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Spectrophotometers measure wavelengths of light emitted from samples to ensure product consistency, desired color output, and other industry-relevant factors. 

What Is Reflectance?

Reflectance can be defined as the ratio of reflected to incident radiation, and it's an essential measurement in several fields. It corresponds to the amount of light that your sample reflects (emits) as compared to a specific reference material. Reflectance color measurement is required samples that are opaque as apposed to translucent or transparent. 

The percentage you get when measuring reflectance is a spectrum of reflection percentage per wavelength or nanometer (nm). For example, if you test a red sample with white light, the expected spectrum should hit the maximum reflectance, usually around 700 nanometers for red visible spectra. 

How to Measure Reflectance

A spectrophotometer attains a measurement of reflectivity by fulfilling the following actions: 

1. The spectrophotometer transmits all the wavelengths of light while shining a light on the sample. 
2. It measures the light that the sample reflects back. 
3. It converts that measurement into a quantified graph showing the dominant wavelengths of the color reflectance. 

The reflectance consists of diffuse reflected light and specular reflected light. 

Diffuse reflected light is the permeating light that scatters in a variety of directions, observed when light beams reflect from microscopically rough surfaces.

Specular reflected light reflects at the same angle as the incident angle of light, creating a mirror image. Essentially, this beam of light when reflecting off of smooth surfaces remains as a beam of light. 

The Purpose of Measuring Reflectance

Reflectance measurement shows which part of the light spectrum a particular object belongs to. Humans have evolved to see a small part of the spectrum, known as visible light. The range of visible light stops at around 700 nanometers, but most of the light spectrum falls outside that continuum. 

Measuring reflectance ensures that an item meets the aesthetic and quality control requirements of various items. It shows the dominant wavelengths that allow you to check that the product meets the desired wavelength range. Here are the typical wavelength ranges of different colors: 

• Yellow: 570-585 nm
• Blue: 440-490 nm
• Indigo: 420-440 nm
• Red: 620-780 nm
• Orange: 585-620 nm
• Green: 490-570 nm
• Violet: 400-420 nm

Every material absorbs, reflects, or transmits light, which determines the color we see when looking at an object. Transmission refers to the light waves that pass through a translucent or transparent sample, and spectrophotometers provide quantitative measurements of that data. These devices make it possible to evaluate a sample's color scientifically and objectively, which is vital for ensuring color consistency in various applications. Spectrophotometer transmittance measurements enable better quality control and enhanced consumer perception of your products.

What Is Transmittance in a Spectrophotometer?

The definition of transmittance is the measured light that passes through a sample, meaning the light that is not absorbed or reflected. Three primary transmittance measurements include:

• Diffuse transmittance (Td, n): This measurement is the ratio of the flux transmitted by a specimen to the incident flux, with the transmitted flux being measured at all forward angles except the regular transmission angle. When light is diffusely transmitted, its direction changes as it passes through the material, causing scattering.
• Regular transmittance (tr, n): This measurement is the ratio of undiffused transmitted flux to incident flux. In other words, it is straight-through light.
• Total transmittance (tt, n): This measurement is the ratio of the flux transmitted at all forward angles to the incident flux. Total transmittance is the sum of regular transmittance and diffuse transmission.

These values are essential to maintaining quality in products like beverages and edible oils. It's best to measure color with a spectrophotometer to capture more accurate information, as these devices provide exact quantitative data regarding the amount of light that passes through a sample. 

The transmittance formula is:

T = PT/P0

The values in this formula represent:

• T: Transmittance
• PT: The source's radiation power as it exits the sample
• P0: The source's radiation power as it enters the sample

The resulting calculation is a ratio, comparing the light before it enters the sample to the light after it passes through it. A value closer to 1 means most light passes through, while lower values mean that less light passes through. 

What Is Percent Transmittance in a Spectrophotometer?

Percent transmittance displays the light that passes through the sample as a percentage rather than the ratio created by the above formula. You can get the value by multiplying the ratio by 100. 

A sample with 100% transmittance is transparent, allowing all light to pass through. Lower numbers indicate more translucence than transparency. At least 30% of the light must pass through the material for effective transmittance measurements.

The transmission percentage is also helpful for absorbance calculations. A commonly used absorbance formula is:

A = 2-log10(%T)

This formula determines the amount of light absorbed using the percentage of light that passes through the sample. 

Resin samples typically measured in Gardner Color

The APHA/Pt-Co/Hazen and Gardner visual color scales were both originally based on liquid chloroplatinate color standards but have different history and intended use.

Color is a key indicator of product quality, safety, and efficacy in pharmaceutical manufacturing. As a result, this critical attribute demands precise control to meet consumer expectations and regulatory mandates. Yet, manufacturers must carefully balance color measurement needs with sterility to preserve safety and hygiene. Many still rely on visual color assessment, but this method is subjective, hard to track over the long term, and error-prone, increasing your risks of batch rejections and compliance failures.

Understanding the potential costs of outdated measurement approaches and how spectrophotometers/colorimeters address the challenges can help you achieve repeatability and preserve sample integrity.

The High Costs of Uncertainty — and How to Avoid Them

Where visual assessment fails, spectrophotometry excels, helping you build a more reliable quality system.

Visual Assessments Create Risks

Color measurement in the pharmaceutical industry requires precision that visual methods simply cannot deliver. Human observers often deliver inconsistent results, influenced by their fatigue, color perception, and environmental variations. These factors make that approach unreliable for the data-driven quality systems pharmaceutical manufacturing demands.

Subjective assessments become potential liabilities where the business consequences may compound quickly:

• Subjective color data can be challenging to substantiate during regulatory audits or inspections. While some regulatory authorities recognize visual assessment, documentation without objective numerical data could lack the rigor expected.
• Batch-to-batch color variations that pass visual checks may fail when instrumentally quantified against EP 2.2.2 or USP 1061 standards. That reality can translate into increased batch rejections, costly rework, and expensive API wastage.
• Tracking color shifts over time with subjective data is an issue. A lack of exact quantification makes it harder to identify patterns before they trigger failures.

Spectrophotometry Is the Best Color Measurement Solution for Pharmaceutical Manufacturing

Spectrophotometry eliminates the trade-offs. These powerful solutions capture precise, objective color data while preserving sterility. Unlike visual assessment methods, this equipment delivers numerical color data that represents objective evidence of appearance. The technology quantifies color variations that the human eye cannot consistently detect, providing the documentation pharmaceutical quality systems require. This measurement approach also integrates seamlessly with existing laboratory workflows while maintaining the sterile conditions pharmaceutical manufacturing demands.

The effects of moisture on color measurement are all too real for manufacturers. Wet samples can appear dramatically different from their dry counterparts — darker, more saturated, or with altered surface properties that can cast doubt on result accuracy. This reality creates a dilemma for quality control (QC) teams in many industries. Do you wait for samples to dry and slow throughput, or measure wet samples and risk inaccuracies?

Understanding how sample moisture content can affect color can help you pivot to a more predictable framework that addresses the variable. With spectrophotometry, you can build reliable measurement protocols that transform wet samples from obstacle into QC advantage.

Why Does Moisture Change a Sample’s Appearance?

Moisture settles into air gaps on a sample's surface, or — in the case of textiles — becomes absorbed by the fibers. Both cases can impact how light interacts with the surface and influence the sample's appearance.

In the simplest terms, wet samples will often appear darker because there is less light scattering and more light absorption. The more moisture-saturated the sample is, the more pronounced the perception shift can become, with highly porous materials often showing the most dramatic effects of moisture on color measurement.

How Does Moisture Impact Different Materials?

This phenomenon can manifest in different ways in key industries.

Powders and coatings present issues with color measurement when moisture is present. Structural clumping is a common concern, and the particle aggregation can cause light scattering that impacts appearance. This clumping can also create issues with sample flowability and further impact data consistency and repeatability.

Absorption is the primary challenge with textiles, reinforcing the need for accurate sample preparation. The type of fabric also makes color measurement more complex in wet samples. For example, cotton and polyester have vastly different absorption capabilities, so color alterations may appear much more pronounced in one than the other.

What Are the Business Costs of Uncontrolled Moisture Variables?

Uncontrolled moisture variables add measurable costs that affect your bottom line through reduced efficiency, increased waste, and elevated quality risks.

Production Bottlenecks

You know downtime is expensive. When your production line stops to wait for samples to dry for color measurement, it creates delayed quality decisions and scheduling backlogs. The end result? Throughput and efficiency suffer.

Consider the cascading effects of a single wet sample that requires 30 minutes of drying time. It pushes back every subsequent sample in the queue. Lab technicians stand idle, production managers scramble to adjust schedules, and quality decisions that should take minutes stretch into hours.

The ripple effects extend beyond the lab. Manufacturing lines may continue producing potentially off-spec product while waiting for color approval, creating inventory that might need reworking or disposal. Meanwhile, shipping schedules slip and customer commitments become harder to meet.

One inconsistent product batch can trigger a cascade of problems — customer complaints, product waste, and costly rework cycles that impact your bottom line. Whether you're measuring color in paprika or breakfast cereal, particle size variations can compromise brand consistency across product lines.

Measurement inconsistency is only a symptom. The real issue is choosing the wrong spectrophotometer/colorimeter for your specific particle size. Fine spice powders demand different approaches than irregular snack pieces, yet many food manufacturers continue using solutions that weren't designed for their sample's characteristics. Understanding particle size effects on color measurement and exploring the best way to address the problems is the first step toward consistent, repeatable results.

What Color Measurement Challenges Do Different Food Particle Sizes Create?

Capturing accurate data in fine powders involves distinctly different challenges than quantifying appearance in snack foods.

When Small Particles Create Big Problems

Finer particles, like spices, seasonings, and powdered flavorings, can form relatively uniform surfaces when properly handled. Yet, achieving that uniformity consistently is where many powder color measurement programs fail. As samples settle and pack during preparation, surface texture variations often form. Even minor variations affect how light interacts with the material, creating measurement variabilities that have nothing to do with the actual product color.

For fine particles like these, a 1-inch area of view (AOV) provides sufficient coverage when the sample surface is properly prepared. The key is eliminating operator-introduced errors that cause artificial surface variations. When handling procedures remain consistent across samples and lab operators, you create more predictable measurement conditions.

Large Particle Measurement Pitfalls

Cereals, nuts, snacks, whole beans, and other large or irregular particles create entirely different measurement challenges.

Unlike fine powders that can settle into smooth surfaces, large particles maintain their individual shapes and create voids between pieces. These gaps allow light to penetrate differently depending on how particles settle in the sample container. One measurement might capture more shadow areas, while another captures more direct particle surfaces, leading to inconsistent results.

Surface texture variations in large particle samples further complicate precise data capture. Individual pieces may have curved surfaces, irregular edges, or varying thicknesses, creating shadows and light-reflection patterns that change with each sample presentation. Traditional color spectrophotometers with a 1-inch AOV designed for smooth, uniform surfaces struggle with these texture variations because they rely on single measurements of limited areas.

High-quality cookie products should be consistent in color from batch to batch. Image Source: Shutterstock user Natali Zakharova

Few people can resist nibbling on a perfectly-baked cookie. Many find the mouthwatering, nostalgic aromas of caramelized sugar coupled with the rich golden brown crumbles downright irresistible. But if you’ve ever eaten an under or overbaked cookie, you know that not all cookies are as delicious as they should be. Factors like setting the oven temperature too high or too low or baking the cookies for too long or too little time could negatively impact the texture and color of the cookie as well as someone’s experience eating it.

This is why large-scale cookie manufacturers need to carefully measure the color of their cookie products. Using a smart spectrophotometer that is capable of accurately measuring the color of textured samples (like crumbly cookies), you can ensure that every batch of cookies is baked to perfection and that they all appear uniform in color to your customers. Whether you produce just a few thousand packages of gourmet cookies every year, or you make millions of cookies in a wide variety of flavors and textures, a smart spectrophotometer can help you refine your manufacturing process.

Color is a Sign of Cookie Quality

The color of cookies and other baked foods can tell you a great deal about how the product will likely taste. This is due to caramelization and the Maillard reaction, a complex chemical process that causes food to turn brown as it’s exposed to heat.1 During the Maillard reaction, the proteins and sugars in the food are broken down by high temperatures, which results in deeper coloration and a significant change in flavor and aroma. In the case of cookies, the Maillard reaction browns the color of the raw cookie dough and produces strong aromatic compounds—that classic freshly-baked cookie scent.

For cookie manufacturers, paying close attention to caramelization and the Maillard reaction is important because too much exposure to heat over time could result in an overly bitter, brittle cookie. Likewise, too little heat exposure will impede the development of the Maillard reaction or caramelization, and the cookie will be soft, pale, and somewhat bland in flavor. Customers are aware of the connection between cookie color, texture, and quality, and the appearance of the cookie may impact a customer’s buying habits.2 This is why you need a smart spectrophotometer to ensure that all of your cookie products have gone through just the right amount of caramelization and appear consistent in color overall.

Significant variations in cookie color could be a sign that your industrial oven temperature is set too high or too low. Image Source: Shutterstock user Brent Hofacker