For every test run, the Delvotest® Accelerator Smart (DAS) is able to automatically figure out the right Control Time accurately determining whether each sample is NEGATIVE or POSITIVE. So, how's it able to do this so effortlessly? It's in great part thanks to how its algorithm works, the DAS's 'Working Principle'. In this article, we will explore that logic in detail, highlighting some of the key implications you will need to keep in mind when testing — in particular, the importance that reference controls play in ensuring your DAS performs as expected.
Z-values, 'Control Time' and 'Cut-off Value'
Every minute during a test run (incubation), the DAS takes a picture of your Delvotest® plate or plate-blocks. It then converts this image into a 'digital map', translating the colour of every well into a numerical value. This colour value is based on a three-dimensional colour scale, from which Z-values are calculated. The Z value ranges from high (+, plus) numbers to low (-, minus) values and cover the color range from purple to yellow color which is seen on Delvotest® due to the color indicator. The DAS calibrations position, uniformity and color ensure the correct performance on reading each well.
As microbial tests like Delvotest® are a dynamic system that contain spores of bacteria, every batch of plates will have a slightly different 'Control Time'. This time can also change throughout shelf life or due to storage conditions. Testing with known negative milk allows the DAS to define the correct reading moment in every run and accurately define the results for all other wells by comparing them against a threshold level. This is why NEGATIVE samples play such an important role in how the DAS operates — without them, it would not be able to figure out the right Control Time to read the results with the best sensitivity.
So, this is how it works:
- For every test run, the DAS presumes that at least a representative proportion of plate-wells will produce NEGATIVE results. For an entire plate (i.e., 96 wells), this presumption is set by default at 10% of all total plate-wells rounded up to the next integer (i.e., 10 wells); and, for when only one plate-block is incubated, this 10% principle is overridden by the minimum expectation that at least 4 wells will produce NEGATIVE results.
- Of course, plate-wells falling under the '-' square on the image above would still constitute a NEGATIVE result, as the NEGATIVE colour range spectrum (i.e., '---' to '-') is broader than just a Z = -10 result. For this reason, the DAS has also established a cut-off value (for Delvotest® T, cut-off Z = -4). Thus, all results below this level at Control Time are considered NEGATIVE, and all others above are considered POSITIVE.
Altogether, this is the DAS's underlying Working Principle. However, because of the way it operates, it is very important to take some key implications into account when testing.
Key Implications
The first obvious implication is that the DAS will not be able to define Control Time without the help of sufficient NEGATIVE samples to represent a leading cohort. To illustrate, consider this scenario:
Let's say you want to incubate a whole Delvotest® plate (i.e., 96 wells) with as many known/expected POSITIVE samples as possible.
10% of 96 wells, equals 9.6, rounded up to the next integer equals 10 — which is greater than the overriding 'minimum of 4 samples' principle explained earlier.
Therefore at least 10 of the plate-wells being incubated must be NEGATIVE in order for the DAS to be able to successfully establish the correct Control Time and read the other wells.
In this scenario, to achieve the goal of incubating as many known/expected POSITIVE samples as possible, you'd need to make sure at least 10 NEGATIVE reference controls (i.e., known beforehand to produce a NEGATIVE result) would be included as samples.
In reality, however, POSITIVE results are rare. This is why it's reasonable to generally expect at least 10 samples in any given full plate to produce negative results. However, when testing one or two plate-blocks, because of 𝑓(N) and the fact that the total pool of samples is smaller, we advise making sure at least 4 samples are validated NEGATIVE reference controls. Importantly, in such cases the use of NEGATIVE reference controls are about operational performance and verifiability. This distinction is helpful because it unveils a second, key implication.
Even though users can 'tag' whether an individual plate-well has either a NEGATIVE or POSITIVE reference control added to it, the DAS does not use this data at all to figure out Control Time. Instead, it will also measure these plate-wells, determining if they produce 'TRUE NEGATIVE' or 'TRUE POSITIVE' results — in other words, it doesn't take that data from the user at face value.
That means it is the user’s responsibility to verify that your reference control samples are performing as expected. For this reason, we advise you to add at least one NEGATIVE and one POSITIVE reference control sample to every plate or plate-block(s) you test. The point of adding these reference control samples is not to form a 'base' from which results are determined, rather, they're simply there to help you make sure things are running as they're expected. So, if there's ever a problem with your reference controls, this level of continuous verifiability helps you conduct a Root Cause Analysis (RCA).
Summarising these two implications, here's a layout guide:
No. of Plate-Wells | No. of Plate Blocks | Min. No. of NEGATIVE Samples Expected | Min. No. of Reference Controls Advised |
96 | 6 (Full Plate) | 10 | 1x NEGATIVE |
80 | 5 | 8 | 1x NEGATIVE |
64 | 4 | 7 | 1x NEGATIVE |
48 | 3 | 5 | 1x NEGATIVE |
32 | 2 | 4 | 4x NEGATIVE |
16 | 1 | 4 | 4x NEGATIVE 1x POSITIVE |
Incidentally, this is why we advise to never leave any plate-wells empty. Adding NEGATIVE reference control contributes to the control time estimation.
The third implication is that although Z-values can be an enticing, 'quantified indication' of 'how negative' or 'how positive' a result is, it's not that simple. Broad-spectrum microbial inhibition tests can only be a semi-qualitative detection method, and therefore never act as quantitative technology. Delvotest® are qualitative tests that give Negative or Positive results based on microbial inhibition.
Last but not least, if the matrix being tested deviates from the standard, raw co-mingled cow milk, as with, for example, raw milk with preservatives, the DAS's standard analysis 'method' needs to be adapted with our help. Typically, this requires a more extensive local validation framework🔗, from which reproducible data forms the basis for targeted, auditable parametric changes. This is why we advise under no circumstances altering 'methods' without our guidance, as doing so will generate results and outcomes we will not be able to support.
💬 Got any questions? Need help? Contact us at support@delvotest.com