Land use at landscape and field scales can increase the diversity and abundance of natural enemies for pest control. In this study, we investigated interactions between landscape elements (semi-natural vegetation, olive orchards, vineyards, other agricultural areas) and inter-row management (vegetation cover vs. bare soil) in relation to arthropod populations in Andalusian vineyards. Arthropods were collected from grapevine foliage in 15 vineyards using suction sampling. Landscape structure was analyzed within a 750 m radius surrounding the studied vineyards. Arthropods were categorized into functional groups (predators, parasitoids, herbivores), and their responses to the most influencing factors were analyzed by likelihood methods and model selection. Of the total of 650 arthropods collected, 48% were predators, 33% herbivores and 19% parasitoids. Numbers of predatory aeolothrips, parasitoids and herbivorous cicadas in the study vineyards decreased with an increased proportion of vineyards in the surroundings. Spider populations in vineyards increased with increasing proportions of other agricultural fields (non-flowering crops) in the surroundings. Semi-natural elements and olive orchards had no influence on the abundance of collected arthropods. We observed synergistic effects between landscape elements and inter-row management. The total numbers of arthropods, herbivores and parasitoids in vineyards benefitted from inter-row vegetation, while spiders benefitted from bare soil. Our findings underline the importance of both surrounding landscape elements and vineyard ground cover management to promote beneficial arthropods for potential natural pest control.

Food fraud is a major threat to the integrity of the nut supply chain. Strategies using a wide range of analytical techniques have been developed over the past few years to detect fraud and to assure the quality, safety, and authenticity of nut products. However, most of these techniques present the limitations of being slow and destructive and entailing a high cost per analysis. Nevertheless, near-infrared (NIR) spectroscopy and NIR imaging techniques represent a suitable non-destructive alternative to prevent fraud in the nut industry with the advantages of a high throughput and low cost per analysis. This review collects and includes all major findings of all of the published studies focused on the application of NIR spectroscopy and NIR imaging technologies to detect fraud in the nut supply chain from 2018 onwards. The results suggest that NIR spectroscopy and NIR imaging are suitable technologies to detect the main types of fraud in nuts.

A common fraud in the sweet almond industry is the presence of bitter almonds in commercial batches. The presence of bitter almonds not only causes unpleasant flavours but also problems in the commercialisation and toxicity for consumers. Hyperspectral Imaging (HSI) has been proved to be suitable for the rapid and non-destructive quality evaluation in foods as it integrates the spectral and spatial dimensions. Thus, we aimed to study the feasibility of using an HSI system to identify single bitter almond kernels in commercial sweet almond batches. For this purpose, sweet and bitter almond batches, as well as different mixtures, were analysed in bulk using an HSI system which works in the spectral range 946.6–1648.0 nm. Qualitative models were developed using Partial Least Squares-Discriminant Analysis (PLS-DA) to differentiate between sweet and bitter almonds, obtaining a classification success of over the 99%. Furthermore, data reduction, as a function of the most relevant wavelengths (VIP scores), was applied to evaluate its performance. Then, the pixel-by-pixel validation of the mixtures was carried out, identifying correctly between 61–85% of the adulterations, depending on the group of mixtures and the cultivar analysed. The results confirm that HSI, without VIP scores data reduction, can be considered a promising approach for classifying the bitterness of almonds analysed in bulk, enabling identifying individual bitter almonds inside sweet almond batches. However, a more complex mathematical analysis is necessary before its implementation in the processing lines.

Failure of passive immunity transfer is one of the main causes of increased susceptibility to infectious agents in newborn kids. To ensure successful transfer of passive immunity, kids need to be fed high-quality colostrum, containing an adequate concentration of IgG. This work evaluated the quality of colostrum obtained in the first 3 days postpartum from Malagueña dairy goats. The IgG concentration in colostrum was measured using an ELISA as a reference method, and it was estimated by optical refractometer. Colostrum composition in terms of fat and protein was also determined. The mean concentration of IgG was 36.6 ± 2.3 mg/mL, 22.4 ± 1.5 mg/mL and 8.4 ± 1.0 mg/mL on days 1, 2 and 3 after parturition, respectively. Brix values obtained using the optical refractometer were 23.2%, 18.6% and 14.1% for days 1, 2 and 3, respectively. In this population, 89% of goats produced high-quality colostrum with IgG concentrations of >20 mg/mL on the day of parturition, but this percentage declined dramatically over the following 2 days. The quality of the fresh colostrum estimated with the optical refractometer was positively correlated with those obtained using ELISA (r = 0.607, p = 0.001). This study highlights the importance of feeding first-day colostrum to newborn kids and demonstrates that the optical Brix refractometer is suitable for the on-farm estimation of IgG content in colostrum.

This study was carried out using a spectral database consisting of 394 samples of fresh total mixed ration (TMR) from dairy farms located at Northern Spain. Cloning sets of different size and structure were evaluated for the transfer of the large TMR spectral database obtained on a Foss NIRSystems monochromator to 2 different portable near-infrared devices: one diode array instrument and another based on linear variable filters. The cloning matrix that produced the best matching between instruments was then used to transfer the TMR spectral library to the 2 portable instruments. Once the database had been transferred, calibration equations were developed to compare the predictive ability of the equations obtained in the benchtop and portable instruments. In comparison with the monochromator predictive ability, the calibration equations developed with the near-infrared portable instruments displayed a high and similar accuracy for most of the studied parameters related to TMR composition, enabling their use for predicting TMR quality at the farm level.

Cheese quality is affected by seasonal variations. These variations can influence several aspects of cheese, including its flavor, texture, nutritional content, and overall sensory qualities. The aim of this study was to assess the performance of near-infrared (NIR) instrumentation in terms of its ability to detect seasonal variations in Halloumi cheese samples when applying limited sample preparation compared to traditional protocols. Therefore, the use of NIR spectroscopy was examined for the determination of seasonal variations in Halloumi cheese samples from Cyprus in combination with chemometrics. Partial Least Squares Discriminant Analysis (PLS-DA) was applied. We found that NIR and chemometrics successfully discriminated the Halloumi cheese samples based on different climate conditions, the four seasons in the year when the milk collection took place. To externally validate the model, the dataset was divided into training and test sets. The innovation of this study is that Halloumi cheese was studied regarding seasonal variations by applying NIR for the first time. The outcome of this preliminary study is positive in terms of the capability of NIR to distinguish seasonal variations in Halloumi cheese, especially those due to differences in fatty acid molecules throughout the year. Future studies will include more samples to increase the current database.