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  • Jordan Gunn

AI-Assisted Hyperspectral Imaging Over NIR Spectroscopy in the Analysis of Food Safety and Quality

The Advantages of AI-Assisted Hyperspectral Imaging Over NIR Spectroscopy in the Analysis of Food Safety and Quality

Picture Credit: Science Photo Library


Near-Infrared Spectroscopy, aka NIR, is an optical-based test method that uses a spectrophotometer to analyze the light spectrum reflected off a test sample when it’s exposed to infrared light. The resulting reflectance is characteristic of the chemicals within a sample, giving information about its chemical composition.


NIR Spectroscopy is routinely used in the flow of food steps in a company’s Hazard Analysis Critical Control Point (HACCP) Plan for quality control and to stay within FDA and USDA mandated standards that specify if a chemical constituent is allowed to be present, and if so, at what concentration. For these purposes NIR is used:

  • Commonly to test broad routine parameters such as moisture, fat, and protein

  • Can also test specific chemical components like caffeine, lactose, vomitoxin, etc.


NIR is thought to have benefits over chemical tests that lend themselves to being used for quality control and hazard analysis in a process environment

  1. Little to no sample prep

  2. High speed - when compared to chemical tests

  3. Low cost - because it doesn’t use consumables


But NIR as a method for quality control also has a number of disadvantages

  1. Point-based - forcing sampling techniques to make up for a small amount of samples representing the whole

  2. Fixed wavelength range - limiting parameters and making certain chemical analyses impossible

  3. Low Accuracy compared to chemical tests


AI-Hyperspectral Imaging (AI-HSI), like NIR spectroscopy, is an imaging-based technology that gives information about the chemical composition by measuring its interaction with light.


However, AI-HSI has a number of advantages over NIR spectroscopy in quality control and the analysis of food safety issues

  1. Larger sample sizes - giving higher throughput of material and better representation

  2. 2D spatial distribution of parameters - telling you not just what, but where

  3. Expanded list of parameters - both because the wavelength range can be adjusted and because it’s able to make determinations on datasets NIR can’t

  4. Better accuracy - produces better results from the same data due to special algorithms

  5. Built-in feedback loop - Learns from samples to improve future analyses

  6. Less data points - Requires less data to make the same statistical conclusions


Because of these advantages, AI-HSI is rapidly replacing NIR as a superior method of analysis and is gaining a strong foothold in the food industry.


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