Control and inspection operations within the context of safety and quality assessment of bulk foods and feeds are not only of particular importance, they are also demanding challenges, given the complexity of food/feed production systems and the variability of product properties. Existing methodologies have a variety of limitations, such as high costs of implementation per sample or shortcomings in early detection of potential threats for human/animal health or quality deviations. Therefore, new proposals are required for the analysis of raw materials in situ in a more efficient and cost-effective manner. For this purpose, a pilot laboratory study was performed on a set of bulk lots of animal by-product protein meals to introduce and test an approach based on near-infrared (NIR) spectroscopy and geostatistical analysis. Spectral data, provided by a fiber optic probe connected to a Fourier transform (FT) NIR spectrometer, were used to predict moisture and crude protein content at each sampling point. Variographic analysis was carried out for spatial structure characterization, while ordinary Kriging achieved continuous maps for those parameters. The results indicated that the methodology could be a first approximation to an approach that, properly complemented with the Theory of Sampling and supported by experimental validation in real-life conditions, would enhance efficiency and the decision-making process regarding safety and adulteration issues.

El objetivo fue caracterizar y tipificar un grupo de explotaciones de dehesa asociadas a una cooperativa de cebo, sacrificio y comercialización de terneros, analizando los sistemas de producción de las distintas tipologías de explotaciones. Se realizaron 114 encuestas, en las que se recolectó información sobre la mano de obra, la base animal, la base territorial y el manejo del ganado vacuno en las explotaciones. Se utilizaron estadísticos descriptivos y análisis multivariantes para definir las relaciones entre las variables y establecer tipologías de explotaciones. En general, las explotaciones disponen de una superficie pequeña (224 ha), predominando la tierra en propiedad (73 %) y la mano de obra familiar (61 %). La mayoría de las explotaciones combinan varias especies ganaderas, destacando las asociaciones vacuno-porcino ibérico (53 %) y vacuno-ovino-porcino ibérico (25 %). La intensificación de la producción es habitual, como se refleja en la superficie cultivada anualmente (47 % de la tierra arable) y en la carga ganadera (0.73 unidades de ganado mayor/ha). Se observa una gran variabilidad entre explotaciones. Se establecieron cuatro tipologías en función de su tamaño, sus estrategias de diversificación de la producción y sus prácticas de manejo. La tipología más numerosa es la formada por las explotaciones más pequeñas (122 ha). La mayoría de las explotaciones grandes de la zona no participan en la cooperativa, o la han abandonado, porque tienen más facilidad que las pequeñas en seguir una estrategia de venta o cebo de los terneros a título individual.

Compliance checks at reception of olive oil in bulk before unloading is an essential step for packing plants to meet quality standards and ensure traceability. Nevertheless, classic procedures based on the withdrawal of samples followed by at-line analysis need to be improved. Near-infrared spectroscopy features can make it an ideal technology to enhance efficiency and decision-making processes. This article presents a new approach whose main pillar is the automated use of a fiber-optic sensor to sample and analyse olive oils in bulk, directly in the tank before the downloading at the reception point. Moreover, a preliminary assessment of the sensor performance is also reported.

The control of conformity at reception of bulk olive oil before unloading is an essential step for packing plants to meet quality standards and ensure traceability. New approaches, different to sampling and analysis at-line, are needed to improve efficiency and decision-making processes. Near-infrared spectroscopy (NIRS) provides fast, cost-effective and in situ analytical measurements without sample preparation, which makes it an ideal technology towards this goal. The purpose of this work is to assess and optimize the spectral acquisition with a fiber-optic probe, especially designed to sample and analyze tank trucks. This probe features two fiber optic bundles, one of measurement and one of illumination. First of all, two olive oils samples, one of extra virgin and one of lampante category, were used to evaluate the noise and repeatability of the NIRS spectral signals of the target probe. A set of 20 spectra were recorded for each sample, and the sequence of measurements lampante-extra virgin was repeated three times per day for seven days spread over five weeks. The noise level was evaluated using a first derivative pre-treatment, and the optimum working range (after removing the noisy regions) was 1150-2149 nm. The Root Mean Square (RMS) statistic and the MEAN values were calculated in each case, and the spectral repeatability results showed that the RMS (MEAN) values for lampante were higher than for extra virgin olive oil (12,630 and 2998 µlog(1/R), respectively). Furthermore, a standardization procedure was performed to make a comparative study with a laboratory probe. Both probes were connected to a MATRIX-F Fourier transform-NIR instrument. A set of 42 samples of different nature were used for standardization purposes. Two matrices of standardization were developed, selecting 1 (STD1) and 10 (STD2) samples, and a validation set of 32 samples was used. The spectral adjustment was evaluated using the validation set by calculating the RMS(c) statistic. The RMS(c) values obtained in the master (laboratory probe, LP) versus satellite (in situ analysis probe, IAP) before standardization showed important differences that were significantly corrected by the best standardization matrix (STD1).

Feed grains are typically transported in bulk and a statistically representative sample of the grain in the truckload is usually required to be taken to the laboratory for wet chemistry or at-line near infrared (NIR) spectroscopy analysis. Currently, most methodologies make use of a physical sampling probe, which mechanically or pneumatically withdraws samples from various depths. Nevertheless, not only is the implementation of this approach expensive and time-consuming, but it is also limited by low sample throughput. In this context, the authors' group is involved in a large research and development project to find more efficient and cost-effective ways of sampling and analyzing bulk raw materials at the reception level. This work presents a piece of this research focused on the evaluation of the optical performance of two fiber-optic probes designed for automated use as immersion probes in truckloads. It is worth noting the rather different optical design of these two diffuse reflectance probes. Probe A features eight bundles (37 fibers/bundle), four for measurement and four for illumination, 0.5 m in length, and four sapphire windows located around the probe diameter. Probe B has one fiber-optic bundle for measurement (7 fibers) and one for illumination (19 fibers), 3 m in length, and a stainless-steel head with two sapphire windows. The experimental design of this laboratory study aimed at imitating the control of bulk lots of two sort of cereals (maize and wheat). For this purpose, a sample of each cereal was placed into a container (0.34 m in width, 0.4 m in length and 0.25 in height) for analysis. To avoid interferences caused by design, both probes were attached to the same Fourier transform-NIR instrument (Matrix-F, Bruker Optics), and spectra were acquired in the range 834.2-2502.4 nm using the same settings. Two different strategies for recording reference spectra were followed in each case (before the first scan and either after every measurement or after every set of 10 measurements). Noisy regions and spectral repeatability were assessed as a first step towards the evaluation of the feasibility of these probes for performing on-site analysis.