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The Spoon

Daily news and analysis about the food tech revolution

food inspection

QCify Goes 3D for Quality Control and Fair Pricing in the Food Supply Chain

As the economy is barraged daily by some kind of pandemic-related bad news, many businesses remain closed (or serving far fewer customers), job losses continue to pile up and people all over are being more cautious about how much they spend.

The food supply chain is not immune from this belt-tightenting. Looking to save some money, food buyers may haggle more vigorously over what they pay per pound for something like almonds, costing the growers and processors money. This problem, Raf Peeters told me, is where QCify can help.

Peeters is the CEO of QCify (pronounced kew-sih-fye), which uses a combination of computer vision and artificial intelligence (AI) to perform quality control on food items at processing plants. Right now QCify inspects almonds and pistachios by running samples through a special machine that uses six cameras to capture a 3D image of each almond. The company’s AI then analyzes the image and grades the almonds based on USDA (or other) criteria such as size, color, insect damage, imperfections, etc.

All that data collected by the inspection machines are sent back to to QCify HQ, where it is incorporated into the company’s algorithms. Twice a year QCify then sends out updates to all of its installed machines, which means that even if a customer bought a QCify system a couple years ago, it will run the newest AI. “Customers feel like they have the latest and greatest,” Peeters told me by phone this week.

Qcify explainer video

The result of all this computer vision and machine learning is that nut processors can set a fair price for their wares, based on objective criteria (like the USDA grading). Right now, QCify works with almonds and pistachios, and has customers in both the U.S. and Australia. A buyer can’t argue over the quality of the almonds (and thereby demand a lower price) because the processor not only has the grade from the QCify system, but it can also produce the sample images to show exactly what quality the almonds or pistachios are in.

QCify isn’t the only company looking to remove biases from the food supply chain using computer vision and AI. AgShift and Intello Labs do much the same thing. Peeters said that QCify is different from the competition because its six-camera setup captures 3D images of the nuts, instead of just scanning the top an bottom of the food, which Peeters claims is what his competition does.

QCify was founded in 2015 and Peeters said they company has only raised an unspecified amount of angel investment money. The company sells the machines themselves and charges a monthly/annual subscription fee for updates and calibration. While he wouldn’t reveal pricing, Peeters said that customers can earn their money back within a year.

In these cash-strapped times, a faster ROI isn’t just peanuts, which, coincidentally is one of the next nut categories QCify is expanding into.

“Inspecto” Gadget Promises Real-Time Contaminant Testing in Food Supply Chain

Whether or not you care that your food is organic, locally sourced or non-GMO, one thing I think we can all agree on is that we’d like our food free of harmful chemical contaminants. But testing food for such contaminants requires samples to be sent to a lab, which is a time-consuming and expensive process.

Inspecto is looking to simplify and speed up this process for food producers and manufacturers with its device dubbed, appropriately, Inspecto. Using the small appliance, samples of food can be loaded into a special Inspecto capsule on-site, whether that’s at a farm or further along the supply chain. The capsule is inserted into the machine, which uses Raman Spectroscopy to analyze it for contaminants.

Don’t know what Raman Spectroscopy is? We didn’t either, here’s a quick introduction from Wikipedia:

Raman spectroscopy (/ˈrɑːmən/); named after Indian physicist Sir C. V. Raman) is a spectroscopic technique used to observe vibrational, rotational, and other low-frequency modes in a system.[1] Raman spectroscopy is commonly used in chemistry to provide a structural fingerprint by which molecules can be identified.

The fingerprints Inspecto looks for are the chemical contaminants (more on those in a minute) that can wind up in our favorite foods. After testing the sample, Inspecto beams the physical analysis to the cloud where Inspecto’s algorithms process the findings and deliver results back, usually within a half hour, depending on what is being tested and tested for.

The result is the ability for food producers to test their ingredients for contaminants in the field in real time. So a Japanese coffee company could test green coffee beans for a particular contaminant in South America before the beans ever get on a boat, without needing (or waiting) to send samples off to a lab.

I spoke with Inspecto Co-Founder and CEO, Avner Avidan, who told me that while his company’s technology can be used to detect just about any liquid or solid contaminant, right now, Inspecto is focusing on analyzing big crops like coffee, wheat, rice and soy for chemical contaminants such as acrylamide, which, Avidan says it can detect all the way down to 50 parts per billion.

While Inspecto can be installed along the supply chain, one thing it can’t do is broad scanning and analysis. That means that there won’t be some gigantic Inspecto-beam situated above a conveyor belt scanning food in real-time as it passes underneath like ImpactVision and P&P Optica do using hyperspectral imaging to detect foreign matter. Inspecto is more like the Nima sensor, using a combination of special hardware and capsules that analyze a particular food for one contaminant.

Right now, Inspecto is finalizing its exact pricing plan, but Avidan said the company will sell both the device and single-use capsules. For larger customers, there may also be a data subscription for greater access to analytics.

Based in Tel Aviv, Israel, has raised $1.7 million USD in seed funding and is currently in trials with three food companies in Europe and one in the U.S.. Inspecto’s plan is to continue trialing throughout this year and go to market in 2020.

AgShift Launches Hydra, its AI-Powered Food Quality Analyzer for Bulk Inspections

AgShift is on a mission to remove human biases from food quality inspection by using computer vision and artificial intelligence. It started off doing this by having inspectors use smartphone cameras to snap pictures of food (like berries), which were then analyzed by AgShift‘s machine learning algorithms to assess quality.

While the company’s software platform may bring objectivity to quality assessment, having inspectors manually take photos of fruit was still mostly manual. And when you consider that one state, California, produces one billion pounds of one fruit, strawberries, each year, these manual inspections can still take quite a bit of time. In addition to accuracy, there is a need for speed in the supply chain.

This is why AgShift created Hydra F100 BQ, a new hardware analyzer that the company officially announced today. With Hydra, companies can do more bulk inspection and thereby faster assessment of food like berries and edible nuts.

We wrote about this hardware analyzer before, but that was when it was in the prototype/development stage. Today’s news takes the wraps off the full industrial version of the device.

The Hydra is a kiosk like machine with a touchscreen that is installed at a food processing facility. Instead of inspectors manually selecting and inspecting samples, whole trays of samples can now be inserted into the machine to be assessed at once. The Hydra has cameras above and below the fruit to capture images of this bulk sample, which is sent to AgShift’s cloud platform to analyze it for weight, size, color and to check for defects like mold or bruising. The result is the same objective analysis, but AgShift says it’s now done in half the time of manual inspections.

“When you do a manual inspection [of strawberries], you are inspecting roughly 4 to 6 clamshells in a sample size, roughly 100 berries in total,” said Miku Jha, Founder and CEO of AgShift. “[That] takes 6 to 8 minutes with manual inspection. Hydra does it in under three minutes.”

AgShift’s Hydra has already been running in trials with both Driscoll’s for strawberry inspection and Olam for cashews.

By removing manual inspections by hand, AgShift says it can also reduce waste because. As we reported last June:

[Jha] said that traditionally cashews are examined by hand, with inspectors looking at one or two pounds of nuts at a time. That takes time, and after being touched, those particular nuts need to be discarded. Both time and waste can add up when you’re processing literally tons of cashews. Using AgShift’s analyzer, sampling can be done faster and samples do not need to be thrown out because of the workflow at the processing facility.

In a recent phone interview, Jha told The Spoon that the company was still determining the business model around Hydra, but that it wasn’t in the business of selling boxes. Instead, the Hydra would most likely be leased with the price of the software subscription coming in around $4,000 per month, depending on the volume of assessments.

AgShift isn’t alone in the computer-vision-for-food-inspection space, Bengaluru-based Intello Labs does much the same thing for farmers in India.

Earlier this month, AgShift raised another seed round from CerraCap Ventures. The amount wasn’t disclosed at the time, but in the Hydra press announcement, AgShift says it has raised $5 million in seed funding. Since just about a year ago AgShift announced a $2 million raise, it looks like the recent raise was for $3 million.