Full-spectrum Partial Least Squares Research Articles

Towards improvement in prediction of iodine value in edible oil system based on chemometric analysis of portable vibrational spectroscopic data

Iodine value (IV) is a significant parameter to illustrate the quality of edible oil. In this study, three portable spectroscopy devices were employed to determine IV in mixed edible oil system, a new Micro-Electro-Mechanical-System (MEMS) Fourier Transform Infrared Spectrometer (MEMS-FTIR), a MicroNIRTM1700 and an i-Raman Plus-785S. Quantitative model was built by Partial least squares (PLS) regression model and four variable selection methods were applied before PLS model, which are Monte Carlo uninformative variables elimination (MCUVE), competitive reweighted sampling (CARS), bootstrapping soft shrinkage approach (BOSS) and variable combination population analysis (VCPA). The coefficient of determination (R2), and the root mean square error prediction (RMSEP) were used as indicators for the predictability of the PLS models. In MicroNIRTM1700 dataset, MCUVE gave the lowest RMSEP (2.3440), in MEMS-FTIR dataset, CARS showed the best performance with RMSEP (2.2185), in i-Raman Plus-785S dataset, BOSS gave the lowest RMSEP (2.5058). They all had great improvements than full spectrum PLS model. Four variable selection methods take a smaller number of variables and perform significant superiority in prediction accuracy. It was demonstrated that three new portable instruments would be suitable for the on-site determination of edible oil quality in infrared and Raman field.

Optimization of COD determination by UV–vis spectroscopy using PLS chemometrics algorithms

Ultraviolet–visible (UV–vis) spectroscopy combined with chemometrics tools were used to determine chemical oxygen demand (COD) content in the water. 144 samples needed for the research were collected from the Qian Lake in Nanjing. UV–vis spectra (193.91–1121.69 nm) were collected and processed by various preprocessing methods. The samples were divided into calibration set and prediction set by sample set portioning based on joint x–y distance (SPXY) method. Then the spectra were optimized and modeled by interval partial least squares (iPLS), synergy interval partial least squares (siPLS) and moving windows partial least squares (mwPLS). Optimized COD prediction models were established and compared with full-spectrum partial least squares (PLS) models. Model performance was evaluated by the correlation coefficient of the prediction set (Rpred) and the root mean square error of prediction (RMSEP). The results demonstrate that the prediction results of PLS models established by the three spectral interval selection methods are superior to the full-spectrum PLS model. Furthermore, the siPLS model has the best performance (Rpred = 0.8334; RMSEP = 2.63). Therefore, the siPLS is the best COD prediction model obtained in this research, which can effectively select optimal spectral intervals and improve the prediction performance of the model.

Canopy Hyperspectral Sensing of Paddy Fields at the Booting Stage and PLS Regression can Assess Grain Yield

Canopy hyperspectral (HS) sensing is a promising tool for estimating rice (Oryza sativa L.) yield. However, the timing of HS measurements is crucial for assessing grain yield prior to harvest because rice growth stages strongly influence the sensitivity to different wavelengths and the evaluation performance. To clarify the optimum growth stage for HS sensing-based yield assessments, the grain yield of paddy fields during the reproductive phase to the ripening phase was evaluated from field HS data in conjunction with iterative stepwise elimination partial least squares (ISE-PLS) regression. The field experiments involved three different transplanting dates (12 July, 26 July, and 9 August) in 2017 for six cultivars with three replicates (n = 3 × 6 × 3 = 54). Field HS measurements were performed on 2 October 2017, during the panicle initiation, booting, and ripening growth stages. The predictive accuracy of ISE-PLS was compared with that of the standard full-spectrum PLS (FS-PLS) via coefficient of determination (R2) values and root mean squared errors of cross-validation (RMSECV), and the robustness was evaluated by the residual predictive deviation (RPD). Compared with the FS-PLS models, the ISE-PLS models exhibited higher R2 values and lower RMSECV values for all data sets. Overall, the highest R2 values and the lowest RMSECV values were obtained from the ISE-PLS model at the booting stage (R2 = 0.873, RMSECV = 22.903); the RPD was >2.4. Selected HS wavebands in the ISE-PLS model were identified in the red-edge (710–740 nm) and near-infrared (830 nm) regions. Overall, these results suggest that the booting stage might be the best time for in-season rice grain assessment and that rice yield could be evaluated accurately from the HS sensing data via the ISE-PLS model.

Near-infrared spectroscopy coupled chemometric algorithms for prediction of antioxidant activity of black goji berries (Lycium ruthenicum Murr.)

In this study, near-infrared (NIR) spectroscopy coupled with partial least-squares (PLS) regression and various efficient variable selection algorithms, synergy interval-PLS (Si-PLS), backward interval PLS (Bi-PLS) and genetic algorithm-PLS (GA-PLS) were applied comparatively for the prediction of antioxidant activity in black wolfberry (BW). The eight assays were used for quantification of antioxidant content. The developed models were assessed using correlation coefficients (R2) of the calibration (Cal.) and prediction (Pre.); root mean square error of prediction, RMSEP; standard Error of Cross-Validation, RMSECV and residual predictive deviation, RPD. The performance of the built model greatly improved by the application of Si-PLS, Bi-PLS and GA-PLS compared with full spectrum PLS. The R2 values determined for calibration and prediction set ranged from 0.8479 to 0.9696 and 0.8401 to 0.9638, respectively. These findings revealed that NIR spectroscopy combined with chemometric algorithms can be used for quantification of antioxidant activity in BW samples.

Monitoring alcohol concentration and residual glucose in solid state fermentation of ethanol using FT-NIR spectroscopy and L1-PLS regression

This study aimed to investigate the potential of FT-NIR spectroscopy technique combined with chemometrics method, which employed to monitor time-related changes of alcohol concentration and residual glucose during solid state fermentation (SSF) of ethanol. Characteristic wavelength variables were firstly selected by use of L1-norm regularization approach. Then, the partial least squares (PLS) regression model was finally developed using the variables selected by L1-norm regularization method to quantitative determine alcohol concentration and residual glucose in SSF of ethanol. Compared with the best results of full-spectrum PLS, the L1-PLS model obtained better results as follows: RMSECV = 1.0392 g/L, Rc = 0.9911, RMSEP = 1.0910 g/L, Rp = 0.9917 for alcohol concentration; RMSECV = 1.7002 g/L, Rc = 0.9880, RMSEP = 2.1859 g/L, Rp = 0.9896 for residual glucose. The overall results sufficiently demonstrate that FT-NIR spectroscopy technique coupled with appropriate chemometrics method is a promising tool for monitoring the process of SSF of ethanol.

Advanced chemometrics manipulation of UV-spectroscopic data for determination of three co-formulated drugs along with their impurities in different formulations using variable selection and regression model updating

Multivariate calibration models manipulating UV-spectroscopic data of three anti-productive cough drugs namely ambroxol, guaifenesin and theophylline were constructed for the intent of simultaneous determination in presence of their impurities; guaiacol and caffeine. Both interval partial least squares (iPLS) and synergy interval partial least square (siPLS) algorithms were adopted for variables selection to extract useful information and improve the models' performance. The optimal spectral range and their combinations were assigned according to the lowest value of Root Mean Square Error of Prediction (RMSEP), Standard Error of Prediction (SEP) and Correlation Coefficient (R2). The results obtained from full spectrum PLS were compared with those obtained by iPLS and siPLS. The siPLS method exhibited better performance. The combination of four subintervals, 2, 9, 13, and 16, showed the best effect, with RMSEP of 0.1039, 0.3548 and 0.207 μg/mL, for ambroxol, guaifenesin and theophylline, respectively and correlation coefficient of 0.9999, 0.9975 and 0.9994 for ambroxol, guaifenesin and theophylline, respectively. The proposed methods were used for the simultaneous determination of the three drugs in presence of their impurities in bulk powder and in pharmaceutical formulation.

The Detection and Quantification of Adulteration in Ground Roasted Asian Palm Civet Coffee Using Near-Infrared Spectroscopy in Tandem with Chemometrics

In the present research, an evaluation of using near infrared (NIR) spectroscopy in tandem with full spectrum partial least squares (FS-PLS) regression for quantification of degree of adulteration in civet coffee was conducted. A number of 126 ground roasted coffee samples with degree of adulteration 0-51% were prepared. Spectral data were acquired using a NIR spectrometer equipped with an integrating sphere for diffuse reflectance measurement in the range of 1300-2500 nm. The samples were divided into two groups calibration sample set (84 samples) and prediction sample set (42 samples). The calibration model was developed on original spectra using FS-PLS regression with full-cross validation method. The calibration model exhibited the determination coefficient R2=0.96 for calibration and R2=0.92 for validation. The prediction resulted in low root mean square error of prediction (RMSEP) (4.67%) and high ratio prediction to deviation (RPD) (3.75). In conclusion, the degree of adulteration in civet coffee have been quantified successfully by using NIR spectroscopy and FS-PLS regression in a non-destructive, economical, precise, and highly sensitive method, which uses very simple sample preparation.

Identifying camellia oil adulteration with selected vegetable oils by characteristic near-infrared spectral regions

In this paper, a methodology based on characteristic spectral bands of near infrared spectroscopy (1000–2500[Formula: see text]nm) and multivariate analysis was proposed to identify camellia oil adulteration with vegetable oils. Sunflower, peanut and corn oils were selected to conduct the test. Pure camellia oil and that adulterated with varying concentrations (1–10% with the gradient of 1%, 10–40% with the gradient of 5%, 40–100% with the gradient of 10%) of each type of the three vegetable oils were prepared, respectively. For each type of adulterated oil, full-spectrum partial least squares partial least squares (PLS) models and synergy interval partial least squares (SI-PLS) models were developed. Parameters of these models were optimized simultaneously by cross-validation. The SI-PLS models were proved to be better than the full-spectrum PLS models. In SI-PLS models, the correlation coefficients of predition set (Rp) were 0.9992, 0.9998 and 0.9999 for adulteration with sunflower oil, peanut oil and corn oil seperately; the corresponding root mean square errors of prediction set (RMSEP) were 1.23, 0.66 and 0.37. Furthermore, a new generic PLS model was built based on the characteristic spectral regions selected from the intervals of the three SI-PLS models to identify the oil adulterants, regardless of the adultrated oil types. The model achieved with Rp[Formula: see text] 0.9988 and RMSEP [Formula: see text] 1.52. These results indicated that the characteristic near infrared spectral regions could determine the level of adulteration in the camellia oil.

Vis-NIR Spectroscopy and PLS Regression with Waveband Selection for Estimating the Total C and N of Paddy Soils in Madagascar

Visible and near-infrared (Vis-NIR) diffuse reflectance spectroscopy with partial least squares (PLS) regression is a quick, cost-effective, and promising technology for predicting soil properties. The advantage of PLS regression is that all available wavebands can be incorporated in the model, while earlier studies indicate that PLS models include redundant wavelengths, and selecting specific wavebands can refine PLS analyses. This study evaluated the performance of PLS regression with waveband selection using Vis-NIR reflectance spectra to estimate the total carbon (TC) and total nitrogen (TN) in soils collected mainly from the surface of upland and lowland rice fields in Madagascar (n = 59; after outliers were removed). We used iterative stepwise elimination-based PLS (ISE-PLS) to estimate soil TC and TN and compared the predictive ability with standard full-spectrum PLS (FS-PLS). The predictive abilities were assessed using the coefficient of determination (R2), the root mean squared error of cross-validation (RMSECV), and the residual predictive deviation (RPD). Overall, ISE-PLS using first derivative reflectance (FDR) showed a better predictive accuracy than ISE-PLS for both TC (R2 = 0.972, RMSECV = 0.194, RPD = 5.995) and TN (R2 = 0.949, RMSECV = 0.019, RPD = 4.416) in the soil of Madagascar. The important wavebands for estimating TC (12.59% of all wavebands) and TN (3.55% of all wavebands) were selected from all 2001 wavebands over the 400–2400 nm range using ISE-PLS. These findings suggest that ISE-PLS based on Vis-NIR diffuse reflectance spectra can be used to estimate soil TC and TN contents in Madagascar with an improved predictive accuracy.

Determination of fat content in chicken hamburgers using NIR spectroscopy and the Successive Projections Algorithm for interval selection in PLS regression (iSPA-PLS)

Determining fat content in hamburgers is very important to minimize or control the negative effects of fat on human health, effects such as cardiovascular diseases and obesity, which are caused by the high consumption of saturated fatty acids and cholesterol. This study proposed an alternative analytical method based on Near Infrared Spectroscopy (NIR) and Successive Projections Algorithm for interval selection in Partial Least Squares regression (iSPA-PLS) for fat content determination in commercial chicken hamburgers. For this, 70 hamburger samples with a fat content ranging from 14.27 to 32.12mgkg−1 were prepared based on the upper limit recommended by the Argentinean Food Codex, which is 20% (ww−1). NIR spectra were then recorded and then preprocessed by applying different approaches: base line correction, SNV, MSC, and Savitzky-Golay smoothing. For comparison, full-spectrum PLS and the Interval PLS are also used. The best performance for the prediction set was obtained for the first derivative Savitzky-Golay smoothing with a second-order polynomial and window size of 19 points, achieving a coefficient of correlation of 0.94, RMSEP of 1.59mgkg−1, REP of 7.69% and RPD of 3.02. The proposed methodology represents an excellent alternative to the conventional Soxhlet extraction method, since waste generation is avoided, yet without the use of either chemical reagents or solvents, which follows the primary principles of Green Chemistry. The new method was successfully applied to chicken hamburger analysis, and the results agreed with those with reference values at a 95% confidence level, making it very attractive for routine analysis.

UV-Vis and ATR-FTIR spectroscopic investigations of postmortem interval based on the changes in rabbit plasma.

Estimating PMI is of great importance in forensic investigations. Although many methods are used to estimate the PMI, a few investigations focus on the postmortem redistribution. In this study, ultraviolet–visible (UV–Vis) measurement combined with visual inspection indicated a regular diffusion of hemoglobin into plasma after death showing the redistribution of postmortem components in blood. Thereafter, attenuated total reflection–Fourier transform infrared (ATR–FTIR) spectroscopy was used to confirm the variations caused by this phenomenon. First, full-spectrum partial least-squares (PLS) and genetic algorithm combined with PLS (GA-PLS) models were constructed to predict the PMI. The performance of GA-PLS model was better than that of full-spectrum PLS model based on its root mean square error (RMSE) of cross-validation of 3.46 h (R2 = 0.95) and the RMSE of prediction of 3.46 h (R2 = 0.94). The investigation on the similarity of spectra between blood plasma and formed elements also supported the role of redistribution of components in spectral changes in postmortem plasma. These results demonstrated that ATR-FTIR spectroscopy coupled with the advanced mathematical methods could serve as a convenient and reliable tool to study the redistribution of postmortem components and estimate the PMI.

Application of fluorescence spectroscopy combined with interval partial least squares to the determination of enantiomeric composition of tryptophan

The application of interval partial least squares (iPLS) method to the fluorescence spectroscopy analysis was investigated. The practicability of spectral region selection in the fluorescence spectroscopy analysis was studied. A method which combines fluorescence spectroscopy and iPLS was proposed for determining the enantiomeric composition of tryptophan (Trp). Fluorescence spectroscopy was used to measure the diastereomeric interaction between the enantiomers of Trp and bovine serum albumin, which plays the role of chiral selector. iPLS was used to select the spectral region and build the calibration model between the fluorescence spectral data and enantiomeric composition of Trp. The spectral region from 300nm to 356nm was selected and used to build the calibration model. Leave-one-out cross validation and external test validation were used to validate the obtained models. By selecting the spectral region, iPLS provided better prediction performance, in comparison to the full-spectrum PLS model. The root mean square relative error (RMSRE) of leave-one-out validation has decreased from 10.24 to 7.17, and the RMSRE of external test validation has decreased from 8.59 to 7.06. In addition, four local-spectrum PLS models were developed in order to make a comparison to the iPLS model. The result demonstrates the iPLS model is superior to the four local-spectrum PLS models. The result of this work demonstrates the proposed method is practicable for determining the enantiomeric composition of Trp at trace level. When there is 2.50μmol·L−1 Trp in the samples, the enantiomeric composition can be accurately determined. Moreover, the result demonstrates the selection of spectral region has significant influence on the fluorescence spectroscopy analysis and iPLS is a practicable method for spectral region selection.

Spectroscopic quantitation of tetrazolium formazan in nano-toxicity assay with interval-based partial least squares regression and genetic algorithm

Spectrophotometric quantitation of formazan in tetrazolium-based nanoparticle (NP) toxicity assay requires a robust calibration model immune to optical interference. For the first time, variant of partial least squares (PLS) regression models, such as, full-spectrum (250–700nm) PLS, interval PLS (iPLS), backward interval PLS (biPLS), and synergy interval PLS (siPLS) models have been adopted for formazan quantitation. Models were evaluated based on root mean square error of cross-validation (RMSECV), and prediction (RMSEP). The spectral variables in optimal iPLS, biPLS and siPLS models, as well as variables retained above a selection frequency threshold (for all intervals), were further refined in a genetic algorithm (GA). The results suggest that the optimal biPLS (140 variables, 5 LVs, RMSECV: 0.4438, RMSEP: 0.2936) and siPLS (88 variables, 5 LVs, RMSECV: 0.4401, RMSEP: 0.316) models were superior either to the full-spectrum PLS (4 LVs, RMSECV: 0.9674, RMSEP: 0.4618) or traditional single wavelength calibration (414nm, RMSECV: 2.0864, RMSEP: 2.1628). Minimum RMSEP (0.2976) was observed when GA was performed on spectral variables retained (above a threshold frequency) from the cumulative frequency distribution of all siPLS models. Finally, applicability of the selected PLS regression models in real NP toxicity assay is demonstrated.

Interval partial least squares and moving window partial least squares in determining the enantiomeric composition of tryptophan by using UV-Vis spectroscopy

The application of interval partial least squares (IPLS) and moving window partial least squares (MWPLS) to the enantiomeric analysis of tryptophan (Trp) was investigated. A UV-Vis spectroscopy method for determining the enantiomeric composition of Trp was developed. The calibration model was built by using partial least squares (PLS), IPLS and MWPLS respectively. Leave-one-out cross validation and external test validation were used to assess the prediction performance of the established models. The validation result demonstrates the established full-spectrum PLS model is impractical for quantifying the relationship between the spectral data and enantiomeric composition of L-Trp. On the contrary, the developed IPLS and MWPLS model are both practicable for modeling this relationship. For the IPLS model, the root mean square relative error (RMSRE) of external test validation and leave-one-out cross validation is 4.03 and 6.50 respectively. For the MWPLS model, the RMSRE of external test validation and leave-one-out cross validation is 2.93 and 4.73 respectively. Obviously, the prediction accuracy of the MWPLS model is higher than that of the IPLS model. It is demonstrated UV-Vis spectroscopy combined with MWPLS is a commendable method for determining the enantiomeric composition of Trp. MWPLS is superior to IPLS for selecting spectral region in UV-Vis spectroscopy analysis.

Determination of K value for fish flesh with ultraviolet–visible spectroscopy and interval partial least squares (iPLS) regression method

The objective of this study is to develop a high performance prediction model for K value of fish flesh determination based on ultraviolet–visible (UV–VIS) spectra of fish eye fluid using an interval partial least squares (iPLS) regression method. Several preprocessing methods were initially compared during this study, i.e. moving weighted average smoothing, normalisation, multiplicative scatter correction (MSC), Savitzky-Golay (SG) first and second-order derivative. Full spectrum partial least squares (FS-PLS) and interval partial least squares (iPLS) regression methods were used to develop calibration models using these different pre-processed spectra. Compared to models developed by FS-PLS, the results showed that the regression model developed by iPLS based on MSC preprocessed spectra gave the best performance model with a determination coefficient of prediction ( R p r e d 2 ) of 0.96, and a root mean square error of prediction (RMSEP) of 5.12%.

Estimation of herbage biomass and nutritive status using band depth features with partial least squares regression in Inner Mongolia grassland, China

AbstractAlthough herbage biomass and nutrient status are widely assessed from hyperspectral measurements, certain difficulties are encountered in semiarid and arid regions with low canopy cover. This study investigated the potential of band depth approaches using partial least squares (PLS) regression to estimate herbage biomass and the concentrations of nitrogen (N) and phosphorus (P) in the Inner Mongolia grassland. Field hyperspectral measurements and plant sampling were conducted in desert and typical steppes with different fertilizer levels. The PLS analyses of typical steppe, desert steppe and combined datasets were based on canopy reflectance and first derivative reflectance (FDR) at wavelengths of 400–1000 nm, with consideration of six band depth features extracted from the red absorption region (580–740 nm). The predictive accuracy of the standard full‐spectrum PLS (FS‐PLS) was compared with that of the iterative stepwise elimination PLS (ISE‐PLS) via the cross‐validated coefficient of determination (R2cv) and the ratio of prediction to standard deviation (RPD). In most of the datasets, the ISE‐PLS provided better predictive results than the FS‐PLS. The final models used band depth features to estimate herbage biomass (R2cv = 0.624–0.952, RPD = 1.506–4.539) and pasture N (R2cv = 0.437–0.888, RPD = 1.331–2.869) and reflectance and FDR to estimate pasture P (R2cv = 0.686–0.815, RPD = 1.754–2.267). The models could accurately estimate most of the grass parameters (RPD >1.5), with the exception of pasture N concentrations in the desert steppe dataset due to a range of variation that was too small. The band depth approach with ISE‐PLS improved the predictive ability of the method for estimating herbage biomass and the nutrient contents of grasses in sparse grasslands.

Assessment of Interval PLS (iPLS) Calibration for the Determination of Peroxide Value in Edible Oils

AbstractPartial least squares (PLS) is a commonly used (and sometimes misused) chemometric technique for calibrating Fourier transform infrared spectroscopy, and allows the analysis of a variety of quality parameters associated with edible oils. Peroxide value (PV) is a typical parameter of interest; however, developing a robust, optimal, and reliable calibration method can be a daunting task. This paper examines and compares the use of interval PLS as a tool to develop a PLS PV calibration method for a single‐bounce attenuated total reflectance accessory relative to full spectrum PLS and experienced PLS, making use of correlation, variance, and pure component spectra. Using mixtures of fresh and oxidized oil covering a PV range of 1–20 meq/kg, backward interval PLS could systematically produce quality calibrations without the need to resort to experienced PLS. The experienced PLS requires a degree of spectral knowledge as well as diligent and tedious spectral examination, including largely unstructured iterative calibrations and cross‐validations to improve calibration performance. The backward interval PLS is also better than the full spectrum PLS in terms of model performance. In addition, the general model developed could account for the errors caused by oil types.

A fast and direct spectrophotometric method for the simultaneous determination of methyl paraben and hydroquinone in cosmetic products using successive projections algorithm.

To develop a simple and efficient spectrophotometric technique combined with chemometrics for the simultaneous determination of methyl paraben (MP) and hydroquinone (HQ) in cosmetic products, and specifically, to: (i) evaluate the potential use of successive projections algorithm (SPA) to derivative spectrophotometric data in order to provide sufficient accuracy and model robustness and (ii) determine MP and HQ concentration in cosmetics without tedious pre-treatments such as derivatization or extraction techniques which are time-consuming and require hazardous solvents. The absorption spectra were measured in the wavelength range of 200-350 nm. Prior to performing chemometric models, the original and first-derivative absorption spectra of binary mixtures were used as calibration matrices. Variable selected by successive projections algorithm was used to obtain multiple linear regression (MLR) models based on a small subset of wavelengths. The number of wavelengths and the starting vector were optimized, and the comparison of the root mean square error of calibration (RMSEC) and cross-validation (RMSECV) was applied to select effective wavelengths with the least collinearity and redundancy. Principal component regression (PCR) and partial least squares (PLS) were also developed for comparison. The concentrations of the calibration matrix ranged from 0.1 to 20 μg mL(-1) for MP, and from 0.1 to 25 μg mL(-1) for HQ. The constructed models were tested on an external validation data set and finally cosmetic samples. The results indicated that successive projections algorithm-multiple linear regression (SPA-MLR), applied on the first-derivative spectra, achieved the optimal performance for two compounds when compared with the full-spectrum PCR and PLS. The root mean square error of prediction (RMSEP) was 0.083, 0.314 for MP and HQ, respectively. To verify the accuracy of the proposed method, a recovery study on real cosmetic samples was carried out with satisfactory results (84-112%). The proposed method, which is an environmentally friendly approach, using minimum amount of solvent, is a simple, fast and low-cost analysis method that can provide high accuracy and robust models. The suggested method does not need any complex extraction procedure which is time-consuming and requires hazardous solvents.

Analysis of Antioxidant Activity of Chinese Brown Rice by Fourier-Transformed Near Infrared Spectroscopy and Chemometrics

This paper develops a rapid method using near infrared (NIR) spectroscopy for analyzing the antioxidant activity of brown rice as total phenol content (TPC) and radical scavenging activity by DPPH (2,2-diphenyl-2-picrylhydrazyl) expressed as gallic acid equivalent (GAE). Brown rice (n=121) collected from five producing areas was analyzed for TPC and DPPH by reference methods. The NIR reflectance spectra were measured with compact powders of samples and no treatment was used. Full-spectrum partial least squares (FS-PLS) and interval PLS (iPLS) were used as the regression methods to relate the antioxidant activity values to the NIR data. The spectral range of 4800–5600 cm−1plus 6000–6400 cm−1has the best correlation with TPC, while the range of 4400–5200 cm−1plus 6000–6400 cm−1is the most suitable for predicting DPPH. With standard normal variate (SNV) transformation and the selected wavelength ranges, the root mean squared error of prediction (RMSEP) is 0.062 mg GAE g−1for TPC and 0.141 mg GAE g−1for DPPH radical, respectively. The multiple correlation coefficients of predictions for TPC and DPPH are 0.962 and 0.974, respectively. The developed NIR method might have a potential application to quality control of brown rice in the domestic market.

SPXY Sample Classification Method and Successive Projections Algorithm Combined with Near-Infrared Spectroscopy for the Determination of Total Sugar Content of Southern Xinjiang Jujube

Sugar degree is an important indicator of red jujube internal quality. The main objectives of this paper are to minimize the collinearity between spectral variables, to find the variable groups which containing the lowest redundant information,and establish the model with better robustness by means of fewer variables. This paper uses SPXY (sample set partitioning based on joint x-y distances) to divide calibrating samples,and applies successive projections algorithm (SPA) to select the near-infrared spectral characteristic variable of southern Xinjiang jujube total sugar. To further establish the partial least squares (PLS) model with selected variables. The root mean square error of prediction (RMSEP) of the model is 2.8804. The correlation coefficient of prediction r is 0.9005.To compare the established PLS model results between SPA selecting variables and full spectrum. The results showed that: Firstly, the divided calibrating samples is reasonable in SPXY way.Secondly, SPA optimizes 9 variables of the full spectrum 1557 variables,and prediction effect of the established PLS model is better than the full spectrum PLS model.Finally,SPA can effectively select characteristic wavelength of component under test.