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Most quality systems reduce wheat and flour quality to a handful of predicted parameters. Protein. Moisture. Ash.
Those metrics matter. But they don't capture functional complexity.
Manna™ does.
Standard NIR models extract one value at a time - protein, moisture, falling number - and discard everything else. But the full spectral signal contains far more: functional patterns that reveal how a wheat lot will behave under milling pressure, how flour will absorb water, how dough will respond in the mixer.
Traditional systems compress that signal into thresholds. Manna™ preserves it.
Every NIR scan produces thousands of data points — a complete biochemical fingerprint of the material. Manna™ analyzes the entire fingerprint, because functional intelligence lives in the relationships between spectral features, not in any single variable. That's true whether the material entering the scanner is incoming wheat at the mill gate or a flour lot arriving at the bakery.
Raw spectra aren't useful on their own. What matters is what they mean in production.
Manna™ transforms spectral data into actionable guidance through AI models purpose-built for grain and flour functionality - trained on spectral data, wet-lab measurements, and real milling and baking outcomes.
From NIR scan to operational decision in seconds. Built for quality managers and production teams — not data scientists.
Capture full-spectrum NIR data at intake, in the mill, or on the bakery floor. Millers use their existing NIR device. Bakeries without NIR can get started immediately using a compact handheld device from Equinom.
AI models classify lots by expected performance behavior.
For millers, that means connecting spectral signals to wheat quality, blending behavior, and flour consistency. For bakers, it means predicting the adjustments needed - water levels, mixing time, and more - before production begins.
Clear operational guidance for the decisions that matter: wheat intake and silo allocation for mill operators; water and mixing adjustments for bakery teams. Built for quality managers, procurement leaders, and production staff - not data scientists.
Our AI models are built on the chemistry and biology of grain and flour functionality - how protein composition affects dough strength, how starch structure influences water absorption, how enzyme activity shapes crumb and crust. These are the properties that determine what happens in both the mill and the mixer.
The spectral signal is the bridge between ingredient biology and real behavior on the production floor - whether that floor belongs to a miller or a baker. The system understands why wheat and flour behave as they do, not just that they correlate with certain outcomes.
Variability never stops. Neither does the model. As new harvests enter the milland new flour lots arrive at the bakery, spectral data, wet-lab results, and realproduction feedback feed back into the models - making predictions sharperwith every season.
Manna™ is a decision platform, not a lab tool - integrating into daily workflowsacross grain intake, blending, flour validation, quality control, and productionmanagement.
Delivered as a cloud-based platform. No complex infrastructure. No heavyimplementation.
Scan. Read. Act.
Equinom Ltd.
Givat Brenner,
6094800, Israel
Equinom Inc.
Indianapolis, IN
46236, USA
Equinom Ltd.
Givat Brenner,
6094800, Israel
Equinom Inc.
Indianapolis, IN
46236, USA