Real-Time Release Testing of Herbal Extract Powder by Near-Infrared Spectroscopy considering the Uncertainty around Specification Limits

The concept of real-time release testing (RTRT) has recently been adopted by the production of pharmaceuticals in order to provide high-level guarantee of product quality. Process analytical technology (PAT) is an attractive and efficient way for realizing RTRT. In this paper, near-infrared (NIR) determination of cryptotanshinone and tanshinoneIIA content in tanshinone extract powders was taken as the research object. The aim of NIR analysis is to reliably declare the extract product as compliant with its specification limits or not. First, the NIR quantification method was developed and the parameters of the multivariate calibration model were optimized. The reliable concentration ranges covering the specification limits of two APIs were successfully verified by the accuracy profile (AP) methodology. Then, with the designed validation data from AP, the unreliability graph as the decision tool was built. Innovatively, the β-content, γ-confidence tolerance intervals (β-CTIs) around the specification limits were estimated. During routine use, the boundary of β-CTIs could help decide whether the NIR prediction results are acceptable. The proposed method quantified the analysis risk near the specification limits and confirmed that the unreliable region was useful to release the product quality in a real-time way. Such release strategy could be extended for other PAT applications to improve the reliability of results.

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New Hybrid Algorithm for Maintaining Multivariate Quantitative Calibrations of a Near-Infrared Spectrometer

Applied Spectroscopy ◽

10.1366/0003702021955169 ◽

2002 ◽

Vol 56 (5) ◽

pp. 605-614 ◽

Cited By ~ 27

Author(s):

Christine M. Wehlburg ◽

David M. Haaland ◽

David K. Melgaard ◽

Laura E. Martin

Keyword(s):

Least Squares ◽

Real Time ◽

Hybrid Model ◽

Hybrid Algorithm ◽

Near Infrared ◽

Multivariate Calibration ◽

Calibration Model ◽

Spectral Variation ◽

Calibration Data

Our newly developed prediction-augmented classical least-squares/partial least-squares (PACLS/PLS) hybrid algorithm can correct for the presence of unmodeled sources of spectral variation such as instrument drift by explicitly incorporating known or empirically derived information about the unmodeled spectral variation. We have tested the ability of the new hybrid algorithm to maintain a multivariate calibration in the presence of instrument drift using a near-infrared (NIR) spectrometer (7500–11 000 cm−1) to quantitate dilute aqueous solutions containing glucose, ethanol, and urea. The spectral variations required to update the multivariate models for both short- and long-term drift were obtained using a single representative midpoint sample whose spectrum was repeatedly measured during collection of calibration data and during collection of separate validation sample spectra on three subsequent days. The performance of the PACLS/PLS model for maintaining a calibration was compared to PLS with subset recalibration, a method that has previously been applied to maintenance and transfer of calibration. Without drift corrections, both PACLS/PLS and PLS had poor predictive ability on sample spectra collected on subsequent days. Unlike previous maintenance of calibration studies that corrected for long-term drift only, the PACLS/PLS and PLS models demonstrated the best predictive abilities when short-term drift was also corrected. The PACLS/PLS hybrid model outperformed PLS with subset recalibration for near real-time predictions when instrument drift was determined from the repeat samples closest in time to the measurement of the unknown. Near real-time standard errors of prediction (SEPs) for the hybrid model were comparable to the cross-validated SEPs obtained with the original calibration model.

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

Journal of Innovative Optical Health Sciences ◽

10.1142/s1793545814500229 ◽

2014 ◽

Vol 07 (06) ◽

pp. 1450022 ◽

Cited By ~ 4

Author(s):

Lian Li ◽

Baoyang Ding ◽

Qi Yang ◽

Shang Chen ◽

Huaying Ren ◽

...

Keyword(s):

Infrared Spectroscopy ◽

Chondroitin Sulfate ◽

Near Infrared Spectroscopy ◽

Near Infrared ◽

Multivariate Calibration ◽

Process Analytical Technology ◽

Precipitation Process ◽

Calibration Model ◽

Optical Cell ◽

Ethanol Precipitation

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 modified carbazole method. The relevance between NIRS and CS was studied throughout optical pathlength, pretreatment 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 calibration model was established using PLS algorithm for determining the content of CS, and the root mean square error of prediction (RMSEP) is 3.934 g⋅L-1. This method will have great potential in process analytical technology in the future.

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Content uniformity determination of pharmaceutical tablets using five near-infrared reflectance spectrometers: A process analytical technology (PAT) approach using robust multivariate calibration transfer algorithms

Analytica Chimica Acta ◽

10.1016/j.aca.2008.02.016 ◽

2008 ◽

Vol 611 (2) ◽

pp. 143-150 ◽

Cited By ~ 39

Author(s):

Yusuf Sulub ◽

Rosario LoBrutto ◽

Richard Vivilecchia ◽

Busolo Wa Wabuyele

Keyword(s):

Near Infrared ◽

Multivariate Calibration ◽

Process Analytical Technology ◽

Content Uniformity ◽

Infrared Reflectance ◽

Near Infrared Reflectance ◽

Calibration Transfer ◽

Pharmaceutical Tablets ◽

Analytical Technology

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Dryer Effluent Monitoring in a Chemical Pilot Plant via Fiber-Optic Near-Infrared Spectroscopy

Applied Spectroscopy ◽

10.1366/0003702981944139 ◽

1998 ◽

Vol 52 (5) ◽

pp. 717-724 ◽

Cited By ~ 10

Author(s):

Charity Coffey ◽

Alex Predoehl ◽

Dwight S. Walker

Keyword(s):

Real Time ◽

Pilot Plant ◽

Near Infrared ◽

Fiber Optic ◽

Tunable Filter ◽

Calibration Model ◽

Gas Cell ◽

Rotary Dryer ◽

Vapor Stream ◽

Effluent Stream

The monitoring of the effluent of a rotary dryer has been developed and implemented. The vapor stream between the dryer and the vacuum is monitored in real time by a process fiber-optic coupled near-infrared (NIR) spectrometer. A partial least-squares (PLS) calibration model was developed on the basis of solvents typically used in a chemical pilot plant and uploaded to an acousto-optic tunable filter NIR (AOTF-NIR). The AOTF-NIR is well suited to process monitoring as it electrically scans a crystal and hence has no moving parts. The AOTF-NIR continuously fits the PLS model to the currently collected spectrum. The returned values can be used to follow the drying process and determine when the material can be unloaded from the dryer. The effluent stream was monitored by placing a gas cell in-line with the vapor stream. The gas cell is fiber-optic coupled to a NIR instrument located 20 m away. The results indicate that the percent vapor in the effluent stream can be monitored in real time and thus be used to determine when the product is free of solvent.

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Near-Infrared Spectroscopy Combined with Multivariate Calibration to Predict the Yield of Sesame Oil Produced by Traditional Aqueous Extraction Process

Journal of Food Quality ◽

10.1155/2017/2515476 ◽

2017 ◽

Vol 2017 ◽

pp. 1-5

Author(s):

Yong-Dong Xu ◽

Yan-Ping Zhou ◽

Jing Chen

Keyword(s):

Near Infrared ◽

Multivariate Calibration ◽

Nir Spectroscopy ◽

Extraction Process ◽

Sesame Oil ◽

Aqueous Extraction ◽

Oil Yield ◽

Support Vector ◽

Calibration Model ◽

Aqueous Extraction Process

Sesame oil produced by the traditional aqueous extraction process (TAEP) has been recognized by its pleasant flavor and high nutrition value. This paper developed a rapid and nondestructive method to predict the sesame oil yield by TAEP using near-infrared (NIR) spectroscopy. A collection of 145 sesame seed samples was measured by NIR spectroscopy and the relationship between the TAEP oil yield and the spectra was modeled by least-squares support vector machine (LS-SVM). Smoothing, taking second derivatives (D2), and standard normal variate (SNV) transformation were performed to remove the unwanted variations in the raw spectra. The results indicated that D2-LS-SVM (4000–9000 cm−1) obtained the most accurate calibration model with root mean square error of prediction (RMSEP) of 1.15 (%, w/w). Moreover, the RMSEP was not significantly influenced by different initial values of LS-SVM parameters. The calibration model could be helpful to search for sesame seeds with higher TAEP oil yields.

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Assessment of internal quality attributes of mandarin fruit. 2. NIR calibration model robustness

Australian Journal of Agricultural Research ◽

10.1071/ar04299 ◽

2005 ◽

Vol 56 (4) ◽

pp. 417 ◽

Cited By ~ 36

Author(s):

J. A. Guthrie ◽

D. J. Reid ◽

K. B. Walsh

Keyword(s):

Near Infrared ◽

Model Updating ◽

Multivariate Calibration ◽

Mean Squared Error ◽

Sample Selection ◽

Model Performance ◽

Soluble Solids ◽

Internal Quality ◽

Calibration Model ◽

Validation Population

The robustness of multivariate calibration models, based on near infrared spectroscopy, for the assessment of total soluble solids (TSS) and dry matter (DM) of intact mandarin fruit (Citrus reticulata cv. Imperial) was assessed. TSS calibration model performance was validated in terms of prediction of populations of fruit not in the original population (different harvest days from a single tree, different harvest localities, different harvest seasons). Of these, calibration performance was most affected by validation across seasons (signal to noise statistic on root mean squared error of prediction of 3.8, compared with 20 and 13 for locality and harvest day, respectively). Procedures for sample selection from the validation population for addition to the calibration population (‘model updating’) were considered for both TSS and DM models. Random selection from the validation group worked as well as more sophisticated selection procedures, with approximately 20 samples required. Models that were developed using samples at a range of temperatures were robust in validation for TSS and DM.

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