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Plastics and foods can be differentiated by their material characteristic fluorescence properties, especially their fluorescence lifetimes. An areal measurement of fluorescent lifetimes of these materials can be done using Frequency-Domain Fluorescence Lifetime Imaging Microscopy (FD-FLIM). Up until now, most plastic detection is done using NIR or X-ray, while most applications of FD-FLIM are in biomedicalfields. The application of FD-FLIM in a food safety setting presents a promising approach to the detection of plastic contaminants. A Multilayer Perceptron (MLP) based neural network is developed to reliably identify the presence of plastic in a food/plastic sample via FD-FLIM. Features like the mean, median, standard deviation, variance, range, and interquartile range are calculated from the intensity image, the phase shift and modulation index along with the according phase- and modulation-dependent fluorescence lifetimes from the FD-FLIM data. For training, test and validation, a total of 3520 FD-FLIM measurements have been taken at 445nm excitation of sixteen samples with the labels food and plastic. To rank the performance of the 3888 trained networks, Fl-score, accuracy, precision, and recall are used as metrics. The best performing network reaches a Fl-score of 98.86% proving that a differentiation of foods and plastics using a MLP classification based on FD- FLIM data is possible with a low error rate.
Ziel des Projektes Fluoreszenz ID von Altholz (FrIDAH)5 ist die Entwicklung eines Demonstrators gewesen, welcher die automatisierte Sortierung von Altholzproben gemäß der Altholzverordnung unter Verwendung der Messung von Fluoreszenzabklingzeiten ermöglicht. In diesem Beitrag werden der entwickelte Messaufbau, die Software, das Automatisierungssystem, sowie der Klassifikator vorgestellt. Die Ergebnisse zeigen, dass die verwendete Technologie zur zuverlässigen Klassifikation von Altholz geeignet ist und für die automatisierte Sortierung angewendet werden kann.
Microplastics have emerged as a significant environmental concern, particularly due to their potential impact on food safety and human health. This study uses frequency-domain fluorescence lifetime imaging microscopy to investigate the presence and effects of microplastics in four food types—ham, honey, fish, and lettuce. Samples were prepared with known quantities of high-density polyethylene particles, and their phase-dependent fluorescence lifetimes were analyzed to distinguish between contaminated and uncontaminated food. The results indicate that fluorescence lifetime analysis can effectively identify microplastic contamination, revealing distinct fluorescence characteristics for each food matrix. This research underscores the importance of innovative detection methods in ensuring food safety, highlighting the need for further studies on automated microplastic detection.
