BIG Interview: Felix Instruments' Galen George on faster fruit testing and new applications 

For fruit growers and packhouses, quality measurements are most valuable when they arrive in time to guide a decision. Whether assessing an incoming avocado lot or checking grape maturity, teams need reliable information without creating a bottleneck in their workflow. 

Felix Instruments is developing tools to make that testing faster and easier to perform routinely. Its instruments use near-infrared spectroscopy to estimate internal fruit quality traits without cutting or juicing the fruit for each measurement. Recent work includes a streamlined avocado sampling configuration for packhouses, rapid grape quality testing and models for additional commodities. 

Fructidor spoke with Galen George, Director of Applied Science at Felix Instruments, about these developments, the importance of representative sampling and how the company adapts its models to different fruits and growing conditions. 

Fructidor: You've developed a new configuration for avocado quality control in packhouses. What is the idea behind it? 

Galen George: We've taken our portable avocado measurement technology, which can be used in the field, in the packhouse or at other stages in the cold chain, and adapted the workflow specifically for packhouse quality control. 

The idea is to have the instrument in a lab or receiving area, where a technician can take a representative sample from an incoming lot, scan the fruit and see the average dry matter content. 

That gives the team information to support decisions about accepting the lot, how to pack it and how to manage storage. The focus is on making those measurements easy to collect as part of the normal receiving process. 

Fructidor: What are those new features, and how do they make the measurement process easier? 

Galen George: We've added automatic fruit detection. You place an avocado on the device, it recognizes that the fruit is there, and a green indicator tells you when the measurement is complete. You don't have to manually trigger each scan. 

You can also set the number of measurements in a batch. Five fruit is a common sample size in the Mexican packhouses we've worked with, but operators can adjust that number to suit their sampling protocol. A larger, well-selected sample can give a more representative picture of the lot. 

You can scan different avocados to assess variation across the sample, or take repeated measurements of an individual avocado. Those serve different purposes: scanning the same fruit five times does not replace sampling five different fruit when you're assessing a lot. 

We've also reduced the need for reference measurements between scans when the instrument is operating under stable lighting conditions. In the field, changing external light makes frequent referencing important. In a controlled packhouse setting, we can streamline that process. 

Once the selected number of scans is complete, the instrument displays the average dry matter content. 

Fructidor: What other information can operators obtain from a batch of avocado measurements? 

Galen George: Along with average dry matter content, the device reports the standard deviation, which shows how much the readings vary. 

When you're measuring different avocados, that helps you understand how consistent the sampled fruit are. Two samples can have the same average dry matter but different levels of variation, which is useful context for quality control. 

If you're taking repeated scans of one avocado, the standard deviation describes variation in those readings. It doesn't tell you how much variation there is across the lot. 

There's also a label-printing option to support pallet identification and traceability. A QR-code feature provides a way to transfer measurement information into a quality management or tracking workflow, depending on how that system is set up. 

Fructidor: Is it also possible to measure individual avocados rather than working with a batch? 

Galen George: Yes. You can take an individual fruit measurement or work in batches, depending on the question you're trying to answer. 

The measurement process itself requires minimal operator training: place the fruit on the lens and wait for the scan to finish. Teams still need a clear procedure for selecting representative fruit and interpreting the results, but we've made collecting the readings straightforward. 

That's particularly useful during busy harvest periods, when packhouses need to move quickly and may be bringing in seasonal employees. The goal is to make routine testing easier to carry out consistently. 

Fructidor: Turning to grapes, what does the F-751 Grape Quality Meter measure? 

Galen George: It provides estimates of Brix, pH and titratable acidity. Those are the three parameters we focused on following discussions with the California Table Grape Commission about the information most useful to the industry. 

Together, they give growers and quality teams insight into grape sweetness and acidity. Conventional acidity testing involves preparing juice and carrying out a laboratory test, so it can be labor-intensive. 

The grape meter provides those estimates from a scan in approximately 10 to 12 seconds, without juicing the berries. That makes it easier to collect quality information in the field or packhouse. 

Fructidor: How does that compare with conventional grape quality testing? 

Galen George: It depends on the equipment and procedure. A manual testing workflow can take around 15 minutes per sample, including preparation and testing. 

Typically, you're bringing samples into the lab, juicing the berries and running separate tests. With the grape meter, the scan itself takes approximately 10 to 12 seconds, and the results appear immediately on the screen.

Sample selection and handling still take time, but you remove much of the preparation involved in destructive testing. 

The instrument takes a reference measurement with every scan. You can also collect scans as a batch and view the sample count, average Brix, pH and titratable acidity, along with the standard deviation, minimum and maximum. 

That lets you look beyond a single reading and understand the range within the fruit you've sampled. 

Fructidor: What can customers do with the measurement data? 

Galen George: Data can be exported over Wi-Fi or from the device's SD card. It also has GPS functionality and can be used with Fruit Maps, in the same way as our avocado, kiwi and mango instruments. 

That helps customers connect measurements to where they were collected and review quality patterns across a growing area. 

We're also exploring applications for juice and wine grapes. We've seen promising results with wine grapes, and their smaller berries and tightly packed clusters can make measurement in the field practical. 

However, our wine grape work is not at the same level of model development and validation as our table grape applications. Customers should expect to evaluate performance for their varieties and growing conditions, with model tailoring where needed. 

Fructidor: With so many grape varieties, how do you account for differences between them? 

Galen George: For table grapes, we have separate models for black, green and red grapes. Users select the appropriate color model before taking measurements. 

Wine grapes present a broader challenge because there are many varieties and substantial differences between growing regions. At this stage, we see that as an application best addressed with individual customers, rather than assuming one model will perform equally well everywhere. 

We can help customers evaluate the existing models and determine whether adjustments are needed for their fruit. We offer custom modeling and model-tailoring services, and customers can also use the same model-building software we use. 

The software, downloadable models and online training materials are available free of charge, with no subscription. That gives customers the flexibility to develop their own capabilities or work with us for additional support. 

Fructidor: The F-750 Produce Quality Meter supports a broader range of fruit. How does it differ from the more specialized instruments? 

Galen George: The F-750 is our flagship instrument for broader research and application development. It has a higher-powered spectrometer and can also load models developed for the F-751 instruments. 

The specialized instruments focus on particular commodities, while the F-750 gives users more flexibility to explore different fruits and quality traits. 

We've developed starter models for blueberries, blackberries, raspberries and strawberries, exploring traits such as dry matter, acidity, Brix and pH. The available traits and level of validation vary by application, so these should be treated as starting points for evaluation and further development. 

We've also revisited pineapple, where we initially had questions about how well the technology would work. In our development work, we've been able to build promising models for Brix and acidity. 

That's an important part of the F-750's role: giving us and our customers a platform to investigate new applications and establish where the technology can be useful. 

Fructidor: Are these models available on the F-751 instruments too? 

Galen George: Right now, the berry and pineapple models we've discussed are exclusive to the F-750. 

We plan to bring these applications to the F-751 platform in the future. The goal is to take what we learn through development and validation and turn it into a more specialized tool for routine use. 

Fructidor: What other fruit commodities are you looking at? 

Galen George: Tomato, pepper and citrus are also in our development pipeline. We've received a lot of requests for banana, so that's another application we plan to explore. 

Our approach is to focus on a commodity, identify the traits that matter to customers and build models supported by data across relevant varieties and conditions. 

Ultimately, the goal is to help growers and packhouses get useful quality information in time to act on it. Faster measurements and simpler workflows make it more practical to test routinely and use those results in everyday decisions. 

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