This article aims to enhance technological advancements in the classification of polyethylene terephthalate (PET) bottle plastic, positively impacting sustainable development and providing effective solutions for collection centers (CC) in Mexico. Three experimental designs and machine learning tools for data processing were developed. The experiments considered three factors: bottle size, liquid volume, and bottle labels. The first experiment focused on determining the sensor distance from post-consumer PET bottles. The second experiment aimed to evaluate the sensor’s detection ability with varying liquid levels, while the third experiment assessed its detection capability for bottle labels. A digital lux meter integrated with a microcontroller was developed to monitor illuminance in post-consumer PET bottles containing liquid as they moved through a conveyor belt at an average rate of three bottles per second. The implemented methodology successfully detected liquids inside transparent PET bottles when they contained beverages ranging from 25% to 100% of their capacity. This study highlights the feasibility of implementing an affordable design for identifying bottles with liquids at CC.

This article seeks to strengthen efforts in technological developments based on the classification of PET plastic that positively impacts sustainable development and contributes to suitable solutions in collection centers in Mexico. Three experiment designs and machine learning tools for data processing were developed. For the experimentation, three factors were considered: the bottle's size, the amount of liquids inside the bottle, and the label on the bottle. The first experiment was to identify the distance of the sensor from post-consumer PET bottles. The second experiment was to determine the detection ability of the sensor with different levels of liquids inside the bottles, and the third was to determine the detection ability on the bottle labels. A digital lux meter on a microcontroller was developed to monitor illuminance on post-consumer PET plastic when it contains liquid as it passes through a conveyor belt for processing at an average rate of three bottles per second. With the implemented methodology, liquids were satisfactorily detected inside the transparent PET bottles when they had beverages between 25% and 100% of their capacity. Finally, this paper highlights that it is possible to implement an affordable design to identify bottles with liquids for collection centers.