Simultaneous determination of total polyphenols and caffeine contents of green tea by near-infrared reflectance spectroscopy

This paper indicates the possibility to use near infrared (NIR) spectroscopy as a rapid method to predict quantitatively the content of caffeine and total polyphenols in green tea. A partial least squares (PLS) algorithm is used to perform the calibration. To decide upon the number of PLS factors included in the PLS model, the model is chosen according to the lowest root mean square error of cross-validation (RMSECV) in training. The correlation coefficient R between the NIR predicted and the reference results for the test set is used as an evaluation parameter for the models. The result showed that the correlation coefficients of the prediction models were R = 0.9688 for the caffeine and R = 0.9299 for total polyphenols. The study demonstrates that NIR spectroscopy technology with multivariate calibration analysis can be successfully applied as a rapid method to determine the valid ingredients of tea to control industrial processes.     

Feasibility study for the use of near infrared spectroscopy in the qualitative and quantitative analysis of green tea, Camellia sinensis (L.)

This paper indicates the possibility to use near infrared spectroscopy (NIR) combined with PLS as a rapid method to estimate the quality of green tea. NIR is used to build calibration models to predict the content of caffeine, epigallocatechin gallate (EGCG) and epicatechin (EC) and for the prediction of the total antioxidant capacity of green tea. For the determination of the total antioxidant capacity, the trolox equivalent antioxidant capacity (TEAC) method is used. Until now, the prediction of the antioxidant capacity as such by use of NIR has not been reported. For caffeine and TEAC, models are build for the whole green tea leaves and also for the ground leaves. For the polyphenols (EGCG and EC), only models for the whole leaves are investigated. A partial least squares (PLS) algorithm is used to perform the calibration. To decide upon the number of PLS factors included in the PLS model, the model with the lowest root mean square error of cross-validation (RMSECV) for the training set is chosen. The correlation coefficient (r) between the predicted and the reference results for the test set is used as an evaluation parameter for the models: for the TEAC results r=0.90 for the model with the whole leaves, r=0.86 for the model with the powdered leaves are obtained. The caffeine prediction model has a correlation coefficient r=0.96 for the whole leaves and r=0.93 for the ground leaves. The correlation coefficient for the EGCG and the EC content models are, respectively 0.83 and 0.44.     

Rapid monitoring of grapevine reserves using ATR–FT-IR and chemometrics

Predictions of grapevine yield and the management of sugar accumulation and secondary metabolite production during berry ripening may be improved by monitoring nitrogen and starch reserves in the perennial parts of the vine. The standard method for determining nitrogen concentration in plant tissue is by combustion analysis, while enzymatic hydrolysis followed by glucose quantification is commonly used for starch. Attenuated total reflectance Fourier transform infrared spectroscopy (ATR–FT-IR) combined with chemometric modelling offers a rapid means for the determination of a range of analytes in powdered or ground samples. ATR–FT-IR offers significant advantages over combustion or enzymatic analysis of samples due to the simplicity of instrument operation, reproducibility and speed of data collection. In the present investigation, 1880 root and wood samples were collected from Shiraz, Semillon and Riesling vineyards in Australia and Germany. Nitrogen and starch concentrations were determined using standard analytical methods, and ATR–FT-IR spectra collected for each sample using a Bruker Alpha instrument. Samples were randomly assigned to either calibration or test data sets representing two thirds and one third of the samples respectively. Signal preprocessing included extended multiplicative scatter correction for water and carbon dioxide vapour, standard normal variate scaling with second derivative and variable selection prior to regression. Excellent predictive models for percent dry weight (DW) of nitrogen (range: 0.10–2.65% DW, median: 0.45% DW) and starch (range: 0.25–42.82% DW, median: 7.77% DW) using partial least squares (PLS) or support vector machine (SVM) analysis for linear and nonlinear regression respectively, were constructed and cross validated with low root mean square errors of prediction (RMSEP). Calibrations employing SVM-regression provided the optimum predictive models for nitrogen (R² = 0.98 and RMSEP = 0.07% DW) compared to PLS regression (R² = 0.97 and RMSEP = 0.08% DW). The best predictive models for starch was obtained using PLS regression (R² = 0.95 and RSMEP = 1.43% DW) compared to SVR (R² = 0.95; RMSEP = 1.56% DW). The RMSEP for both nitrogen and starch is below the reported seasonal flux for these analytes in Vitis vinifera. Nitrogen and starch concentrations in grapevine tissues can thus be accurately determined using ATR–FT-IR, providing a rapid method for monitoring vine reserve status under commercial grape production.     

Infrared spectroscopy and outer product analysis for quantification of fat, nitrogen, and moisture of cocoa powder

The combination of the near infrared (NIR) and Fourier-transform infrared (FTIR) absorbance spectra (1100-2500 nm and 4000-600 cm⁻¹) of 100 cocoa powder samples was used to build calibration models for the determination of the content of fat, nitrogen, and moisture. The samples that comprised the dataset had an average composition of 13.51% of fat, 3.77% nitrogen, and 3.98% moisture. The fat content ranged from 2.42 to 22.00%, the nitrogen from 0.88 to 4.48%, and moisture from 1.60 to 7.80%. For NIR, the relative root mean square error of cross-validation (RMSECV) was 7.0% (R² = 0.96) for fat, 1.7% (R² = 0.98) for nitrogen, and 5.2% (R² = 0.94) for moisture. For FTIR, the relative RMSECV was 10.4% (R² = 0.94) for fat and 3.9% (R² = 0.95) for nitrogen. However, for moisture, it was not possible to build a calibration model with suitable predictability. The combination of the NIR and FTIR domains (data fusion) by outer product analysis PLS1 allowed to predict these parameters and to characterise frequencies in one domain based on the information of the other domain. This work allows to conclude that the second derivative of NIR is the recommended procedure to quantify fat, nitrogen, and moisture content in cocoa powders by infrared spectroscopy.     

Discrimination of transgenic tomatoes based on visible/near-infrared spectra

VIS-NIR spectroscopy combined with multivariate analysis after the appropriate spectral data pre-treatment has been proved to be a very powerful tool for judgment of the relative pattern of the objects that have very similar properties. In this study, seventy transgenic tomatoes with antisense LeETR2 and 94 of their parents, non-transgenic ones were measured in VIS-NIR diffuse reflectance mode. Principal component analysis (PCA), discriminant analysis (DA) and partial least-squares discriminant analysis (PLSDA) were applied to classify tomatoes with different genes into two groups. Calibrations were developed using PLS regression with the leave-one-out cross-validation technique. The results show that differences between transgenic and non-transgenic tomatoes do exist and excellent classification can be obtained after optimizing spectral pre-treatment. The correct classifications for transgenic and non-transgenic tomatoes were both 100% using PLSDA after derivative spectral pre-treatment. The raw spectra with PLSDA model after the second derivative pre-treatment had the best satisfactory calibration and prediction abilities, with r_c = 0.97964, root mean square error of calibration (RMSEC) = 0.099, r_cv = 0.97963, root mean square error of cross-validation (RMSECV) = 0.0993 and a factor. The results in the present study show VIS-NIR spectroscopy together with chemometrics techniques could be used to differentiate transgenic tomato, which offers the benefit of avoiding time-consuming, costly and laborious chemical and sensory analysis.     

Multi-analyte quantification in bioprocesses by Fourier-transform-infrared spectroscopy by partial least squares regression and multivariate curve resolution

This paper presents the quantification of Penicillin V and phenoxyacetic acid, a precursor, inline during Pencillium chrysogenum fermentations by FTIR spectroscopy and partial least squares (PLS) regression and multivariate curve resolution – alternating least squares (MCR-ALS). First, the applicability of an attenuated total reflection FTIR fiber optic probe was assessed offline by measuring standards of the analytes of interest and investigating matrix effects of the fermentation broth. Then measurements were performed inline during four fed-batch fermentations with online HPLC for the determination of Penicillin V and phenoxyacetic acid as reference analysis. PLS and MCR-ALS models were built using these data and validated by comparison of single analyte spectra with the selectivity ratio of the PLS models and the extracted spectral traces of the MCR-ALS models, respectively. The achieved root mean square errors of cross-validation for the PLS regressions were 0.22 g L⁻¹ for Penicillin V and 0.32 g L⁻¹ for phenoxyacetic acid and the root mean square errors of prediction for MCR-ALS were 0.23 g L⁻¹ for Penicillin V and 0.15 g L⁻¹ for phenoxyacetic acid. A general work-flow for building and assessing chemometric regression models for the quantification of multiple analytes in bioprocesses by FTIR spectroscopy is given.     

Rapid analysis of Radix puerariae by near-infrared spectroscopy

A new, rapid analytical method using near-infrared spectroscopy (NIRS) was developed to differentiate two species of Radix puerariae (GG), Pueraria lobata (YG) and Pueraria thomsonii (FG), and to determine the contents of puerarin, daidzin and total isoflavonoid in the samples. Five isoflavonoids, puerarin, daidzin, daidzein, genistin and genistein were analyzed simultaneously by high-performance liquid chromatography-diode array detection (HPLC-DAD). The total isoflavonoid content was exploited as critical parameter for successful discrimination of the two species. Scattering effect and baseline shift in the NIR spectra were corrected and the spectral features were enhanced by several pre-processing methods. By using linear discriminant analysis (LDA) and soft independent modeling class analogy (SIMCA), samples were separated successfully into two different clusters corresponding to the two GG species. Furthermore, sensitivity and specificity of the classification models were determined to evaluate the performance. Finally, partial least squares (PLS) regression was used to build the correlation models. The results showed that the correlation coefficients of the prediction models are R = 0.970 for the puerarin, R = 0.939 for daidzin and R = 0.969 for total isoflavonoid. The outcome showed that NIRS can serve as routine screening in the quality control of Chinese herbal medicine (CHM).     

Potential of visible-near infrared spectroscopy combined with chemometrics for analysis of some constituents of coffee and banana residues

Banana (stalk, leaf, rhizome, rachis and stem) and coffee (leaf and husks) residues are promising feedstock for fuel and chemical production. In this work we show the potential of near-infrared spectroscopy (NIR) and multivariate analysis to replace reference methods in the characterization of some constituents of coffee and banana residues. The evaluated parameters were Klason lignin (KL), acid soluble lignin (ASL), total lignin (TL), extractives, moisture, ash and acid insoluble residue (AIR) contents of 104 banana residues (B) and102 coffee (C) residues from Brazil. PLS models were built for banana (B), coffee (C) and pooled samples (B + C). The precision of NIR methodology was better (p < 0.05) than the reference method for almost all the parameters, being worse for moisture. With the exception of ash (B and C) and ASL (C) content, which was predicted poorly (R² < 0.80), the models for all the analytes exhibited R² > 0.80. The range error ratios varied from 4.5 to 16.0. Based on the results of external validation, the statistical tests and figures of merit, NIR spectroscopy proved to be useful for chemical prediction of banana and coffee residues and can be used as a faster and more economical alternative to the standard methodologies.     

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.     

Determination of polyphenols in oats by near-infrared spectroscopy (NIRS) and two-dimensional correlation spectroscopy

Two-dimensional correlation spectroscopy (2DCOS) and near-infrared spectroscopy (NIRS) were used to determine the polyphenol content in oat grain. A partial least squares (PLS) algorithm was used to perform the calibration. A total of 116 representative oat samples from four locations in China were prepared and the corresponding near-infrared spectra were measured. Two-dimensional correlation spectroscopy was employed to select wavelength bands for the PLS regression model for the polyphenol determination. The number of PLS components and intervals was optimized according to the coefficients of determination (R²) and root mean square error of cross validation (RMSECV) in the calibration set. The performance of the final model was evaluated using the correlation coefficient (R) and the root mean square error of validation (RMSEV) in the prediction set. The results showed the band corresponding to the optimal calibration model was between 1350 and 1848?nm and the optimal spectral preprocessing combination was second derivative with second smoothing. The optimal regression model was obtained with an R² of 0.8954 and an RMSECV of 0.06651 in the calibration set and R of 0.9614 and RMSEV of 0.04573 in the prediction set. These measurements reveal the calibration model had qualified predictive accuracy. The results demonstrated that the 2DCOS with PLS was a simple and rapid method for the quantitative determination of polyphenols in oats.     

Fourier transform near- and mid-infrared spectroscopy can distinguish between the commercially important Pelargonium sidoides and its close taxonomic ally P. reniforme

Pelargonium sidoides is indigenous to South Africa and abundant in the Eastern Cape Province. Several herbal products have been formulated using P. sidoides of which Umckaloabo^® is probably the most popular and successfully marketed in Germany. The objective of this study was to discriminate between P. sidoides and Pelargonium reniforme by FT-IR spectroscopy. Absorbance spectra were collected for P. sidoides (n = 96) and its close taxonomic ally P. reniforme (n = 57) in the near infrared (NIR) and mid infrared (MIR) regions. The spectroscopic data were analysed using chemometric computations including principal component analysis and orthogonal projections to latent structures discriminant analysis. Phytochemical variation of 5.79% in the NIR dataset (R²X(cum) = 0.962; Q²(cum) = 0.918) and 9.22% variation in the MIR dataset (R²X(cum) = 0.497; Q²(cum) = 0.658) was responsible for the separation of the two species. Seven absorption areas were identified as putative biomarkers responsible for the differences between the two species. These results indicate that FT-NIR and FT-MIR spectroscopy can be used to discriminate between these two closely related species which occupy a sympatric distribution in South Africa.     

Prediction of phenolic compounds in red wine fermentations by visible and near infrared spectroscopy

Near infrared (NIR) spectroscopy was used to simultaneously predict the concentrations of malvidin-3-glucoside (M3G), pigmented polymers (PP) and tannins (T) in red wine. A total of 495 samples from 32 commercial scale red wine fermentations over two vintages using two grape varieties (Cabernet Sauvignon and Shiraz), and also including as additional variables two types of fermenters, two different yeasts, and three fermentation temperatures were used. Samples were scanned in transmission mode (400-2500 nm) using a monochromator instrument (NIRSystems6500). Calibration equations were developed from high performance liquid chromatography (HPLC) and NIR data using partial least squares (PLS) regression with internal cross validation. Using PLS regression, very good calibration statistics (R_cal²>0.80) were obtained for the prediction of M3G, PP and T with standard deviation (S.D.)/standard error in cross validation (SECV) ratio (residual predictive deviation, RPD)) ranging from 1.8 to 5.8. It was concluded that near infrared spectroscopy could be used as rapid alternative method for the prediction of the concentration of phenolic compounds in red wine fermentations.     

Melamine detection by mid- and near-infrared (MIR/NIR) spectroscopy: a quick and sensitive method for dairy products analysis including liquid milk, infant formula, and milk powder

Melamine (2,4,6-triamino-1,3,5-triazine) is a nitrogen-rich chemical implicated in the pet and human food recalls and in the global food safety scares involving milk products. Due to the serious health concerns associated with melamine consumption and the extensive scope of affected products, rapid and sensitive methods to detect melamine's presence are essential. We propose the use of spectroscopy data-produced by near-infrared (near-IR/NIR) and mid-infrared (mid-IR/MIR) spectroscopies, in particular—for melamine detection in complex dairy matrixes. None of the up-to-date reported IR-based methods for melamine detection has unambiguously shown its wide applicability to different dairy products as well as limit of detection (LOD) below 1 ppm on independent sample set. It was found that infrared spectroscopy is an effective tool to detect melamine in dairy products, such as infant formula, milk powder, or liquid milk. ALOD below 1 ppm (0.76 ± 0.11 ppm) can be reached if a correct spectrum preprocessing (pretreatment) technique and a correct multivariate (MDA) algorithm—partial least squares regression (PLS), polynomial PLS (Poly-PLS), artificial neural network (ANN), support vector regression (SVR), or least squares support vector machine (LS-SVM)—are used for spectrum analysis. The relationship between MIR/NIR spectrum of milk products and melamine content is nonlinear. Thus, nonlinear regression methods are needed to correctly predict the triazine-derivative content of milk products. It can be concluded that mid- and near-infrared spectroscopy can be regarded as a quick, sensitive, robust, and low-cost method for liquid milk, infant formula, and milk powder analysis.     

Partial least square analysis of lysozyme near-infrared spectra

Proteins possess strong absorption features in the combination range (5000-4000 cm⁻¹) of the near infrared (NIR) spectrum. These features can be used for quantitative analysis. Partial least squares (PLS) regression was used to analyze NIR spectra of lysozyme with the leave-one-out, full cross-validation method. A strategy for spectral range optimization with cross-validation PLS calibration was presented. A five-factor PLS model based on the spectral range between 4720 and 4540 cm⁻¹ provided the best calibration model for lysozyme in aqueous solutions. For 47 samples ranging from 0.01 to 10 mg/mL, the root mean square error of prediction was 0.076 mg/mL. This result was compared with values reported in the literature for protein measurements by NIR absorption spectroscopy in human serum and animal cell culture supernatants.     

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.     

Analysis of berberine and total alkaloid content in cortex phellodendri by near infrared spectroscopy (NIRS) compared with high-performance liquid chromatography coupled with ultra-visible spectrometric detection

This paper developed a rapid method using near infrared spectroscopy (NIRS) to differentiate two species of Cortex Phellodendri (CP), Cortex Phellodendri Chinensis (PCS) and Cortex Phellodendri Amurensis (PAR), and to predict quantitatively the content of berberine and total alkaloid content in all Cortex Phellodendri samples. Three alkaloids, berberine, jatrorrhizine and palmatine were analyzed simultaneously with a Thermo ODS Hypersil column by gradient elution with a new mobile phase under high-performance liquid chromatography-diode array detection (HPLC-DAD). Berberine content determined by HPLC-DAD was exploited as a critical parameter for successful discrimination between them. Multiplicative scatter correction (MSC), second derivative and Savitsky-Golay (S.G.) were utilized together to correct the scattering effect and eliminate the baseline shift in all near infrared diffuse reflectance spectra as well as to enhance spectral features in order to give a better correlation with the results obtained by HPLC-DAD. With the use of principal component analysis (PCA), samples datasets were separated successfully into two different clusters corresponding to two species. Furthermore, a partial least squares (PLS) regression method was built on the correlation model. The results showed that the correlation coefficients of the prediction models were R = 0.996 for the berberine and R = 0.994 for total alkaloid content. The influences of water absorption bands present in the NIR spectra on the models were also investigated in order to explore the practicability of NIRS in routine use. The outcome showed that NIRS possibly acts as routine screening in the quality control of Chinese herbal medicine.     

Characterization and quantification of secondary metabolite profiles in leaves of red and white clover species by NIR and ATR-IR spectroscopy

Different varieties of two clover species (Trifolium pratense L. and Trifolium repens L.), cultivated in 2008 and 2009 were analysed by near-infrared (NIR) and mid-infrared (MIR) spectroscopy for establishing a fast and reliable quantification protocol for isoflavones and phenolic acids. Based on HPLC–UV/MS reference data, good results were obtained by PLS regression for the prediction of total isoflavone (R² = 0.90) as well as for glycitin content (R² = 0.88). Because of the lower concentration of formononetin and phenolic acids, their prediction quality was generally slightly lower (R² = 0.73 and R² = 0.64, respectively) compared to those of the isoflavones. The applicability of ‘leave one out’ cross validation for such a large data set is proven by comparison to an averaged randomized test-set validation leading to similar results. Additionally, the large sample set (n = 624) was screened by hierarchical cluster analysis allowing a fast evaluation of influences resulting from different cultivation parameters on the isoflavone and phenolic acid content. Climatic changes (cultivation year, date of harvest) seem to have the most impact on the metabolic profile as indicated by higher variability in the referring spectra when both cultivation years were simultaneously regarded. This work offers a new vibrational spectroscopic approach for the qualitative and quantitative determination of isoflavone and phenolic acid profiles, directly performed in the plant material without any laborious sample preparation and time-consuming chromatography. Once validated by HPLC reference, MIR and NIR spectroscopy can be used for the reliable prediction of secondary metabolites in clover as well as for fast screening and pre-evaluation of the diversity of a large sample set, aiming to reduce analytical costs, chemical waste and expenditure of time.     

Using near-infrared overtone regions to determine biodiesel content and adulteration of diesel/biodiesel blends with vegetable oils

This work evaluates the use of near-infrared (NIR) overtone regions to determine biodiesel content, as well potential adulteration with vegetable oil, in diesel/biodiesel blends. For this purpose, NIR spectra (12,000–6300 cm⁻¹) were obtained using three different optical path lengths: 10 mm, 20 mm and 50 mm. Two strategies of regression with variable selection were evaluated: partial least squares (PLS) with significant regression coefficients selected by Jack-Knife algorithm (PLS/JK) and multiple linear regression (MLR) with wavenumber selection by successive projections algorithm (MLR/SPA). For comparison, the results obtained by using PLS full-spectrum models are also presented. In addition, the performance of models using NIR (1.0 mm optical path length, 9000–4000 cm⁻¹) and MIR (UATR – universal attenuated total reflectance, 4000–650 cm⁻¹) spectral regions was also investigated. The results demonstrated the potential of overtone regions with MLR/SPA regression strategy to determine biodiesel content in diesel/biodiesel blends, considering the possible presence of raw oil as a contaminant. This strategy is simple, fast and uses a fewer number of spectral variables. Considering this, the overtone regions can be useful to develop low cost instruments for quality control of diesel/biodiesel blends, considering the lower cost of optical components for this spectral region.