AIMC Topic: Product Packaging

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Contaminant detection in flexible polypropylene packaging waste using hyperspectral imaging and machine learning.

Waste management (New York, N.Y.) Feb 10, 2025
Flexible plastic packaging (FPP) constitutes one of the largest post-consumer plastic streams processed in recycling facilities. To address the key challenges of its sorting and quality control, this study developed and tested a classification proced...
Machine Learning Spectroscopy, Fourier Transform Infrared Principal Component Analysis Hyperspectral Imaging Polypropylenes Recycling Product Packaging
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The development of classification-based machine-learning models for the toxicity assessment of chemicals associated with plastic packaging.

Journal of hazardous materials Nov 30, 2024
Assessing chemical toxicity in materials like plastic packaging is critical to safeguarding public health. This study presents the development of classification-based machine learning models to predict the toxicity of chemicals associated with plasti...
Humans Machine Learning Support Vector Machine Plastics Toxicity Tests Hazardous Substances Product Packaging
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A Novel Two-Stage Induced Deep Learning System for Classifying Similar Drugs with Diverse Packaging.

Sensors (Basel, Switzerland) Aug 19, 2023
Dispensing errors play a crucial role in various medical errors, unfortunately emerging as the third leading cause of death in the United States. This alarming statistic has spurred the World Health Organization (WHO) into action, leading to the init...
Humans Patient Safety Deep Learning Cognition Benchmarking Product Packaging
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Nondestructive and multiplex differentiation of pathogenic microorganisms from spoilage microflora on seafood using paper chromogenic array and neural network.

Food research international (Ottawa, Ont.) Oct 17, 2022
Non-destructive detection of human foodborne pathogens is critical to ensuring food safety and public health. Here, we report a new method using a paper chromogenic array coupled with a machine learning neural network (PCA-NN) to detect viable pathog...
Humans Neural Networks, Computer Machine Learning Seafood Food Safety Product Packaging Shewanella putrefaciens
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Sensor-based particle mass prediction of lightweight packaging waste using machine learning algorithms.

Waste management (New York, N.Y.) Oct 26, 2021
Sensor-based material flow characterization (SBMC) promises to improve the performance of future-generation sorting plants by enabling new applications like automatic quality monitoring or process control. Prerequisite for this is the derivation of m...
Machine Learning Algorithms Particle Size Physical Phenomena Product Packaging
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Review on the photonic techniques suitable for automatic monitoring of the composition of multi-materials wastes in view of their posterior recycling.

Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA Mar 21, 2021
In the increasingly pressing context of improving recycling, optical technologies present a broad potential to support the adequate sorting of plastics. Nevertheless, the commercially available solutions (for example, employing near-infrared spectros...
Artificial Intelligence Plastics Polymers Recycling Product Packaging
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