In biosensor performance, which statement about linearity error is true?

In biosensor performance, which statement about linearity error is true?

• A. Linearity error is the deviation from linear response across the measurement range.
• B. Linearity error is the baseline drift over time.
• C. Linearity error is the difference between actual output and a nonlinear model.
• D. Linearity error is the noise in the output.

Answer: (A)

Linearity error measures how well the sensor’s response follows a straight-line relationship between input concentration and output across the measurement range. In an ideal biosensor, the signal changes proportionally with concentration, so the line through the calibration data would be flat in the sense of a constant slope. Linearity error quantifies any departure from that straight-line behavior: you compare the actual output to what a linear model would predict, and the deviations indicate how far the response is from linear. This is often expressed as a percentage of full-scale or as the maximum deviation along the range. Baseline drift is about slow shifts in the starting signal over time, not how the signal changes with varying concentration. Using a nonlinear model to describe the data relates to nonlinearity, not linearity error, which specifically targets deviations from a linear response. Noise refers to random fluctuations around the signal, not a systematic departure from a line. So the statement that linearity error is the deviation from linear response across the measurement range is the best description.

Linearity error measures how well the sensor’s response follows a straight-line relationship between input concentration and output across the measurement range. In an ideal biosensor, the signal changes proportionally with concentration, so the line through the calibration data would be flat in the sense of a constant slope. Linearity error quantifies any departure from that straight-line behavior: you compare the actual output to what a linear model would predict, and the deviations indicate how far the response is from linear. This is often expressed as a percentage of full-scale or as the maximum deviation along the range.

Baseline drift is about slow shifts in the starting signal over time, not how the signal changes with varying concentration. Using a nonlinear model to describe the data relates to nonlinearity, not linearity error, which specifically targets deviations from a linear response. Noise refers to random fluctuations around the signal, not a systematic departure from a line. So the statement that linearity error is the deviation from linear response across the measurement range is the best description.