Partial Least-squares Calibration Research Articles

Geographical origin discrimination and polysaccharides quantitative analysis of Radix codonopsis with micro near-infrared spectrometer engine

At present, Tradition Chinese Medicine (TCM) industry in China is in the stage from the empirical development to industrial production. Near infrared (NIR) spectroscopy has been widely used in the quality control of TCM’s modernization with its characteristics including rapidness, nondestruction, simplicity, economy, and so on. In this study, as one type of a portable micro NIR spectrometer, Micro NIR 1700 was used to establish the qualitative models for identification of geographical region and authenticity of Radix codonopsis based on discriminant analysis (DA) method. Both of the DA models had better predictive ability with 100% accuracy. In addition, a method for rapid quantitative analysis of polysaccharide in Radix codonopsis was also developed based on Micro NIR 1700 spectrometer with partial least-squares (PLS) algorithm. In the PLS calibration model, the NIR spectra of samples were pretreated with different preprocessing methods and the spectral region was selected with different variable selection methods as well. The performance of the final PLS model was evaluated according to correlation coefficient of calibration ([Formula: see text]), correlation coefficient of prediction ([Formula: see text]), root mean squared error of cross validation (RMSECV), and root mean squared of prediction (RMSEP). The values of [Formula: see text], [Formula: see text], RMSECV, and RMSEP were 0.9775, 0.9602, 2.496, and 2.734[Formula: see text]g/mL, respectively. This work demonstrated that micro infrared spectrometer could be more convenient and rapid for quality control of Radix codonopsis, and the presented models would be a useful reference for quality control of other similar raw materials of TCM.

Quantification of amine functional groups and their influence on OM/OC in the IMPROVE network

Recently, we developed a method using FT-IR spectroscopy coupled with partial least squares (PLS) regression to measure the four most abundant organic functional groups, aliphatic C-H, alcohol OH, carboxylic acid OH and carbonyl C=O, in atmospheric particulate matter. These functional groups are summed to estimate organic matter (OM) while the carbon from the functional groups is summed to estimate organic carbon (OC). With this method, OM and OM/OC can be estimated for each sample rather than relying on one assumed value to convert OC measurements to OM. This study continues the development of the FT-IR and PLS method for estimating OM and OM/OC by including the amine functional group. Amines are ubiquitous in the atmosphere and come from motor vehicle exhaust, animal husbandry, biomass burning, and vegetation among other sources. In this study, calibration standards for amines are produced by aerosolizing individual amine compounds and collecting them on PTFE filters using an IMPROVE sampler, thereby mimicking the filter media and collection geometry of ambient standards. The moles of amine functional group on each standard and a narrow range of amine-specific wavenumbers in the FT-IR spectra (wavenumber range 1 550–1 500 cm−1) are used to develop a PLS calibration model. The PLS model is validated using three methods: prediction of a set of laboratory standards not included in the model, a peak height analysis and a PLS model with a broader wavenumber range. The model is then applied to the ambient samples collected throughout 2013 from 16 IMPROVE sites in the USA. Urban sites have higher amine concentrations than most rural sites, but amine functional groups account for a lower fraction of OM at urban sites. Amine concentrations, contributions to OM and seasonality vary by site and sample. Amine has a small impact on the annual average OM/OC for urban sites, but for some rural sites including amine in the OM/OC calculations increased OM/OC by 0.1 or more.

An enantioselective electrochemical sensor for simultaneous determination of mandelic acid enantiomers using dexamethasone-based chiral nanocomposite coupled with chemometrics method

A nanocomposite-based sensor that responds selectively to mandelic acid (MA) enantiomers was constructed with Dexamethasone (DEX) as a chiral recognition element. The DEX-modified electrode was selective towards MA enantiomers compared with the glassy carbon electrode (GCE) modified by graphene (GR) nanosheets or bare GCE. The entrapment of DEX within the overoxidised polypyrrole (OPPy) film occurred on the surface of the GR-modified electrode using a two-step electrodeposition approach. The deposited film was characterized by scanning electron microscopy (SEM), differential pulse voltammetry (DPV), cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The OPPy-DEX/GR/GCE sensor for sensing of MA enantiomers required ∼0.3V less overpotential than the GC electrode. Upon exposure to (S)- and (R)-MA, the sensor showed the current signals with different ratios, which were differentiated at the voltages of 1.38 and 1.43V (vs. Ag/AgCl), respectively. Under optimal conditions, the chiral sensor exhibited a good linear relationship with MA enantiomers concentrations ranging from 1 to 25mM with a detection limit of 0.25mM. Due to the highly overlapping signals, partial least squares (PLS) regression was applied to distinguish the MA enantiomers in synthetic mixtures. In addition, the genetic algorithm-based potential selection procedure was applied to optimize the number of PLS factors used for the building of the PLS calibration models, and also its predictive ability was studied.

Simultaneous analysis of Cr and Pb in contaminated pork by laser-induced breakdown spectroscopy.

Laser-induced breakdown spectroscopy (LIBS) as a rapid and green method was used to detect heavy metals Cr and Pb in pork contaminated in the lab. The laser-induced plasma was generated by a Q-switched Nd:YAG laser, and the LIBS signal was collected by a spectrometer with a charge-coupled device detector. The traditional calibration curves (CC) and multivariate partial least squares (PLS) algorithm were applied and compared to validate the accuracy in predicting the content of heavy metals in samples. The results demonstrated that the correlation coefficient of CC is poor by the classical univariate calibration method, so the univariate calibration analysis cannot effectively serve the quantitative purpose in analyzing heavy metals' residue in pork with a complex matrix. The analysis accuracy was improved effectively by the PLS method, and the correlation coefficient is 0.9894 for Cr and 0.9908 for Pb. The concentration of Cr and Pb in samples from a prediction set was obtained using the PLS calibration method, and the average relative errors for the 21 samples in the prediction set are lower than 6.53% and 7.82% for Cr and Pb, respectively. The investigated results display that the matrix effect would be reduced effectively during the quantitative analysis of pork by a LIBS-combined PLS model, and the predictive accuracy would be improved greatly compared to traditional univariate analysis.

Phenolic profiling of grapes, fermenting samples and wines using UV-Visible spectroscopy with chemometrics

Phenolic compounds play and important role on colour, flavour and mouthfeel attributes of wines. The acquisition of information related to phenolic compounds during the winemaking process is therefore becoming a necessity. Ultraviolet-Visible (UV-Vis) spectroscopy appears as an affordable option to monitor phenolic composition and levels during winemaking. To investigate this, a large number of samples collected from industrial fermentations over two vintages as well as commercial wine samples, spanning a wide range of vintages, were analysed for phenolic compounds using high performance liquid chromatography (HPLC). Methyl cellulose precipitable (MCP) tannins, anthocyanins, total phenols and colour density were also analysed. Partial least-squares (PLS) calibration models, based on UV-VIS spectra and reference measurements, were constructed and their performance evaluated in terms of the residual predictive deviation values. The accuracy and robustness of the calibrations were further evaluated by a combined test on slope and intercept, interclass correlation coefficients and standard error of measurement. Limit of detection and limit of quantification of the PLS models were also reported and evaluated. Furthermore, PLS models for an additional data set including 130 grape samples was also investigated for MCP tannins, anthocyanins, total phenols and colour density measurements. Phenolic compounds were extracted following two different protocols, namely wine-like and homogenate methods.

High-throughput analysis of chemical components and theoretical ethanol yield of dedicated bioenergy sorghum using dual-optimized partial least squares calibration models

BackgroundDue to its chemical composition and abundance, lignocellulosic biomass is an attractive feedstock source for global bioenergy production. However, chemical composition variations interfere with the success of any single methodology for efficient bioenergy extraction from diverse lignocellulosic biomass sources. Although chemical component distributions could guide process design, they are difficult to obtain and vary widely among lignocellulosic biomass types. Therefore, expensive and laborious “one-size-fits-all” processes are still widely used. Here, a non-destructive and rapid analytical technology, near-infrared spectroscopy (NIRS) coupled with multivariate calibration, shows promise for addressing these challenges. Recent advances in molecular spectroscopy analysis have led to methodologies for dual-optimized NIRS using sample subset partitioning and variable selection, which could significantly enhance the robustness and accuracy of partial least squares (PLS) calibration models. Using this methodology, chemical components and theoretical ethanol yield (TEY) values were determined for 70 sweet and 77 biomass sorghum samples from six sweet and six biomass sorghum varieties grown in 2013 and 2014 at two study sites in northern China.ResultsChemical components and TEY of the 147 bioenergy sorghum samples were initially analyzed and compared using wet chemistry methods. Based on linear discriminant analysis, a correct classification assignment rate (either sweet or biomass type) of 99.3% was obtained using 20 principal components. Next, detailed statistical analysis demonstrated that partial optimization using sample set partitioning based on joint X–Y distances (SPXY) for sample subset partitioning enhanced the robustness and accuracy of PLS calibration models. Finally, comparisons between five dual-optimized strategies indicated that competitive adaptive reweighted sampling coupled with the SPXY (CARS-SPXY) was the most efficient and effective method for improving predictive performance of PLS multivariate calibrations.ConclusionsAs a dual-optimized methodology, sample subset partitioning combined with variable selection is an efficient and straightforward strategy to enhance the accuracy and robustness of NIRS models. This knowledge should facilitate generation of improved lignocellulosic biomass feedstocks for bioethanol production. Moreover, methods described here should have wider applicability for use with feedstocks incorporating multispecies biomass resource streams.

Mid-infrared and near-infrared spectroscopy for rapid detection of Gardeniae Fructus by a liquid-liquid extraction process

Gardeniae Fructus is widely used in the pharmaceutical industry, and many studies have confirmed its medical and economic value. In this study, samples collected from different liquid-liquid extraction batches of Gardeniae Fructus were detected by mid-infrared (MIR) and near-infrared (NIR) spectroscopy. Seven analytes, neochlorogenic acid (5-CQA), cryptochlorogenic acid (4-CQA), chlorogenic acid (3-CQA), geniposidic acid (GEA), deacetyl-asperulosidic acid methyl ester (DAAME), genipin-gentiobioside (GGB), and gardenoside (GA), were chosen as quality property indexes of Gardeniae Fructus. The two kinds of spectra were each used to build models by single partial least squares (PLS). Additionally, both spectral data were combined and modeled by multiblock PLS. For single spectroscopy modeling results, NIR had a better prediction for high-concentration analytes (3-CQA, DAAME, GGB, and GA) whereas MIR performed better for low-concentration analytes (5-CQA, 4-CQA, and GEA). The multiblock methodology was found to be better compared to single spectroscopy models for all seven analytes. Specifically, the coefficients of determination (R2) of the NIR, MIR, and multiblock PLS calibration models of all seven components were higher than 0.95. Relative standard errors of prediction (RSEP) were all less than 7%, except for models of GGB, which were 10.36%, 13.24%, and 8.15% for the NIR-PLS, MIR-PLS, and multiblock models, respectively. These results indicate that MIR and NIR spectrographic techniques could provide a new choice for quality control in industrial production of Gardeniae Fructus.

In‐line monitoring of hydrogen peroxide in two‐phase reactions using raman spectroscopy

Hydrogen peroxide is an environment‐friendly oxidizer, which is used in several chemical processes. However, safety necessitates the determination and control of the concentration of hydrogen peroxide during oxidation reactions. We propose a methodology to monitor hydrogen peroxide in disperse two‐phase reaction mixtures based on in‐line Raman spectroscopy. We compare indirect hard modeling (IHM), peak integration (PI), and partial least squares (PLS). Building predictive PLS and PI calibration models is challenging, whereas the IHM calibration is easy to develop. These methods show good accuracy for known samples (root mean square error of cross validation [RMSECV] of 0.3–0.7 wt %) compared to the classic titration method (RMSECV of 0.4 wt %). After calibration, inline monitoring during reaction is performed demonstrating that the concentration of hydrogen peroxide can be successfully monitored in a fast and reliable way by Raman spectroscopy. The IHM seems to give slightly better inline predictions. © 2017 American Institute of Chemical Engineers AIChE J, 63: 3994–4002, 2017

Prediction of nutritive values, morphology and agronomic characteristics in forage maize using two applications of NIRS spectrometry

ABSTRACTThis study evaluates nutritive, morphological and agronomic characteristics of forage maize predicted by using a high-quality near-infrared (NIR) spectrometer and an NIR hyperspectral-imaging technique using partial least squares (PLS) regression models. The study includes 132 samples of dried milled whole-plant homogenates of forage maize with variation in maturity, representing two growing seasons, three locations in Sweden and three commercial maize hybrids. The samples were measured by a classical sample cup NIR spectrometer and by a pushbroom hyperspectral-imaging instrument. The spectra and a number of variables (crude protein, CP, neutral detergent fibre, starch, water soluble carbohydrates (WSC) and organic matter digestibility), morphological variables (leaves, stems & ears) and crop yield were used to make PLS calibration models. Using PLS modelling allowed the determination of how well maize variables can be predicted from NIR spectra and a comparison of the two types of instruments. Most examined variables could be determined equally well, by both instruments, but the pushbroom technique gave slightly better predictions and had higher analytical capacity. Predictions of CP, starch, WSC and the proportions of ears in the maize gave robust. The findings open new possibilities to further utilise the technology in plant breeding, crop management, modelling and forage evaluation.

Simultaneous determination of valsartan, amlodipine besylate and hydrochlorothiazide in tablets by near infrared

Interval partial least-squares calibration (iPLS) and synergical partial least-squares calibration (siPLS) methods in combination with near infrared spectroscopy coupled with integrating sphere (NIRA) have been developed for simultaneous determination of amlodipine (AML), valsartan (VAL) and hydrochlorothiazide (HCT) in raw material powder mixtures used for production commercial pharmaceutical product (tablets). Variable selection methods (iPLS and siPLS) were applied to select a spectral range that provided the lowest prediction error in comparison to the full-spectrum model. Spectral data were recorded between 10000 and 4000 cm-1 by NIRA and subdivided into 32 intervals. A relative standard error of prediction (RSEP) of 1.27% for VAL, 1.92% for HCT and 5.19% for AML was obtained after selection of better intervals by siPLS. Results shown that multivariate regression model associated to NIRA is a relatively simple, free-solvent and non-destructive procedure that could be applied to the simultaneous determination of AML, VAL and HCT in routine quality control of pharmaceuticals.

The Effect of Path Length on the Measurement Accuracies of Wine Chemical Parameters by UV, Visible, and Near-Infrared Spectroscopy

The use of spectral measurements using either UV, visible (VIS), or near-infrared (NIR) spectroscopy to characterize wines or to predict wine chemical composition has been extensively reported. However, little is known about the effect of path length on the UV, VIS, and NIR spectrum of wine and the subsequent effect on the performance of calibrations used to measure chemical composition. Several parameters influence the spectra of organic molecules in the NIR region, with path length and temperature being one of the most important factors affecting the intensity of the absorptions. In this study, the effect of path length on the standard error of UV, VIS, and NIR calibration models to predict phenolic compounds was evaluated. Nineteen red and 13 white wines were analyzed in the UV, VIS, and NIR regions (200–2500 nm) in transmission mode using two effective path lengths 0.1 and 1 mm. Principal component analysis (PCA) and partial least squares (PLS) regression models were developed using full cross validation (leave-one-out). These models were used to interpret the spectra and to develop calibrations for phenolic compounds. These results indicated that path length has an effect on the standard error of cross validation (SECV) absolute values obtained for the PLS calibration models used to predict phenolic compounds in both red and white wines. However, no statistically significant differences were observed (p > 0.05). The practical implication of this study was that the path length of scanning for wines has an effect on the calibration accuracies; however, they are non-statistically different. Main differences were observed in the PCA score plot. Overall, well-defined protocols need to be defined for routine use of these methods in research and by the industry.

Lubricant-Sensitivity Assessment of SPRESS® B820 by Near-Infrared Spectroscopy: A Comparison of Multivariate Methods

The predictability of multivariate calibration models, calculated with offline near-infrared spectroscopy (NIRS), assessing impact of magnesium stearate (MgSt) fraction, blending time, and compression force on the tablet breaking force (TBF) of SPRESS® B820 was statistically compared. Tablets of lubricated SPRESS® B820 were prepared by varying lubrication and compression conditions using 24 full factorial design. Tablets were scanned in reflection mode on a benchtop NIRS. A qualitative principal component analysis and quantitative principal component regression (PCR) and partial least square (PLS) regression relationship between lubricant concentration, blending time, compression force, preprocessed NIR spectra, and measured TBF was calculated with calibration data set. The predictability of calibration models was validated with independent data set. Expected qualitative correlations between MgSt blending time and TBF and a nonlinear relationship between MgSt fraction and TBF were observed. Predictability of PLS comprehensive (0.25%-1% w/w MgSt) model was significantly different from individual 0.25%, 0.5%, and 1.0% w/w MgSt PLS models. In addition, PLS calibration models' predictability was different from PCR calibration models. Thus, added lubrication fraction and adopted multivariate methodology should be selected carefully during the calibration and validation stages as it may have a significant impact on the predictability of the developed models.

Screening for Adulterants in Liquid Milk Using a Portable Raman Miniature Spectrometer with Immersion Probe.

A portable Raman system with an immersion fiber optic probe was assessed for point-of-collection screening for the presence of adulterants in liquid milk. N-rich adulterants and sucrose were measured in this proof-of-concept demonstration. Reproducibility, limit of detection range and other figures of merit such as specificity, sensitivity, ratio of predicted to standard deviation, standard error of prediction and root mean squared error for cross validation were determined from partial least squares (PLS) and partial least squares with discriminant analysis (PLS-DA) calibrations of milk mixtures containing 50-1000 ppm (parts per million) of melamine, ammonium sulphate, Dicyandiamide, urea and sucrose. The spectra were recorded by immersing the fiber optic probe directly in the milk solutions. Despite the high scattering background which was easily and reliably estimated and subtracted, the reproducibility for four N-rich compounds averaged to 11% residual standard deviation (RSD) and to 5% RSD for sucrose. PLS calibration models predicted the concentrations of separate validation sets with standard errors of prediction of between 44 and 76 ppm for the four N-rich compounds and 0.17% for sucrose. The sensitivity and specificity of the PLS-DA calibration were 92% and 89%, respectively. The study shows promise for use of portable mini Raman systems for routine rapid point-of-collection screening of liquid milk for the presence of adulterants, without the need for sample preparation or addition of chemicals.

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.

Simultaneous determination of neutral and uronic sugars based on UV–vis spectrometry combined with PLS

A method using partial least squares (PLS) for simultaneous determination of neutral and uronic sugars was developed in this paper. This method is based on the development of the reaction between the analytes and anthrone. The calibration set was built with 25 binary solutions at the concentrations ranging from 20 to 100μg/mL for glucose and from 10 to 50μg/mL for glucuronic acid. An independent prediction set was utilized to check the robustness of the PLS calibration model. The root-mean-square error of prediction (RMSEP) values for neutral and uronic sugars are 1.2233 and 1.9367, respectively. The correlation coefficient for the prediction set (Rp2) values for them are 0.9971 and 0.9767, respectively. Compared with the univariate method, the proposed method improves detection accuracy. In addition, it was also applied to commercial polysaccharides and Glycyrrhiza uralensis polysaccharides (GUPs), and the results indicated that the PLS model was suitable for simultaneous determination of neutral and uronic sugars.

Feasibility of the Simultaneous Determination of Monomer Concentrations and Particle Size in Emulsion Polymerization Using in Situ Raman Spectroscopy.

An immersion Raman probe was used in emulsion copolymerization reactions to measure monomer concentrations and particle sizes. Quantitative determination of monomer concentrations is feasible in two-monomer copolymerizations, but only the overall conversion could be measured by Raman spectroscopy in a four-monomer copolymerization. The feasibility of measuring monomer conversion and particle size was established using partial least-squares (PLS) calibration models. A simplified theoretical framework for the measurement of particle sizes based on photon scattering is presented, based on the elastic-sphere-vibration and surface-tension models.

Simultaneous Determination of Metamizole, Thiamin and Pyridoxin Using UV-Spectroscopy in Combination with Multivariate Calibration.

Analysis of drugs in multicomponent system officially is carried out using chromatographic technique, however, this technique is too laborious and involving sophisticated instrument. Therefore, UV-VIS spectrophotometry coupled with multivariate calibration of partial least square (PLS) for quantitative analysis of metamizole, thiamin and pyridoxin is developed in the presence of cyanocobalamine without any separation step. The calibration and validation samples are prepared. The calibration model is prepared by developing a series of sample mixture consisting these drugs in certain proportion. Cross validation of calibration sample using leave one out technique is used to identify the smaller set of components that provide the greatest predictive ability. The evaluation of calibration model was based on the coefficient of determination (R(2)) and root mean square error of calibration (RMSEC). The results showed that the coefficient of determination (R(2)) for the relationship between actual values and predicted values for all studied drugs was higher than 0.99 indicating good accuracy. The RMSEC values obtained were relatively low, indicating good precision. The accuracy and presision results of developed method showed no significant difference compared to those obtained by official method of HPLC. The developed method (UV-VIS spectrophotometry in combination with PLS) was succesfully used for analysis of metamizole, thiamin and pyridoxin in tablet dosage form.

Evaluation of MLSR and PLSR for estimating soil element contents using visible/near-infrared spectroscopy in apple orchards on the Jiaodong peninsula

Conventional methods for soil element content determination based on laboratory analyses are costly and time-consuming. A soil reflectance spectrum is an alternative approach for soil element content estimation with the advantage of being rapid, non-destructive, and cost effective. Visible/near-infrared spectra (350nm to 2500nm) were measured from 105 soil samples originating from 30 apple orchards on the Jiaodong peninsula. The Savitzky–Golay (FD-SG) technique for spectral data was implemented to reduce the signal noise. Logarithm of the reciprocal of reflectance (logR−1) and the first derivative transformation (DR) were used to accentuate the features and to prepare the data for use in quantitative estimation models. The SI (sum index), DI (difference index), PI (product index), RI (ratio index), and NDI (normalized difference index) were calculated to extract sensitive waveband combinations that are significantly related to soil element contents. Soil element contents were retrieved based on sensitive waveband combinations by multiple linear stepwise regression (MLSR) and partial least square (PLSR) models. The results showed that DR performed better than logR−1 in eliminating the interfering factors of soil particle size and spectral noise. The MLSR and PLSR calibration models based on PI performed better than those based on SI or DI did. The MLSR performed better than PLSR in estimating soil elemental content. The contents of total nitrogen (TN), arsenic (As), and mercury (Hg) could be estimated well using MLSR and PLSR calibration models developed with PI. The MLSR calibration model developed with PI performed well in estimating available potassium (A-K) content. However, the contents of available phosphorus (A-P), ammonium nitrogen (NH4+-N), nitric nitrogen (NO3--N), and soil organic matter (SOM) could not be estimated using MLSR or PLSR calibration models. These outcomes will provide the theoretical basis and technical support for estimations of soil element content using visible/near-infrared spectra. Although they were shown to be useful in apple orchards of the Jiaodong peninsula, these models and methods should be further tested in soil samples from other regions and countries to prove their validity.

Using Vis/NIR Diffuse Transmittance Spectroscopy and Multivariate Analysis to Predicate Soluble Solids Content of Apple

The objectives of this research were to compare the effect of different fruit orientations on the quality of acquired spectra and to provide a suitable calibration model for further online determination of soluble solids content (SSC) of “Fuji” apples using visible and near-infrared (Vis/NIR) diffuse transmittance. The diffuse transmittance spectra between 650 and 910 nm were collected with the designed spectrum measurement system in two fruit orientations: stem-calyx axis horizontal (T1) and stem-calyx axis vertical (T2). Area change rate (ACR) was used to evaluate the stability of spectra collected in two fruit orientations. Results showed that the fruit orientation T1 was better for our designed spectrum measurement system. Then, the performance of partial least squares (PLS) models based on spectral data after the pretreatment of several preprocessing methods was analyzed and compared. Finally, the modified competitive adaptive reweighted sampling (MCARS), successive projection algorithm (SPA), and their combination were investigated to select the effective variables for the determination of SSC. It concluded that the MCARS-SPA-PLS model based on the spectra after preprocessing of Savitzky-Golay (SG) smoothing achieved better results for SSC prediction. The correlation coefficients between measured and predicted SSC were 0.962 and 0.946, and the root mean square errors were 0.510 and 0.527°Brix for calibration and prediction set, respectively. Moreover, the physicochemical properties of 27 variables selected by MCARS-SPA were discussed to obtain a better interpretation of the calibration model. The overall results indicated that the designed diffuse transmittance spectrum measurement system together with the PLS calibration model with 27 effective variables selected by MCARS-SPA method had a potential application for online SSC detection of apple.

In-vivo evaluation of clindamycin release from glyceryl monooleate-alginate microspheres by NIR spectroscopy

The purpose of this study was to use near-infrared (NIR) transmission spectroscopic technique to determine clindamycin plasma concentration after oral administration of clindamycin loaded GMO-alginate microspheres using rabbits as animal models. Lyophilized clindamycin–plasma standard samples at a concentration range of 0.001–10μg/ml were prepared and analyzed by NIR and HPLC as a reference method. NIR calibration model was developed with partial least square (PLS) regression analysis. Then, a single dose in-vivo evaluation was carried out and clindamycin–plasma concentration was estimated by NIR. Over 24h time period, the pharmacokinetic parameters of clindamycin were calculated for the clindamycin loaded GMO-alginate microspheres (F3) and alginate microspheres (F2), and compared with the plain drug (F1). PLS calibration model with 7-principal components (PC), and 8000–9200cm−1 spectral range shows a good correlation between HPLC and NIR values with root mean square error of cross validation (RMSECV), root mean square error of prediction (RMSEP), and calibration coefficient (R2) values of 0.245, 1.164, and 0.9753, respectively, which suggests that NIR transmission technique can be used for drug-plasma analysis without any extraction procedure. F3 microspheres exhibited controlled and prolonged absorption Tmax of 4.0 vs. 1.0 and 0.5h; Cmax of 2.37±0.3 vs. 3.81±0.8 and 5.43±0.7μg/ml for F2 and F1, respectively. These results suggest that the combination of GMO and alginate (1:4 w/w) could be successfully employed for once daily clindamycin microspheres formulation which confirmed by low Cmax and high Tmax values.