Tea leaves identified using ICP and neural networks

A team of chemists from the University of Seville, Spain, has managed to distinguish between different kinds of tea leaves on the basis of their mineral content and by using artificial neural networks. This technique makes it possible to differentiate between the five main varieties of tea—white, green black, Oolong and red tea.

White, black, green and red tea (Oolong is simlar to black tea). Image: J. Marcos Jurado et al.

First, the leaves were analysed by inductively-coupled plasma atomic emission spectroscopy, which showed the most abundant elements to be calcium, magnesium, potassium, aluminium, phosphorus and sulphur. Other essential elements were also identified in the tea, such as copper, manganese, iron and zinc, according to this study, which has been published online in Food Chemistry (doi: 10.1016/j.foodchem.2010.05.007).

Once the mineral content of the leaves was established, probabilistic neural networks were used to find out which type of tea a sample belonged to. This generates a model that receives an input signal (chemical data) and produces an output one, making it possible to predict the type of tea in the sample with a probability of 97%.

STYLE-->

A team of chemists from the University of Seville, Spain, has managed to distinguish between different kinds of tea leaves on the basis of their mineral content and by using artificial neural networks. This technique makes it possible to differentiate between the five main varieties of tea—white, green black, Oolong and red tea.

First, the leaves were analysed by inductively-coupled plasma atomic emission spectroscopy, which showed the most abundant elements to be calcium, magnesium, potassium, aluminium, phosphorus and sulphur. Other essential elements were also identified in the tea, such as copper, manganese, iron and zinc, according to this study, which has been published online in Food Chemistry (doi: 10.1016/j.foodchem.2010.05.007).

Once the mineral content of the leaves was established, probabilistic neural networks were used to find out which type of tea a sample belonged to. This generates a model that receives an input signal (chemical data) and produces an output one, making it possible to predict the type of tea in the sample with a probability of 97%.

User Rating: / 0
PoorBest