The Sagitto Blog

We believe that our customers should subscribe to our services willingly, because of the value that they receive and not because they are locked in to using us. That is why we take particular care to provide a smooth pathway, should our corporate customers decide to no longer use Sagitto's services.

'English lavender' oil is extracted from the flowers of Lavandula angustifolia, while 'Lavandin' oil is made from Lavandula x. intermedia, a hybrid cross between Lavandula angustifolia and Lavandula latifolia (Spike Lavender). Near infrared spectroscopy not only gives a very rapid and inexpensive method to tell the difference between these two types of oil, but also allows the composition of oils to be accurately measured.

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?