Management Control Points Related to the Lag Phase of Fungal Growth in a Stored Rapeseed Ecosystem

Identification and Safety Evaluation of Ochratoxin A Transformation Product in Rapeseed Oil Refining Process

Ochratoxin A (OTA) is an important mycotoxin detected in edible oil, and it can be effectively removed by classical edible oil refining processes. However, the fate of OTA in the refining process has not been reported. In this study, we systematically tracked the OTA changes during the oil refining process by fortifying 100 μg/kg OTA in crude rapeseed oil. Results showed that about 10.57%, 88.85%, and 0.58% of OTA were removed during the degumming, deacidification, and decolorization processes. Among them, 16.25% OTA was transferred to the byproducts, including 9.85% in degumming wastewater, 5.68% in soap stock, 0.14% in deacidification wastewater, and 0.58% in the decolorizer; 83.75% OTA was found to transform into the lactone ring opened OTA (OP-OTA) during the deacidification stage, which is attributed to the hydrolysis of the lactone ring of OTA in the alkali refining. The OP-OTA was verified to distribute in the soap stock, and small amounts of OP-OTA could be transferred to deacidified wastewater when the OTA pollution level reached 500 μg/kg in crude rapeseed oil. The OP-OTA exhibited strong toxicity, especially nephrotoxicity, as reflected by the cell viability assay and in silico toxicity. Therefore, the safety of the soap stock processing products from OTA-contaminated rapeseed deserves attention.

An endeavor of "deep-underground agriculture": storage in a gold mine impacts the germination of canola (Brassica napus L.) seeds

Exploring and utilizing the agronomic potential of deep-underground is one of the ways to cope with the challenges of sudden environmental change on agriculture. Understanding the effects of environmental stresses on the morphological and physiological indicators of crop seeds after their storage deep-underground is crucial to developing and implementing strategies for agriculture in the deep-underground space. In this study, we stored canola seeds in tunnels with horizontal depths of 0, 240, 690, and 1410 m in a gold mine. Seeds in envelopes were retrieved at 42, 66, 90, and 227 days of storage, whereas seeds in sealed packages were retrieved at 66 and 227 days of storage. The germination tests were conducted to investigate the effects of storage depth, duration, and packing method on stored and non-stored seeds. Results showed that increased depth and duration reduced seed germination rate, with the germination and vigor indexes also descending to varying degrees. Increased hypocotyl length and biomass accumulation suggested that deep-underground environment had a more significant compensatory effect on seed germination. For all indicators, the performance of seeds sealed in packages was superior to those stored in envelopes. Regression analysis showed that it was difficult to obtain the optimal value of each indicator simultaneously. The successful germination experiment foreshadowed the possibilities of deep-underground agriculture in the future.

Predictive Models of Phytosterol Degradation in Rapeseeds Stored in Bulk Based on Artificial Neural Networks and Response Surface Regression

The need to maintain the highest possible levels of bioactive components contained in raw materials requires the elaboration of tools supporting their processing operations, starting from the first stages of the food production chain. In this study, artificial neural networks (ANNs) and response surface regression (RSR) were used to develop models of phytosterol degradation in bulks of rapeseed stored under various temperatures and water activity conditions (T = 12–30 °C and a_w = 0.75–0.90). Among ANNs, networks based on a multilayer perceptron (MLP) and a radial basis function (RBF) were tested. The model input constituted a_w, temperature and storage time, whilst the model output was the phytosterol level in seeds. The ANN-based modeling turned out to be more effective in estimating phytosterol levels than the RSR, while MLP-ANNs proved to be more satisfactory than RBF-ANNs. The approximation quality of the ANNs models depended on the number of neurons and the type of activation functions in the hidden layer. The best model was provided by the MLP-ANN containing nine neurons in the hidden layer equipped with the logistic activation function. The model performance evaluation showed its high prediction accuracy and generalization capability (R² = 0.978; RMSE = 0.140). Its accuracy was also confirmed by the elliptical joint confidence region (EJCR) test. The results show the high usefulness of ANNs in predictive modeling of phytosterol degradation in rapeseeds. The elaborated MLP-ANN model may be used as a support tool in modern postharvest management systems.

Model of Fungal Development in Stored Barley Ecosystems as a Prognostic Auxiliary Tool for Postharvest Preservation Systems

Postharvest preservation and storage have a crucial impact on the technological quality and safety of grain. The important threat to stored grain quality and nutritional safety of cereal products is mould development and their toxic metabolites, mycotoxins. Models based on predictive microbiology, which are able to estimate the kinetics of fungal growth, and thus, the risks of mycotoxin accumulation in a mass of grain are promising prognostic tools that can be applied in postharvest management systems. The study developed a modelling approach to describe total fungal growth in barley ecosystems stored at different temperatures (T = 12–30 °C) and water activity in grain (aw = 0.78–0.96). As the pattern of fungal growth curves was sigmoidal, the experimental data were modelled using the modified Gompertz equation, in which constant coefficients reflecting biological parameters of mould development (i.e. lag phase duration (τlag), maximum growth rate (μmax) and the maximum increase in fungal population level (Δmaxlog(CFU)) were expressed as functions of storage conditions, i.e. aw and T. The criteria used to evaluate the overall model performance indicated its good precision (R2 = 0.95; RMSE = 0.23) and high prediction accuracy (bias factor and accuracy factor Bf = 1.004, Af = 1.035). The formulated model is able to estimate the extension of fungal contamination in a bulk of grain versus time by monitoring temperature and intergranular relative humidity that are readily measurable in practice parameters; therefore, it may be used as a prognostic support tool in modern postharvest management systems.

Application of Deep and Machine Learning Using Image Analysis to Detect Fungal Contamination of Rapeseed

This paper endeavors to evaluate rapeseed samples obtained in the process of storage experiments with different humidity (12% and 16% seed moisture content) and temperature conditions (25 and 30 °C). The samples were characterized by different levels of contamination with filamentous fungi. In order to acquire graphic data, the analysis of the morphological structure of rapeseeds was carried out with the use of microscopy. The acquired database was prepared in order to build up training, validation, and test sets. The process of generating a neural model was based on Convolutional Neural Networks (CNN), Multi-Layer Perceptron Networks (MLPN), and Radial Basis Function Networks (RBFN). The classifiers that were compared were devised on the basis of the environments Tensorflow (deep learning) and Statistica (machine learning). As a result, it was possible to achieve the lowest classification error of 14% for the test set, 18% classification error for MLPN, and 21% classification error for RBFN, in the process of recognizing mold in rapeseed with the use of CNN.