The SaltbyPep Blog
Technical writing from the SaltbyPep team. Enzyme kinetics, sensor data, humidity control, and the messy reality of building AI for commercial beef aging.
Field installation notes from our first commercial aging facilities. The sensor stream dropout problem, the recovery protocol we built, and why the redundancy design took four iterations to stop failing at the same point.
Read articleHow we validate that our model's endpoint call matches what a skilled practitioner would call. The measurement protocol, the disagreement cases, and what they told us about model gaps.
Temperature fluctuations during transport and holding compound with the enzymatic process in ways most aging programs do not account for. This is what the data looks like when you start logging from slaughter date.
The intramuscular fat distribution in Hanwoo creates a different collagen hydrolysis dynamic than Angus or wagyu. Our profile calibration data and what it took to stop over-predicting the endpoint by 6 to 8 hours.
Why we moved the inference step from cloud to edge, the latency arithmetic, and what the hardware constraint meant for our model architecture. The memory and compute budget are smaller than you think.
Wet aging is less predictable than dry aging because the lactobacillus culture activity is harder to instrument. This is our current approach to using CO2 as a proxy for fermentation state in sealed packages.
We use Warner-Bratzler shear force to validate that sensor-predicted endpoints correspond to actual texture outcomes. This is the correlation data from our first six months of pilot operation.
The difference between 78% and 83% relative humidity in a dry-aging chamber is the difference between surface crust development and unwanted surface flora growth. What precision really means in an environment with repeated door openings and product load changes.
We spent three days at an aging facility in Icheon studying how an experienced practitioner reads a chamber. This is what the sensor array misses, and how we are thinking about closing that gap in the next model iteration.
The foundational biochemistry behind what we are measuring. Calpain and cathepsin activity rates, their temperature sensitivity, and why the Arrhenius model is both a useful approximation and a source of persistent prediction error.
