Stop Guessing, Start Sampling: Practical Strategies for Pet Food QA Teams

Let’s be honest—every pet food brand has faced that dreaded phone call. A bag looks off-color. A kibble crumbles too easily. Or worse, a customer posts a photo online, wondering if their “premium” purchase fell short. Before long, the brand calls the manufacturing team asking what went wrong, and whether it could happen again.   

Sometimes, the root cause is hard to predict. But other times, the issue could have been caught earlier if the sampling plan had been designed just a little better.   

Sampling may sound like a dry statistical exercise, but in reality, it’s the foundation of product quality, consistency, and brand trust. It’s how you know that what’s inside the bag really matches the label—not just once, but every time. Yet, sampling isn’t cheap. Each test costs time, product, and labor.  

So, how do manufacturers find the sweet spot between too little and too much?   

A strong sampling plan doesn’t happen by accident. It’s a carefully engineered balance of science, practicality, and foresight. Here are some key questions and insights to help pet food brands—from boutique startups to large co-manufacturers—rethink how they collect, test, and trust their data. 

Start with the “Why,” Define the Goal 

Before grabbing a scoop or writing a procedure, ask, “What am I trying to learn?” 

Sampling for surveillance is very different from sampling for compliance. Surveillance sampling acts like a routine health check; it helps detect whether the process is drifting out of specification or if normal variation is creeping wider. It’s about trend spotting, not fault finding.  

Compliance sampling, on the other hand, is about confirmation—proving that the product meets a defined quality level or defect rate. That means knowing what percentage of defects you’re willing to tolerate and how frequently they might appear.   

In short:   

• Surveillance sampling = “How is my process behaving?”   

• Compliance sampling = “Does my product meet the bar?”    

Without clarity on that goal, even the most well-intentioned sampling plan risks collecting plenty of data, just not the right data.   

Destructive vs. Non-Destructive Sampling   

Once the purpose is clear, think about the impact of sampling itself.    

Non-destructive sampling is the most common approach in pet food manufacturing. You measure, inspect, or test a product—say, a bag’s seal strength or moisture level—and then return the sample to the lot. These “check and return” tests help maintain lot integrity and reduce waste.    

Destructive sampling, by contrast, means the product can’t go back into circulation. It’s common for nutrient analysis, digestibility studies, or pathogen testing. Once tested, that product is out of spec by definition: it’s been opened, blended, or consumed during analysis.    

Choosing between destructive and non-destructive testing often comes down to cost and risk. Destructive testing provides richer data but increases product loss. Non-destructive methods are cheaper and faster but can miss subtle quality shifts. A well-balanced plan often uses both in strategic combination.   

Population Sampling vs. Quality Sampling   

Not all sampling is created equal, or equally useful.   

Quality sampling focuses on process checks: spot tests that catch issues quickly enough to trigger corrective action. Think of case weight checks every 30 minutes or seal integrity checks every hour. These checks may not be statistically perfect, but they generate ongoing process data and help keep operators alert to trends.   

Population sampling, by contrast, takes a more statistical approach. Here, the number of samples depends on the size of the lot and the acceptable percentage of defects. For example, if you require 99% of bag seals to meet a certain tensile strength, your sample size must be large enough to confidently verify that performance.   

 The key is to know what level of assurance you need and to match your sample size and frequency to that confidence level. Over-sampling wastes resources; under-sampling risks undetected quality failures.  

Random Sampling: Avoiding Bias   

Even the most carefully written plan can fail if bias sneaks in. Bias is what happens when sampling doesn’t truly represent the process. 

It’s surprisingly easy to introduce—collecting from the same conveyor lane, sampling behind your best operator, or always testing early in the shift when everyone’s sharpest. 

Even visual judgment can bias results; for instance, “cherry-picking” samples that look closest to target color.  
 
To minimize bias:   

• Automate where possible. Let equipment trigger sample pulls at set intervals. 

• Use scoops, not fingers. Grabbing random handfuls introduces unconscious selection. 

• Composite your samples. Mix several small grabs into one composite—it’s more representative of the lot. 
• Randomize timing. Use random-number generators or counters to decide when to pull samples, not the clock on the wall.   

Eliminating bias won’t make your process perfect, but it will make your data honest, which is far more valuable.   

When the Process Is Stable: Skip-Lot and Stratified Sampling   

Once you’ve demonstrated process control, there may be opportunities to optimize.   

Skip-lot sampling allows you to test less frequently, maybe every second or third lot, once performance is proven stable. It’s a cost-saving strategy, but it requires vigilance. One failed test can trigger resampling of skipped lots, erasing those savings fast.    

Stratified random sampling can also help balance coverage and cost. Imagine you have 300 different ingredients and need to verify their safety or quality over time. The FDA requires monitoring programs and record retention for three years. Instead of testing all 300 at the same time, you could test 100 each year, ensuring all are tested at least once within that three-year window. This method maintains oversight without overburdening your QA budget.   

Both strategies rely on confidence in your mean time between failures (MTBF)—the average interval between defects under normal conditions. Knowing your MTBF helps you design smarter, risk-based sampling frequencies.  

The Power of SPC—and Knowing When Not to React   

If you want to elevate your sampling strategy, consider integrating Statistical Process Control (SPC).    

SPC tools provide near real-time data, showing variation trends and control limits visually. Operators can see when a process is trending toward a problem and when it’s just normal noise. The beauty of SPC lies in its balance: it teaches teams when not to overcorrect.  

Properly implemented, SPC reduces waste, scrap, and unnecessary downtime. It empowers operators to take proactive action without chasing false alarms. In a well-trained team, SPC turns data into decision-making confidence.  

Sample Upstream, Not Just at the Finish Line   

Many facilities concentrate sampling at the end of the line when the product is bagged, sealed, and ready to ship. The logic makes sense: that’s what the customer receives. But waiting until the end to find problems is costly.   

A smarter approach is to sample and error-proof upstream. By monitoring variation early—ingredient quality, mix consistency, equipment calibration—you prevent defects before they reach finished goods.   

For example, using screens to filter oversized kibble or magnets to remove metal fragments prevents those defects from ever reaching packaging. It’s the classic “garbage in, garbage out” principle. Sampling earlier may seem like more work, but it drastically reduces rework, waste, and recalls down the line.  

Don’t Forget the Cost of Conformance   

Everyone tracks the cost of non-conformance—product holds, recalls, or rework. But few track the cost of conformance, or the money spent to stay compliant. 

Sampling costs include labor, product loss, and analytical testing fees. While they’re necessary, they add up fast. Benchmarking these costs against industry peers can reveal whether you’re under- or over-sampling compared to your competitors.  

Unfortunately, this data isn’t always easy to find. Industry benchmarking groups or co-manufacturing partnerships can help you gauge where your efforts stand. Ideally, your conformance costs should align with your brand’s quality positioning. A premium brand might justify heavier sampling than an economy line—as long as the return on quality is there.  

Parting Thoughts   

Sampling isn’t glamorous. It doesn’t get ad campaigns or shelf talkers. But it’s one of the most powerful tools a brand has to protect its integrity and its customers’ trust.   

Sample too little, and your next viral post might not be the kind you want. Sample too much, and you’ll drown in data and costs. The art lies in finding the equilibrium, enough precision to catch meaningful variation, but not so much that you slow the process to a crawl.  

In pet food manufacturing, where ingredients, processes, and consumer expectations all shift rapidly, a great sampling plan isn’t static. It evolves—just like your product.   

And if building that plan feels daunting, remember: you don’t have to do it alone. At BSM Partners, we’ve helped manufacturers large and small design smart, data-driven sampling programs that reduce cost, improve consistency, and keep tails wagging.   

Because when it comes to pet food, what’s really in that scoop should never be a surprise. 

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About the Author

August Konie has been a Food Safety, Quality and Regulatory Professional for over 30 years. He was worked in many sectors of the food industry including fisheries, beverages, poultry, pork and pet food, under both FDA and USDA regulatory oversight. As an active committee member in various trade organization for food and pet food organizations, he was successful of implementing new regulatory guidance. He has worked with various teams across Asian, Europe, North and South American on various food safety, quality and import/export concerns. He currently serves as the Principal of BSM Assurance overseeing FSQAR activities at BSM Partners.