Gold nanoparticles (Au NPs) were almost chosen as the first option for biological and biosensor applications due to their enhancement and their outstanding properties. The combining of optical fiber with localized surface plasmon resonance (LSPR) for forming a biosensor is widely used in diagnosis. In this work, we report a fiber optical biosensor based on LSPR of Au NPs for the detection of bovine serum albumin (BSA) protein. BSA was functionalized on Au NPs immobilized fiber optic sensing head (length of 1 cm) via methanesulfonic acid (MSA) by carboxylic binding. It is the binding between the analytes with the surface-modified Au NPs that caused refractive index changes in the sensing medium led to changes in optical power at the output of the sensor. The detection limit of the LSPR fiber biosensor was found to be 0.18 ng/mL for the BSA detection with the low coefficient of variation (CV) at under 1%. We have demonstrated the effectiveness of combining multimode fiber with Au NPs to generate the biosensor as the label-free sensor that can be a feasible tool for highly sensitive, rapid response time, stable, and miniaturized point-of-care analytical systems.

Honey represents a natural, agricultural product endowed with valuable nutritional and pharmacological functions. Therefore, the classification and evaluation of honey has always been a challenge for chemical analysis, especially when honey adulteration is increasing. The traditional methods for quality control of honey is currently based on physicochemical methods, which is often relatively high cost and time‐consuming. NMR based metabolomics is a metabolomic fingerprinting approach for NMR data using chemometric tools. This combined approach can be apply in food analysis for origin discrimination, biomarker discovery and authenticity screening. The present study demonstrated the capability of the 1H NMR based metabolomics for evaluation of 27 NMR spectra of 09 selected honey samples from Viet Nam. 1H NMR analysis was conducted immediately on collected honey samples, without extraction. Unsupervised PCA multivariate data analysis, applied on 1H NMR experimental data, used to characterize and classify honey samples according to their origin and quality. Different metabolites specific for each botanical origins of honey samples also determined. The obtained results of the demonstration suggests that this combined approach could be useful to develop generally applicable metabolomic approaches to valuate honey products as well as other agricultural products.

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