Accurate in situ diagnosis and optimal surgical removal of a malignancy constitute key elements in reducing cancer-related morbidity and mortality. In surgical oncology, the accurate discrimination between healthy and cancerous tissues is critical for the postoperative care of the patient. Conventional imaging techniques have attempted to serve as adjuvant tools for in situ biopsy and surgery guidance. However, no single imaging modality has been proven sufficient in terms of specificity, sensitivity, multiplexing capacity, spatial and temporal resolution. Moreover, most techniques are unable to provide information regarding the molecular tissue composition. In this review, we highlight the potential of Raman spectroscopy as a spectroscopic technique with high detection sensitivity and spatial resolution for distinguishing healthy from malignant margins in microscopic scale and in real time. A Raman spectrum constitutes an intrinsic “molecular finger-print” of the tissue and any biochemical alteration related to inflammatory or cancerous tissue state is reflected on its Raman spectral fingerprint. Nowadays, advanced Raman systems coupled with modern instrumentation devices and machine learning methods are entering the clinical arena as adjunct tools towards personalized and optimized efficacy in surgical oncology.

Advanced Raman spectroscopy (RS) systems have gained new interest in the field of medicine as an emerging tool for in vivo tissue discrimination. The coupling of RS with artificial intelligence (AI) algorithms has given a boost to RS to analyze spectral data in real time with high specificity and sensitivity. However, limitations are still encountered due to the large amount of clinical data which are required for the pre-training process of AI algorithms. In this study, human healthy and cancerous colon specimens were surgically resected from different sites of the ascending colon and analyzed by RS. Two transfer learning models, the one-dimensional convolutional neural network (1D-CNN) and the 1D–ResNet transfer learning (1D-ResNet) network, were developed and evaluated using a Raman open database for the pre-training process which consisted of spectra of pathogen bacteria. According to the results, both models achieved high accuracy of 88% for healthy/cancerous tissue discrimination by overcoming the limitation of the collection of a large number of spectra for the pre-training process. This gives a boost to RS as an adjuvant tool for real-time biopsy and surgery guidance.

Raman spectroscopy (RS) techniques are attracting attention in the medical field as a promising tool for real-time biochemical analyses. The integration of artificial intelligence (AI) algorithms with RS has greatly enhanced its ability to accurately classify spectral data in vivo. This combination has opened up new possibilities for precise and efficient analysis in medical applications. In this study, healthy and cancerous specimens from 22 patients who underwent open colorectal surgery were collected. By using these spectral data, we investigate an optimal preprocessing pipeline for statistical analysis using AI techniques. This exploration entails proposing preprocessing methods and algorithms to enhance classification outcomes. The research encompasses a thorough ablation study comparing machine learning and deep learning algorithms toward the advancement of the clinical applicability of RS. The results indicate substantial accuracy improvements using techniques like baseline correction, L2 normalization, filtering, and PCA, yielding an overall accuracy enhancement of 15.8%. In comparing various algorithms, machine learning models, such as XGBoost and Random Forest, demonstrate effectiveness in classifying both normal and abnormal tissues. Similarly, deep learning models, such as 1D-Resnet and particularly the 1D-CNN model, exhibit superior performance in classifying abnormal cases. This research contributes valuable insights into the integration of AI in medical diagnostics and expands the potential of RS methods for achieving accurate malignancy classification.

In this work, aqueous nutrient solutions replicating bioreactor culture media for microalgae were analyzed using spontaneous Raman spectroscopy. Focusing on nitrate, sulfate, glucose, and phosphate, the study evaluated their potential for real‐time monitoring in cell cultivations such as Chlorella vulgaris . Univariate analysis, based on Raman intensities of specific nutrient peaks, was conducted and compared to multivariate analysis results. Four multivariate calibration models were developed using partial least squares regression (PLSR), achieving high calibration and validation performance, with R ² values above 0.99 and low RMSE _CV , indicating strong calibration accuracy. The study also examined the limit of detection (LOD) for each nutrient, finding that LODs for nitrate, sulfate, and glucose reached levels relevant for algae bioreactors even without the application of enhanced Raman techniques. To further validate the PLS models, independent real bioreactor samples were analyzed, showing strong predictive accuracy (R _P ² : 0.9661–0.9892) and low RMSE _P values. Additional testing with five samples collected over a Chlorella vulgaris cultivation run (day 0 to day 9) confirmed the models' robust performance under real bioprocess conditions. Limitations in practical applications, such as phosphate's relatively high LOD, were also identified. The results suggest that Raman spectroscopy, combined with multivariate analysis, could deliver precise and reliable detection of critical nutrients and their concentrations in bioreactor culture media. This potential of the Raman technique, along with insights into nutrient LODs, PLS model accuracy, and practical application challenges, provides a solid foundation for future research and development in industrial bioprocess monitoring.