Multivariate detection limits of on-line NIR model for extraction process of chlorogenic acid from Lonicera japonica

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In this work, multivariate detection limits (MDL) estimator was obtained based on the microelectro-mechanical systems-near infrared (MEMS-NIR) technology coupled with two sampling accessories to assess the detection capability of four quality parameters (glycyrrhizic acid, liquiritin, liquiritigenin and isoliquiritin) in licorice from di®erent geographical regions. 112 licorice samples were divided into two parts (calibration set and prediction set) using KennardStone (KS) method. Four quality parameters were measured using high-performance liquid chromatography (HPLC) method according to Chinese pharmacopoeia and previous studies. The MEMS-NIR spectra were acquired from¯ber optic probe (FOP) and integrating sphere, then the partial least squares (PLS) model was obtained using the optimum processing method. Chemometrics indicators have been utilized to assess the PLS model performance. Model assessment using chemometrics indicators is based on relative mean prediction error of all concentration levels, which indicated relatively low sensitivity for low-content analytes (below 1000 parts per million (ppm)). Therefore, MDL estimator was introduced with alpha error and beta error based on good prediction characteristic of low concentration levels. The result suggested that MEMS-NIR technology coupled with¯ber optic probe (FOP) and integrating sphere was able to detect minor analytes. The result further demonstrated that integrating sphere mode (i.e., MDL 0:05;0:05 , 0.22%) was more robust than FOP mode (i.e., MDL 0:05;0:05 , 0.48%). In conclusion, this research ‡ Corresponding authors. This is an Open Access article published by World Scienti¯c Publishing Company. It is distributed under the terms of the Creative Commons Attribution 3.0 (CC-BY) License. Further distribution of this work is permitted, provided the original work is properly cited. Vol. 8, No. 5 (2015) proposed that MDL method was helpful to determine the detection capabilities of low-content analytes using MEMS-NIR technology and successful to compare two sampling accessories. Journal of Innovative Optical Health Sciences

Section: Introductionmentioning

confidence: 88%

Section: Pls Model Performance Using Mems Technology For Two Samplingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation of multivariate detection limits of four quality parameters in licorice using MEMS–NIR spectrometry coupled with two sampling accessories

Shi

Zhao

et al. 2015

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“…[9][10][11][12][13][14][15] NIR spectroscopy has also shown great power and gained wide acceptance in TCM manufacturing industry. 11,16 The most wellknown applications of NIR spectroscopy in the manufacturing processes of TCM include the separation monitoring, 17 end point judgement of extraction process, [18][19][20] alcohol precipitation monitoring, 21 etc. Obviously, NIR spectroscopy is a promise technology for the understanding of the TCM manufacturing processes.…”

Section: Introductionmentioning

confidence: 99%

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

Tian

Zang

et al. 2017

Near infrared (NIR) spectroscopy has been developed into one of the most important process analytical techniques (PAT) in a wide¯eld of applications. The feasibility of NIR spectroscopy with partial least square regression (PLSR) to monitor the concentration of paeoni°orin, albi-°o rin, gallic acid, and benzoyl paeoni°orin during the water extraction process of Radix Paeoniae Alba was demonstrated and veri¯ed 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 coe±cients (R 2 cal and R 2 pred ), 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, 0.0545 mg/mL and 8.4 for paeoni°orin; 0.9879, 0.9888, 0.0303 mg/mL, 0.0321 mg/mL and 9.1 for albi°orin; 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 paeoni°orin, respectively. The results turned out that this approach was very e±cient and environmentally friendly for the quantitative monitoring of the water extraction process of Radix Paeoniae Alba.

“…The result demonstrated that NIR allowed the detection of a minor analyte (50 ppm) in CMM. 13 Wang et al illuminated that the best spectral band was selected to detect alcohol concentration using siPLS analysis. It showed that the model had good predictive ability and reliability.…”

Section: Introductionmentioning

confidence: 99%

NIR assignment of isopsoralen by 2D-COS technology and model application in Yunkang Oral Liquid

Pei

Shi

et al. 2015

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Near infrared (NIR) assignment of Isopsoralen was performed using deuterated chloroform solvent and two-dimensional correlation spectroscopy (2D-COS) technology. Yunkang Oral Liquid was applied to study Isopsoralen, the characteristic bands by spectral assignment as well as the bands by interval partial least squares (iPLS) and synergy interval partial least squares (siPLS) were used to establish partial least squares (PLS) model. The coe±cient of determination in calibration (R