Fruit: Towards Virtual Taste Tests
When it comes to fresh fruit, looks can be deceiving.
The prettiest apples may be tasteless or their texture mealy. Intact, ruby-hued skin may hide a large, mushy bruise. As a result, each purchase becomes somewhat of a gamble.
Federal engineers with the Agricultural Research Service hope to up a buyer’s odds with a system they’re developing in a laboratory at Michigan State University. They shine near infrared (NIR) light onto apples and then analyze the light both that’s reflected back at a slightly altered wavelength and that’s scattered.
The first type of spectral data offers some information on the chemical composition of the sample, the second probes its physical structure, explains Renfu Lu, one of the technique’s developers. Taken together, the two types of data should provide a good gauge of a fruit’s sweetness and firmness.
Currently, food-quality inspectors obtain such information on random pieces of fruit by taste and by punching a steel cylinder into the apple to a specified depth. Measurements of the force needed on the cylinder indicate the apple’s firmness. The drawback is that these tests damage the sampled items, rendering them unavailable for sale even if they prove perfect.
Because the new technology is noninvasive, sampled fruit can remain in the food pipeline. Moreover, Lu says, the NIR analyses should be inexpensive enough to use on every apple moving through packinghouses.
Scanning more than skin deep
In Michigan, a major apple-producing state, most packinghouses currently employ digital imaging to sort fruit by size or to identify externally defective apples that need to be culled. However, Lu points out, “that system is literally skin-deep. It can’t detect bruises.”
By coupling a camera’s conventional visual data with analyses of reflected light outside the visual range, Lu can find bruises that don’t show on the surface. But his primary focus, at this point, is optimizing the system to evaluate two other features.
Some of the NIR light directed at fruit will penetrate 10 millimeters or so–about the same depth that mechanical firmness gauges now reach, Lu says. Some of that light will bounce back at a different wavelength. Lu has developed software that correlates the relative sugar content of that internal tissue to the wavelength of reemitted light.
Some shoppers want their apples sweet and others prefer them tart. Lu’s data would enable packers to sort the fruit by sugar content–and perhaps tangy acidity–and tailor the marketing.
What’s more, Lu explains, data on the light scattering offers clues to the fruit’s texture–specifically, to its density, which correlates with crispness. “Traditional NIR technology cannot measure firmness with the required accuracy,” Lu says. So, his group is looking to go beyond NIR to a form of imaging spectroscopy “to detect firmness as well as sugar content.”
Conventional NIR spectroscopy, he says, “basically gives you a point measurement,” with no spatial information. The imaging component of the new spectral studies broadens the sampling to give a “spectral signature” for the entire sample of fruit hit by the light beam. A more detailed description of the technique must await patent filings on the technology, Lu says.
Pilot testing of integrated systems offering these sampling capabilities may begin in a couple years, Lu says. He envisions packinghouses eventually incorporating them for the evaluation of every piece moving along the line. With the appropriate calibration, he predicts, the technology should extend such remote evaluation of similar sensory characteristics to cherries, peaches, pears, and even oranges.
