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BACKGROUND: Strawberry is a rich source of antioxidants, including ascorbic acid (ASA) and polyphenols, which have numerous health benefits. Antioxidant content and activity are often determined manually using laboratory equipment, which is destructive and time‐consuming. This study constructs a prediction model for antioxidant compounds utilizing machine learning (ML) and multiple linear regression based on environmental, plant growth and agronomic fruit quality‐related parameters as well as antioxidant levels. These were studied in three farms at two‐week intervals during two years of cultivation. RESULTS: During the ML model screening, artificial neural network (ANN)‐boosted models displayed a moderate coefficient of determination (R2) at 0.68–0.78 and relative root mean square error (RRMSE) at 3.8–4.8% in polyphenols and total ASA levels, as well as a high R2 of 0.96 and low RRMSE at <3.0% in antioxidant activity. Additionally, we developed variable selection models regarding the antioxidant activity, and variables two and five (environmental parameters and leaf length, respectively) with high accuracy were selected. The linear regression analysis between the actual and predicted data of antioxidants in the ANN‐boosted models revealed high fitness with all parameters in almost all training, validation and test sets. Furthermore, environmental parameters are essential in developing such reliable models. CONCLUSION: We conclude that ANN‐boosted, stepwise and double‐Lasso regression models can predict antioxidant compounds with enhanced accuracy, and the relevant parameters can be easily acquired on‐site without the need for any specific equipment. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

DNA methylation is a crucial epigenetic modification used as a biomarker for early cancer progression. However, existing methods for DNA methylation analysis are complex, time-consuming, and prone to DNA degradation. This work demonstrates selective capture of unmethylated DNAs using ZnO nanowires without chemical or biological modifications, thereby concentrating methylated DNA, particularly those with high methylation levels that can predict cancer risk. We observe varying affinities between methylated and unmethylated DNA on ZnO nanowires, which may be influenced by differences in hydrogen bonding strength, potentially related to the effects of methylation on DNA strand behavior, including self-aggregation and stretching inhibition. As a result, the nanowire-based microfluidic device effectively collects unmethylated DNA, leading to a significantly increased ratio of methylated to unmethylated DNA, particularly for collecting low-concentration methylated DNA. This simplified microfluidic device, composed of ZnO nanowires, enables direct separation of specific methylated DNA, offering a potential approach for DNA methylation mapping in clinical disease diagnostics.