, the samples were divided into calibration set and validation set by Kennard-Stone algorithm. The models of baicalin, wogonoside, baicalein, wogonin were established with partial least squares (PLS) algorithm and the optimal principal component (PC) numbers were selected with Leave-One-Out (LOO) cross-validation. The established models were evaluated with the root mean square error of prediction (RMSEP) and corresponding correlation coefficients. The correlation coefficients of all the four calibration models are above 0.920, and the RMSEPs of baicalin, wogonoside, baicalein and wogonin are 0.752%, 0.094%, 0.418% and 0.139%, respectively. This research indicated that the NIR diffuse reflection spectroscopy could be used for the rapid analysis of R. scutellariae, which is beneficial to the quality control of this raw material in TCM pharmaceutical factory, and will also help to solve analogous problems.     

Coffee varietal differentiation based on near infrared spectroscopy

Near infrared spectroscopy (NIRS) was used to discriminate between arabica and robusta pure coffee varieties and blends of varied varietal composition. Direct orthogonal signal correction (DOSC) pre-processing method was applied on a set of 191 roasted coffee NIR spectra from both pure varieties and blends varying the final robusta content from 0 to 60% (w/w) in order to remove information unrelated to the actual varietal composition of samples. The corrected NIR spectra, as well as raw NIR spectra, were used to develop separate classification models using the potential functions method as class-modelling technique, exploring several options more or less restrictive according to the final number of considered categories. All constructed classification models were compared to evaluate their respective qualities and to show the suitability of applying DOSC method as pre-processing step for developing improved classification models for coffee varietal identification purposes.     

Sample classification for improved performance of PLS models applied to the quality control of deep-frying oils of different botanic origins analyzed using ATR-FTIR spectroscopy

The selection of an appropriate calibration set is a critical step in multivariate method development. In this work, the effect of using different calibration sets, based on a previous classification of unknown samples, on the partial least squares (PLS) regression model performance has been discussed. As an example, attenuated total reflection (ATR) mid-infrared spectra of deep-fried vegetable oil samples from three botanical origins (olive, sunflower, and corn oil), with increasing polymerized triacylglyceride (PTG) content induced by a deep-frying process were employed. The use of a one-class-classifier partial least squares-discriminant analysis (PLS-DA) and a rooted binary directed acyclic graph tree provided accurate oil classification. Oil samples fried without foodstuff could be classified correctly, independent of their PTG content. However, class separation of oil samples fried with foodstuff, was less evident. The combined use of double-cross model validation with permutation testing was used to validate the obtained PLS-DA classification models, confirming the results. To discuss the usefulness of the selection of an appropriate PLS calibration set, the PTG content was determined by calculating a PLS model based on the previously selected classes. In comparison to a PLS model calculated using a pooled calibration set containing samples from all classes, the root mean square error of prediction could be improved significantly using PLS models based on the selected calibration sets using PLS-DA, ranging between 1.06 and 2.91% (w/w).     

基于粒子群算法的最小二乘支持向量机在红花提取液近红外定量分析中的应用

提出一种基于粒子群算法的最小二乘支持向量机(PSO-LS-SVM)方法,用于建立红花提取过程关键质控指标的定量分析模型.近红外光谱数据经波段选择、预处理和主成分分析(降维)后,利用粒子群优化(PSO)算法对最小二乘支持向量机算法中的参数进行优化,然后使用最优参数建立固含量和羟基红花黄色素A(HSYA)浓度的定量校正模型.将校正结果与偏最小二乘法回归(PLSR)和BP神经网络(BP-ANN)比较,并将所建的3个模型用于红花提取过程未知样本的预测.结果表明,BP-ANN校正结果优于PSO-LS-SVM和PLSR,但是对验证集和未知样品集的预测能力较差,而PSO-LS-SVM和PLSR模型的校正、验证结果相近,相关系数均大于0.987,RMSEC和RMSEP值相近且小于0.074,RPD值均大于6.26,RSEP均小于5.70％.对于未知样品集,pSO-LS-SVM模型的RPD值大于8.06,RMSEP和RSEP值分别小于0.07％和5.84％,较BP-ANN和PLSR模型更低.本研究所建立的PSO-LS-SVM模型表现出较好的模型稳定性和预测精度,具有一定的实践意义和应用价值,可推广用于红花提取过程的近红外光谱定量分析.     

FT-MIR and UV–vis data fusion strategy for origins discrimination of wild Paris Polyphylla Smith var. yunnanensis

Paris Polyphylla Smith var. yunnanensis (Franch.) Hand.-Mazz has multiple therapeutic properties and the origins may affect clinical efficacy. Tracing the geographical origin is important to the authentication and quality assessment of this species. 177 wild samples collected from central, southeast and northwest Yunnan Province, China, were analyzed by single analytical method and data fusion strategies (low- and mid-levels) using Fourier transform mid-infrared (FT-MIR) and ultraviolet-visible (UV–vis) spectroscopies combined with chemometrics (partial least squares discrimination analysis (PLS-DA) and support vector machines grid search (SVM-GS)), for categorizing samples from different geographic origins. According to the results, mid-level data fusion strategy presented a better generalization performance and accuracy rates based on latent variables selected by PLS-DA than single analytical method and low-level data fusion strategy. Accuracy rates were almost 100% when both of the PLS-DA and SVM-GS were employed for classifying samples picked from southeast and northwest districts based on mid-level dataset. For samples collected from central of Yunnan where was divided into seven categories in this paper, the accuracy rates of training set and test set of PLS-DA and SVM-GS were preferable (>87%). Based on the mid-level data set, both of the classification results of PLS-DA and SVM-GS presented satisfying accuracy for 177 samples. Additionally, as small as possible parameters showed in mid-level data set, it suggested that this method was robust and generalized. Therefore, the comprehensive method was established for the origin traceability of wild P. Polyphylla Smith var. yunnanensis, which is meaningful for the quality control of herbal medicines.     

Multivariate calibration on NIR data: development of a model for the rapid evaluation of ethanol content in bakery products

A new NIR method based on multivariate calibration for determination of ethanol in industrially packed wholemeal bread was developed and validated. GC-FID was used as reference method for the determination of actual ethanol concentration of different samples of wholemeal bread with proper content of added ethanol, ranging from 0 to 3.5% (w/w). Stepwise discriminant analysis was carried out on the NIR dataset, in order to reduce the number of original variables by selecting those that were able to discriminate between the samples of different ethanol concentrations. With the so selected variables a multivariate calibration model was then obtained by multiple linear regression. The prediction power of the linear model was optimized by a new “leave one out” method, so that the number of original variables resulted further reduced.     

Application of probabilistic neural networks in qualitative analysis of near infrared spectra: determination of producing area and variety of loquats

Near infrared (NIR) spectra of a sample can be treated as a signature, allowing samples to be grouped on basis of their spectral similarities. Near infrared spectroscopy (NIRS) combined with probabilistic neural networks (PNN) have been used to discriminate producing area and variety of loquats. Two varieties of loquats (‘Dahongpao’ and ‘Jiajiaozhong’) picked from two producing areas of ‘Tangxi’ and ‘Cunan’ in Zhejiang province were analyzed in this study. Principal component analysis (PCA) was applied before PNN modeling and the results indicated that the dimension of the vast spectral data can be effectively reduced. For each model, half samples were used to train the network and the remaining half were used to test the network. The results of the PCA-PNN models for discriminating the variety of samples from the same producing area or for discriminating the producing area of the same variety samples were much better than those of the PCA-PNN models for discriminating variety or producing area of all loquat samples. The results of this study show that NIRS combined with PCA-PNN is a feasible way for qualitative analysis of discriminating fruit producing areas and varieties.     

Discrimination of biological and chemical threat simulants in residue mixtures on multiple substrates

The potential of laser-induced breakdown spectroscopy (LIBS) to discriminate biological and chemical threat simulant residues prepared on multiple substrates and in the presence of interferents has been explored. The simulant samples tested include Bacillus atrophaeus spores, Escherichia coli, MS-2 bacteriophage, α-hemolysin from Staphylococcus aureus, 2-chloroethyl ethyl sulfide, and dimethyl methylphosphonate. The residue samples were prepared on polycarbonate, stainless steel and aluminum foil substrates by Battelle Eastern Science and Technology Center. LIBS spectra were collected by Battelle on a portable LIBS instrument developed by A3 Technologies. This paper presents the chemometric analysis of the LIBS spectra using partial least-squares discriminant analysis (PLS-DA). The performance of PLS-DA models developed based on the full LIBS spectra, and selected emission intensities and ratios have been compared. The full-spectra models generally provided better classification results based on the inclusion of substrate emission features; however, the intensity/ratio models were able to correctly identify more types of simulant residues in the presence of interferents. The fusion of the two types of PLS-DA models resulted in a significant improvement in classification performance for models built using multiple substrates. In addition to identifying the major components of residue mixtures, minor components such as growth media and solvents can be identified with an appropriately designed PLS-DA model.     

Classification of Brazilian Coffee Using Near-Infrared Spectroscopy and Multivariate Calibration

This work describes the use of near infrared spectroscopy (NIRS) and chemometric techniques calibration for the classification of coffee samples from different lots and producers acquired in supermarkets and roasting industries in some Brazilian cities. Seventy-three samples of finely ground roasted coffee were acquired in the market and 91 samples of roasted ground Arabica beans were analyzed in the full NIR spectral range (800–2500 nm) using a diffuse reflectance accessory coupled to an MB160 Bomem spectrophotometer. Two classification models were constructed: Soft Independent Modeling Class Analogy (SIMCA) and PLS Discriminant Analysis (PLS-DA). All findings reveal that NIR spectroscopy, coupled with either SIMCA or PLS-DA multivariate models, can be a useful tool to differentiate roasted coffee grains and to replace sensory tests.     

Pareto calibration with built-in wavelength selection

In multivariate regression, it is often reported that wavelength selection can improve results. Improvement is often solely based on bias measures such as the root mean square error of calibration (RMSEC) and root mean square error of validation (RMSEV), R² for the calibration and validation, etc. In recent studies, it has been shown that when variance measures are included, Pareto optimal models can be determined. However, variance measures used to date do not provide the ability to choose wavelength subset models relative to full wavelength models when wavelength subset models may be the Pareto models. In this paper, simplex optimization is used with a more complete variance measure to generate Pareto optimal models. The standard basis set is used as well a basis set that includes the range and null space of the calibration spectra. Results show that it is possible to identify Pareto optimal models and if a wavelength subset is best, these are the models found. Regression coefficients for non-essential wavelengths are zero to near zero.