Rising volumes of textile waste necessitate the development of more efficient recycling systems, with a primary focus on the optimization of sorting technologies. Near-infrared (NIR) spectroscopy is a state-of-the-art method for fiber identification; however, its accuracy in quantifying textile blends, particularly common polyester/cotton blend textiles, still requires refinement. This study explores the potential and limitations of NIR spectroscopy for quantifying cotton content in post-consumer textiles. A lab-scale NIR sorter and a handheld NIR spectrometer in complementary wavelength ranges were applied to a diverse range of post-consumer textile samples to test model accuracies. Results show that the commonly assumed 10% accuracy threshold in industrial sorting can be exceeded, especially when excluding textiles with <35% cotton content. Identifying and excluding the range of non-linearity significantly improved the model’s performance. The final models achieved an RMSEP of 6.6% and bias of −0.9% for the NIR sorter and an RMSEP of 3.1% and bias of −0.6% for the handheld NIR spectrometer. This study also assessed how textile characteristics—such as color, structure, product type, and alkaline treatment—affect spectral behavior and model accuracy, highlighting their importance for refining quantification when high-purity inputs are needed. By identifying current limitations and potential sources of errors, this study provides a foundation for improving NIR-based models.

The production of polyhydroxybutyrate (PHB) by autotrophic fermentation of cyanobacteria has received increasing interest in the light of carbon emission reducing process strategies. Biotechnological approaches are in development to optimize the yield of PHB, including adapted cultivation media, characterized by a limitation of key nutrients: cyanobacteria accumulate PHB as energy storage molecules under limited growth conditions. Since there is an increasing demand for fast, simple and reliable analytics, we report the establishment of surface enhanced Raman spectroscopy (SERS) as a suitable monitoring tool for up scaled PHB production processes. Both, pure Ag-colloids mixed with bacterial culture, and in situ prepared colloids (Ag-Synechocystis), generated on the cell surface directly, were successfully applied and evaluated for this purpose. SERS measurements with in situ prepared Ag-colloids improved the reproducibility of Raman signals from 54.8% to 93.9%. The measurement time could be reduced significantly, completing our secondary goal. The quality of classically and in situ prepared Ag-colloids was monitored by zeta potential measurements and scanning electron microscopy (SEM) respectively. For data interpretation and statistical model-building an in house written code in the open source software RStudio was implemented. It was applied for the differentiation of PHB producers at the cellular level, revealing heterogeneities within sample groups regarding the PHB amount accumulated. The results obtained using the statistical model were validated as well and were complementary to the reference HPLC analysis. Therefore, a fast and reliable identification in situ SERS tool for the selection of the most promising cyanobacterial PHB production was established.

Accurate quantification of cotton content is a key prerequisite for efficient textile recycling. However, it remains challenging due to material heterogeneity and technical limitations. Near-infrared spectroscopy (NIR) combined with advanced data analysis offers a rapid, non-destructive approach. However, systematic evaluations across instrument classes and analysis strategies for industrial textile sorting remain limited. In this study, a unique set of cotton/polyester blends from the same starting material with varying cotton content was analyzed using three NIR systems representing laboratory, handheld, and industrial sensor-based applications. Multiple spectral preprocessing strategies were systematically combined with partial least squares regression and advanced machine learning models. Model performance was evaluated using cross-validation and independent test sets. The benchtop NIR system delivered the highest and most consistent performance, achieving RMSEP values below 1.0% with advanced regression models. The handheld and imaging sensor system exhibited higher RMSEP values (1.2–1.6%), reflecting not only differences in preprocessing and model selection, but also intrinsic instrumental limitations. Overall, the results demonstrate that each NIR instrument class exhibits distinct strengths and limitations with respect to accuracy, sensitivity, and robustness. Consequently, instrument-specific preprocessing, models, and hyperparameters are required, and no universally transferable pipeline was identified.

This study investigates the use of quantitative LC-MS/MS-based proteomics and surface-enhanced Raman spectroscopy (SERS) for biomarker detection in classical Hodgkin lymphoma (HL). Two HL cell models with distinct TP53 status were utilized to evaluate the effects of etoposide, a DNA-damaging chemotherapeutic agent, and resveratrol, a polyphenolic compound with known chemosensitizing activity. For SERS, the best performance was achieved by applying logistic regression to classify different treatment conditions and identify discriminative spectral features in the data. Proteomics showed highly reproducible and accurate results with relative standard deviations of below 5% for the sample preparation and about 2% for the measurements. Proteomic profiling revealed a TP53-dependent organization of metabolic and stress-response pathways and demonstrated that cryopreserved aliquots yielded the most consistent proteomic signatures. Treatment-dependent regulation of key biomarker proteins showed direct correspondence to specific SERS features, such as reduced nucleotide/cytochrome-associated signals and enhanced amide and aromatic amino acid signals. Our findings highlight the strength of applying reproducible proteomic profiling with machine learning–guided SERS analysis to improve molecular interpretation and to validate potential biomarkers in cancer research. Future work will focus on refining the analytical workflow and extending it toward integrative multi-omics applications, enabling more comprehensive biomarker detection and mechanistic insight into classical Hodgkin lymphoma. This strategy will be extended to additional model systems—such as metastatic melanoma, melanocytes, and ultimately patient-derived leukemia cells—to help bridge the gap toward clinical translation. Graphical abstract An analytical approach that enables reproducible and complementary biomarker discovery in Hodgkin lymphoma cells by applying robust SERS-based spectral classification with quantitative LC-MS/MS proteomics for molecular validation. It emphasizes reproducibility and data complementarity while identifying treatment-specific molecular signatures.

Depending on the respective research question, LC-MS/MS based bottom-up proteomics poses challenges from the initial biological sample all the way to data evaluation. The focus of this study was to investigate the influence of sample preparation techniques and data analysis parameters on protein identification in Tribolium castaneum by applying free software proteomics platform Max Quant. Multidimensional protein extraction strategies in combination with electrophoretic or chromatographic off-line protein pre-fractionation were applied to enhance the spectrum of isolated proteins from T. castaneum and reduce the effect of co-elution and ion suppression effects during nano-LC-MS/MS measurements of peptides. For comprehensive data analysis, MaxQuant was used for protein identification and R for data evaluation. A wide range of parameters were evaluated to gain reproducible, reliable, and significant protein identifications. A simple phosphate buffer, pH 8, containing protease and phosphatase inhibitor cocktail and application of gentle extraction conditions were used as a first extraction step for T.castaneum proteins. Furthermore, a two-dimensional extraction procedure in combination with electrophoretic pre-fractionation of extracted proteins and subsequent in-gel digest resulted in almost 100% increase of identified proteins when compared to chromatographic fractionation as well as one-pot-analysis. The additionally identified proteins could be assigned to new molecular functions or cell compartments, emphasizing the positive effect of extended sample preparation in bottom-up proteomics. Besides the number of peptides during post-processing, MaxQuant’s Match between Runs exhibited a crucial effect on the number of identified proteins. A maximum relative standard deviation of 2% must be considered for the data analysis. Our work with Tribolium castaneum larvae demonstrates that sometimes – depending on matrix and research question − more complex and time-consuming sample preparation can be advantageous for isolation and identification of additional proteins in bottom-up proteomics.