Publications
Seeking natural colors for foods and beverages |
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PepsiCo is evaluating different natural color ingredients and technologies to replace Yellow #5, Yellow #6, Red #40, and Blue #1 for multiple food and beverage brands in order to address consumer demand. Specific challenges are in the Red #40 (food) and Blue #1 (food & beverage) replacers. |
Seeking technologies to reduce GHGs in all aspects of the supply chain and manufacturing process. |
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PepsiCo is committed to reducing GHG emissions from seed to shelf. We are looking to engage with partners who can assist us in our goal of reducing GHG 20% by 2030 and achieving net-zero emissions by 2050. As new technologies emerge, efficiencies will be gained and PepsiCo aims to remain at the forefront of best practices in the food and beverage industry. PepsiCo is seeking technologies to reduce GHGs in all aspects of the supply chain and manufacturing process. Solutions may be in one or more of the following areas: • Agriculture – sustainable farming and the reduction of GHG intensive fertilizers • Manufacturing – adopting energy efficient manufacturing processes; novel methods for utilizing waste; use of renewable thermal energy at scale • Packaging – alternative packaging materials and formats that reduce GHGs • Distribution – technologies to assist in the efficiency of our company-owned and third party fleet |
Seeking Recycling technologies for biodegradable films |
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We are looking for solutions to convert bio-based packaging back to starting materials or other high-value added chemicals (no fuels). |
Regenerative medicine for drug screening |
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The project, termed CardioSlice, includes: An effective in vitro tool for drug screening heart disease models A new technology for the generation of mature human cardiomyocytes |
Image processing method for glaucoma detection and computer program |
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We have developed a new algorithm for ordering anterior chamber OCT images in such a way that it is possible to classify them, in a fully unsupervised manner, in meaningful groups according to relevant features. We have tested the algorithm with a large set of images classified by two expert ophthalmologists, and with a larger set of annotated images. We have verified that the separation in the different classes defined by the ophthalmologists (closed, narrow, open, and wide open_ figure 6) is similar when using the manually extracted features, or when using the features that are returned by the unsupervised algorithm (View figures). Therefore, the abstract features generated by the algorithm provide novel tools for assessing OCT images of the anterior chamber. They can be used for direct classification of the images and, furthermore, they can be linked to established quantities used for characterizing diseased eyes (like chamber depth, iris-corneal angle) resulting in an automatic detection system. As the algorithm is fully unsupervised, it can be easily automated and set up in OCT imaging systems to aid technicians and doctors in an early diagnosis. The two main advantages of the algorithm demonstrated here over previous works are that it doesn’t need any ground truth or gold standard for training, and it does not rely on specific landmarks; thus, it can analyze images in which relevant landmarks are not visible or not easy to locate. |
Electrochemical biosensor for detection and quantification of alkylphenols |
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Fast, efficient and accurate method for the quantification of alkylphenols (APs). These sensors are based on a network of single-walled carbon nanotubes functionalized with a highly specific anti-AP antibody, which is at the core of the AP quantification reaction. This quantification is determined as a function of the changes in the electrical current, which varied according not only to the presence of absence of these com-pounds, but also in proportion to their concentration. |
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