How Can Chemometrics Improve Microfluidic Research?

Paper-based optical sensor arrays for simultaneous detection of multi-targets in aqueous media: A review

Sensor arrays, which draw inspiration from the mammalian olfactory system, are fundamental concepts in high-throughput analysis based on pattern recognition. Although numerous optical sensor arrays for various targets in aqueous media have demonstrated their diverse applications in a wide range of research fields, practical device platforms for on-site analysis have not been satisfactorily established. The significant limitations of these sensor arrays lie in their solution-based platforms, which require stationary spectrophotometers to record the optical responses in chemical sensing. To address this, this review focuses on paper substrates as device components for solid-state sensor arrays. Paper-based sensor arrays (PSADs) embedded with multiple detection sites having cross-reactivity allow rapid and simultaneous chemical sensing using portable recording apparatuses and powerful data-processing techniques. The applicability of office printing technologies has promoted the realization of PSADs in real-world scenarios, including environmental monitoring, healthcare diagnostics, food safety, and other relevant fields. In this review, we discuss the methodologies of device fabrication and imaging analysis technologies for pattern recognition-driven chemical sensing in aqueous media.

Benchtop volatilomics supercharged: How machine learning based design of experiment helps optimizing untargeted GC-IMS gas phase metabolomics

Gas chromatography-ion mobility spectrometry (GC-IMS) plays a significant role in both targeted and non-targeted analyses. However, the non-linear behavior of IMS and its complex ion chemistry pose challenges for finding optimal experimental conditions using existing methodologies. To address these issues, integrating machine learning (ML) strategies offers a promising approach. In this study, we propose a hybrid strategy, combining design of experiment (DOE) and machine learning (ML) for optimizing GC-IMS conditions in non-targeted volatilomic/flavoromic analysis, with saffron volatiles as a case study. To begin, a rotatable circumscribed central composite design (CCD) is used to define five influential GC-IMS factors of sample amount, headspace temperature, incubation time, injection volume, and split ratio. Subsequently, two ML models are utilized: multiple linear regression (MLR) as a linear model and Bayesian regularized-artificial neural network (BR-ANN) as a nonlinear model. These models are employed to predict the response variables of total peak areas (PAs) and the number of detected peaks (PNs) in GC-IMS. The findings show that there is a direct correlation between the factors in GC-IMS and the PNs, suggesting that MLR is a suitable approach for building a model in this scenario. However, the PAs exhibit nonlinear behavior, suggesting that BR-ANN is better suitable to capture this complexity. Notably, Derringer's desirability function is utilized to integrate the PAs and PNs, and in this scenario, MLR demonstrates satisfactory performance in modeling the GC-IMS factors.

Internet-of-medical-things integrated point-of-care biosensing devices for infectious diseases: Toward better preparedness for futuristic pandemics

Microbial pathogens have threatened the world due to their pathogenicity and ability to spread in communities. The conventional laboratory-based diagnostics of microbes such as bacteria and viruses need bulky expensive experimental instruments and skilled personnel which limits their usage in resource-limited settings. The biosensors-based point-of-care (POC) diagnostics have shown huge potential to detect microbial pathogens in a faster, cost-effective, and user-friendly manner. The use of various transducers such as electrochemical and optical along with microfluidic integrated biosensors further enhances the sensitivity and selectivity of detection. Additionally, microfluidic-based biosensors offer the advantages of multiplexed detection of analyte and the ability to deal with nanoliters volume of fluid in an integrated portable platform. In the present review, we discussed the design and fabrication of POCT devices for the detection of microbial pathogens which include bacteria, viruses, fungi, and parasites. The electrochemical techniques and current advances in this field in terms of integrated electrochemical platforms that include mainly microfluidic- based approaches and smartphone and Internet-of-things (IoT) and Internet-of-Medical-Things (IoMT) integrated systems have been highlighted. Further, the availability of commercial biosensors for the detection of microbial pathogens will be briefed. In the end, the challenges while fabrication of POC biosensors and expected future advances in the field of biosensing have been discussed. The integrated biosensor-based platforms with the IoT/IoMT usually collect the data to track the community spread of infectious diseases which would be beneficial in terms of better preparedness for current and futuristic pandemics and is expected to prevent social and economic losses.

An integrated resource-efficient microfluidic device for parallelised studies of immobilised chiral catalysts in continuous flow via miniaturized LC/MS-analysis

Dual-μReactor catalysis screening: a novel method combining multiple miniaturized packed-bed reactors and on-line HPLC/MS-analysis on one single microfluidic device.

Chiral recognition coupled with chemometrics using boronate ensembles containing D-π-A cyanostilbenes

Two types of boronic acid-appended D-π-A cyanostilbenes were synthesized to produce chiral boronate ensembles via dehydration with tartaric acid. The aggregation-induced high sensitivity and positional effect of the CN group on the emission properties allowed for chemometrics-coupled chiral recognition.

Wicking in Paper Strips under Consideration of Liquid Absorption Capacity

Paper-based microfluidic devices have the potential of being a low-cost platform for diagnostic devices. Electrical circuit analogy (ECA) model has been used to model the wicking process in paper-based microfluidic devices. However, material characteristics such as absorption capacity cannot be included in the previous ECA models. This paper proposes a new model to describe the wicking process with liquid absorption in a paper strip. We observed that the fluid continues to flow in a paper strip, even after the fluid reservoir has been removed. This phenomenon is caused by the ability of the paper to store liquid in its matrix. The model presented in this paper is derived from the analogy to the current response of an electric circuit with a capacitance. All coefficients in the model are fitted with data of capillary rise experiments and compared with direct measurement of the absorption capacity. The theoretical data of the model agrees well with experimental data and the conventional Washburn model. Considering liquid absorption capacity as a capacitance helps to explain the relationship between material characteristics and the wicking mechanism.

Accurate chiral pattern recognition for amines from just a single chemosensor

The current work proposes a novel method for accurate pattern recognition of (mono- and di-) amines and determination of enantiomeric excess (ee) using molecular self-assembly.

Simple Colorimetric Chemosensor Array for Oxyanions: Quantitative Assay for Herbicide Glyphosate

Although the determination of oxyanions due to correlation with metabolic processes and diseases is in high demand, most of the developed methods are suffering from a shortage of a capability of on-site analysis, sensitivity, and user-friendliness. This paper introduces the first colorimetric chemosensor array targeting various anions including glyphosate. The proposed sensor benefits from some notable features such as utilizing only commercially available reagents, recognizing similarly structured compounds by biomaterial-free sensors, and providing a fingerprint-like response originating from pattern recognition. The detection mechanism is based on an anion sensing strategy named coordination binding-based sensor array (CBSA). In CBSA, competitive coordinative bonding of a metal ion (Zn2+) between a catechol dye (i.e., indicator) and target anions occurs, and changes in the optical properties of the dye represent the target's concentration. For data processing, two chemometrical techniques including linear discrimination analysis (LDA) and an artificial neural network (ANN) for pattern classification and regression/prediction purposes were successfully employed, respectively. Finally, the proposed chemosensor was subjected to glyphosate samples (commercial herbicide and tap water samples) and produced satisfactory results.