Search

Abstract In this study, a surface plasmon resonance sensor for detecting haemoglobin concentration based on a D‐type optical fibre with a graphene‐gold surface architecture is proposed. The graphene‐gold surface architecture included a 50 nm thick gold film and monolayer graphene film, which were decorated on a 10 mm long D‐type sensing region. The proposed sensor worked in wavelength interrogation mode, with the light wavelength ranging from 400 to 1100 nm. The authors realised the D‐type optical fibre surface polishing process, the gold film vacuum coating process, and the chemical‐vapour‐deposited graphene's wet transfer process. Furthermore, the fabricated sensors were used to detect the refractive index (RI) of haemoglobin samples, which varied from 1.331 to 1.346. Experiment results show that the fitted RI sensitivity of the sensor decorated with gold and graphene reaches 1874.41 nm/RIU, 4.995% higher than that of the sensor decorated only with gold. The concentration sensitivity of the sensor coated with gold and graphene film is 4.96 nm/(g/dL), and the proposed sensor can provide a resolution of 20.2 mg/dL for haemoglobin concentration detection.

The production season is one of the judgment standards of the green tea quality and spring tea is generally considered of higher quality. Moreover, early spring tea is usually more precious and sells for a higher price. Therefore, a multifaceted strategy that integrates lipidomics and metabolomics, based on UPLC-Triple-TOF/MS coupled with chemometrics, was developed to discriminate early spring green tea (ET) and late spring green tea (LT). Twenty-six lipids and forty-five metabolites were identified as characteristic components. As for characteristic lipids, most of glycerophospholipids and acylglycerolipids have higher contents in ET. By contrast, glycoglycerolipids, sphingolipids and hydroxypheophytin a were shown higher levels in LT samples. Most of the differential metabolites identified were more abundant in ET samples. LT samples have much higher catechin, procyanidin B2, and 3',8-dimethoxyapigenin 7-glucoside contents. Based on the integration of differential lipids and metabolites, the reconstructed orthogonal partial least squares discriminant analysis (OPLS-DA) model displayed 100% correct classification rates for harvest time discrimination of green tea samples. These results demonstrated that the integration of lipidomics and metabolomics approaches is a promising method for the discrimination of tea quality.