Optical analysis capable of predicting taste of fruit
accurately predict whether fruit and vegetables taste as good as
they look.
The American Research Service (ARS) team behind the project claim that the machine vision tool can predict the quality of fruit or vegetable flavor - right after picking and in the packing plant - without ever touching the product.
Renfu Lu, an agricultural engineer with the ARS Sugar Beet and Bean Research Unit in East Lansing, has developed machine vision prototypes that 'taste' every single piece of produce from right after harvest to when it passes by on the packing line.
Such a device could revolutionise production in the future by speeding up the inspection process and reducing the amount of waste.
This is because batches of fruits and vegetables are usually judged by sample tastings, where there is no guarantee that all of the produce in the batch will taste the same. Samples are also tested for firmness by mechanically stabbing them with a thick, steel probe.
With both methods, the tested produce has to be thrown away.
But while there are machine vision tools that can check skin-deep traits such as size, color and bruising, it is difficult to judge deep, internal qualities such as the taste and texture of apples.
The new machine vision concept uses optical sensors to inspect objects instead. Lu and his ARS colleagues claim that the machine should work with any produce that is at least as large as an apple or peach.
The detector focuses four laser beams, each a different light wavelength, into one sharp beam that shines into individual fruits. Laser light photons momentarily scatter all the way to the fruit's core and back.
The amount of light bounced back after interacting with tissue reflects firmness. Peaches and apples are separated by whether they are soft, firm or hard.
Since scattered light also indicates the amount of light absorbed by the fruit. Because absorption is affected by sugar levels, this technology can be used to predict flavors, such as the sweetness in apples.
You can read more about this research in the May 2005 issue of Agricultural Research magazine.
