Net Analyte Signal Based Statistical Quality Control

Abstract

Net analyte signal statistical quality control (NAS-SQC) is a new methodology to perform multivariate product quality monitoring based on the net analyte signal approach. The main advantage of NAS-SQC is that the systematic variation in the product due to the analyte (or property) of interest is separated from the remaining systematic variation due to all other compounds in the matrix. This enhances the ability to flag products out of statistical control. Using control charts, the analyte content, variation of other compounds, and residual variation can be monitored. As an example, NAS-SQC is used to appreciate the control content uniformity of a commercially available pharmaceutical tablet product measured with near-infrared spectroscopy. Using the NAS chart, the active pharmaceutical ingredient (API) content is easily monitored for new tablets. However, since quality is a multivariate property, other quality parameters of the tablets are also monitored simultaneously. It will be demonstrated that, besides the API content, the water content of the tablets as well as the homogeneity of the other compounds is monitored.

Raman spectroscopy and multivariate analysis for the rapid discrimination between native-like and non-native states in freeze-dried protein formulations

“…As we directly use the unprocessed Raman spectra of a freeze-dried formulation (without the usual blank subtraction), we have to investigate the influence of the excipient signals on the discriminating power of the model. Therefore we applied an orthogonal projection approach [21][22][23][24]. The contributions within the variable space of the calibration matrix X (containing the spectra of the protein formulations) are originating from the pure protein signals (X p ), interferences such as the excipient signals (X b ) and other undefined spectral variance (ε) (eq.…”

Multivariate control charts based on NAS and mid‐infrared spectroscopy for quality control of B5 blends of methyl soybean biodiesel in diesel

“…One principal component was used, which explained the 100.00% data variance. PCA is the most widely used method for the construction of interference space because it is a well known and established method, which has shown to be very efficient for separation of systematic and non-systematic variation of blank samples [11,15,24]. However, it showed disadvantages in its use when samples of blank are difficult to obtain, as would be, for example, the case of natural products or protein in food (e.g., milk or meat), where they would be involved in a lengthy and difficult implementation methods to try to separate the analytes of interest, without changing the composition of the blank.…”

“…Multivariate control charts based on NAS were developed by Skibsted et al [24] and is divided into two stages: the development of system model and the determination of statistics limits.…”

“…Thus, control charts require a smaller quantity of samples than other methods. In addition, quality monitoring is a methodology, which allows separated monitoring, but simultaneous, of systematic variation because of biodiesel, diesel, and MIR spectra noise, one of the main advantages of using control charts based on NAS [11,24]. Regarding the assessment of the efficiency of the built charts for monitoring adulterated samples with several kinds of oils and solvents, adulteration sets were divided into three types: (a) adulteration with partial replacement of biodiesel by several types of vegetable oil and waste frying oil (sets F to J) and whose diesel content is 95.00 ± 0.50%; (b) adulteration with partial replacement of diesel by lubricant automotive residual oil, kerosene, and gasoline (sets K to M) whose biodiesel content was maintained at 5.00 ± 0.50%; and (c) the addition of all adulterants cited earlier directly added to the B5 blend, which leads to alterations in the concentration of both biodiesel and diesel (sets N to U), which are shown in Table I.…”

Multivariate data modeling using modified kernel partial least squares

“…In order to remove undesirable systematic variation and achieve better models in multivariate calibration, Lorber proposed a signal preprocessing method called NAP (Lorber, 1986). Many different NAP methods have been proposed in recent years (Skibsted et al, 2005;Lorber et al, 1997;Bro and Andersen, 2003;Ferre and Faber, 2003;Goicoechea and Olivier, 2001). In Goicoechea and Olivier (2001), Goicoechea proposed a NAP method, which was called simple NAP (SNAP) method.…”