Nickel oxide nanoparticles-modified glassy carbon electrodes for non-enzymatic determination of total sugars in commercial beverages

Biomimic models for in vitro glycemic index: Scope of sensor integration and artificial intelligence

The accurate quantification of glycemic index (GI) remains crucial for diabetes management, yet current methodologies are constrained by resource intensiveness and methodological limitations. In vitro digestion models face challenges in replicating the dynamic conditions of the human gastrointestinal tract, such as enzyme variability and multi-time point analysis, leading to suboptimal predictive accuracy. This review proposes an integrated technological framework combining non-enzymatic electrochemical sensing with artificial intelligence to revolutionize GI assessment. Non-enzymatic sensors offer superior stability and repeatability in complex matrices, enabling real-time glucose quantification across multiple timepoints without enzyme degradation constraints. Machine learning algorithms, both supervised and unsupervised, enhance predictive accuracy by elucidating complex relationships within digestion data. This technological convergence represents a paradigm shift in food science analytics, promising improved throughput and precision in GI assessment. Future developments should focus on system scalability and broader applications across nutritional science, advancing diabetic management and personalized nutrition strategies.

Electrochemical Sensors and Biosensors for the Determination of Food Nutritional and Bioactive Compounds: Recent Advances

The significance of food nutrients and bioactive compounds in human health has driven the development of many methods for their determination in different matrices. Among these, electroanalysis has gained popularity due to its cost-effectiveness, rapidity, and, in many cases, portability and minimal sample treatment. This review highlights key advances in electrochemical sensors and biosensors from 2019 to the present. Given the variability and the challenges of managing food matrices, the focus is limited to methods that have been thoroughly assessed for their applicability to real samples. The technical characteristics and analytical performance of the proposed sensors are discussed, along with breakthrough features and future trends.

Electrochemical Detection of Bisphenol A Based on Gold Nanoparticles/Multi-Walled Carbon Nanotubes: Applications on Glassy Carbon and Screen Printed Electrodes

Bisphenol A (BPA) has been classified as an endocrine-disrupting substance that may cause adverse effects on human health and the environment. The development of simple and sensitive electrochemical biosensors is crucial for the rapid and effective quantitative determination of BPA. This work presents a study on electrochemical sensors utilizing gold nanoparticle-modified multi-walled carbon nanotubes (CNT/AuNPs). Glassy carbon electrodes (GCEs) and screen-printed electrodes (SPEs) were conveniently modified and used for BPA detection. AuNPs were electrodeposited onto the CNT-modified electrodes using the galvanostatic method. The electrodes were properly modified and characterized by using Raman spectroscopy, cyclic voltammetry (CV), and electrochemical impedance analysis (EIS). The electrochemical response of the sensors was studied using differential pulse voltammetry (DPV) and constant potential amperometry (CPA) for modified GCE and SPE electrodes, respectively, and the main analytical parameters were studied and optimized. Problems encountered with the use of GCEs, such as sensor degradation and high limit of detection (LOD), were overcome by using modified SPEs and a flow injection device for the measurements. Under this approach, an LOD as low as 5 nM (S/N = 3) was achieved and presented a linear range up to 20 μM. Finally, our investigation addressed interference, reproducibility, and reusability aspects, successfully identifying BPA in both spiked and authentic samples, including commercial and tap waters. These findings underscore the practical applicability of our method for accurate BPA detection in real-world scenarios. Notably, the integration of SPEs and a flow injection device facilitated simplified automation, offering an exceptionally efficient and reliable solution for precise BPA detection in water analysis laboratories.

New Electrochemical Sensor Based on Hierarchical Carbon Nanofibers with NiCo Nanoparticles and Its Application for Cetirizine Hydrochloride Determination

A new electrochemical sensor based on hierarchical carbon nanofibers with Ni and Co nanoparticles (eCNF/CNT/NiCo-GCE) was developed. The presented sensor may be characterized by high sensitivity, good electrical conductivity, and electrocatalytic properties. Reproducibility of its preparation expressed as %RSD (relative standard deviation) was equal to 9.7% (n = 5). The repeatability of the signal register on eCNF/CNT/NiCo-GCE was equal to 3.4% (n = 9). The developed sensor was applied in the determination of the antihistamine drug—cetirizine hydrochloride (CTZ). Measurement conditions, such as DPV (differential pulse voltammetry) parameters, supporting electrolyte composition and concentration were optimized. CTZ exhibits a linear response in three concentration ranges: 0.05–6 µM (r = 0.988); 7–32 (r = 0.992); and 42–112 (r = 0.999). Based on the calibration performed, the limit of detection (LOD) and limit of quantification (LOQ) were calculated and were equal to 14 nM and 42 nM, respectively. The applicability of the optimized method for the determination of CTZ was proven by analysis of its concentration in real samples, such as pharmaceutical products and body fluids (urine and plasma). The results were satisfactory and the calculated recoveries (97–115%) suggest that the method may be considered accurate. The obtained results proved that the developed sensor and optimized method may be used in routine laboratory practice.