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Raman spectroscopy represents an advanced process analytical technology to monitor and control chemical and biochemical processes. This study presents an autoencoder-based methodology that simulates Raman spectra from process variables and predicts the concentrations of different chemicals. The methodology accurately predicts concentrations from the spectra, even considering the temperature influences, and can work as an anomaly detector in process monitoring. The proposed methodology has significant implications for the optimization of industrial processes, improving process efficiency, reducing waste, and minimizing costs. It can also be extended to other industrial processes and imaging spectroscopy techniques, making it a valuable tool for process monitoring. This study highlights the effectiveness of autoencoders in simulating spectra and quantitative analysis, contributing significantly to the field of process monitoring. It has the potential to revolutionize industrial process monitoring and optimization, leading to substantial improvements in productivity and sustainability. [Display omitted] • Raman spectroscopy is a cutting-edge PAT for (bio)chemical process monitoring. • A method is introduced for simulating Raman spectra from process variables. • The method accurately predicts chemical concentrations with temperature variations. • The method can serve as an effective anomaly detector during process monitoring. [ABSTRACT FROM AUTHOR]

Salmon fillet was analysed via absorption spectroscopy with a hyperspectral camera (400–1000 nm) for four consecutive storage days. Optimised K-Nearest Neighbours analyses was performed on all variables (wavelengths) returning an average classification accuracy of 77.0 % for storage day prediction (across all days). A five principal component (PC) model returned an average accuracy of 73.7 % (all days). A histogram analysis of spectral pixels in the hyperspectral images matching reference spectra displayed increases in spoilt regions as the fillet aged for a Tail Bottom fillet section. Overall, five out of 12 of the fillet areas showed a monotonic increase in spoilage (p-value≈0.01). While Principal Component Analysis showed minimal separation in foremost PCs between days, this was improved by using 'fresh' and 'spoilt' class labels prescribed by the hyperspectral data. Via mean spectrum analysis, the dampening of an absorbance band around 600 nm was identified as the main discriminator between fresh and spoilt datasets. Therefore, spatial inhomogeneity of sample freshness should be considered in fish freshness studies, and hyperspectral imaging can be useful as tool to do so. [Display omitted] • Spectroscopy is emerging as a powerful tool to rapidly characterise the composition of different foods, which can be applied to food security. • Hyperspectral imaging, which combines spectroscopic and location information, can be used to ascertain spatial inhomogeneity in food samples. • Histogram analysis of images using a similarity algorithm isuseful to visualize spectroscopic differences. [ABSTRACT FROM AUTHOR]