George Hill

Sagitto Ltd
Sagitto's founder, George Hill, first started working with artificial intelligence during the 1980s, while developing 'expert systems' within Bank of America in London. On returning to New Zealand, he undertook part-time study with the University of Waikato's Machine Learning Group while working for Hill Laboratories, a well-known New Zealand commercial testing laboratory. This led to the formation of Sagitto Limited, dedicated to combining the power of artificial intelligence and machine learning with spectroscopy.
Authentication

Is This Pure Silk?

Silk has been a premium fibre for textiles for millennia. Sagitto provides a rapid and non-destructive way of determining whether a textile is genuine pure silk, a blend of silk with another fibre, or made entirely from another fibre. This is especially valuable for collectors of antique clothing or rugs, and buyers of premium label garments.

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Vendor Lockin

You Should Be Free To Leave

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.

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Authentication

Know Your Lavender Oil

'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.

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Data Science

Benchmark Against Machine Learning Models

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.

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Data Science

Outlier Detection - Sorting The Sheep From The Goats

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.

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Food

Variety Matters When Testing Apples With NIR

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.

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Recycling

The Problem With Carbon Black

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.

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Food

70 Bars of Chocolate Later

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.

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Data Science

Are We There Yet?

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?

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