Color & Appearance Measurement

The Benefits of Hyperspectral Imaging for Frozen Food Moisture Content Analysis

In the ever-evolving landscape of food technology, maintaining the quality and safety of frozen foods is a vital concern for both manufacturers and consumers. One of the critical parameters influencing the quality of frozen foods is the Moisture Content Ratio (MCR). Traditional methods of moisture analysis can be time-consuming and may need more precision for modern food processing. Enter hyperspectral imaging, a cutting-edge technology revolutionizing how we assess and manage moisture content in frozen foods.

Moisture Content Ratio Explained

Moisture Content Ratio refers to the amount of water present in a substance, typically expressed as a percentage of the total weight of that substance. The proper MCR is crucial for frozen foods because it directly affects the product's texture, stability, and overall quality/sensory attributes.

A more significant portion of the freezing process for frozen food includes lowering the temperature of the product to below its freezing point, leading to the formation of ice crystals, which, in turn, plays a key role in determining the texture and tastefulness of the product after thawing.

Below are reasons as to why Moisture Content Ratio is important in frozen foods:

1. Texture
   The freezing process can affect the texture of certain food items. If the moisture content is too high, it can result in larger ice crystals, which may damage the food's cell structure, leading to a mushy or undesirable texture upon thawing. On the other hand, if the moisture content is too low, the frozen food may become dry and lose its original texture and color, commonly referred to as "freezer burn."
2. Stability
   MCR also influences the microbial stability of frozen foods. While freezing inhibits the growth of most microorganisms, it does not entirely eliminate them. Controlling the moisture content helps to minimize the risk of spoilage during storage.

3. Quality
   The moisture content in frozen foods contributes to their overall quality and shelf life. Proper moisture levels help maintain the product's original flavor, color, and nutritional content, ensuring a more satisfactory consumer experience.

Understanding Hyperspectral Imaging

Hyperspectral imaging (HSI) involves capturing and processing information from a wide range of the electromagnetic spectrum. Unlike traditional imaging systems that use only three color channels (red, green, and blue), hyperspectral imaging captures information across numerous bands, providing a detailed spectral signature for each pixel in an image. This capability opens up new possibilities for analyzing the composition of materials, making it particularly valuable in the frozen food industry.

Detecting Moisture Content of Frozen Foods using Hyperspectral Imaging 

1. Non-destructive Analysis
   Hyperspectral imaging allows for non-destructive analysis, letting manufacturers assess moisture content without altering the integrity of the frozen food product. This is particularly advantageous for testing processes where samples need to be preserved for further analysis, ultimately contributing to improving frozen food quality.
2. High Precision and Accuracy
   Moisture content analysis may need more precision for modern food processing. Hyperspectral imaging is becoming the new gold standard with high-resolution data, allowing for accurate and precise measurement of moisture content. Meeting regulatory standards and ensuring the consistency of frozen food products is more crucial than ever.

Benefits of Hyperspectral Imaging for Frozen Food Moisture Content

1. Rapid Assessment:
   Time is of the essence in food processing, and hyperspectral imaging offers a rapid and efficient solution for moisture content analysis. The technology can scan large quantities of frozen food, providing real-time data that allows manufacturers to make timely adjustments to their processes.
   As seen below, traditional processes require manual measurement/intervention, which can delay or even halt a production line. Real-time monitoring of MCR with a hyperspectral imaging system, including a camera such as with the FX-17, allows frozen food producers to save time with their analysis and continue their production processes without stoppage.
2. Uniformity Detection
   Frozen foods are often composed of various ingredients with different moisture levels. Hyperspectral imaging can detect and quantify the uniformity within a product, providing insights into the moisture content distribution. This capability is particularly valuable in ensuring even quality throughout a batch of frozen food, with the ability to maintain the texture, stability, and quality of the products (as mentioned above) as it relates to the effects of MCR.
3. Quality Assurance and Process Optimization 
   Manufacturers and OEMs can easily integrate hyperspectral imaging into a production line and/or a laboratory, where quality assurance protocols and optimizing processing parameters can be enhanced. A hyperspectral imaging system allows for continuous monitoring of the graphic above. Random and manual "spot checks" may miss products with MCR issues, affecting product quality and consistency.
4. Reduce Waste and Save Money
   Utilizing Hyperspectral imaging technology enables precise moisture content measurement, prevents over-processing and waste, and results in cost savings, promoting eco-friendly practices in real time. Stoppage of the production line is unnecessary, and catching any issues early in the production cycle will drastically reduce waste in the product due to Moisture Content Ratio issues.

Moisture Content Ratio is critically important to the frozen foods industry. Maintaining the correct moisture content is essential throughout the freezing process and subsequent storage. The use of Hyperspectral imaging presents an innovative advancement in the field of frozen food quality control, specifically in moisture content analysis. Its non-destructive nature, high precision, rapid assessment capabilities, and ability to detect proper product diversity make it a valuable tool for manufacturers and OEMs striving to meet the demands of an increasingly discerning consumer market. As technology advances, hyperspectral imaging will likely play a pivotal role in ensuring the quality and safety of frozen foods for years to come.

Products Mentioned in this Blog:

FX-17 Hyperspectral Camera

High sensitivity and detection accuracy beyond the capability of any other inspection method make the FX17 hyperspectral imaging camera an industry stand-out. As it operates in the near infra-red region, FX17 informs us of the finest details, many not detectable by the human eye. Small and lightweight, its built-in self-correcting image feature makes the workload even lighter, with non-uniformity, bad pixels and automatic image enhancement (AIE) being addressed by the camera automatically. Integration with standard analysis software is another benefit of this hyperspectral imaging camera, designed for ease of use in every possible way.

by Mary-Anne Milne

Enhancing EV Battery Manufacturing with Colorimeters and Spectrophotometers

The electric vehicle (EV) industry is rapidly evolving, with technological advancements crucial in improving efficiency and sustainability. One critical aspect of EV battery manufacturing is quality control, where colorimeters and spectrophotometers are invaluable tools. These devices ensure the reliability and performance of electric vehicle batteries by precisely measuring and controlling color parameters during manufacturing.

Understanding Colorimeters and Spectrophotometers:

Colorimeters and spectrophotometers are instruments designed to measure and analyze color. While both devices serve a similar purpose, they operate on different principles. Colorimeters are color instruments that perform "tristimulus" color measurement based on light passing through three primary filters (red, green, and blue), which simulate how the human eye is sensitive to light. Colorimeters can determine colorimetric values under a single illuminant and are used primarily for reflectance measurements. However, a colorimeter is inappropriate for complex color analysis such as metamerism, colorant strength, or recipe formulation.

Spectrophotometers are the most precise, accurate, and sophisticated color measurement instruments for color quality control and formulation. These instruments measure an object's spectral reflectance (solid/opaque) or transmittance (transparent/translucent) across the full spectrum of visible wavelengths. A spectrophotometer has high precision and increased versatility. It is suitable for more complex color analysis because it can determine the spectral reflectance/transmittance at all visible or specific/designated wavelengths.

Common Use of Colorimeters in EV Battery Manufacturing:

1. Quality Control

The color of cathode and anode powdered materials in EV batteries, especially lithium-ion batteries, can indicate their purity and composition. Consistent color is often a visual indicator of uniformity in manufacturing, ensuring that the materials meet specified standards.

2. Identification and Classification

Different battery chemistries use various materials in cathode and anode powders, and the color of these materials can help identify and classify them during manufacturing, helping to ensure the correct materials for specific battery types.

The CR-410 (seen here with optional Granular Measurement Attachment) offers a larger measurement area of 50mm, perfect for cumulative color analysis of granular/powdered materials.

3. Chemical Stability

The color of electrolytes and additives in EV batteries can provide insights into their chemical stability. Color changes may indicate chemical reactions or degradation, which can impact the overall performance and lifespan of the battery.

4. Process Monitoring

Certain powders may undergo coating and deposition processes during manufacturing to improve their properties. Monitoring the color of coated materials can be crucial for ensuring uniformity in thickness and coverage, which affects the battery's performance.

5. Batch Consistency

Maintaining consistent color across batches of cathode, anode, and other powdered/granular components helps ensure uniformity in the manufacturing process. Any deviation in color may signal a variation in the composition or processing parameters that could impact the battery's performance.

6. Research and Development

In the research and development of new battery materials, manufacturers can use color as one of the parameters for characterizing and understanding the properties of the materials. It aids scientists and engineers in studying the behavior and stability of different materials under various conditions.

Spectrophotometers for Precise Color Analysis in Opaque Materials:

1. Wavelength-specific Analysis

Spectrophotometers provide a more in-depth analysis by measuring light intensity at different wavelengths, allowing manufacturers to identify subtle color variations that may be invisible to the human eye. Benchtop spectrophotometers such as the CM-5 and CM-3700A are great solutions for R&D labs, while portable instruments like the CM-700d are ideal for in-process/floor inspection.

2. Coating Thickness Inspection

EV battery components often undergo coating processes to enhance their durability. Spectrophotometers aid in assessing the thickness and uniformity of these coatings, ensuring they meet the required specifications.

3. Batch Consistency/Quality Control

Spectrophotometers play a vital role in maintaining batch-to-batch consistency during the manufacturing process. By precisely measuring color parameters, manufacturers can identify and rectify any deviations that may impact the overall quality of the batteries. Some instruments, such as the CM-26dG and the CM-36dG, offer simultaneous gloss measurements to ensure desired consistency further.

Spectrophotometers for Accurate Color Assessment in Transparent/Translucent Liquid Materials:

1. Quality Control

Color is often an indicator of impurities, contamination, or variations in the composition of liquids used in battery manufacturing. Monitoring and controlling the color of liquids helps ensure the purity and consistency of materials, which is critical for the performance and longevity of EV batteries.

2. Material Uniformity

Consistent color in liquids signifies uniformity in material composition. This is essential for achieving consistent performance across battery cells. Any deviation in color may indicate an uneven distribution of materials, potentially leading to variations in the battery's electrical and mechanical properties. Benchtop instruments like the CM-5 and CM-36dG can offer transmittance measurements and reflective (opaque) as mentioned above.

3. Chemical Reactions and Stability

Color changes in liquids can indicate chemical reactions occurring within the battery. Monitoring color helps identify any undesired chemical reactions that may affect the stability of the battery components. Ensuring a stable and uniform color in the liquids is crucial for maintaining the reliability of the battery over its lifespan.

Benefits of Incorporating Color Measurement Tools

1. Improved Quality Control

Colorimeters and spectrophotometers enable manufacturers to implement stringent quality control measures, leading to consistently high-quality EV batteries.

2. Increased Efficiency

By implementing color measurement processes, manufacturers can streamline their production lines, reducing the risk of errors, reducing rework/waste, and increasing overall efficiency.

3. Enhanced Consumer Trust

Reliable color control ensures that EV batteries meet stringent quality standards, contributing to consumer trust and confidence in the brand.

In the swiftly changing electric vehicle battery production world, integrating colorimeters and spectrophotometers is proving to be a game-changer. Many companies plan to implement color measurement tools in their manufacturing processes. These instruments contribute to improving product quality and play a pivotal role in advancing the efficiency and sustainability of electric vehicles. As the electric vehicle industry continues to grow, the role of color measurement tools will likely become even more integral in ensuring the reliability and performance of EV batteries.

Want to learn more about color measurement in battery production, contact us for a quick discussion.

by Mary-Anne Milne

Detecting Food Fraud Using Hyperspectral Imaging: A Cutting-Edge Solution

Food fraud is a pervasive issue affecting the entire global food supply chain, posing risks to consumers' health, and undermining the industry's integrity. Unscrupulous practices such as mislabeling, adulterating, and substituting food products continue to challenge food safety authorities and producers. To combat this growing concern, innovative technologies like hyperspectral imaging are emerging as a powerful tool in detecting and preventing food fraud. In this blog post, we will explore how hyperspectral imaging can revolutionize the fight against food fraud and ensure the authenticity and safety of our food.

Understanding Hyperspectral Imaging

Hyperspectral imaging involves capturing and analyzing a wide range of electromagnetic wavelengths across the visible, near-infrared (NIR), and infrared (IR) spectra. Unlike traditional imaging techniques that rely on color or RGB data, hyperspectral imaging provides a detailed spectral profile for each pixel in an image, offering a wealth of information about the composition and characteristics of an object.

 Detecting Food Fraud with Hyperspectral Imaging

1. Authenticity Verification: Hyperspectral imaging can verify the authenticity of food products by analyzing their spectral fingerprints. Each food item has a unique spectral signature, which can act as a reference to detect adulteration or substitution. Comparing the acquired spectral data of a sample against a database of authentic samples allows the identification of inconsistencies and deviations, flagging potential instances of fraud.
2. Quality Assessment: Food fraud involves misrepresentation and can also encompass the intentional degradation of food quality. Hyperspectral imaging can assess various quality attributes, such as ripeness, freshness, and nutritional composition. By examining the spectral signatures of different quality grades, the system can quickly determine if a product meets the specified quality standards or is compromised.
3. Packaging and Labeling Verification: By analyzing the spectral characteristics of packaging materials and printed labels, the technology can identify counterfeit packaging or labels that do not match the product's contents. This capability helps prevent mislabeling, misleading claims, and unauthorized use of brand identities.

Benefits of Hyperspectral Imaging in Food Fraud Detection

1. Non-destructive Analysis: Hyperspectral imaging allows for non-destructive analysis, meaning examining food products without altering their integrity or rendering them unfit for sale. This is especially important when dealing with high-value or limited-supply products.
2. Rapid and Objective Analysis: The technology enables rapid analysis of large quantities of food samples, reducing the time and effort required for manual inspections. Additionally, hyperspectral imaging provides objective and quantitative data, minimizing human error and subjectivity in the detection process.
3. Enhancing Consumer Trust: The industry can strengthen consumer trust and confidence by utilizing hyperspectral imaging to identify and prevent food fraud. Ensuring the authenticity and safety of food products demonstrates a commitment to transparency, quality, and consumer protection.

The battle against food fraud requires a multi-layered approach that combines regulations, testing protocols, and innovative technologies. Hyperspectral imaging presents an innovative solution for detecting and preventing food fraud, enabling producers and regulators to make informed decisions based on accurate and reliable data. As technology advances, it can transform the food industry, mitigating risks, safeguarding public health, and upholding the integrity of our global food supply chain.

by Mary-Anne Milne

Fat Content In Ground Meat

Quality control is crucial in the food industry. Monitoring the product’s nutritive property protects and increases a brand’s reputation. Fat content is one of the properties consumers look at when buying meat. Its quantitative level needs to be precisely documented. Also, monitoring the fat content while transforming meat plays a cost-effective role. New automation technologies are therefore required to improve brand competitiveness.

For this study, five meat samples were obtained and mixed to create ten samples for analysis. To know their fat content accurately, Specim ordered measurements from a 3rd party laboratory, Seilab. According to the Gerber method, they are certified in fat analysis, using a butyrometer (Seilab in Seinäjoki, Finland; method NMKL 181, 2005; see Table 1 below).

Table 1: fat content on each sample included in this study. Samples 3 and 4 were used for validation purposes.

We measured the samples with Specim FX17 hyperspectral camera (Fig.1). Hyperspectral imaging is a non-destructive method that combines spectroscopy and imaging. It collects NIR spectra for each pixel of the acquired image (900 – 1700 nm). Those can be converted into fat content employing relevant processing algorithms. Here a regression model was built and calibrated on eight samples and applied to the two remaining ones (indicated with * in Table 1).

Figure 1: FX17 on the 40×20 scanner (left) and example of a sample on the scanner sample tray (right).

Table 1 and Fig. 2 show the regression model results. It indicates that the FX17 is a suitable tool for measuring the fat content in ground meat.

Figure 2: Regression plot of the quantitative model for fat content prediction. Red dots relate to calibration samples, whereas green ones relate to validation samples.

In addition to measuring the fat content in samples, hyperspectral imaging is suitable for measuring its distribution (Fig.3).

Conclusions:

Specim FX17 in machine vision systems will provide meat transformers with crucial and accurate fat quantification information. This fast and non-destructive method is also suitable for detecting other properties such as moisture and freshness. Furthermore, this method can sort contaminants such as pieces of wood and plastics. FX17 is a perfect tool for industrial quality control. Besides, the methods’ flexibility allows a fast adaptation to new regulations.

Hyperspectral imaging offers cost reduction and quick adaptation to new regulations by providing real-time information about the manufacturing process for decision-making and real-time control of meat processing factory input and output within specification.

by Mary-Anne Milne