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In this study, an innovative odor imaging system capable of detecting adulteration in quince seed edible oils mixed with sunflower oil and sesame oil based on their volatile organic compound (VOC) profiles was developed. The system comprises a colorimetric sensor array (CSA), a data acquisition unit, and a machine learning algorithm for identifying adulterants. The CSA was created using a method that involves applying a mixture of six different pH indicators (methyl violet, chlorophenol red, Nile blue, methyl orange, alizarin, cresol red) onto a Thin Layer Chromatography (TLC) silica gel plate. Subsequently, difference maps were generated by subtracting the "initial" image from the "final" image, with the resulting color changes being converted into digital data, which were then further analyzed using Principal Component Analysis (PCA). Following this, a Support Vector Machine was employed to scrutinize quince seed oil that had been adulterated with varying proportions of sunflower oil and sesame oil. The classifier was progressively supplied with an increasing number of principal components (PCs), starting from one and incrementally increasing up to five. Each time, the classifier was optimized to determine the hyperparameters utilizing a random search algorithm. With one to five PCs, the classification error accounted for a range of 37.18% to 1.29%. According to the results, this novel system is simple, cost-effective, and has potential applications in food quality control and consumer protection. [ABSTRACT FROM AUTHOR]

In many European rural areas, agriculture is not only an economic activity, but it is strictly linked to environmental and social characteristics of the area. Thus, sometimes, a pathogen can become a social threat, as in the case of Xylella fastidiosa and olive trees (Olea europaea L.) in Salento. Fast and systemic response to threats represents the key to success in stopping pest invasions, and proves a great help in managing lots of data in a short time or coordinating large-scale monitoring coming from applying Information Technology tools. Regarding the field of applications, the advantages provided by new technologies are countless. However, is it the same in agriculture? Electronic identification tools can be applied for plant health management and certification. Treatments, agrochemical management or impact assessment may also be supported by dematerialization of data. Information Technology solution for urban forestry management or traceability of commodities belonging to "Food from Somewhere" regimes were analyzed and compared to protection from pests of a unique tree heritage such as olive trees in Salento. [ABSTRACT FROM AUTHOR]

This paper investigates the influences of several limiting factors on the performance of ground penetrating radar (GPR) in accurately detecting huanglongbing (HLB)-infected citrus roots and determining their main structural characteristics. First, single-factor experiments were conducted to evaluate GPR performance. The factors that were evaluated were (i) root diameter; (ii) root moisture level; (iii) root depth; (iv) root spacing; (v) survey angle; and, (vi) soil moisture level. Second, two multi-factor field experiments were conducted to evaluate the performance of the GPR in complex orchard environments. The GPR generated a hyperbola in the radar profile upon root detection; the diameter of the root was successfully determined according to the width of the hyperbola when the roots were larger than 6 mm in diameter. The GPR also distinguished live from dead roots, a capability that is indispensable for studying the effects of soil-borne and other diseases on the citrus tree root system. The GPR can distinguish the roots only if their horizontal distance is greater than 10 cm and their vertical distance is greater than 5 cm if two or more roots are in proximity. GPR technology can be applied to determine the efficacy of advanced crop production strategies, especially under the pressures of disease and environmental stresses. [ABSTRACT FROM AUTHOR]