Glucose measurement is a fundamental tool in the daily care of Diabetes Mellitus (DM) patients and healthcare professionals. While there is an established market for glucose sensors, the rising number of DM cases has promoted intensive research to provide accurate systems for glucose monitoring. Polyaniline (PAni) is a conductive polymer with a linear conjugated backbone with sequences of single C–C and double C=C bonds. This unique structure produces attractive features for the design of sensing systems such as conductivity, biocompatibility, environmental stability, tunable electrochemical properties, and antibacterial activity. PAni-based glucose sensors (PBGS) were actively developed in past years, using either enzymatic or non-enzymatic principles. In these devices, PAni played roles as a conductive material for electron transfer, biocompatible matrix for enzymatic immobilization, or sensitive layer for detection. In this review, we covered the development of PBGS from 2015 to the present, and it is not even exhaustive; it provides an overview of advances and achievements for enzymatic and non-enzymatic PBGB PBGS for self-monitoring and continuous blood glucose monitoring. Additionally, the limitations of PBGB PBGS to advance into robust and stable technology and the challenges associated with their implementation are presented and discussed.

The high incidence of Diabetes Mellitus in low-income regions has promoted the development of low-cost alternatives to replace blood-based procedures. In this work, we present a bienzymatic paper-based sensor suitable for the naked-eye detection of glucose in saliva samples. The sensor was obtained by a stamping procedure and modified with chitosan to improve the colorimetric readout. The bienzymatic reaction of GOx-HRP coupled with 2,4,6-tribromo-3-hydroxy benzoic acid was applied for the detection of glucose within a range from 0 to 180 mgdL⁻¹ in buffer and artificial saliva solutions. The visual readout was perceived by the naked eye and registered with an office scanner to evaluate the analytical performance. The results showed a limit of detection of 0.37 mgdL⁻¹ (S/N = 3) with an R.S.D. of 1.69% and a linear range from 1 to 22.5 mgdL⁻¹ with an R² of 0.99235. The analysis of human saliva samples was performed without pre-processing, achieving recoveries from 92 to 114%. The naked-eye detection was evaluated under two different light settings, showing average recoveries of 108.58 and 90.65% for standard and low illumination. The proposed device showed potential for easy-to-use, sensitive, low-cost, fast, and device-free detection of salivary glucose suitable for untrained personnel operation and limited facilities.

Given the limited access to healthcare resources, low-income settings require the development of affordable technology. Here we present the design and evaluation of a low-cost colorimeter applied to the non-invasive monitoring of Diabetes Mellitus through the detection of glucose in salival fluid. Samples were processed by the glucose oxidase-peroxidase enzymatic system and analyzed with the development equipment. A light emission diode of 532.5 nm was used as an excitation source and a RGB module was used as a receptor. A calibration curve to quantify the concentration of salivary glucose (0 to 18 mg/dL) was carried out by relating the RGB components registered with glucose concentrations, achieving a limit of detection of 0.17 mg/dL with a CV of 5% (n = 3). Salivary samples of diabetic and healthy volunteers were processed with the equipment showing an average concentration of 1.5519 ± 0.4511 mg/dL for the first and 4.0479 ± 1.6103 mg/dL for the last, allowing a discrimination between both groups. Results were validated against a UV-Vis-NIR spectrophotometer with a correspondence of R² of 0.98194 between both instruments. Results suggest the potential application of the developed device to the sensitive detection of relevant analytes with a low-cost, user-friendly, low-power and portable instrumentation.

This paper describes a new method based on a voltammetric electronic tongue (ET) for the recognition of distinctive features in coffee samples. An ET was directly applied to different samples from the main Mexican coffee regions without any pretreatment before the analysis. The resulting electrochemical information was modeled with two different mathematical tools, namely Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). Growing conditions (i.e., organic or non-organic practices and altitude of crops) were considered for a first classification. LDA results showed an average discrimination rate of 88% ± 6.53% while SVM successfully accomplished an overall accuracy of 96.4% ± 3.50% for the same task. A second classification based on geographical origin of samples was carried out. Results showed an overall accuracy of 87.5% ± 7.79% for LDA and a superior performance of 97.5% ± 3.22% for SVM. Given the complexity of coffee samples, the high accuracy percentages achieved by ET coupled with SVM in both classification problems suggested a potential applicability of ET in the assessment of selected coffee features with a simpler and faster methodology along with a null sample pretreatment. In addition, the proposed method can be applied to authentication assessment while improving cost, time and accuracy of the general procedure.