Multivariate Control Charts for Simultaneous Quality Monitoring of Isoniazid and Rifampicin in a Pharmaceutical Formulation Using a Portable Near Infrared Spectrometer

Costa, Fernanda S. L.; Pedroza, Ricardo H. P.; Porto, Dayanne Lopes; Lima, Kássio M. G.

doi:10.5935/0103-5053.20140214

Cited by 9 publications

(12 citation statements)

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“…Multivariate control charts based on NAS provide multivariate product quality monitoring and they are carried out in two stages: (i) model building and (ii) calculation of statistical limits [ 26 ]. The first stage consists on the decomposition of a sample spectrum r into three vectors: a vector r NAS that is unique for the analyte; a vector r INT that is related to the other compounds in the sample (interfering constituents); and a residual vector r res [ 25 ]:…”

Section: Theorymentioning

confidence: 99%

“…The projected “under control” spectra occupy a restricted region on the interference space, wherein the pharmaceutical formulation is constructed with placebo and blank samples. The validation of these control charts is made by using “in-control” and “out-of-control” samples, based on the concentration of the active pharmaceutical ingredient [ 25 , 26 ]. The residual charts are obtained after calculation of NAS and interference vector, in which its control limits are estimated based on Q-statistics by fitting a chi-squared distribution to the reference distribution obtained from NOC data [ 25 , 26 ].…”

Section: Theorymentioning

confidence: 99%

“…The validation of these control charts is made by using “in-control” and “out-of-control” samples, based on the concentration of the active pharmaceutical ingredient [ 25 , 26 ]. The residual charts are obtained after calculation of NAS and interference vector, in which its control limits are estimated based on Q-statistics by fitting a chi-squared distribution to the reference distribution obtained from NOC data [ 25 , 26 ]. The Q-statistics is the first type of statistical calculation recommend to test significance of an individual observation vector [ 32 ].…”

Section: Theorymentioning

confidence: 99%

“…An interesting approach for quality multivariate control chart is based on Net Analyte Signal (NAS) [ 21 - 24 ]. This method is carried out by the decomposition of a sample spectrum into a vector that is unique for the analyte; a vector related to the other compounds in the sample (interfering constituents); and a remaining residual vector [ 25 ]. Thereafter, the statistical limits for the NAS control charts are derived from the NAS value for each of the NOC spectra calculated [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Multivariate Control Chart Approach for Calibration Transfer between NIR Spectrometers for Simultaneous Determination of Rifampicin and Isoniazid in Pharmaceutical Formulation

Andrade

Morais

Costa

et al. 2018

CAC

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Background: Multivariate transfer techniques have become a widely accepted concept over the past few years, since they avoid full recalibration procedures when instruments are changed to analyze a specific sample.Objective: This paper reports a multivariate control chart transfer approach between two near infrared (NIR) spectrometers for simultaneous determination of rifampicin and isoniazid in pharmaceutical formu-lation using Direct Standardization (DS).Method: The control charts are based on the calculation of Net Analyte Signal (NAS) models and the transfer samples are selected by the Kennard-Stone (KS) algorithm. Three control charts (NAS, interfer-ence and residual) transferred on both the master and slave instruments were measured.Results: As a result, a classification model for rifampicin and isoniazid developed on a primary instrument has been successfully transferred to a secondary instrument. The spectral differences after the standardiza-tion procedure were considerably reduced and errors values found in the charts for both analytes were comparable with the errors obtained for the original chart models.Conclusion: The proposed approach appears to be a valid alternative to the commonly used transfer of multivariate calibration models in simultaneous determination of isoniazid and rifampicin in pharmaceuti-cal formulation

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Section: Theorymentioning

confidence: 99%

Section: Theorymentioning

confidence: 99%

Section: Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Multivariate Control Chart Approach for Calibration Transfer between NIR Spectrometers for Simultaneous Determination of Rifampicin and Isoniazid in Pharmaceutical Formulation

Andrade

Morais

Costa

et al. 2018

CAC

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“…By applying multivariate control charts, out-of-control signals can be detected and this allows manufacturers to diagnose and identify the factors in the process that affect the behavior of these variables, as well as establish corrective actions (Costa et al 2015). There is a large number of published studies describing the role of control charts in industrial processes.…”

Section: Introductionmentioning

confidence: 99%

Application of robust multivariate control chart with Winsorized Mean: a case study

2019

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Water pH and active ingredient concentration are two of the most important variables to consider in the manufacturing process of fungicides. If these variables do not meet the required standards, the quality of the product may be compromised and lead to poor fungicide performance when water is used as the application carrier, which is in most cases. Given the correlation between the variables, these kinds of manufacturing processes must be analyzed in multivariate settings. Thus, this paper analyzes the variables involved in the process using the multivariate control chart |S| introduced by J. A. Vargas. In the original chart, the arithmetic mean is used as the mean vector estimator. However, in this investigation the arithmetic mean was replaced by the Winsorized Mean for the purpose of evaluating the chart performance with a robust estimator. The results show that using the new estimator, the control chart is able to detect shifts in the variation of the mean vector that the traditional estimator did not. Furthermore, different subgroup sizes for the data were studied in order to examine the performance of the chart in each case. It was found that the proposed control chart is more sensible to changes when the subgroups consist of less observations, since it is able to better identify the outliers in the sample.

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Quality Control of Biodiesel Content of B7 Blends of Methyl Jatropha and Methyl Crambe Biodiesels Using Mid-Infrared Spectroscopy and Multivariate Control Charts Based on Net Analyte Signal

Sitoe¹,

Mitsutake²,

Guimarães³

et al. 2016

Energy Fuels

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In this work, mid-infrared spectroscopy and multivariate control charts based on the net analyte signal (NAS) were applied for quality control of B7 (7% biodiesel and 93% diesel) biodiesel/diesel blends of jatropha oil methyl esters (JOME) and crambe oil methyl esters (COME) relative to the biodiesel content. The construction of the multivariate control charts was based on the decomposition of the spectral signal of samples prepared within quality specifications. Three charts were generated: (i) a NAS chart to monitor the content of the biodiesel in the blend, (ii) an interference chart to monitor the diesel content, and (iii) a residual chart to monitor any systematic variations. For each chart developed, statistical limits were established using the same samples prepared under normal operating conditions, with biodiesel content between 6.5% and 7.5%, considering the range of ±0.5% (v/v) permitted by the Brazilian National Agency of Petroleum, Natural Gas, and Biofuels. To validate the control charts, new in-control and out-of-control samples relative to the biodiesel content were prepared. Of the two sets of out-of-control samples, the biodiesel content of one set (D) was below 6.5% (v/v) and the other set (E) was above 7.5%. The third set (C) of validation samples was prepared with biodiesel content within control of the quality specifications. The analytical methodology developed for each model (JOME and COME) was rapid, efficient, and able to correctly monitor the compliant and noncompliant samples relative to the biodiesel content in B7 biodiesel/diesel blends, demonstrating that this methodology is an alternative route to be used by the supervisory bodies for quality control of these types of biodiesel.

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Multivariate Control Charts for Simultaneous Quality Monitoring of Isoniazid and Rifampicin in a Pharmaceutical Formulation Using a Portable Near Infrared Spectrometer

Cited by 9 publications

References 1 publication

A Multivariate Control Chart Approach for Calibration Transfer between NIR Spectrometers for Simultaneous Determination of Rifampicin and Isoniazid in Pharmaceutical Formulation

A Multivariate Control Chart Approach for Calibration Transfer between NIR Spectrometers for Simultaneous Determination of Rifampicin and Isoniazid in Pharmaceutical Formulation

Application of robust multivariate control chart with Winsorized Mean: a case study

Quality Control of Biodiesel Content of B7 Blends of Methyl Jatropha and Methyl Crambe Biodiesels Using Mid-Infrared Spectroscopy and Multivariate Control Charts Based on Net Analyte Signal

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