Genetic characterization of chestnut (Castanea sativa Mill.) orchards and traditional nut varieties in El Bierzo, a glacial refuge and major cultivation site in Northwestern Spain

This paper presents a detailed genetic study of Castanea sativa in El Bierzo, a major nut production region with interesting features. It is located within a glacial refuge at one extreme of the distribution area (northwest Spain); it has a centenary tradition of chestnut management; and more importantly, it shows an unusual degree of genetic isolation. Seven nuclear microsatellite markers were selected to analyze the genetic variability and structure of 169 local trees grafted for nut production. We analyzed in the same manner 62 local nuts. The selected loci were highly discriminant for the genotypes studied, giving a combined probability of identity of 6.1 × 10−6. An unprecedented density of trees was sampled for this project over the entire region, and nuts were collected representing 18 cultivars marketed by local producers. Several instances of misclassification by local growers were detected. Fixation index estimates and analysis of molecular variance (AMOVA) data are supportive of an unexpectedly high level of genetic differentiation in El Bierzo, larger than that estimated in a previous study with broader geographical scope but based on limited local sampling (Pereira-Lorenzo et al., Tree Genet Genomes 6: 701–715, 2010a). Likewise, we have determined that clonality due to grafting had been previously overestimated. In line with these observations, no significant spatial structure was found using both a model-based Bayesian procedure and Mantel’s tests. Taken together, our results evidence the need for more fine-scale genetic studies if conservation strategies are to be efficiently improved.

Hyperspectral to multispectral imaging for detection of tree nuts and peanut traces in wheat flour

In current industrial environments there is an increasing need for practical and inexpensive quality control systems to detect the foreign food materials in powder food processing lines. This demand is especially important for the detection of product adulteration with traces of highly allergenic products, such as peanuts and tree nuts. Manufacturing industries dealing with the processing of multiple powder food products present a substantial risk for the contamination of powder foods with traces of tree nuts and other adulterants, which might result in unintentional ingestion of nuts by the sensitised population. Hence, the need for an in-line system to detect nut traces at the early stages of food manufacturing is of crucial importance. In this present work, a feasibility study of a spectral index for revealing adulteration of tree nut and peanut traces in wheat flour samples with hyperspectral images is reported. The main nuts responsible for allergenic reactions considered in this work were peanut, hazelnut and walnut. Enhanced contrast between nuts and wheat flour was obtained after the application of the index. Furthermore, the segmentation of these images by selecting different thresholds for different nut and flour mixtures allowed the identification of nut traces in the samples. Pixels identified as nuts were counted and compared with the actual percentage of peanut adulteration. As a result, the multispectral system was able to detect and provide good visualisation of tree nut and peanut trace levels down to 0.01% by weight. In this context, multispectral imaging could operate in conjuction with chemical procedures, such as Real Time Polymerase Chain Reaction and Enzyme-Linked Immunosorbent Assay to save time, money and skilled labour on product quality control. This approach could enable not only a few selected samples to be assessed but also to extensively incorporate quality control surveyance on product processing lines.

Hyperspectral to multispectral imaging for detection of tree nuts and peanut traces in wheat flour.

In current industrial environments there is an increasing need for practical and inexpensive quality control systems to detect the foreign food materials in powder food processing lines. This demand is especially important for the detection of product adulteration with traces of highly allergenic products, such as peanuts and tree nuts. Manufacturing industries dealing with the processing of multiple powder food products present a substantial risk for the contamination of powder foods with traces of tree nuts and other adulterants, which might result in unintentional ingestion of nuts by the sensitised population. Hence, the need for an in-line system to detect nut traces at the early stages of food manufacturing is of crucial importance. In this present work, a feasibility study of a spectral index for revealing adulteration of tree nut and peanut traces in wheat flour samples with hyperspectral images is reported. The main nuts responsible for allergenic reactions considered in this work were peanut, hazelnut and walnut. Enhanced contrast between nuts and wheat flour was obtained after the application of the index. Furthermore, the segmentation of these images by selecting different thresholds for different nut and flour mixtures allowed the identification of nut traces in the samples. Pixels identified as nuts were counted and with the actual percentage of peanut adulteration. As a result, the multispectral system was able to detect and provide good visualisation of tree nut and peanut trace levels down to 0.01% by weight. In this context, multispectral imaging could operate in conjuction with chemical procedures, such as Real Time Polymerase Chain Reaction and Enzyme-Linked Immunosorbent Assay to save time, money and skilled labour on product quality control. This approach could enable not only a few selected samples to be assessed but also to extensively incorporate quality control surveyance on product processing lines.