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  • Jordan Gunn

How to Solve the Problem of Inhomogeneity in Coffee Quality Control

Updated: May 25

In this blog we look at the heterogeneous nature of coffee and how your current testing methods of optical sorting and chemical-based spot tests don’t adequately account for this heterogeneity. We then discuss how that ultimately hurts your business, and what you can do about it.


What Perfection Looks Like

Famously, the perfect quality coffee beans can be obtained with palm civets, a mongoose relative who eats only the ripest, sweetest coffee drupes, which are then collected from the animal’s droppings to make the $500/lb coffee known as Kopi Luwak. Unfortunately, the use of these creatures isn’t exactly scaleable. Humans can perform a similar task (without eating them) but this practice is often deemed too labor intensive for its worth, as it only increases bean quality by some 15%. Consequently, harvesting is often performed with strip picking where all the cherries of a branch are collected at once. A good picker can harvest 45 to 90 kilos of coffee cherries per day using strip picking which ultimately produces roughly 18 kilos of coffee beans.

Herein Lies the Problem

Strip picking is the only practical harvesting method for large-scale coffee production in an industry that already spends around 70% of its production costs on labor, but it causes one very challenging problem for the processing steps that follow. That problem is that the coffee beans it produces vary wildly with respect to age, size, ripeness, sweetness, alkaloid content, etc.

Since you’re using optical sorting and chemical-based spot tests to conduct quality control in your coffee processing operation you aren’t properly measuring or accounting for variability because of:


  • Small representative area: You are currently testing individual beans or a ground sample of several beans to produce an average value which is theoretical and not truly representative of your material as a whole.

  • Slow method: Your current methods of ELISA and HPLC, etc. measure parameters individually and require specialized staff to conduct multiple steps that take significant time thereby reducing your testing capacity.

  • Limited parameters: Optical sorters can’t test chemical properties e.g. caffeine content, polyphenol content, fermentation index, etc. necessary for optimal sorting.


The Fallout

Unfortunately, the drawbacks to your currently used methods are leading to poor business outcomes in the form of:


  1. Loss of Product: Because small discrepancies in your quality checks of raw ingredients lead to large batches of final product not meeting specifications.

  2. Poor Quality Product: Because of the variability you are allowing in the final product

  3. Increased Labor: With your current efforts to adequately assess variability with slow chemical-based methods and trying to correct for variability down the production line.


But let’s be clear - It’s not that heterogeneity is to be avoided, it’s that you need to assess the variability within coffee beans adequately and provide actionable information for future use. That’s because how sweet or caffeine-rich a bean is will, or should at any rate, play into decisions you make about what type of coffee that bean will then be used to make (e.g. decaf or flavored coffee). Taking averages or small samples as representative of entire batches can not adequately provide this type of actionable information, and it will inevitably fail if it pretends to.


The Solution

Hyperspectral imaging is a technology that works by nearly instantaneously collecting comprehensive spectral data from every pixel of an image of the coffee beans. Capturing this type and amount of data has several advantages:


  1. Larger areas: This technology scans across an entire group of coffee beans, giving you data about chemical distribution within and between beans.


2. Faster testing: This method scans samples start to finish in 30 seconds compared to the minutes, hours, or days needed to complete HPLC and ELISA tests.

3. Less labor intensive: No sample prep, no complicated SOPs, just put it in front of the sensor and let it scan.

4. Multiple parameters: Group all your individual tests in one comprehensive test that does it all.



5. Better parameters: Not just color or size, but a detailed chemical analysis that better predicts the flavors, aromas, and potency of your coffee.

Business Value


With the use of AI-HSI strategic advantages in quality testing will manifest as increased business value in the form of:


  1. More useable product: Because it’s more likely to meet specifications at the end of the line

  2. Greater product consistency: You won’t be surprised by outliers in the final product.

  3. Higher quality products: You won’t let outliers dilute the beneficial characteristics of your product.

  4. Lower operating costs: Reduce your QC budget by as much as 70% both by making it easier to do your testing, and being able to test more with the same amount of effort.


Conclusion

Coffee production brings unique challenges with respect to its quality control because of its heterogeneous nature. Using outdated quality control methods in this environment are causing a number of problems for your business. Luckily, technology that addresses these problems does exist in the form of AI-HSI and provides a high quality product that is consistent and conducive to a healthy business model. Through detailed and comprehensive chemical analysis, variability within coffee raw materials can be adequately accounted for and actionable intelligence can be gained to address it before it hurts your bottom line.

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