In recent years, very many incidences of contamination with aflatoxin B₁ (AFB₁) in pistachio nuts have been reported as a major global problem for the crop. In Europe, legislation is in force and 12 μg/kg of AFB₁ is the maximum limit set for pistachios to be subjected to physical treatment before human consumption. The goal of the current study was to develop a mechanistic, weather-driven model to predict Aspergillus flavus growth and the AFB₁ contamination of pistachios on a daily basis from nut setting until harvest. The planned steps were to: (i) build a phenology model to predict the pistachio growth stages, (ii) develop a prototype model named AFLA-pistachio (model transfer from AFLA-maize), (iii) collect the meteorological and AFB₁ contamination data from pistachio orchards, (iv) run the model and elaborate a probability function to estimate the likelihood of overcoming the legal limit, and (v) manage a preliminary validation. The internal validation of AFLA-pistachio indicated that 75% of the predictions were correct. In the external validation with an independent three-year dataset, 95.6% of the samples were correctly predicted. According to the results, AFLA-pistachio seems to be a reliable tool to follow the dynamic of AFB₁ contamination risk throughout the pistachio growing season.

Aflatoxins are highly toxic fungal secondary metabolites that often contaminate food and feed commodities. An electrochemical immunosensor for the determination of aflatoxin B₁ (AFB₁) was fabricated by immobilizing monoclonal AFB₁ antibodies onto a screen-printed gold electrode that was modified with carbo-methyldextran by N-(3-dimethylaminopropyl)-N′-ethylcarbodiimide hydrochloride/N-hydroxysuccinimide cross-linking. An electrochemical interfacial modelling of biomolecular recognition was suggested and reasonably interpreted. Impedance technology was employed for the quantitative determination of AFB₁. The limit of detection concentration of AFB₁ for standard solutions and spiked pistachio samples was 0.5 ng/mL and 1 ng/mL, respectively. The immunosensor was able to successfully determine AFB₁ concentrations in the range of 4.56–50.86 ng/mL in unknown pistachio samples. Comparative chromatographic analysis revealed that AFB₁ concentrations that were higher than 345 ng/mL were not within the immunosensor’s upper limits of detection. Selectivity studies against Ochratoxin A and Aflatoxin M₁ demonstrated that the proposed AFB₁ immunosensor was able to differentiate between these other fungal mycotoxins. The novel electrochemical immunosensor approach has the potential for rapid sample screening in a portable, disposable format, thus contributing to the requirement for effective prevention and the control of aflatoxin B₁ in pistachios.

Various diseases and pests cause serious damage to vegetable crops during the growing season and after harvesting. Growers attempt to minimize losses by protecting their crops, starting with seed and seedling treatments and followed by monitoring their stands. In many cases, synthetic pesticide treatments are applied. Integrated pest management is currently being employed to minimize the impact of pesticides upon human health and the environment. Over the last few years, “smart” approaches have been developed and adopted in practice to predict, detect, and quantify phytopathogen occurrence and contamination. Our review assesses the currently available ready-to-use tools and methodologies that operate via visual estimation, the detection of proteins and DNA/RNA sequences, and the utilization of brand-new innovative approaches, highlighting the availability of solutions that can be used by growers during the process of diagnosing pathogens.