The Sagitto Blog

The success of generative AI applications such as ChatGPT and DALL-E has increased public awareness of the power of artificial intelligence software. Sagitto's Benchmarking Service allows users of infrared spectroscopy instruments to benchmark their current models against models generated by machine learning.

Outlier detection is an important step in preparing spectroscopy data for machine learning models. Hotelling's T2 and Q-Residuals are two outlier detection methods commonly used in chemometrics. However, Sagitto has found that they need to be used with caution to avoid discarding unusual but valid data.

Innovations in chip-scale sensors and NIR LEDs are creating exciting opportunities for consumers to accurately measure fruit quality with tiny, inexpensive spectral devices. We have demonstrated that we can build robust predictive models for a wide range of apple varieties.

Carbon black is a common black pigment, traditionally produced from charring organic materials such as wood or bone. It appears black because it reflects very little light in the visible part of the spectrum.

Just for fun, we tested 70 different bars of chocolate using our hand-held NIR spectrometer and a tiny NIR spectral sensor from ams-Osram. Here's what we found.

When building a machine learning model, our customers often ask "How much training data will we need?" It's rather like kids in the back seat of the car on a long journey, asking how much further until we get there?