The aim of the present study was to critically evaluate the potential of using NIR and Raman spectroscopy for prediction of fatty acid features and single fatty acids in salmon muscle. The study was based on 618 homogenized salmon muscle samples acquired from Atlantic salmon representing a one year-class nucleus, fed the same high fish oil feed. NIR and Raman spectra were used to make regression models for fatty acid features and single fatty acids measured by gas chromatography. The predictive performance of both NIR and Raman was good for most fatty acids, with R² above 0.6. Overall, Raman performed marginally better than NIR, and since the Raman models generally required fewer components than respective NIR models to reach high and optimal performance, Raman is likely more robust for measuring fatty acids compared to NIR. The fatty acids of the salmon samples co-varied to a large extent, a feature that was exacerbated by the overlapping peaks in NIR and Raman spectra. Thus, the fatty acid related variation of the spectroscopic data of the present study can be explained by only a few independent principal components. For the Raman spectra, this variation was dominated by functional groups originating from long-chain polyunsaturated FAs like EPA and DHA. By exploring the independent EPA and DHA Raman models, spectral signatures similar to the respective pure fatty acids could be seen. This proves the potential of Raman spectroscopy for single fatty acid prediction in muscle tissue.

This study investigated the effects of supplementing the diet of a slow-growing autochthonous chicken breed with dehydrated or live Black Soldier Fly Larvae (BSFL) on meat quality and sensory attributes. The research, conducted at the University of Turin, Italy, involved 144 male birds distributed in three experimental groups. The control group (C) was fed a basal diet in which soybean meal was completely substituted with alternative ingredients. The 2 experimental groups were administered a diet identical to the control group but supplemented with either whole dehydrated black soldier fly larvae (DL) or whole live black soldier fly larvae (LL) at a level equal to 5% expected daily feed intake of dry matter. We evaluated the following parameters: nutrient intake, slaughtering performance, physical and nutritional meat quality, fatty acid composition, proteomics, and sensory characteristics. The results demonstrated BSFL supplementation to have no detrimental effects on overall meat quality or sensory attributes. Specifically, there were no significant differences in physical meat quality parameters, nutritional composition, lipid oxidation, or protein digestibility between control and BSFL-fed groups. Fatty acid analysis revealed higher concentrations of lauric and myristic acids in BSFL-fed chicken breast (p < 0.005), suggesting potential nutritional benefits from the supplement. The proteomic analysis also showed no significant differences in the expression of abundant proteins in the breast meat between groups, indicating minimal physiological impact of BSFL supplementation. Overall, this study provides reassurance to consumers and industries about the suitability of BSFL as a sustainable feed supplement for poultry that also offers potential benefits in terms of optimizing the fatty acid profile of chicken meat.

When vibrational spectroscopy is used for quantification purposes, multivariate analysis is often used to extract information from covariances between the spectra and any given reference values. In complex samples, there is a high risk that the constituents covary with each other. In such scenarios many methods may confuse the analytes and use signal from several analytes, rather than just the analyte of interest. While this allows the method to use more signal, and thus have a better effective signal-to-noise ratio, it also makes them less robust to changes to the chemical composition in the samples. This effect has been termed the cage of covariance. In order to avoid cage of covariance to affect predictive performances, it is highly important to have simple diagnostic tools to analyze and review this effect. Therefore, in the present paper, a systematic overview of tools for diagnosing and quantifying the cage of covariance in spectroscopic calibration models is provided. A collection of previously published methods with some expansions is provided, as well as two completely new tools: covariance ratio and virtual spiking. Practical applications of the tools on three different datasets are also shown.

Deploying a calibration based on near infrared NIR spectroscopy in a factory setting often includes first creating a calibration based on laboratory data and then a calibration based on data from the factory. This has been done in this work, where the aim was to establish in-line monitoring of fat content in drycured sausages directly after the sausage stuffer. The laboratory data was also used to create an industrial calibration, with factory data used for tuning and testing. This approach has the potential to reduce the required factory samples in similar scenarios if the performance of a model based on laboratory data is sufficient. The root mean square error of prediction of the final factory validation set was 0.9% for the laboratory model and 0.8% for the factory model. The calibrated NIR spectroscopy instrument was used in process to monitor the fat variation in the sausages over two periods of 2 days each. The in-line measurements revealed that there was 0.5% variation in fat content between different batches using the same raw materials, and 0.6% variation within batches. The strategy of deploying an in-line NIR spectroscopy instrument gives efficient insight into the process variation and clues about how it can be improved for better raw material utilization and more reliable end-product quality. In this case, there was a factory upgrade between the two measurement trials. The measurements showed that the factory upgrade resulted in a reduction of the standard deviation of the within-batch fat content from 0.60% to 0.32% (after taking the measurement error into account).