Food testing leaps ahead with space-age technology

Increased food safety regulations and the cost of recalls due to contaminated foods are driving processors and researchers to search for better solutions to reduce pathogens in plants.

The Agricultural Research Service (ARS) said it has developed advanced technology to support human inspection of food and equipment. Using filtered light to scan individual fruits, carcasses and processing areas, the developed devices expose contamination previously invisible to the naked eye.

At the Instrumentation and Sensing Laboratory (ISL), a team of scientists led by Yud-Ren Chen are designing advanced portable inspection devices using various technologies to assist processors and food safety workers.

One device is a high-speed on-line imagining system that can be used for chicken inspection. The system uses multiple images at selected wavelengths, a technology originally developed for remote sensing of Earth by satellites.

In an agreement with Stork-Gamco, a processing equipment manufacturer, a prototype will be tested within the industry. Similar systems are currently being developed for other foods, including fruit and vegetables.

Another design includes a hardhat with a small mounted camera and a flashlight that emits a specially filtered light. Another is a pair of safety glasses that contain miniature computer monitor displaying test data, which informs an inspector of the presence of any fecal matter on poultry carcasses.

The ARS is also developing binocular lenses that filter special bands of light to check for disease, defects, or fecal matter on the meat, produce, or equipment.

A hand-held device that shines filtered light to highlight contamination in processing plants has also made it from the drawing board.

The device has a camera that sends images to another eyewear-mounted computer display, which reveals fecal matter as white specks to the inspector looking through the lenses.

All the devises can connect to systems that use optically filtered light and opto-electronics to "see" contamination, often called "machine vision" or "optical sensing" systems.

At the heart of these machine vision systems is the latest digital multispectral camera that can take photos at different wavelengths simultaneously and can even detect light invisible to the naked eye, claims the ARS.

All the systems rely on two or three wavelengths chosen to do the best job of seeing special features.

Machine vision can be used to supplement human inspectors with instruments that shine light on every single fruit, vegetable, meat, or poultry product as it travels through processing lines at up to speeds of 360 fruit and 180 poultry carcasses per minute, claims the ARS.

The system developed by Chen's team spots almost all biological conditions that cause inspectors to take a second look at chicken carcasses, such as signs of diseases that pose food safety risks or otherwise mar a chicken's consumer appeal.

Chen's team is now focusing its attention on apples, developing a system that could be used for other fresh produce as well. It can detect contaminants on the apple surface, such as fecal matter.

Additional research is being conducted at the ISL to inspect wheat and grain.

Agricultural engineer Stephen Delwiche, is working with the ARS Grain Marketing and Production Research Center in, Kansas, on high-speed optical inspection that uses near-infrared reflected light to detect proteins in wheat can be detected as well as scab and other molds.

In Michigan, ARS engineer Renfu Lu, is leading a research team that uses similar optical technologies to judge taste and other quality aspects of produce. Working with apples, peaches, and cherries the prototype optical detector uses laser beams to detect fruit sweetness and firmness.

Lu and colleagues are refining the mathematical equations and the imaging sensor used by the prototype to judge the internal quality of fruit.

"We should have an improved machine vision prototype for 'tasting' apples and other fruit very soon,"​ Lu says.

The next step to advance the machine vision inspection system to pickling cucumbers for bruises and other defects as well as internal quality factors, such as firmness, dry matter content, and color.

"We want to select the best cucumbers-those that are firm, have a fresh green color, and aren't too soggy," says Lu.

Lu's research program is also fully funded by USDA, with additional funds from industry, and it partners with MSU's Biosystems and Agricultural Engineering Department.

Once commercialized, Lu's optical sensors could be used to sort fruits and vegetables just after they had been picked and again on the processing line.