Fat marbling, the amount, and distribution of intramuscular fat, is an important quality trait for beef loin (Longissimus lumborum) and is closely connected to sensory properties such as tenderness, juiciness, and flavor. For meat producers, it would be of value to grade and sort whole loins according to marbling on the production line. The main goal of this study was to evaluate high-speed NIR hyperspectral imaging in interaction mode (760–1047 nm) for in-line measurement of sensory assessed marbling in both intact loins and loin slices. The NIR system was calibrated based on 28 whole striploins and 412 slices. Marbling scores were assessed for all slices on a scale from 1 to 9 by a trained sensory panel. The calibrated NIR system was tested for in-line measurements on 30 loins and 60 slices at a commercial meat producer. Satisfactory accuracy for prediction of marbling was obtained by partial least squares regression for both slices and whole loins (R² = 0.81 & 0.82, RMSEP = 0.95 & 0.88, respectively). The concentration of myoglobin in the meat and its state of oxygenation has a strong impact on the NIR spectra and can give deviations in the estimated marbling scores. This must be carefully considered in industrial implementation.

Refining approaches to measuring, monitoring and appraising animal welfare in aquaculture research is key to (i) protecting and optimizing it, (ii) documenting the severity of how and when it deviates, and (iii) ensuring good scientific quality, reliable results and reproducibility, amongst other factors. However, different fish species and life stages can have varying welfare needs and assessing their welfare can be challenging. An array of welfare indicators (WIs) can be utilized when documenting fish welfare, and there is currently little consensus on which WIs are most applicable to the key fish species used in European aquaculture research. The aim of this review is to propose updated, fit for purpose and comprehensive WI toolboxes for aquaculture research involving Atlantic salmon ( Salmo salar ), rainbow trout ( Oncorhynchus mykiss ), European seabass ( Dicentrarchus labrax ), gilthead seabream ( Sparus aurata ), and the common carp ( Cyprinus carpio ). Where possible, these toolboxes will also include life‐stage considerations. It also provides information on utilizing WIs in deciding humane end‐points as well as information on how to sample different types of indicators. The review closes with information on how digitalization can affect the collection, collation and analysis of WI data in aquaculture research, including both practical and theoretical considerations. The toolboxes incorporate a range of WIs that go beyond those required for legally safeguarding fish welfare in both laboratory and operational experimental facilities in the current European 2010/63/EU Directive on the protection of animals used for scientific purposes and its amendment, the Commission Delegated Directive (EU) 2024/1262.

Quality testing in the food industry is usually performed by manual sampling and at/off-line laboratory analysis, which is labor intensive, time consuming, and may suffer from sampling bias. For many quality attributes such as fat, water and protein, in-line near-infrared spectroscopy (NIRS) is a viable alternative to grab sampling. The aim of this paper is to document some of the benefits of in-line measurements at the industrial scale, including higher precision of batch estimates and improved process understanding. Specifically, we show how the decomposition of continuous measurements in the frequency domain, using power spectral density (PSD), may give a useful view of the process and serve as a diagnostic tool. The results are based on a case regarding the large-scale production of Gouda-type cheese, where in-line NIRS was implemented to replace traditional laboratory measurements. In conclusion, the PSD of in-line NIR predictions revealed unknown sources of variation in the process that could not have been discovered using grab sampling. PSD also gave the dairy more reliable data on key quality attributes, and laid the foundation for future improvements.

It is important to utilize the entire animal in meat and fish production to ensure sustainability. Rest raw materials, such as bones, heads, trimmings, and skin, contain essential nutrients that can be transformed into high-value products. Enzymatic protein hydrolysis (EPH) is a bioprocess that can upcycle these materials to create valuable proteins and fats. This paper focuses on the role of spectroscopy and chemometrics in characterizing the quality of the resulting protein product and understanding how raw material quality and processing affect it. The article presents recent developments in chemical characterisation and process modelling, with a focus on rest raw materials from poultry and salmon production. Even if some of the technology is relatively mature and implemented in many laboratories and industries, there are still open challenges and research questions. The main challenges are related to the transition of technology and insights from laboratory to industrial scale, and the link between peptide composition and critical product quality attributes.

Conditional Independence (CI) testing is fundamental in statistical analysis. For example, CI testing helps validate causal graphs or longitudinal data analysis with repeated measures in causal inference. CI testing is difficult, especially when testing involves categorical variables conditioned on a mixture of continuous and categorical variables. Current parametric and non-parametric testing methods are designed for continuous variables and can quickly fall short in the categorical case. This paper presents a computational approach for CI testing suited for categorical data types, which we call computational conditional independence (CCI) testing. The test procedure is based on permutation and combines machine learning prediction algorithms and Monte Carlo cross-validation. We evaluated the approach through simulation studies and assessed the performance against alternative methods: the generalized covariance measure test, the kernel conditional independence test, and testing with multinomial regression. We find that the computational approach to testing has utility over the alternative methods, achieving better control over type I error rates. We hope this work can expand the toolkit for CI testing for practitioners and researchers.

Problems concerning covariance among independent variables are well understood and dealt with by inverse regression methods like partial least squares regression. However, covariance between dependent variables has only received minor attention. Biological samples are often complex mixtures of multiple covarying compounds. During multivariate calibration, analyte predictions may be mediated through relationships with interfering compounds, which implies that the calibration model is not providing a direct link between the multivariate measurements and the analyte of interest. This compromises robustness and validity of the calibration model—important aspects when applying the model to future samples and data sets. This study discusses issues of calibration modeling when strong covariance structures exist among the analyte of interest and interfering compounds.We propose a projection‐based method to diagnose whether indirect covariance structures dominate the calibration model. The proposed method is tested on a two‐constituent Beer's law system consisting of 20 aqueous samples with covarying amounts of fructose (analyte of interest) and riboflavin (interfering compound). Transmission measurements are obtained on all samples in the visual and near‐infrared wavelength ranges. Riboflavin has strong absorption in the visual region, whereas fructose exclusively absorbs in the near‐infrared region. Hence, predictions of fructose concentrations, obtained from the visual wavelength range only, are fully mediated through riboflavin, whereas fructose predictions obtained from the near‐infrared wavelength range may be obtained independent of riboflavin.