Construction of universal quantitative models for the determination of cefoperazone sodium/sulbactam sodium for injection from different manufacturers using near-infrared reflectance spectroscopy

To develop near-infrared （NIR） reflectance spectroscopic methods for the quantitative analysis of cefoperazone sodium/ sulbactam sodium from different manufacturers for injection powder medicaments. Various powders of cefoperazone sodium/ sulbactam sodium were directly analyzed by non-destructive NIR reflectance spectroscopy using the spectrometer EQUINOX55. Two quantitative methods via integrating sphere （IS） and fiberoptic probe （FOP） models were explored from 6 batches of commercial samples and 42 batches of laboratory samples at a content ranging from 30% to 70% for cefoperazone and 60% to 20% for sulbactam. The root mean square errors of cross validation （RMSECV） and the root mean square errors of prediction （RMSEP） of IS were 1.79% and 2.85%, respectively, for cefoperazone sodium, and were 1.86% and 3.08%, respectively, for sulbactam sodium; and those of FOP were 2.93% and 2.92%, respectively, for cefoperazone sodium, and were 2.23% and 3.01%, respectively, for sulbactam sodium. Based on the ICH guidelines and Ref. 12, the quantitative models were then evaluated in terms of specificity, linearity, accuracy, precision, robustness and model transferability. The non-destructive quantitative NIR methods used in this study are applicable for rapid analysis of injectable powdered drugs from different manufacturers.

Application of Near Infrared Diffuse Reflectance Spectroscopy with Radial Basis Function Neural Network to Determination of Rifampincin Isoniazid and Pyrazinamide Tablets

Partial least squares（PLS）,back-propagation neural network（BPNN）and radial basis function neural network（RBFNN）were respectively used for estalishing quantative analysis models with near infrared（NIR）diffuse reflectance spectra for determining the contents of rifampincin（RMP）,isoniazid（INH）and pyrazinamide（PZA）in rifampicin isoniazid and pyrazinamide tablets.Savitzky-Golay smoothing,first derivative,second derivative,fast Fourier transform（FFT）and standard normal variate（SNV）transformation methods were applied to pretreating raw NIR diffuse reflectance spectra.The raw and pretreated spectra were divided into several regions,depending on the average spectrum and RSD spectrum.Principal component analysis（PCA）method was used for analyzing the raw and pretreated spectra in different regions in order to reduce the dimensions of input data.The optimum spectral regions and the models＇ parameters were chosen by comparing the root mean square error of cross-validation（RMSECV）values which were obtained by leave-one-out cross-validation method.The RMSECV values of the RBFNN models for determining the contents of RMP,INH and PZA were 0.00288,0.00226 and 0.00341,respectively.Using these models for predicting the contents of INH,RMP and PZA in prediction set,the RMSEP values were 0.00266,0.00227 and 0.00411,respectively.These results are better than those obtained from PLS models and BPNN models.With additional advantages of fast calculation speed and less dependence on the initial conditions,RBFNN is a suitable tool to model complex systems.

Determination of Protein and Starch Content in Whole Maize Kernel by Near Infrared Reflectance Spectroscopy

Using 128 bulk-kernel samples of inbred lines and hybrids, a study was conducted toinvestigate the feasibility and method of measuring protein and starch contents inintact seeds of maize by near infrared reflectance spectroscopy (NIRS). The chemometricalgorithms of partial least square (PLS) regression was used. The results indicated thatthe calibration models developed by the spectral data pretreatment of firstderivative+multivariate scattering correction within the spectral region of 10000-4000cm-1, and first derivative + straight line subtraction in 9000-4000cm-1 were thebest for protein and starch, respectively. All these models yielded coefficients ofdetermination of calibration (R2cal) above 0.97, while R2cv and R2val of cross and externalvalidation ranged from 0.92 to 0.95, respectively; however, the root of mean squareerrors of calibration, cross and external validation (RMSEE, RMSECV and RMSEP) werebelow 1(ranged 0.3-0.7),respectively. This study demonstrated that it is feasible touse NIRS as a rapid, accurate, and none-destructive technique to predict protein andstarch contents of whole kernel in the maize quality improvement program.

Geographic Classification of Chinese Grape Wines by Near-Infrared Reflectance Spectroscopy

Near-infrared reflectance spectroscopy (NIRS) was applied to classify grape wines of different geographical origins (Changli, Huailai, and Yantai, China). Near infrared (NIR) spectra were collected in transmission mode in the wavelength range of 800-2500 nm. Wines (n=90) were randomly split into two sets, calibration set (n=54) and validation set (n=36). Discriminant analysis models were developed using BP neural network and discriminant partial least-squares discriminant analysis (PLS-DA). The prediction performance of calibration models in different wavelength range was also investigated. BP neural network models and PLS-DA models correctly classified 100% of the wines in calibration set. When used to predict wines in validation set, BP neural network models correctly classified 100%, 81.8%, and 90.9% of the wines from Changli, Huailai, and Yantai respectively, and PLS-DA models correctly classified 100% of all samples. The results demonstrated that NIRS could be used to discriminate Chinese grape wines as a rapid and reliable method.

Near-infrared spectroscopy method for rapid proximate quantitative analysis of nutrient composition in Pacific oyster Crassostrea gigas

Glycogen,amino acids,fatty acids,and other nutrient components affect the flavor and nutritional quality of oysters.Methods based on near-infrared reflectance spectroscopy(NIRS)were developed to rapidly and proximately determine the nutrient content of the Pacific oyster Crassostreagigas.Samples of C.gigas from 19 costal sites were freeze-dried,ground,and scanned for spectral data collection using a Fourier transform NIR spectrometer(Thermo Fisher Scientific).NIRS models of glycogen and other nutrients were established using partial least squares,multiplication scattering correction first-order derivation,and Norris smoothing.The R_(C) values of the glycogen,fatty acids,amino acids,and taurine NIRS models were 0.9678,0.9312,0.9132,and 0.8928,respectively,and the residual prediction deviation(RPD)values of these components were 3.15,2.16,3.11,and 1.59,respectively,indicating a high correlation between the predicted and observed values,and that the models can be used in practice.The models were used to evaluate the nutrient compositions of 1278 oyster samples.Glycogen content was found to be positively correlated with fatty acids and negatively correlated with amino acids.The glycogen,amino acid,and taurine levels of C.gigas cultured in the subtidal and intertidal zones were also significantly different.This study suggests that C.gigas NIRS models can be a cost-effective alternative to traditional methods for the rapid and proximate analysis of various slaughter traits and may also contribute to future genetic and breeding-related studies in Pacific oysters.

NIRS Prediction of SOM,TN and TP in a Meadow in the Sanjiang Plain,China

The aim of this study was to establish the applicability of near-infrared reflectance spectroscopy(NIRS)as a rapid method for the accurate estimation of nutrient components in agricultural soils.Focusing on the soil of the Sanjiang Plain,NIRS was used to predict soil organic matter(SOM),the total nitrogen(TN)and the total phosphorus(TP).A total of 540 samples were collected from the three different depths(180 samples from each depth:0-10,10-20 and 20-30 cm),from 2015 to 2017,from the Sanjiang Plain in Heilongjiang Province,China.From every depth,120 samples were used to construct the calibration set.Other 60 samples were used to check the efficiency of the model.Combining the first-order differentiation with the partial least square(PLS)method,a prediction model was obtained to measure SOM,TN and TP.The correlation coefficient of SOM from 0 to 10 cm was R2=0.9567,from 10 to 20 cm was R2=0.9416,and from 20 to 30 cm was R2=0.9402.The corresponding ratio(standard deviation[SD]/root mean square error of prediction[RMSEP])was>2.96.R2 of TN with the three depths was 0.9154,0.9028 and 0.9024,respectively,all with SD/RMSEP>2.89.Meanwhile,R2 of TP with the three depths was 0.8974,0.8624 and 0.7804,respectively,all with SD/RMSEP>2.50.These results demonstrated that NIRS based on the first-order differentiation and PLS could efficiently predict SOM,TN and TP from different soil depths.

Identification of maize seed varieties based on near infrared reflectance spectroscopy and chemometrics

False seeds can often be seen in the maize seed market,leading to a serious decline in maize yield.Those existing variety identification methods are expensive,time consuming,and destructive to seeds.The aim of this study is to develop a cheap,fast and non-destructive method which can robustly identify large amounts of maize seed varieties based on near-infrared reflectance spectroscopy(NIRS)and chemometrics.Because it is difficult to establish models for every variety in the market,this study mainly investigated the performance of models based on a large number of samples(more than 40 major varieties in the market).The reflectance spectra of maize seeds were collected by two modes(bulk kernels mode and single kernel mode).Both collection modes can be applied to identification,but only the single kernel mode can be applied to purity sorting.The spectra were pretreated with smoothing,the first derivative and vector normalization;and then principal component analysis(PCA),linear discriminant analysis(LDA)and biomimetic pattern recognition(BPR)were applied to establish identification models.The environmental factors such as producing areas and years have a significant influence on the performance of the models.Therefore,the method to improve the robustness of the models was investigated in this study.New indexes(correct acceptance degree(CAD),correct rejection degree(CRD)and correct degree(CD))were defined to analyze the performance of the models more accurately.Finally,the models obtained a mean correct discrimination rate of over 90%,and exhibited robust properties for samples harvested from different areas and years.The results showed that NIR technology combined with chemometrics methods such as PCA,LDA,and BPR could be a suitable and alternative technique to identify the authenticity of maize seed varieties.