Home Green Futures: Technology transformation in packaging recyclability

Revolutionizing packaging recyclability through data-driven sorting, robotics, and e-nose technology for unparalleled precision and sustainability

In the quest for a greener future, one of the most critical challenges is the management of packaging waste. The proliferation of food packaging materials, especially plastics, has created a looming environmental crisis. According to the World Economic Forum, only 9% of plastic packaging is recycled globally. The rest ends up in landfills, incinerators, or the environment. The improper disposal and recycling of this waste contributes significantly to pollution, habitat destruction, and climate change. Traditional recycling methods are often inefficient, leading to low recycling rates and the accumulation of packaging waste in landfills and oceans. It’s clear that we need a paradigm shift in how we approach packaging recyclability.

We will explore how the integration of digital technologies, including smart collectors, electronic noses, machine learning, and AI-driven robotic sorting, can lead to more efficient and precise recyclable waste recovery.


The Key to Transformation

To tackle the packaging waste challenge effectively, brands must harness the power of digital integration. By combining cutting-edge technologies, recyclers and packaging, companies can improve the accuracy, quality, safety, and efficiency of packaging waste management. New technologies such as the Internet of Waste, Artificial Intelligence (AI), and robotics are becoming increasingly important in optimizing resource utilization, minimizing waste, and transitioning towards a more sustainable and circular economy.

Recycling Reimagined with AI and Machine Learning (ML)

The integration of AI and ML into recycling processes represents a monumental leap forward in our journey toward a sustainable future. AI-driven sorting systems can identify and separate materials with unparalleled precision, ensuring that recyclables are directed to appropriate streams. This reduces contamination and improves the overall quality of recycled materials. By automating the sorting process and reducing the need for manual labor, AI and ML solutions can lead to cost savings in waste management and recycling operations.

Researchers at the University of Lorraine have introduced a smart plastic waste collector that promises to reshape waste management and contribute to a closed-loop plastic waste supply chain. This collector focuses on identifying recyclable plastic waste, playing a pivotal role in establishing an open-source hardware ecosystem for plastic recycling. Over the course of nine months, this groundbreaking experiment has successfully collected 60 kilograms of plastic, providing insights into distributed recycling implementation. The potential for reducing plastic waste in our environment is undeniable, and this smart collector is a significant step towards that goal.

Another recent study highlights how certain design elements such as black coloring, large-area printing, and high shrinkage of shrink sleeves negatively affects their sortability during NIR-based sorting processes. Adding spectral data from sleeved bottles to the machine learning training dataset improved classification accuracy. This underscores the importance of diverse data in enhancing sorting capabilities. The research demonstrated that selecting suitable machine learning algorithms and fine-tuning hyperparameters based on the specific sorting task can significantly enhance the accuracy of sorting sleeved materials.


Robots are transforming Trash-to-Treasure

Robotics can help improve the efficiency and effectiveness of the recycling process, which can lead to several benefits including increased recycling rates, reduced contamination and high accuracy. However, the adoption of robotic technologies in recycling will require investment in research and development, integration with existing infrastructure and collaboration between the public and private sectors to drive innovation and sustainable recycling practices.

AMP Robotics system’s Neuron AI platform utilizes computer vision to identify recyclable materials in mixed waste streams. It distinguishes plastic, paper, metal, and multi-layered packages for sorting. This technology converts images into data, classifying materials based on type, color, and form. Through ML, Neuron continually enhances its recognition capabilities using real-world data from global installations. AMP’s uniqueness lies in its extensive practical learning, identifying over 70 billion objects annually, and innovating optimal solutions.

Robotics, especially when combined with AI and computer vision, can significantly enhance the efficiency and effectiveness of recycling processes. They can identify and sort recyclable materials with high accuracy, reducing the need for manual labor and speeding up the recycling operation. Accurate sorting by robots minimizes contamination in recycling streams. This is essential for maintaining the quality of recycled materials and ensuring that they meet industry standards.


Establishing quality control with E-nose

E-noses have the potential to enhance the recycling industry by improving the quality control processes, reducing contamination, increasing the efficiency of recycling operations and ensuring the safety of recycled packaging materials.

E-noses can be used to assess the quality of packaging materials. By analyzing the odor profile of packaging materials, these can detect off-putting odors or signs of contamination. They can detect a wide range of volatile organic compounds (VOCs and can also help identify the presence of harmful or non-recyclable substances in recycled packaging materials. By analyzing the odors emitted during different stages of recycling, E-noses can provide real-time feedback on the efficiency and effectiveness of recycling operations. However, their successful implementation requires the integration of sensor technology into recycling equipment and the development of algorithms for odor analysis and decision-making.



Researchers have developed a novel approach for the quality assessment of polymer materials, specifically high-density polyethylene, in the context of plastic recycling technologies. This method employs an electronic nose featuring aluminum-doped zinc oxide sensing material, combined with the RandomForestClassifier ML tool. It analyzes volatile organic compounds (VOCs) from primary and secondary plastics, both odor-active and odorless, using headspace gas chromatography and mass-spectrometry under optimized conditions. The electronic nose achieves a high accuracy of over 98.5% in differentiating between primary and secondary plastics. Additionally, the study explores the impact of zeolites on reducing off-odors in recycled plastics.


According to a study by Scientists at the University of Cádiz, (https://www.nature.com/articles/s41893-021-00720-8)  the most dominant types of packaging waste found in the ocean are takeaway food and beverage packaging with 80% plastic. Avoiding waste accumulation and closing the loop by recycling technologies will become essential soon to prevent adverse effects. This situation demands innovative solutions that go beyond conventional recycling methods. By integrating digital technologies, such as smart collectors with electronic noses, ML, and AI-driven robotic sorting, the industry can usher in a new era of efficient and precise recyclable waste recovery. This can increase recycling rates, reduce environmental pollution, and conserve valuable resources and energy. To truly revolutionize packaging recyclability, it is imperative that governments, industries, and consumers collaborate in adopting and investing in these transformative technologies. Only through collective efforts can we secure a sustainable and environmentally responsible future for generations to come.

Revolutionize your packaging future with data-driven tech! Connect with us now for insights on cutting-edge sorting, robotics, and e-nose solutions to transform your packaging future



Need a thought partner?

Share your focus area or question to engage with our Analysts through the Business Objectives service.

Submit My Business Objective

Our Clients

Our long-standing clients include some of the worlds leading brands and forward-thinking corporations.