Detection of 5‐ HMF in apple juice with artificial sensing systems

Electro-oxidation and determination 5-hydroxymethylfurfural in food on co-electrodeposited Cu-Ni bimetallic microparticles modified copper electrode

This study presents a sensitive approach for electrochemical determination of 5-hydroxymethylfurfural (5-HMF) in food. The electrochemical sensor was fabricated on a copper electrode (CuE) modified with co-electrodeposited Cu-Ni bimetallic particles. This sensor, fabricated by 30 cycles of cyclic voltametric scanning with a scan rate of 50 mV s^-1, exhibits good electrocatalytic ability to 5-HMF oxidation. Under the optimal conditions, linear scan voltammetry (LSV) and chronoamperometry were conducted for the determination of 5-HMF. The results of LSV show that a linear dependency within the 0.4-10 mM range with a detection limit (LOD) of 3.51 μM (S/N = 3) was achieved, while a linear range in 1 × 10-⁴-11 mM with a LOD of 0.043 μM (S/N = 3) was obtained by chronoamperometric measurement. The electrochemical sensor was finally applied in determination of 5-HMF in various foods, and the reliability and accuracy of the method were assessed by adopting an UV method as a standard method. Results show that the concentrations of 5-HMF in real samples are close to those measured by the standard method. In addition, standard addition method was further performed to evaluate the accuracy of our approach. The recoveries ranged from 90.0% to 110.0% are calculated, demonstrating good accuracy of the electrochemical sensor.

E-sensing and nanoscale-sensing devices associated with data processing algorithms applied to food quality control: a systematic review

Devices of human-based senses such as e-noses, e-tongues and e-eyes can be used to analyze different compounds in several food matrices. These sensors allow the detection of one or more compounds present in complex food samples, and the responses obtained can be used for several goals when different chemometric tools are applied. In this systematic review, we used Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, to address issues such as e-sensing with chemometric methods for food quality control (FQC). A total of 109 eligible articles were selected from PubMed, Scopus and Web of Science. Thus, we predicted that the association between e-sensing and chemometric tools is essential for FQC. Most studies have applied preliminary approaches like exploratory analysis, while the classification/regression methods have been less investigated. It is worth mentioning that non-linear methods based on artificial intelligence/machine learning, in most cases, had classification/regression performances superior to non-liner, although their applications were seen less often. Another approach that has generated promising results is the data fusion between e-sensing devices or in conjunction with other analytical techniques. Furthermore, some future trends in the application of miniaturized devices and nanoscale sensors are also discussed.