Animal feed production involves balancing nutritional quality, profitability and environmental sustainability. Although near-infrared spectroscopy (NIRS) is currently used for real-time quality control of feed ingredients, we demonstrate that NIRS can also predict their environmental sustainability in a resource-efficient way. We use NIRS to determine ingredient origins and combine these with global spatially-explicit life cycle assessment (LCA) to estimate environmental footprints. By incorporating ingredient prices and transport, we then optimize feeds towards the triple goals of quality, profitability and sustainability. We show 3.3-39% reductions in climate change and land stress impacts on biodiversity while reducing profitability by only 0.82-2.4% over current production and ensuring quality. Our approach provides a suite of optimal feed ratios and identifies footprint-profitability trade-offs, aiding decision-makers in moving towards more environmentally sustainable feed. We conclude that NIRS-LCA is a powerful combination for enhancing sustainability that can be extended beyond feed to food, fiber and other biobased commodities.

Many industries see a shifting focus towards performing on-site analysis using handheld spectroscopic devices. A determining factor for decision-making on the commissioning of these devices is available information on the potential performance of the device for specific applications. By now, myriad handheld solutions with very different specifications and pricing are available on the market. Although specifications are generally available for new devices, this does not directly quantify or predict how available devices will perform for targeted cases. We present a novel chemometric method to estimate the prediction performance of handheld NIR hardware and apply it to estimate the performance of two commercially available handheld NIR technologies in predicting protein content (ranging 120 e180 g kg(-1)) in pig feed from existing data of a benchtop device. Adjusting benchtop data to the wavelength range and resolution of the handheld device lead to over-optimistic estimates of the handheld performances. Our method additionally utilizes information on the error structure of the handheld devices for the estimation. It yielded performance estimates differing less than 1 g kg(-1) from the experimentally determined handheld performances and similar model parameters. Our method was effective for linear and nonlinear calibration algorithms, also when estimating performance after averaging multiple scans. Replicate spectra of twenty samples recorded using the handheld were required for replication error estimation to obtain an accurate performance estimation. The error structure could be reported by manufacturers in the future for this approach to be universally employed for predictive quantitative technology assessment. Overall, our method provides estimates of the performance of a handheld device for a specific task with minimal testing required and can thus be used as a device or application screening tool before committing to develop calibrations. (c) 2022 The Authors. Published by Elsevier B.V.

The long-term prediction performance of spectroscopic calibration models is a critical factor to monitor or control many production processes. Over time, new variations may emerge that deteriorate prediction performance. Therefore, models have to be maintained to retain or improve their prediction performance through time, requiring considerable resources and data. Maintenance should improve relevant predictions but also needs to be resource and cost efficient. Current approaches do not consider these trade-offs. We propose a new method to quantify the effectiveness and cost of model maintenance strategies based on historical data. Model performance over time for past, imminent and future samples is evaluated as these may react differently to maintenance. The model performance and required updating resources are translated into relative cost and benefit to compare strategies and determine optimal maintenance parameters. We used this method to evaluate a maintenance strategy that combines adding incoming samples to the calibration data with re-optimization of spectral preprocessing and modelling parameters. Continuously adding samples to the calibration data is shown to improve prediction performance and leads to more robust and generic models for emerging variations in all investigated data streams. Selectively adding incoming sample variations showed a reduced prediction performance but saves considerably in resources. Comparing model performance on the different sampling windows can also be used to determine an optimal updating frequency. This novel strategy to evaluate the expected performance and determine an optimal maintenance strategy is generally applicable and should lead to robust and consistently high prospective and/or retrospective model performance through time, which can be crucial for optimal operation and fault detection in industrial processes.

Preprocessing of near-infrared (NIR) spectra is an essential part of multivariate calibration. It mainly aims to remove artefacts caused during measurement to improve prediction performance or interpretation. However, preprocessing can have undesired side-effects. Additionally, calibration algorithms can learn to deal with artefacts by themselves when enough samples are available. This may influence the effect preprocessing has on prediction performance when the calibration dataset size increases. In this paper we investigate the interaction between the size of the calibration data and preprocessing for NIR calibrations for several datasets. Results show that extending the calibration data with more samples improves prediction performance, regardless of the preprocessing strategy. Although prediction performance almost always benefits from preprocessing, extending the calibration data can reduce the effect of preprocessing on prediction performance. This means the optimal preprocessing strategy may change as a function of the number of samples. It is demonstrated that using a Design of Experiments (DoE) approach to determine the optimal preprocessing strategy leads to equal or better prediction performance for all calibration set sizes compared to the case of not preprocessing at all. Preprocessing is most valuable for small calibration sets, but as the calibration set increases can become obsolete or even harmful. Therefore, we recommend to always evaluate the effect of a preprocessing strategy before making or updating calibration models.

Water-holding capacity is the ability of meat to hold moisture and is subject to postmortem metabolism. The objective of this study was to characterize the loss of moisture from muscle postmortem and investigate whether these losses are useful in predicting the ultimate drip loss of fresh pork. Cotton-rayon absorptive-based devices were inserted in the longissimus dorsi muscles of pork carcasses (n = 51) postmortem and removed at various intervals for 24h. Greatest moisture absorption was observed at 105 min post exsanguination. Drip loss varied (0.6-15.3%) across carcasses. Individual absorption at 75 min correlated (r = 0.33) with final drip loss. Correlations improved using individual absorption values at 90 min (r = 0.48) and accumulated absorption values at 150 min (r = 0.41). Results show that significant moisture is lost from muscle tissue early postmortem and suggest that capture of this moisture may be useful in predicting final drip loss of fresh meat.

Longissimus dorsi samples (685) collected at four processing plants were used to develop prediction equations for meat quality with near infrared spectroscopy. Equations with R(2)>0.70 and residual prediction deviation (RPD)≥2.0 were considered as applicable for screening. One production plant showed R(2) 0.76 and RPD 2.05, other plants showed R(2)<0.70 and RPD<2.0 for drip loss %. RPD values were ≤2.05 for drip loss%, for colour L*≤1.82 and pH ultimate (pHu)≤1.57. Samples were grouped for drip loss%; superior (<2.0%), moderate (2-4%), inferior (>4.0%). 64% from the superior group and 56% from the inferior group were predicted correctly. One equation could be used for screening drip loss %. Best prediction equation for L* did not meet the requirements (R(2) 0.70 and RPD 1.82). pHu equation could not be used. Results suggest that prediction equations can be used for screening drip loss %.

The objective was to study prediction of pork quality by near infrared spectroscopy (NIRS) technology in the laboratory. A total of 131 commercial pork loin samples were measured with NIRS. Predictive equations were developed for drip loss %, colour L*, a*, b* and pH ultimate (pHu). Equations with R(2)>0.70 and residual prediction deviation (RPD)≥1.9 were considered as applicable to predict pork quality. For drip loss% the prediction equation was developed (R(2) 0.73, RPD 1.9) and 76% of those grouped superior and inferior samples were predicted within the groups. For colour L*, test-set samples were predicted with R(2) 0.75, RPD 2.0, colour a* R(2) 0.51, RPD 1.4, colour b* R(2) 0.55, RPD 1.5 and pHu R(2) 0.36, RPD 1.3. It is concluded that NIRS prediction equations could be developed to predict drip loss% and L*, of pork samples. NIRS equations for colour a*, b* and pHu were not applicable for the prediction of pork quality on commercially slaughtered pigs.

Food coatings that remain after swallowing starch-based or CMC-based custard desserts were investigated for 19 subjects. Foods were orally processed for 5s using a pre-defined protocol, after which the food was swallowed. The remaining food coating was assessed sensorially as well as instrumentally using turbidity of rinse water. The instrumental and sensory results indicated a gradual decline of food coatings over intervals up to 180–270s. Decline rates of coatings of individual subjects related significantly to their decline rates in perceived fattiness.Decline rates were somewhat faster for the starch-based custards indicating a role of salivary amylase in clearance of starch-based foods. No evidence was found for mechanical clearance with tongue movements. In stead, decline rates of coating after swallowing were primarily determined by oral movements before swallowing, whereby intense oral movements produced relatively little oral coatings and relatively slow decline rates, and vice versa.