Rapid monitoring the water extraction process of Radix Paeoniae Alba using near infrared spectroscopy

Near infrared(NIR)spectroscopy has been developed into one of the most important process analytical techniques(PAT)in a wide field of applications.The feasibility of NIR spectroscopy with partial least square regression(PLSR)to monitor the concentration of paeoniflorin,albi-florin,gallic acid,and benzoyl paeoniforin during the water extraction process of Radix Paeoniae Alba was demonstrated and verified in this work.NIR spectra were collected in transmission mode and pretreated with smoothing and/or derivative,and then quantitative models were built up using PLSR.Interval partial least squares(iPLS)method was used for the selection of spectral variables.Determination coeficients(R2aI and R2red),root mean squares error of prediction(RMSEP),root mean squares error of calibration(RMSEC),and residual predictive deviation(RPD)were applied to verify the performance of the models,and the corresponding values were 0.9873 and 0.9855,0.0487 mg/mL,0.0545mg/mL and 8.4 for paeoniforin;0.9879,0.9888,0.0303 mg/mL,0.0321 mg/mL and 9.1 for albiflorin;0.9696,0.9644,0.0140 mg/mL,0.0145 mg/mL and 5.1 for gallic acid;0.9794,0.9781,0.00169 mg/mL,0.00171 mg/mL and 6.9 for benzoyl paeoniflorin,respectively.The results turned out that this approach was very efficient and environmentally friendly for the quantitative monitoring of the water extraction process of Radix Paeoniae Alba.

The relevance study of effective information between near infrared spectroscopy and chondroitin sulfate in ethanol precipitation process

Near infrared spectroscopy(NIRS)is based on molecular overtone and combination vibrations.It is difficult to assign specific features under complicated system.So it is necessary to find the relevance between NIRS and target compound.For this purpose,the chondroitin sulfate(CS)ethanol precipitation process was selected as the research model,and 90 samples of 5 different batches were collected and the content of CS was determined by modifed carbazole method.The relevance between NIRS and CS was studied throughout optical pathlength,pretreat ment methods and variables selection methods.In conclusion,the first derivative with Savitzky--Golay(SG)smoothing was selected as the best pretreatment,and the best spectral region was selected using interval partial least squares(iPLS)method under 1 mm optical cell.A multivariate cali-bration model was established using PLS algorithm for determining the content of CS,and the root mean square error of prediction(RMSEP)is 3.934gL-1.This method will have great potential in process analytical technology in the future.

Rapid determination of hyaluronic acid concentration in fermentation broth with near-infrared spectroscopy

Hyaluronic acid(HA)concentration is an important parameter in fermentation process.Cur-rently,carbazole assay is widely used for HA content determination in routine analysis.However,this method is time-consuming,environment polluting and has the risk of microbial contarmi-nation,as well as the results lag behind fermentation process.This paper attempted the feasibility to predict the concentration of HA in fermentation broth by using near infrared(NIR)spec-troscopy in transmission mode.In this work,a total of 56 samples of fermentation broth from 7 batches were analyzed,which contained HA in the range of 2.35-9.69 g/L.Diferent data pre-processing methods were applied to construct calibration models.The final optimal model was obtained with first derivative using Savitzky-Golay smoothing(9 points window,second-order polynomial)and partial least squares(PLS)regression with leave-one-block out CrOsS validation.The correlation cofficient and Root Mean Square Error of prediction set is 0.98 and 0.43 g/L,respetively,which show the possibility of NIR as a rapid method for microanalysis and to be a promising tool for a rapid assay in HA fermentation.