Establishment of optimized in vitro disinfection protocol of Pistacia vera L. explants mediated a computational approach: multilayer perceptron-multi-objective genetic algorithm.

BACKGROUND

Contamination-free culture is a prerequisite for the success of in vitro - based plant biotechnology. Aseptic initiation is an extremely strenuous stride, particularly in woody species. Meanwhile, over-sterilization is potentially detrimental to plant tissue. The recent rise of machine learning algorithms in plant tissue culture proposes an advanced interpretive tool for the combinational effect of influential factors for such in vitro - based steps.

RESULTS

A multilayer perceptron (MLP) model of artificial neural network (ANN) was implemented with four inputs, three sterilizing chemicals at various concentrations and the immersion time, and two outputs, disinfection efficiency (DE) and negative disinfection effect (NDE), intending to assess twenty-seven disinfection procedures of Pistacia vera L. seeds. Mercury chloride (HgCl; 0.05-0.2%; 5-15 min) appears the most effective with 100% DE, then hydrogen peroxide (HO; 5.25-12.25%; 10-30 min) with 66-100% DE, followed by 27-77% DE for sodium hypochlorite (NaOCl; 0.54-1.26% w/v; 10-30 min). Concurrently, NDE was detected, including chlorosis, hard embryo germination, embryo deformation, and browning tissue, namely, a low repercussion with NaOCl (0-14%), a moderate impact with HO (6-46%), and pronounced damage with HgCl (22-100%). Developed ANN showed R values of 0.9658, 0.9653, 0.8937, and 0.9454 for training, validation, testing, and all sets, respectively, which revealed the uprightness of the model. Subsequently, the model was linked to multi-objective genetic algorithm (MOGA) which proposed an optimized combination of 0.56% NaOCl, 12.23% HO, and 0.068% HgCl for 5.022 min. The validation assay reflects the high utility and accuracy of the model with maximum DE (100%) and lower phytotoxicity (7.1%).

CONCLUSION

In one more case, machine learning algorithms emphasized their ability to resolve commonly encountered problems. The current successful implementation of MLP-MOGA inspires its application for more complicated plant tissue culture processes.