All you need to know

Quantification of rapid tests

Lateral flow tests can be performed in both qualitative and quantitative formats. A qualitative test simply detects the presence or absence of a specific analyte and is typically interpreted visually. A well-known example is the pregnancy test, where a clear positive or negative result is sufficient.

However, in many real-world applications, a simple “yes” or “no” isn’t enough. The concentration of the analyte can be crucial—for instance, when monitoring disease progression, detecting low-level infections, or measuring therapeutic drug levels. This is where quantitative testing becomes essential. It allows for precise measurement of analyte levels, providing more detailed and actionable insights.

We believe this field deserves the attention it merits. That’s why we’ve compiled a list of frequently asked questions (FAQs) to help you better understand the value and capabilities of quantitative lateral flow testing.

Unlike qualitative tests, where the presence or absence of a test line can be seen with the naked eye, quantitative analysis requires precise measurement of line intensity—something our eyes can’t accurately judge. To determine the exact concentration of an analyte on a lateral flow test strip, a reader is essential.

The reader captures the line intensity using a calibrated camera or optical detection system. This intensity is then translated into a 2D peak profile, where the peak’s height or area corresponds to the analyte concentration. By referencing a stored calibration curve—which maps known concentrations to measured peak values—the system can accurately calculate the analyte level in the sample.

To accurately quantify a lateral flow test, the reader must meet specific hardware requirements that ensure reliable, consistent, and high-quality results:

Linear camera or sensor: Captures high-resolution raw data along the length of the test strip, enabling precise detection and analysis of line intensity.
Adequate and homogeneous illumination: Uniform lighting across the test field is essential to prevent shadows or uneven brightness, which could interfere with accurate signal measurement.
Dynamic integration times: The reader should be capable of adjusting exposure times to properly detect both faint and strong signals, ensuring the test line is neither under- nor overexposed.

These features work together to provide the stable and accurate data needed for reliable quantitative analysis of lateral flow tests.

A standard curve, also known as a calibration curve, is an experimentally generated graph used to determine the concentration of an unknown sample based on a measurable signal—such as gray value from a test line.

To create this curve, a series of calibration standards with known concentrations are measured, and their corresponding signal intensities are plotted. Typically, the concentration is shown on the x-axis, while the measured value (e.g. gray level or peak height) is plotted on the y-axis.

Once the curve is established, it can be used to calculate the concentration of unknown samples by comparing their measured signal to the standard curve. This process is essential for transforming raw signal data into meaningful, quantitative results.

For a lateral flow assay system to be considered truly quantitative, it must meet several key performance criteria to ensure accuracy, reliability, and consistency across measurements:

Low variability: The entire system—including sampling, assay chemistry, and reader performance—should demonstrate low variability, typically with coefficients of variation (CVs) ≤20%, and ≤25% at the limit of quantification (LoQ).
Accuracy: The system should deliver results that are within ±20% of the nominal (true) concentration, ensuring dependable and clinically relevant output.
Specificity: The assay must selectively detect the target analyte without cross-reacting with similar substances or matrix components, which could lead to false results.
Stability: Both the assay reagents and the test system should remain stable over time and under varying conditions, maintaining performance throughout the intended shelf life and use.

Meeting these requirements is essential to ensure that a quantitative assay system delivers reproducible and trustworthy results, particularly in clinical, research, or regulatory environments.

A successful quantitative lateral flow assay relies on the seamless integration of several key components, each playing a crucial role in ensuring accuracy, reproducibility, and ease of use:

Assay design and components: The overall layout and architecture of the test strip, including the positioning of sample, conjugate, test, and control lines, must be optimized for consistent flow and signal development.
Biological reagents: High-quality antibodies or other binding molecules are essential for specific detection of the target analyte. Their stability and binding efficiency directly impact assay performance.
Labels: Detection labels (e.g., gold nanoparticles, fluorescent dyes, or latex beads) must generate a signal that can be accurately quantified and correlate reliably with analyte concentration.
Sample collection and handling methods: Proper sample collection tools and protocols help ensure that samples are consistent in volume and quality, minimizing variability and preserving analyte integrity.
Reader: A calibrated reader is required to capture and interpret signal intensity, converting it into quantifiable data through a standard curve.
Manufacturing process: Reproducible, quality-controlled manufacturing is critical to ensure consistent assay performance across production batches.

Each of these components must work together to create a robust and reliable quantitative testing system.

Several factors can contribute to variability in lateral flow assays, affecting their reliability and reproducibility. The primary sources of variability include:

Assay design and components: Inconsistent test strip layout or improper placement of test and control lines can lead to uneven flow dynamics and unreliable results. The design must ensure uniformity in sample distribution and signal development.
Biological reagents: Variations in the quality or stability of biological reagents (such as antibodies or detection molecules) can lead to inconsistent binding efficiency, impacting the assay’s sensitivity and specificity.
Sample collection and handling methods: Differences in sample collection volume, storage, and transport conditions can affect the concentration of the target analyte or introduce contaminants, resulting in variability in test performance.
Manufacturing process: Inconsistent production methods, such as variations in reagent application, strip assembly, or coating techniques, can lead to batch-to-batch variability, affecting the overall performance and reproducibility of the assay.

Minimizing these sources of variability is key to ensuring that lateral flow assays deliver consistent and accurate results.

When creating a calibration curve for a lateral flow assay, several important factors must be taken into account to ensure the accuracy and consistency of the results:

Generated for each lot: The calibration curve should be generated individually for each lot of reagents or test strips, as variability between production batches can affect performance.
Loaded on multiple readers: To ensure cross-reader compatibility, the calibration curve should be loaded onto multiple readers. This helps maintain consistency across different devices used in the field or clinical settings.
Programmed into reader via RFID or barcode: Calibration data can be programmed into the reader through unique identifiers like RFID tags or barcodes, allowing the reader to automatically access the correct calibration curve for each test run.
Algorithm determines concentration of test: The reader uses a mathematical algorithm that compares the measured signal (such as peak height or intensity) to the calibration curve to determine the analyte concentration in the sample.

Considering these aspects ensures that the calibration curve provides accurate, reliable, and consistent quantification across different testing environments.

To ensure accurate and reliable quantification, we recommend using at least 7 different concentration levels, ranging from low to high. This range should encompass the entire quantifiable range of the assay, allowing for precise measurement across the spectrum of expected analyte concentrations. Including multiple levels ensures that the calibration curve is robust and can accurately reflect variations in sample concentration.

Yes, algorithms are essential for creating standard curves. To generate an accurate calibration curve, you need to apply fitting algorithms that best match the data. Most immunoassays exhibit sigmoidal behavior, meaning the relationship between signal intensity and analyte concentration follows an S-shaped curve. Depending on the target concentration range (low, medium, or high), different mathematical functions can be used to fit the data, including:

Exponential functions for low concentrations
Linear functions for mid-range concentrations
Logarithmic functions for high concentrations

These algorithms help accurately map the measured signal to the corresponding analyte concentration, ensuring reliable quantification.

No, simply applying a reader to a qualitative lateral flow assay will not transform it into a quantitative one. While a reader can help measure the intensity of the test line, the assay itself must be specifically designed to support quantitative analysis. This includes factors like optimized reagent formulation, appropriate test strip layout, and the ability to detect varying levels of analyte concentration. Without these design elements, the assay may not produce reliable or accurate quantitative results.

To transition from a qualitative to a quantitative lateral flow assay, several key parameters must be optimized:

Capture molecule (antibody): The capture molecule should have high concentration and strong affinity constants to ensure it binds effectively with the target analyte. This maximizes the capacity of the test line, especially for detecting higher concentrations of the analyte.
Gold conjugate optimization: The gold conjugate—which typically consists of gold nanoparticles bound to antibodies or other detection molecules—needs to be carefully optimized. Factors such as the coupling pH, coupling time, concentration of the detection molecule/antibody, and the formulation of the conjugate buffer all influence performance. Additionally, the material used for preparing the Conjugate Release Pad is crucial for ensuring efficient release of the conjugate.
Conjugate concentration: The concentration of the conjugate itself can significantly affect the standard curve and assay sensitivity. Titrating the gold conjugate concentration is an effective strategy to ensure lot-to-lot consistency, which is essential for producing reliable and reproducible results across different production batches.

By carefully optimizing these parameters, the lateral flow test can be made suitable for accurate quantitative measurements.

It’s difficult to give a precise number, as the market for quantitative lateral flow assays is growing rapidly. More and more companies are developing and offering these assays, driven by the significant improvements in lateral flow test quality over time. However, despite these advancements, quantitative lateral flow assays still don’t always receive the recognition they deserve, both in the public and scientific communities.

While many manufacturers are now entering this space, specific names and offerings can vary widely depending on the application area (e.g., medical diagnostics, environmental testing, or food safety). As the field continues to evolve, it’s likely we will see an increasing number of manufacturers adopting quantitative formats.

Not necessarily. The choice of label—whether fluorescence or latex/gold—should depend on the concentration of the analyte being measured and the specific requirements of the assay. For analytes in the μg-ng/mL range, fluorescence detection may not be necessary, and traditional latex or gold labels can provide sufficient sensitivity.

However, for highly sensitive assays, where detecting low concentrations is critical, fluorescence labels (such as organic dyes or quantum dots) may be required to achieve the necessary sensitivity.

Gold conjugates, on the other hand, are simple, flexible, and robust in terms of preparation and application. In contrast, fluorescence-based assays may face challenges such as background fluorescence from the membrane and issues with conjugate diffusion during the test run, which need to be carefully addressed to ensure accurate results.