Food and Feed

Food integrity

Food integrity: an important quality of life

Assure food integrity - quickly and easily

Food integrity is an umbrella concept for the fundamental food properties:

  • Food Authenticity, correct food origin and declaration
  • Food Safety, food is safe to consume
  • Food Quality, food meets the preset quality specifications

Related negative concepts for food integrity degradation are:

  • Food fraud, intentional degradation of food integrity
  • Food adulteration, food fraud by admixture
  • Food counterfeit, food fraud by incorrect product name/manufacturer
  • Food mislabelling, food fraud by incorrect origin, declaration, production/expiration date
  • Food spoilage, microbial degradation of food safety and/or food quality
  • Food contamination, food safety degradation by harmful chemicals, foreign bodies, parasites, or microorganisms

The VideometerLab instrument provides fast, versatile, and non-destructive detection of many types of food fraud as well as food quality and food safety issues.

The VideometerLab 4 for spectral imaging of various productsVideometerLab4: spectral imaging as a fast method for food integrity assessment

Why spectral imaging?

Videometer spectral imaging measures more than 12 million spectra on a food sample within a fraction of a second. Every pixel in the image is a spectrum covering UV, visual color, and NIR ranges of areas down to 30x30 µm. This unique and versatile technology allows fast characterization of food integrity in terms of color, surface chemistry, texture, shape, and size without touching the sample and with little or no sample preparation. Further, spectral imaging provides more informative and specific information than traditional food imaging.

1, 2, 3, go!

Setting up a protocol for food integrity assessment is a three step procedure:

  1. Define sample preparation and presentation, select a feasible way to present your sample in a standardized way
  2. Define conforming product and non-conforming product by example, conforming product definition must be representative for acceptable variations of good products, non-conforming product definition may be non-specific - meaning everything but conforming product - or specific - incorporating definitions of each type of non-conformity
  3. Build analysis recipe, use the built-in machine learning and AI to automatically detect and quantify non-conforming product in terms of count, area, and/or volume, or in terms of percentages of these relative to the full sample

Once the recipe has been defined then it can be stored in a local database or in the cloud for easy deployment to new samples with the corresponding product ID. Having the recipe availabe will mean that new samples are measured and analyzed in VideometerLab in app. 10 seconds including sample handling. Automated presentation by a robot and automated feeding of granular products are available as are customized in-line versions, VideometerLine, of the technology.


Food adulteration

Durum wheat left, common wheat rightDurum wheat left, common wheat right - spectral signature

LGC, a global leader in the life sciences, conducted an extensive study for the Food Standards Agency (FSA) on spectral imaging for food and feed analysis. The final report shows a number of great admixture application examples using the VideometerLab 4 instrument. The study concludes on multispectral imaging (MSI) as a food analytical technique: "MSI benefits from a number of important advantages compared to competitor technologies, which includes a low test cost base due to limited use of consumables, multi-analyte capability, simple workflow and short total analyses time arising from the lack of complex sample preparation/purification and analysis steps. MSI represents an emerging analytical 48 technology within the food sector with the potential to augment or even replace multiple current tests with a single flexible platform." The admixture examples include detection of olive leaves in oregano, plastic rice in rice, pork in beef, ground peanut in ground almond, peanut flour in wheat flour, and ground mahaleb in ground cumin.

A previous study by LGC using the VideometerLab 2 in a comparison with a hyperspectral camera was published in a peer-reviewed paper. Using 21 blind samples with preset adulteration level VideometerLab 2 gave convincing quantitative results for adulteration percentage. In the figure we see the spectral signature for durum versus common wheat and each kernels can be assessed automaically by computing the mean of this signature for the kernel. If positive it is classified as durum, if negative it is common wheat. The classification could also include shape, size, texture and other information, but the seed coat spectrum is sufficient for the purpose and provides at least an order of magnitude better separation than color alone.

Food counterfeit

Counterfeit products are fraudulent imitations of products with well-known and trusted brands. They represent a significant and increasing risk for consumers as well as brand owners. While already recognized as a huge problem in the fashion and pharmaceutical industry then it is also important to address in the field of food, feed, beverage, and household items in general. Detection of counterfeit can be done on the product itself, or on any part of the packaging. With Videometer imaging technology it is not enough that the counterfeiters make the product appear like the genuine product. If the materials or pigments used are not exactly the same, then a difference will typically be revealed. VideometerLab can be used to set of a number of checkpoints on the product and through these classify conforming/genuine product against different types of non-conforming/counterfeit products - for consumer as well as brand protection.

Pfizer probably has one of the most counterfeited pharmaceutical products in the world. See how Pfizer scientists tell the difference between counterfeit drugs and real ones using VideometerLab in this video (from 32 seconds into the video).

Mislabelling: rancid oil may be mislabelled as fresh or used as adulterant in fresh oil

Food mislabelling

Mislabelling is food fraud by putting incorrect origin, declaration, organic status, production date, or expiration date on the product. Organic food fraud have reached headlines a number of times over the last decade and lately in a press release by Eurojust. Fraudulently giving expired products a "new life" by changing the expiration date is a mislabelling fraud. An example could be expired and rancid oil as shown in the figure. Oil rancidity can often be detected by spectral imaging even if the expired oil is mixed with fresh oil in order to disguise the fraud. Undeclared admixtures is a common fraud that can be characterized both as adulteration and mislabelling. 


Food spoilage

Fruit spoilage: test for pathogens on strawberries at Rothamsted Research

Food spoilage is a primary cause of food waste around the world. It typically causes a deviation in appearance from conforming product. Color and/or texture may become different and the microorganism or metabolites from the microorganisms may be directly visible on the surface. Spoilage is often associated with unpleasant odor and taste as well as with an increase in softness. In the video to the right Tom Ashfield of Rothamsted Research is illustrating how VideometerLab may be used to detect spoiling pathogens on strawberries. Another video from the same lab is found here.




Food contamination

Food contamination: Atlantic cod seen in RGB (left) and as spectral signature for Anisakis parasites (right)

Food contaminants are harmful chemicals, foreign bodies, parasites, or microorganisms in the food. Their origin can be from raw materials, transport, storage, packaging,and processing of the food. Examples are agrochemicals (mainly pesticides), bacteria, fungi, mycotoxins, parasites, heavy metals, packaging chemicals, foreign bodies (metal pieces, rodent excreta), and fraudulent chemicals (melamine). VideometerLab can inspect for many contaminants and especially those that are localized and not spread throughput the sample in very low concentrations (like heavy metals).

To the right we see a fillet of Atlantic cod with anisakis larvae. The larvae are not visible in the color image on the left, but can be greatly enhanced through their spectral signature to the right.

Food applications