2020
DOI: 10.1080/15376516.2020.1839612
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Toxicological analyses: analytical method validation for prevention or diagnosis

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Cited by 5 publications
(2 citation statements)
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“…Thus, linear regression using the method of least squares was applied to fit the standard curve (Figure 2A) and showed an excellent correlation (r > 0.99) in the range of 5 -30 μg/mL (Table 2), as recommended by guidelines such as the ICH (ICH, 2005). Since the regression coefficient is closer to 1.0, the dispersion of the set of experimental points is considered smaller, with low uncertainty of the estimated regression coefficients (Abrão et al, 2020;ANVISA, 2017;ICH, 2005). The linear regression equation was y = 0.02711x + 0.2346, where y is the absorbance and x is the nominal concentration of SA (μg/mL).…”
Section: Linearitymentioning
confidence: 99%
“…Thus, linear regression using the method of least squares was applied to fit the standard curve (Figure 2A) and showed an excellent correlation (r > 0.99) in the range of 5 -30 μg/mL (Table 2), as recommended by guidelines such as the ICH (ICH, 2005). Since the regression coefficient is closer to 1.0, the dispersion of the set of experimental points is considered smaller, with low uncertainty of the estimated regression coefficients (Abrão et al, 2020;ANVISA, 2017;ICH, 2005). The linear regression equation was y = 0.02711x + 0.2346, where y is the absorbance and x is the nominal concentration of SA (μg/mL).…”
Section: Linearitymentioning
confidence: 99%
“…The figure also indicates the use of high‐end instruments, such as HPLC, GC‐MS, and LC‐MS, to determine specific CEC concentrations. Analytical validation in the toxicological analysis is important to establish the precise exposure value for risk assessment [8]. Thus, developing an instrumental method in CEC analysis is essential to determine an exact and accurate risk assessment level.…”
Section: Introductionmentioning
confidence: 99%