Wastewater monitoring by means of e-nose, VE-tongue, TD-GC-MS, and SPME-GC-MS

A machine learning-based electronic nose system using numerous low-cost gas sensors for real-time alcoholic beverage classification

This study introduces numerous low-cost gas sensors and a real-time alcoholic beverage classification system based on machine learning. Dogs possess a superior sense of smell compared to humans due to having 30 times more olfactory receptors and three times more olfactory receptor types than humans. Thus, in odor classification, the number of olfactory receptors is a more influential factor than the number of receptor types. From this perspective, this study proposes a system that utilizes distinctive data patterns resulting from heterogeneous responses among numerous low-cost homogeneous MOS-based sensors with poor gas selectivity. To evaluate the performance of the proposed system, learning data were gathered using three alcoholic beverage groups including different aged whiskeys, Korean soju with 99% same compositions, and white wines made from the Sauvignon blanc variety, sourced from various countries. The electronic nose system was developed to classify alcoholic samples measured using 30 gas sensors in real time. The samples were injected into a gas chamber for 60 seconds, followed by a 60-second injection of clean air. After preprocessing the time-series data into four distinct datasets, the data were analyzed using a machine learning algorithm, and the classification results were compared. The results showed a high classification accuracy of over 99%, and it was observed that classification performance varied depending on data preprocessing. As the number of gas sensors increased, the prediction accuracy improved, reaching up to 99.83 ± 0.21%. These experimental results indicated that the proposed electronic nose system's classification performance was comparable to that of commercial electronic nose systems. Additionally, the implementation of an alcoholic beverage classification system based on a pretrained LDA model demonstrated the feasibility of real-time classification using the proposed system.

Allelopathy and Identification of Volatile Components from the Roots and Aerial Parts of Astragalus mongholicus Bunge

The volatile compounds produced by plants play an important role in plant growth, plant communication, and resistance to biological and abiotic stresses. Astragalus membranaceus var. mongholicus (AM) is a perennial herbaceous plant (Leguminosae) that is widely cultivated in northwest China. The bioactive compounds in its root have shown various pharmacological activities. Root rot disease caused by Fusarium spp. often occurs in AM planting with increasing severity in continuous monoculture. It is currently still unclear what are the effects of the volatile compounds produced by fresh AM on itself, other crops cultivated on the same field after AM, pathogen, and rhizobia. In this study, we found that seed germination and seedling growth of AM, lettuce (Lactuca sativa L.), and wheat (Triticum aestivum L.) could be affected if they were in an enclosed space with fresh AM tissue. Additionally, 90 volatile compounds were identified by SPME-GC-MS from whole AM plant during the vegetative growth, 36 of which were specific to aerial parts of AM (stems and leaves, AMA), 17 to roots (AMR), and 37 were found in both AMA and AMR. To further identify the allelopathic effects of these volatile compounds, five compounds (1-hexanol, (E)-2-hexenal, (E,E)-2,4-decadienal, hexanal, and eugenol) with relatively high content in AM were tested on three receptor plants and two microorganisms. We found that (E,E)-2,4-decadienal and (E)-2-hexenal showed significant inhibitory effects on the growth of AM and lettuce. One-hexanol and hexanal suppressed the growth of wheat, while eugenol showed a similar effect on all three plant species. Moreover, the activities of these compounds were dose dependent. Notably, we discovered that (E)-2-hexenal and eugenol also inhibited the growth of the pathogen Fusarium solani by as high as 100%. Meanwhile, all five compounds tested suppressed the rhizobia Sinorhizobium fredii. In summary, this study furthered our understanding of the comprehensive allelopathic effects of the main volatile components of AM.

Improving Recognition Accuracy of Pesticides in Groundwater by Applying TrAdaBoost Transfer Learning Method

Accurate and rapid prediction of pesticides in groundwater is important to protect human health. Thus, an electronic nose was used to recognize pesticides in groundwater. However, the e-nose response signals for pesticides are different in groundwater samples from various regions, so a prediction model built on one region's samples might be ineffective when tested in another. Moreover, the establishment of a new prediction model requires a large number of sample data, which will cost too much resources and time. To resolve this issue, this study introduced the TrAdaBoost transfer learning method to recognize the pesticide in groundwater using the e-nose. The main work was divided into two steps: (1) qualitatively checking the pesticide type and (2) semi-quantitatively predicting the pesticide concentration. The support vector machine integrated with the TrAdaBoost was adopted to complete these two steps, and the recognition rate can be 19.3% and 22.2% higher than that of methods without transfer learning. These results demonstrated the potential of the TrAdaBoost based on support vector machine approaches in recognizing the pesticide in groundwater when there were few samples in the target domain.

Development of a wide-range soft sensor for predicting wastewater BOD5 using an eXtreme gradient boosting (XGBoost) machine

In wastewater monitoring, detecting extremely high pollutant concentrations is necessary to properly calibrate the treatment process. However, existing hardware sensors have a limited linear range which may fail to measure extremely high levels of pollutants; and likewise, the conventional "soft" model sensors are not suitable for the highly-skewed data distributions either. This study developed a new soft sensor by using eXtreme Gradient Boosting (XGBoost) machine learning to 'measure' the wastewater organics (in terms of 5-day biochemical oxygen demand (BOD5)). The soft sensor was tested on influent and effluent BOD5 of two different wastewater treatment plants to validate the results. The model results showed that XGBoost can detect these extreme values better than conventional soft sensors. This new soft sensor can function using a sparse input matrix via XGBoost's sparsity awareness algorithm - which can address the limitation of the conventional soft sensor with the fallibility of supporting hardware sensors even.

R. Rocha C, Teixeira JA, Pereira JAM. Green Extraction Techniques as Advanced Sample Preparation Approaches in Biological, Food, and Environmental Matrices: A Review

Green extraction techniques (GreETs) emerged in the last decade as greener and sustainable alternatives to classical sample preparation procedures aiming to improve the selectivity and sensitivity of analytical methods, simultaneously reducing the deleterious side effects of classical extraction techniques (CETs) for both the operator and the environment. The implementation of improved processes that overcome the main constraints of classical methods in terms of efficiency and ability to minimize or eliminate the use and generation of harmful substances will promote more efficient use of energy and resources in close association with the principles supporting the concept of green chemistry. The current review aims to update the state of the art of some cutting-edge GreETs developed and implemented in recent years focusing on the improvement of the main analytical features, practical aspects, and relevant applications in the biological, food, and environmental fields. Approaches to improve and accelerate the extraction efficiency and to lower solvent consumption, including sorbent-based techniques, such as solid-phase microextraction (SPME) and fabric-phase sorbent extraction (FPSE), and solvent-based techniques (μQuEChERS; micro quick, easy, cheap, effective, rugged, and safe), ultrasound-assisted extraction (UAE), and microwave-assisted extraction (MAE), in addition to supercritical fluid extraction (SFE) and pressurized solvent extraction (PSE), are highlighted.

Novel solid-phase microextraction fiber coatings: A review

The materials used for the fabrication of solid-phase microextraction fiber coatings in the past five years are summarized in the current review, including carbon, metal-organic frameworks, covalent organic frameworks, aerogel, polymer, ionic liquids/poly (ionic liquids), metal oxides, and natural materials. The preparation approaches of different coatings, such as sol-gel technique, in-situ growth, electrodeposition, and glue methods, are briefly reviewed together with the evolution of the supporting substrates. In addition, the limitations of the current coatings and the future development directions of solid-phase microextraction are presented.

An Outlook of Recent Advances in Chemiresistive Sensor-Based Electronic Nose Systems for Food Quality and Environmental Monitoring

An electronic nose (Enose) relies on the use of an array of partially selective chemical gas sensors for identification of various chemical compounds, including volatile organic compounds in gas mixtures. They have been proposed as a portable low-cost technology to analyse complex odours in the food industry and for environmental monitoring. Recent advances in nanofabrication, sensor and microcircuitry design, neural networks, and system integration have considerably improved the efficacy of Enose devices. Here, we highlight different types of semiconducting metal oxides as well as their sensing mechanism and integration into Enose systems, including different pattern recognition techniques employed for data analysis. We offer a critical perspective of state-of-the-art commercial and custom-made Enoses, identifying current challenges for the broader uptake and use of Enose systems in a variety of applications.

VOCs Sensing by Metal Oxides, Conductive Polymers, and Carbon-Based Materials

This review summarizes the recent research efforts and developments in nanomaterials for sensing volatile organic compounds (VOCs). The discussion focuses on key materials such as metal oxides (e.g., ZnO, SnO2, TiO2 WO3), conductive polymers (e.g., polypyrrole, polythiophene, poly(3,4-ethylenedioxythiophene)), and carbon-based materials (e.g., graphene, graphene oxide, carbon nanotubes), and their mutual combination due to their representativeness in VOCs sensing. Moreover, it delves into the main characteristics and tuning of these materials to achieve enhanced functionality (sensitivity, selectivity, speed of response, and stability). The usual synthesis methods and their advantages towards their integration with microsystems for practical applications are also remarked on. The literature survey shows the most successful systems include structured morphologies, particularly hierarchical structures at the nanometric scale, with intentionally introduced tunable "decorative impurities" or well-defined interfaces forming bilayer structures. These groups of modified or functionalized structures, in which metal oxides are still the main protagonists either as host or guest elements, have proved improvements in VOCs sensing. The work also identifies the need to explore new hybrid material combinations, as well as the convenience of incorporating other transducing principles further than resistive that allow the exploitation of mixed output concepts (e.g., electric, optic, mechanic).