The identification/classification of textile fibres is essential in manufacturing, forensic science, cultural heritage preservation, and recycling. Conventional methods, including solubility tests, optical microscopy, and chromatographic techniques, are often destructive, labour-intensive, and limited in scope. Vibrational spectroscopy, particularly near-infrared (NIR), Fourier-transform infrared (FTIR), and Raman spectroscopy, has emerged as a rapid, non-destructive, and accurate alternative for fibre analysis. However, multi-composition textiles, dyes, finishing agents, and ageing effects frequently cause overlapping spectral features, hampering direct interpretation. This review examines the combined use of vibrational spectroscopy and chemometrics for textile fibre discrimination. It critically evaluates the performance of different spectroscopic techniques in classifying natural, synthetic, and blended fibres. The role of multivariate analysis methods, such as PCA, PLS, LDA, SIMCA, and machine learning algorithms, in improving spectral interpretation and classification accuracy is highlighted. Key factors affecting model robustness, including spectral pre-processing, sample heterogeneity, moisture, and colour, are also discussed. The integration of spectroscopy with chemometrics provides a robust, scalable, and sustainable solution for fibre identification, supporting quality control, fraud detection, and circular economy initiatives. This approach demonstrates significant potential for both research and industrial applications.

Advances in Industry 4.0 manufacturing have accelerated the adoption of machine learning (ML) for automated classification. Polyester (PES), a widely used synthetic fiber, competes with natural fibers like cotton and other synthetics, highlighting the need for continuous research and improvement. In the textile sector, distinguishing recycled polyester (rPES) from virgin polyester (vPES) remains challenging due to overlapping chemical signatures and material variability. A combination of Fourier transform infrared (FTIR) spectroscopy and ML has not been explored for this purpose. In this study, we evaluated ML models to discriminate three PES fiber types (45 vPES, 65 rPES, and 55 mixed PES) using 165 FTIR spectra across four spectral regions, R1, R2, R3, and R4, as well as their combined representation. Six ML approaches were tested on data reduced with fast independent component analysis (FastICA) (1–30 components) using an 80/20 train–test dataset split. The Decision Tree classifier achieved the highest Accuracy in four of the five spectral evaluations, with classification accuracies ranging from 66.67% to 77.78% for region R4, which also had a balanced classification profile with an area-under-the-curve (AUC) value of 0.81. Notably, despite the moderate overall Accuracy, the model achieved 100% discrimination of rPES when distinguishing it from both mixed and vPES. Mixed fibers remained the most difficult to classify, highlighting the need for improved feature representation.

Meat spoilage remains one of the major challenges affecting food quality, shelf life, and economic sustainability worldwide. Over the last 25 years, significant advances have been made in understanding the biochemical, microbiological, and environmental mechanisms responsible for spoilage in fresh and processed meat products. Spoilage is primarily driven by microbial proliferation, particularly psychrotrophic and mesophilic bacteria such as Pseudomonas spp., Brochothrix thermosphacta, lactic acid bacteria, and members of Enterobacteriaceae, whose dominance depends strongly on storage temperature, oxygen availability, pH, and packaging conditions. These microorganisms promote proteolysis, lipolysis, and the production of volatile organic compounds (VOCs), biogenic amines (BAs), sulphur-containing metabolites, and other compounds associated with undesirable odour, discoloration, slime formation, and texture deterioration. Attempting to identify biomarkers that allow for the early detection of these compounds may be essential to help prevent their deterioration and subsequent disposal. Parallel progress in preservation technologies has shifted from conventional refrigeration, vacuum packaging, and modified atmosphere packaging toward innovative strategies such as biopreservation, antimicrobial coatings, essential oils, and high-pressure processing, aiming to extend shelf life while maintaining sensory quality. In addition, intelligent packaging systems and non-destructive monitoring tools based on biosensors, spectroscopy, and nanomaterials have emerged as promising approaches for real-time spoilage detection. Despite these advances, spoilage remains highly variable across meat types, processing environments, and supply chains, emphasizing the need for integrated control strategies. Future research should focus on predictive microbiology, sustainable preservation systems, and microbiome-guided interventions to reduce meat losses and improve food safety across global markets.