S3 and Recovery and Resilience Funds: A Case Study Built on the Experience of 10 Spanish Regions.

The EU's response to the COVID-19 crisis, namely the approval of the Next Generation package, provides an opportunity to explore to what extent the existing Smart Specialisation regional strategies and related ecosystems have been taken into account in the highly relevant territorial context in which the national Recovery Plans have been designed. According to our results the potential of the Smart Specialisation approach (S3) in relation with its place-based strategic prioritisation may have been overlooked in the process. The research is based on a desk review of relevant documents and recent literature in this field; followed by semi-structured interviews with regional planners and practitioners from 10 Spanish regions (autonomous communities); complemented, in a second phase, by the organisation of a focus group to validate the initial results. During our research we identified the main contributions that the Smart Specialisation approach has so far made to the regions (mainly in terms of participative governance and creation of regional ecosystems); and the unanimous perception shared by all the practitioners interviewed that the S3 approach has led to a change of vision in public intervention. However, all of the interviewed regions have confirmed that the drafting of the national recovery and resilience plan lacked an ex-ante alignment with the regional S3 strategies, and failed to consider the existing regional S3 ecosystems. The separation of the recovery logic (based on the operation of public consultations at national level to identify strategic projects) from the S3 logic (based on a strategic prioritisation exercise conducted by each regional ecosystem) confirms that an opportunity may have been missed in the recovery planning process to consolidate the multi-actor, multilevel and place-based S3 approach. Although there is a certain degree of disappointment among regional practitioners as a result of this misalignment, the majority of them believe in the possibility of an ex-post alignment between the two processes, that can protect existing regional shared visions. However, without clear recognition of the S3 ecosystems and the S3 managing bodies, the significant role that Smart Specialisation could play in the recovery process may be at risk.

Evaluation of local approaches to obtain accurate near-infrared (NIR) equations for prediction of ingredient composition of compound feeds.

This research work investigated new methods to improve the accuracy of intact feed calibrations for the near-infrared (NIR) prediction of the ingredient composition. When NIR reflection spectroscopy, together with linear models, was used for the prediction of the ingredient composition, the results were not always acceptable. Therefore, other methods have been investigated. Three different local methods (comparison analysis using restructured near-infrared and constituent data [CARNAC]), locally weighed regression [LWR], and LOCAL) were applied to a large (N = 20 320) and heterogeneous population of non-milled feed compounds for the NIR prediction of the inclusion percentage of wheat and sunflower meal, as representative of two different classes of ingredients. Compared with partial least-squares regression, results showed considerable reductions of standard error of prediction values for all methods and ingredients: reductions of 59, 47, and 50% with CARNAC, LWR, and LOCAL, respectively, for wheat, and reductions of 49, 45, and 43% with CARNAC, LWR, and LOCAL, respectively, for sunflower meal. These results are a valuable achievement in coping with legislation and manufacture requirements concerning the labeling of intact feedstuffs.

Pixel selection for near-infrared chemical imaging (NIR-CI) discrimination between fish and terrestrial animal species in animal protein by-product meals.

This paper proposes a method based on near-infrared hyperspectral imaging for discriminating between terrestrial and fish species in animal protein by-products used in livestock feed. Four algorithms (Mahalanobis distance, Kennard-Stone, spatial interpolation, and binning) were compared in order to select an appropriate subset of pixels for further partial least squares discriminant analysis (PLS-DA). The method was applied to a set of 50 terrestrial and 40 fish meals analyzed in the 1000-1700 nm range. Models were then tested using an external validation set comprising 45 samples (25 fish and 20 terrestrial). The PLS-DA models obtained using the four subset-selection algorithms yielded a classification accuracy of 99.80%, 99.79%, 99.85%, and 99.61%, respectively. The results represent a first step for the analysis of mixtures of species and suggest that NIR-CI, providing valuable information on the origin of animal components in processed animal proteins, is a promising method that could be used as part of the EU feed control program aimed at eradicating and preventing bovine spongiform encephalopathy (BSE) and related diseases.

Feasibility of diode-array instruments to carry near-infrared spectroscopy from laboratory to feed process control.

Near-infrared calibrations were developed for the instantaneous prediction of the chemical and ingredient composition of intact compound feeds. Two rather different instruments were compared (diode array vs grating monochromator). The grating monochromator was used in a static mode in the laboratory, whereas the diode-array instrumentbetter adapted to online analysiswas placed on a conveyor belt to simulate measurements at a feed mill plant. Modified partial least squares (MPLS) equations were developed using the same set of samples analyzed in the two instruments. Sample set 1 ( N = 398) was used to predict crude protein (CP) and crude fiber (CF), while sample set 2 ( N = 393) was used for the prediction of one macroingredient (sunflower meal, SFM) and one microingredient (mineral-vitamin premix, MVP). The standard error of cross-validation (SECV) and the coefficient of determination (R2) values for CF were better using the monochromator instrument. However, results obtained for CP, SFM, and MVP using the samples analyzed in the diode-array instrument showed similar or even greater accuracy than those obtained using samples analyzed in the grating monochromator. The excellent predictive ability [R2> 0.95; RPD (ratio of standard deviation to SECV) > 3] obtained for CP, CF, and SFM opens the way for the online use of NIRS diode-array instruments for surveillance and monitoring in the manufacture, processing, and marketing of compound feeds. R2, RPD, and SECV values for MVP showed similar performance for both instruments. Although RPD values did not reach the minimum recommended for quantitative analysis, results are encouraging for an ingredient present in feed compounds in such very low amounts.

Near-infrared reflectance spectroscopy for predicting amino acids content in intact processed animal proteins.

Near-infrared calibrations were developed for the instantaneous prediction of amino acids composition of processed animal proteins (PAPs). Two sample presentation modes were compared (ground vs intact) for demonstrating the viability of the analysis in the intact form, avoiding the need for milling. Modified partial least-squares (MPLS) equations for the prediction of amino acids in PAPs were developed using the same set of samples (N = 92 PAPs) analyzed in ground and intact form and in three cups differing in the optical window size. The standard error for cross validation (SECV) and the coefficient of determination (1-VR) values yielded with the calibrations developed using the samples analyzed in the intact form showed similar or even better accuracy than those obtained with finely ground samples. The excellent predictive ability (1-VR > 0.90; CV < 3.0%) obtained for the prediction of amino acids in intact processed animal proteins opens an enormous expectative for the on-line implementation of NIRS technology in the processing and marketing of these important protein feed ingredients, alleviating the costs and time associated with the routine quality controls.