Understanding Screen Analysis in Particle Size Distribution

In food processing, achieving a consistent grind – whether it’s flour, spices, or sugar – is not a matter of guesswork. It requires a reliable, repeatable method to measure how fine or coarse the particles in a ground product actually are. That method is screen analysis, also known as sieve analysis. It is one of the most widely used techniques to determine particle size distribution after a size reduction operation. By passing a sample through a stack of standard sieves with decreasing mesh sizes, processors get a clear, quantitative picture of their product’s particle makeup – and that picture directly influences quality, texture, and consumer satisfaction.

What is screen analysis? 🔗

Screen analysis is a method of classifying a comminuted (ground) product by passing it through a series of sieves arranged from coarsest to finest. Each sieve has a wire mesh with square openings of a specific, standardised size. When a weighed sample is placed on the top sieve and the entire stack is shaken – either manually or by a mechanical shaker – particles smaller than the openings on a given sieve pass through to the next one below, while larger particles are retained. At the bottom of the stack sits a solid pan (called the receiver) that collects the finest particles that pass through even the smallest sieve.

After shaking is complete, the material retained on each sieve is weighed. These weights, expressed as percentages of the total sample mass, tell you exactly what fraction of the product falls into each size range. This data set is your particle size distribution.

Standard screen series used in screen analysis 🔗

For sieve analysis results to be meaningful and comparable across different labs, facilities, or countries, the sieves must conform to recognised standards. Three major standard series are used globally:

Tyler standard screen series: Developed by the W.S. Tyler company in the early 20th century, this series is based on a 200-mesh screen with an opening of 0.074 mm. The key feature of the Tyler series is that each successive sieve has an opening area exactly twice that of the next finer sieve, meaning the ratio of openings between consecutive screens is √2 (approximately 1.414).

US standard sieve series (ASTM E11): This is the predominant standard in the United States and is widely adopted internationally. Sieves are designated by a US Sieve Number, which approximates the number of openings per linear inch. The actual measurement that matters is the nominal sieve opening in micrometres (µm). Each successive sieve has an opening reduced by a factor of approximately the fourth root of 2 (about 1.189).

ISO 3310 series: This international standard specifies sieve sizes in millimetres and micrometres and is used across Europe and many other regions. For most practical purposes, the Tyler and US sieve series are nearly interchangeable – for instance, a 35-mesh Tyler screen corresponds closely to a US No. 35 sieve.

Understanding mesh number 🔗

The term mesh number refers to the number of openings per linear inch of the sieve. A 4-mesh sieve has 4 openings per inch and therefore has large apertures, while a 200-mesh sieve has 200 openings per inch with very fine apertures. As the mesh number increases, the opening size decreases – and the particles retained become finer. It is important to note that the actual opening size is always slightly smaller than what simple division would suggest, because the wire used to weave the mesh has its own thickness.

How screen analysis is performed 🔗

The procedure for conducting a screen analysis is straightforward, but accuracy depends on careful technique. Here is how it works step by step:

Step 1 – Select and stack sieves: Choose a set of standard sieves that cover the full range of particle sizes expected in your sample. Arrange them in a column with the coarsest (largest opening) sieve on top and the finest at the bottom, with the receiver pan at the base.

Step 2 – Prepare the sample: Take a representative sample of the ground material. A sample size of about 100 grams is often suitable for standard 8-inch diameter sieves, though the USP recommends at least 25 g depending on powder density and sieve diameter. The sample must not be too large (which causes clogging) or too small (which reduces weighing accuracy).

Step 3 – Sieve the sample: Place the sample on the top sieve and secure the stack in a mechanical sieve shaker. The shaker vibrates the stack for a standardised time – usually 10 to 15 minutes. Modern electromagnetic sieve shakers allow digital control of amplitude and duration, ensuring reproducible results.

Step 4 – Weigh retained fractions: After shaking, carefully remove each sieve and weigh the material retained on it. Also weigh the material in the bottom pan. The sum of all fractions should closely match the original sample weight; any significant loss indicates a problem with the procedure.

Step 5 – Calculate and record: Express each retained weight as a percentage of the total sample mass. This gives you the mass fraction retained on each sieve.

Interpreting screen analysis data 🔗

Raw retention percentages are useful, but the real insights come from two types of analysis applied to the data: differential analysis and cumulative analysis.

Differential analysis 🔗

In differential (or incremental) analysis, the mass fraction retained on each individual sieve is plotted as a histogram against the average particle size for that fraction. The average particle size for material retained on a given sieve is typically taken as the arithmetic mean of the opening of that sieve and the opening of the sieve just above it. This histogram gives you a visual snapshot of where most of the particles in your sample fall on the size spectrum.

For example, if you are analysing ground black pepper and find that 35% of the sample is retained on the 60-mesh sieve, that tells you the largest single fraction of your product has particles in that specific size range. A tall, narrow histogram indicates a uniform grind; a broad, spread-out histogram indicates a wide range of particle sizes.

Cumulative analysis 🔗

Cumulative analysis provides a running total. You calculate the cumulative percentage retained by adding up the percent retained on each sieve from the coarsest down. Alternatively, the cumulative percentage passing (also called “percent finer”) is found by subtracting the cumulative retained from 100%. This data is plotted as an S-shaped curve on a graph, typically with a logarithmic scale on the x-axis (particle size) and a linear scale on the y-axis (cumulative percent).

This curve lets you quickly identify important parameters such as the median particle size (D₅₀) – the size at which 50% of the particles are finer and 50% are coarser. It also reveals the overall spread or uniformity of the particle size distribution.

Fineness modulus and its significance 🔗

The fineness modulus (FM) is a single numerical value that summarises the particle size distribution of a sample. It was originally introduced by researcher Duff Abrams in the early 20th century for characterising concrete aggregates, but it is equally valuable in food processing for assessing grind uniformity.

How fineness modulus is calculated 🔗

The calculation involves two steps. First, determine the cumulative percentage of material retained on each sieve in a specified standard series. Then, add up all those cumulative percentages and divide the total by 100. The formula is:

FM = (Sum of cumulative percentages retained on each specified sieve) ÷ 100

For instance, if the cumulative percentages retained across four sieves are 10%, 30%, 60%, and 90%, the fineness modulus would be (10 + 30 + 60 + 90) ÷ 100 = 1.90. A lower FM value indicates a finer product, while a higher FM value indicates a coarser product.

Practical significance in food processing 🔗

In food applications, fineness modulus serves several purposes. It is a quick indicator of grind uniformity – a consistent FM from batch to batch means the grinding equipment is performing reliably. If the FM suddenly shifts, it signals that something has changed: perhaps the grinder blades are worn, the raw material is harder than usual, or the feed rate is too high.

Different food products require different FM ranges. Bread flour, for example, needs very fine particles (low FM) for proper gluten development and water absorption. Semolina for pasta, on the other hand, requires coarser particles (higher FM) to give the pasta its characteristic structure. Spice grinding operations use FM to ensure that ground cinnamon, pepper, or turmeric meets the expected texture and dissolution characteristics.

Regulatory bodies like the FSSAI also mandate sieve analysis for certain food products to ensure consistency and detect adulteration or foreign matter.

Average particle size 🔗

Screen analysis data also allows you to calculate the average particle size of the sample. The average particle diameter for material retained on a given sieve is the mean of the opening of that sieve and the opening of the sieve directly above it in the stack. The overall average particle size for the entire sample is typically calculated as a weighted average, where each sieve fraction’s average diameter is multiplied by its mass fraction, and the results are summed.

This value is important because many product properties – solubility, flowability, packing density, and reaction rate – are closely related to average particle size. Finer powders generally dissolve faster and have greater surface area for chemical reactions, but they may also be more prone to caking and dust formation. Coarser materials tend to flow more freely but may not mix or dissolve as readily.

Factors that affect screen analysis accuracy 🔗

Several practical factors can influence the reliability of sieve analysis results:

Sample size: Too much material overloads the sieves and traps particles that should pass through, a problem known as blinding. Too little material reduces the statistical reliability of the results. For standard 8-inch sieves, a sample of around 25-200 g is typical, depending on the material’s density.

Sieving duration and method: Results change with shaking time. Longer shaking allows more particles to find their way through, but beyond a certain point the gain is minimal. Standardising the shaking time and method (mechanical vs. manual) is essential for reproducible results.

Particle shape: Sieve analysis assumes particles are roughly spherical. Elongated, flat, or fibrous particles may not pass through openings even if their minimum dimension is smaller than the aperture. This is a known limitation of the method.

Moisture content: Damp or oily materials tend to clump and clog sieve openings. In such cases, wet sieving – where the sample is suspended in a liquid and washed through the sieves – can be a better alternative, provided the liquid does not alter the sample.

Sieve condition: Worn, damaged, or distorted meshes give inaccurate results. Regular inspection and calibration of sieves against standards like ISO 3310-1 or ASTM E11 is necessary.

Role of screen analysis in quality control 🔗

In a food processing plant, screen analysis is a frontline quality control tool. It provides objective, quantifiable data that helps maintain product consistency. If a batch of ground spice suddenly shows a different particle size distribution than previous batches, it immediately flags a potential issue – worn equipment, a change in raw material hardness, or a process parameter that has drifted out of range.

Screen analysis data is also used to set and verify product specifications. When a buyer specifies that flour must have a certain percentage of particles passing through a given mesh, screen analysis is the test that confirms compliance. In international trade, standardised sieve analysis data ensures that a product description means the same thing regardless of where it was produced or tested.

Modern food processing facilities often integrate sieve analysis with computerised data systems that automate calculations, generate trend charts, and flag deviations in real time – making it easier to maintain tight control over product quality on a continuous basis.

Limitations of screen analysis 🔗

While screen analysis is simple, well-established, and cost-effective, it does have limitations. It is primarily a two-dimensional measurement – passage through a sieve depends more on a particle’s width and thickness than on its length. Very fine powders (below about 75 µm) are difficult to sieve accurately because surface forces cause particles to stick to each other and to the mesh. For ultra-fine materials, alternative methods such as laser diffraction or air-jet sieving may be more appropriate.

Additionally, the same fineness modulus value can theoretically result from different particle size distributions. Two samples with very different size profiles could still yield an identical FM. This is why FM should always be interpreted alongside the full particle size distribution curve rather than in isolation.

What do you think? How critical is particle size uniformity in the food products you consume daily – and could even slight variations in grind consistency noticeably affect the taste, texture, or shelf life of products like flour, coffee, or spices?

References

1. https://en.wikipedia.org/wiki/Sieve_analysis
2. https://wstyler.com/particle-analysis/test-sieves/
3. https://www.humboldtmfg.com/blog/particle-size-analysis-using-astm-mesh-test-sieves
4. https://www.usp.org/sites/default/files/usp/document/harmonization/excipients/harmonization-april-2022-m99584.pdf
5. https://www.retsch.com/files/5866/the-basic-principles-of-sieve-analysis.pdf
6. https://chemenggcalc.com/screen-analysis-differential-and-cumulative/
7. https://www.globalgilson.com/blog/fineness-modulus-of-concrete-aggregates
8. https://www.studocu.com/in/document/national-institute-of-food-technology-entrepreneurship-and-management/food-processing-engineering/sieve-analysis-of-food-materials-imp/150449703
9. https://www.globalgilson.com/blog/sieve-analysis-of-coarse-aggregate
10. https://en.wikipedia.org/wiki/Mesh_(scale)
11. https://foodtechnotes.com/2020/07/16/sieve-analysis/