Quality, Adulteration, and Traceability (QAT) System

The Industry Need and Solution: SafetySpect has developed food Quality, Adulteration & Traceability (QAT) systems to address current market needs. Portable real-time food testing that goes beyond human vision capability is possible using advanced light sources, detectors and optics that are now smaller, cheaper, and more efficient. Our technology synergistically combines spectral measurements that are capable of compositional and molecular analysis of a food sample in a single system. We envision many applications assessing food quality, adulteration or fraudulent mislabeling and safety. By utilizing intrinsic spectral signatures of food and artificial intelligence software, our technology becomes more accurate and covers more applications in the food industry. Our multimode spectroscopic technology delivers an order of magnitude greater accuracy and specificity than competing machine vision and single mode optical methods. It also provides sampling advantages over laboratory measurements, which are sparse, random and very slow compared to the real-time full sampling of the SafetySpect systems.

USDA – ARS systems to create spectral signatures database.

SafetySpect initially developed a multimode spectral imaging system for laboratory settings, with advanced spectral analysis and classification algorithms. SafetySpect has both imaging and point spectroscopic capabilities and applies them according to specific market needs and the biology and molecular composition of the target. By applying the data analysis results developed using our tabletop laboratory system, we are launching the first portable, handheld commercial device integrating multimode point spectroscopic analysis. This portable handheld device will be deployed along the entire food supply chain including processing and manufacturing, retail, and ports of entry. We plan to deploy rapid conveyor belt imaging systems as well as consumer-based smartphone applications.

Our multimode spectroscopic technology delivers rich data sets that can be used to generate authentication and validation trust certificates and SafetySpect is well-positioned for integration into third party blockchain platforms to enhance supply chain management.

SafetySpect QAT system

SafetySpect QAT technology is based on research and development by our scientists and our USDA-ARS and Chapman University collaborators over several decades and their facilities which are largely funded by the federal government. The SafetySpect QAT systems have been under development since 2016. SafetySpect has designed and built table-top multispectral imaging systems deployed at our collaborator’s laboratories to collect spectral signatures and develop the analysis software. We continued food quality and fraud detection measurements in laboratory testing in early 2019 with the USDA-ARS and SafetySpect under the Cooperation Research and Development Agreement (CRADA) which has been extended two additional years.

USDA – ARS systems to create spectral signatures database.

Our food Quality, Adulteration & Traceability (QAT) product is a wireless, portable, fast device for assessment of food which is field deployable for current measurement needs. This product will solve many food quality/fraud assessment needs and key food safety problems now only measurable with laboratory instruments. We have combined three modes of spectral measurement in our first handheld system:

  • Visible-NIR spectroscopy that surpasses human vision capability (with spectral resolution at nm level) and facilitates compositional analysis,
  • Fluorescence spectroscopy that assesses molecular composition including proteins and harmful contaminants (e.g. toxins, spoilage, harmful bacteria),
  • IR spectroscopy that characterizes water, protein, fat and carbohydrate composition.

These multimode measurements can cross-validate each other to improve total accuracy. For instance, pure fluorescence measurements independent of light absorption can be acquired with concurrent multimode measurements. By analyzing food samples this way our software, augmented by artificial intelligence (AI), can more accurately differentiate a target of interest. It can draw deeper and more discrete conclusions with more targeted and valuable spectral signatures. The AI algorithm can retrain itself over time to be more efficient and provide higher accuracy and faster results as our datasets grow.

Future enhancements can include improved hardware sensitivity and addition or substitution of spectroscopic measurement modes within the QAT portable system to expand the field of use as needed to solve specific problems. This can result in additional product lines or extensions to new markets that requires more sensitive assessment for specific applications.

USDA – ARS systems to create spectral signatures database.

Our data analysis consists of a set of algorithms for each mode of spectroscopy that extracts compositional, chemical, and molecular information about the food sample. We then fuse these algorithms from each mode of measurement using AI methods to construct a multimode algorithm that provides classification with superior accuracy. For example, the QAT system AI algorithm will allow the user to quantify adulteration issues such as percentage of pure extra virgin olive oil, percentage of spoilage in food, or authenticity of a fish species.

Target Customers:

Food Manufacturers and Distributors


Ports of Entry

Grocery Stores

Food Inspectors

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