Real-time and in situ monitoring of Irgacure 907 penetration into food plastic packaging through surface-enhanced Raman spectroscopy

In situ imaging of the spatial and temporal penetration of organic pollutants into microplastics via surface-enhanced Raman spectroscopy

Understanding the spatial and temporal penetration patterns of organic pollutants in microplastics (μP) is important for evaluating their environmental and biological impacts, such as the "Trojan Horse" effect. However, there is a lack of an effective method to monitor the penetration processes and patterns in situ. This study aimed to develop a simple and sensitive approach for in situ imaging of organic pollutant penetration into μP. The novel method was developed using surface-enhanced Raman spectroscopy (SERS) coupled with gold nanoparticles as nanoprobes that could sensitively detect organic pollutants in low-density polyethylene (LDPE) μP spatially and temporally. The detection limit of this SERS-based method was 0.36 and 0.02 ng/mm² for ferbam (pesticide) and methylene blue (synthetic dye), respectively. The results showed that both ferbam and methylene blue could penetrate LDPE μP. The penetration depth and amount increased as the interaction time increased. Most of the absorbed organic pollutants accumulated within the top 90 μm layer of the tested μP. Compared to methylene blue, ferbam was more quickly absorbed and achieved higher accumulation in μP with a maximum of 32.57 ng/mm² after 168 h interaction. This pioneering study clearly demonstrated that SERS mapping is a sensitive and in situ approach to visualize and quantify the penetration patterns of organic pollutants in μP. The new approach developed here can advance our understanding of μP as pollutant carriers and their influence on the environmental fate, behavior, and biological impacts of organic pollutants.

Deep Learning for Chondrogenic Tumor Classification through Wavelet Transform of Raman Spectra

The grading of cancer tissues is still one of the main challenges for pathologists. The development of enhanced analysis strategies hence becomes crucial to accurately identify and further deal with each individual case. Raman spectroscopy (RS) is a promising tool for the classification of tumor tissues as it allows us to obtain the biochemical maps of the tissues under analysis and to observe their evolution in terms of biomolecules, proteins, lipid structures, DNA, vitamins, and so on. However, its potential could be further improved by providing a classification system which would be able to recognize the sample tumor category by taking as input the raw Raman spectroscopy signal; this could provide more reliable responses in shorter time scales and could reduce or eliminate false-positive or -negative diagnoses. Deep Learning techniques have become ubiquitous in recent years, with models able to perform classification with high accuracy in most diverse fields of research, e.g., natural language processing, computer vision, medical imaging. However, deep models often rely on huge labeled datasets to produce reasonable accuracy, otherwise occurring in overfitting issues when the training data is insufficient. In this paper, we propose a chondrogenic tumor CLAssification through wavelet transform of RAman spectra (CLARA), which is able to classify with high accuracy Raman spectra obtained from bone tissues. CLARA recognizes and grades the tumors in the evaluated dataset with 97% accuracy by exploiting a classification pipeline consisting of the division of the original task in two binary classification steps, where the first is performed on the original RS signals while the latter is accomplished through the use of a hybrid temporal-frequency 2D transform.