Rapid quality evaluation of Plantaginis Semen by near infrared spectroscopy combined with chemometrics.

OBJECTIVE: To establish a rapid and comprehensive method for the quality evaluation of Plantaginis Semen (PS) by using near-infrared spectroscopy combined with chemometrics to determine the content of geniposidic acid (GEA), verbascoside (VE), isoverbascoside (IVE) and total flavonoids (TF) in PS, as well as the antioxidant activity (AOA). METHODS: The content of GEA, VE and IVE in PS were determined by HPLC, the content of TF in PS was determined by UV-Vis spectrophotometry, and the AOA of PS was characterized by the DPPH, ABTS and FRAP, respectively. Then, the measured values of each item were used as reference values and were correlated with the near infrared spectra of PS. Seven quantitative models were established by the partial least squares regression. A variety of spectral preprocessing, such as standard normal variation (SNV), multiplicative scatter correction (MSC), Savitzky-Golay smoothing (SG), derivative and their combination methods, were investigated. In addition, genetic algorithm (GA), particle swarm optimization (PSO), as well as competitive adaptive reweighted sampling (CARS) were also compared. All samples were divided into a calibration set and a prediction set at the ratio of 3:1 by the descending order of reference values. The coefficients of determination (R2), root mean square error (RMSE) and residual predictive deviation (RPD) were calculated to evaluate model performance. RESULTS: After optimization, the performance of each model was greatly improved, where the R2 for calibration and prediction were both greater than 0.8, the RPD were both greater than 2. Such satisfactory results indicated that the present models had good prediction accuracy. CONCLUSION: Quantitative models based on near infrared spectroscopy were herein established, which proved to be able to quickly and accurately determine the content of GEA, VE and IVE and TF, as well as AOA in PS, and which might provide a new method for rapid and comprehensive quality evaluation of PS.

Rapid detection and quantification of adulteration in Chinese hawthorn fruits powder by near-infrared spectroscopy combined with chemometrics.

The aim of this study is to explore the feasibility of detection and quantification of two cheap adulterants (maltodextrin and starch) in Chinese functional food, hawthorn fruits powder (HFP), by using near infrared (NIR) spectroscopy coupled with chemometrics methods. The partial least squares discriminant analysis (PLS-DA) models were developed to discriminate the adulterated HFP from the authentic HFP, while the partial least squares regression (PLSR) models were employed to determine the contents of adulterants. In order to yield the best results, various spectra pretreatment methods and wavelength selection methods were carefully investigated. The models' qualities were assessed by the self-consistency test, the independent test and the rigorous leave-one-out cross-validation test. The metrics for the PLS-DA discriminative model included error rate, true positive rate, true negative rate and F1 score, while the metrics for the PLSR quantitative model were determination coefficient, root mean square error and residual prediction deviation. Finally, very satisfying results were obtained, which indicate that our method is quite robust and applicable, and thus has great potential for rapid detection of adulteration in powder of many other herbal plants or functional foods.

Rapid determination of polysaccharides and antioxidant activity of Poria cocos using near-infrared spectroscopy combined with chemometrics.

This study evaluates the feasibility of near-infrared (NIR) spectroscopy combined with chemometrics as a fast and efficient technique to predict the polysaccharide content and antioxidant activity of Poria cocos. The reference values of polysaccharide content were determined by the phenol-sulfuric acid method, and the antioxidant activities were determined by the DPPH scavenge assay, FRAP scavenge assay and ABTS scavenge assay, respectively. The partial least squares regression algorithm was used to relate the spectra to the reference values. Various methods for spectra pretreatment and variable selection were optimized to improve the predictability and stability of the models. As a result, the best models yielded very satisfying results, of which the values of coefficients of determination were all >0.94, and the values of residual predictive deviation were all >4. Such results confirmed that the present method is robust and applicable, and thus has good potential for rapid quality evaluation of Poria cocos.

Quality evaluation of decoction pieces of Rhizoma Atractylodis Macrocephalae by near infrared spectroscopy coupled with chemometrics.

OBJECTIVE: To establish a fast, simple and reliable method for quality evaluation of decoction pieces of Rhizoma Atractylodis Macrocephalae (referred as BZ below) by near infrared spectroscopy coupled with chemometrics. METHOD: Twelve batches of raw medicinal materials of BZ were collected from three main producing location in China. According to the Pharmacopoeia of the People's Republic of China, these raw decoction pieces were stir-fried in wheat bran using a stir-frying machine for 3, 6, 9, 12 and 15 min, respectively. The resulted 60 samples were categorized into three classes (i.e., light, moderate and dark) by experienced pharmacists according to their surface color. After that, these slices were smashed to acquire near infrared spectra and to determine the contents of atractylenolide I, II and III by HPLC method. Qualitative and quantitative models were constructed to relate the spectra to the color labels and to the contents of three atractylenolides. Various chemometrics methods, including calibration methods like principal component analysis, partial least squares discriminant analysis (PLS-DA) and partial least squares regression (PLSR), spectra pretreatment methods like standard normal variate, multiplicative scatter correction, derivation and smoothing, feature selection methods like particle swarm optimization, genetic algorithm (GA) and other fourteen methods were compared in detail. The PLS-DA models were evaluated by jackknife tests with calculating parameters such as error rate (ERR), true positive rate (TPR), true negative rate (TNR) and F1 score, meanwhile the PLSR models were evaluated by five fold cross-validation tests with calculating parameters such as coefficients of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and residual predictive deviation (RPD). RESULTS: The PLS-DA models with spectra pretreated by 1D5S or 1D9S and wavelengths selected by InfFS, Relief-F, MutInfFS, fisher or CFS performed best, yielding 0.00 of ERR, 1.00 of TPR, 1.00 of TNR, and 1.00 of F1 for all three classes. As for quantitative models, the PLSR models by 1D5S spectra pretreatment and GA wavelengths selection performed best, where R2C and R2P were all >0.95, RMSEC and RMSEP were all <0.04%, MAEC and MAEP were all <0.04%, and RPD were all >5. CONCLUSION: The present qualitative and quantitative models can be successfully used to distinguish the degree of suitability of processed BZ, and to determine the contents of three atractylenolides, which thus are of great help for quality evaluation and control of processed BZ and other decoction pieces.