Henan University of Technology

To achieve rapid, non-destructive detection of food quality indicators, this study introduces a novel method that combines near-infrared (NIR) spectroscopy with the Extreme learning machine (ELM) model. Eight spectral preprocessing methods and three wavelength selection algorithms were evaluated for predicting total ginsenoside content (TGC) and protein content (PC), along with a comparative analysis of the ELM model's performance against support vector regression and random forest. Results showed that Savitzky-Golay smoothing with standard normal variate was the best preprocessing method, K-means clustering provided the optimal wavelength selection algorithm, and the ELM model demonstrated the best performance. Specifically, the ELM based on K-means method achieved optimal results: R² of 0.9431, RMSE of 0.2933 mg/g, rRMSE of 0.0824, RPD of 4.2386, and P-time of 4 × 10^-7 s for TGC; and R² of 0.9764, RMSE of 4.1361 mg/g, rRMSE of 0.0337, RPD of 6.1295, and P-time of 2 × 10^-7 s for PC. In summary, combining NIR spectroscopy with the ELM model and clustering-based wavelength selection algorithm offers a reliable and practical solution for rapid, non-destructive, and accurate detection of food quality indicators.

This study investigated the main volatile flavor compounds in maize harvested in seven major production provinces in northern China from 2019 to 2023, using a solid-phase microextraction coupled with gas chromatography-triple quadrupole mass spectrometry (SPME-GC-MS/MS) flavoromics approach. A targeted method of analysis based on SPME-GC-MS/MS was optimized by testing sample amounts, extraction temperatures and extraction times. A total of 99 volatile flavor compounds were identified in 285 maize samples. Eight differentially volatile metabolites were screened based on chemometric models, and the storage ages were separated through partial least squares discriminant analysis (PLS-DA) belonging to the five groups. Based on the non-destructive and automatic profiling of volatile flavor compounds, maize of various storage ages could be successfully differentiated by flavoromics, and a machine learning method that can accurately distinguish fresh and aged maize was established, which will provide the theoretical basis for quality evaluation during maize storage.

Herein, we present an ultrasensitive DNA detection platform based on n-type accumulation-mode vertical organic electrochemical transistors (vOECTs) for the first time. The vOECTs utilize poly(benzimidazobenzophenanthroline) (BBL) as the n-type organic semiconductor channel, operating in accumulation mode with low power consumption and ultrahigh transconductance (up to 53.7 mS). DNA probes were immobilized on Au gate electrodes via Au-S bonds, followed by 6-mercapto-1-hexanol (MCH) blocking to minimize nonspecific adsorption. Target DNA hybridization induces interfacial charge redistribution and electrical double-layer (EDL) modulation, amplifying current responses through the vOECT's high transconductance. The platform achieves an exceptional sensitivity of 155.91 μA/dec-31-fold higher than state-of-the-art DNA sensors-and an ultralow detection limit of 1.07 aM. Notably, the sensor enables label-free, amplification-free detection with rapid response and excellent stability. This work establishes a novel approach for high-precision nucleic acid analysis in biomedical applications.