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Special Issue "Advanced Real-Time On-Site Sensing Technologies in Food and Environment Analysis"

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A special issue of Chemosensors (ISSN 2227-9040). This special issue belongs to the section "Analytical Methods, Instrumentation and Miniaturization".

Deadline for manuscript submissions: 31 March 2024 | Viewed by 4683

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Special Issue Editors

Dr. Zhenbo Wei

E-Mail Website
Guest Editor

Department of Biosystems Engineering, Zhejiang University, Hangzhou 310058, China
Interests: artificial gustatory and olfactory system in the food quality detection and smart agriculture
Special Issues, Collections and Topics in MDPI journals

Dr. Shanshan Qiu

E-Mail Website
Guest Editor

College of Materials and Environmental Engineering, Hangzhou Dianzi University, Hangzhou, China
Interests: gas sensors; data mining

Special Issue Information

Dear Colleagues,

Real-time detection devices and sensors are key in object detection with fast inference while maintaining simple operation and a base level of accuracy. The number of different types of sensors that focus on object detection quantitatively or qualitatively is continuously growing, although their applications in practical utilization are more limited. Compared to laboratory-scale devices, real-time on-site detection devices based on gas sensors, microwave sensors, or spectroscopy sensors are extremely attractive due to their low cost, easy operation, and simplified sample pretreatment.

This Special Issue will provide a forum for the latest research activities in the field of chemical/physical sensors, relevant data mining, and their application. Both review articles and original research papers are solicited in areas including, but not limited to, the following:

Gas sensors, microwave sensors, or spectroscopy sensors;
On-line analysis system design based on micro sensors or sensor arrays;
The application of sensors for food detection or environment monitoring;
Data mining for sensor signal feature extraction, data reduction, classification, prediction, etc.

Dr. Zhenbo Wei
Dr. Shanshan Qiu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Chemosensors is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Research

Open AccessArticle

Utilising Portable Laser-Induced Breakdown Spectroscopy for Quantitative Inorganic Water Testing

and

Chemosensors 2023, 11(9), 479; https://doi.org/10.3390/chemosensors11090479 - 01 Sep 2023

Viewed by 400

Abstract

At present, the majority of water testing is carried out in the laboratory, and portable field methods for the quantification of elements in natural waters remain to be established. In contrast, portable instruments like portable X-ray fluorescence (pXRF) analysis and portable laser-induced breakdown spectroscopy (pLIBS) have become routine analytical methods for the quantification of elements in solids. This study aims to show that pLIBS can also be used for chemical compositional measurements of natural waters. Bottled mineral waters were selected as sample materials. A surface-enhanced liquid-to-solid conversion technique was used to improve the detection limits and circumvent the physical limitations in liquid analysis. The results show that low to medium mineralised waters can be analysed quantitatively for their ions using the documented method. For more highly concentrated samples, typically above an electrical conductivity (EC) of 1000 µS/cm, further adjustment is required in the form of self-absorption correction. However, water with a conductivity up to this limit can be analysed for the main cations (Li⁺, Na⁺, Mg²⁺, K⁺, Ca²⁺, and Sr²⁺) as well as the main anions (SO₄²⁻ and Cl⁻) using the documented method. This study demonstrates that there is significant potential for developing field-based pLIBS as a tool for quantitative water analysis. Full article

(This article belongs to the Special Issue Advanced Real-Time On-Site Sensing Technologies in Food and Environment Analysis)

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Open AccessArticle

Application of an Electronic Nose Technology for the Prediction of Chemical Process Contaminants in Roasted Almonds

Marta Mesías

Juan Diego Barea-Ramos

Jesús Lozano

Francisco J. Morales

and

Daniel Martín-Vertedor

Chemosensors 2023, 11(5), 287; https://doi.org/10.3390/chemosensors11050287 - 11 May 2023

Viewed by 789

Abstract

The purpose of this study was to investigate the use of an experimental electronic nose (E-nose) as a predictive tool for detecting the formation of chemical process contaminants in roasted almonds. Whole and ground almonds were subjected to different thermal treatments, and the levels of acrylamide, hydroxymethylfurfural (HMF) and furfural were analysed. Subsequently, the aromas were detected by using the electronic device. Roasted almonds were classified as positive or negative sensory attributes by a tasting panel. Positive aromas were related to the intensity of the almond odour and the roasted aroma, whereas negative ones were linked to a burnt smell resulting from high-intensity thermal treatments. The electronic signals obtained by the E-nose were correlated with the content of acrylamide, HMF, and furfural (

R_{C V}^{2}

> 0.83;

R_{P}^{2}

> 0.76 in whole roasted almonds;

R_{C V}^{2}

> 0.88;

R_{P}^{2}

> 0.95 in ground roasted almonds). This suggest that the E-nose can predict the presence of these contaminants in roasted almonds. In conclusion, the E-nose may be a useful device to evaluate the quality of roasted foods based on their sensory characteristics but also their safety in terms of the content of harmful compounds, making it a useful predictive chemometric tool for assessing the formation of contaminants during almond processing. Full article

(This article belongs to the Special Issue Advanced Real-Time On-Site Sensing Technologies in Food and Environment Analysis)

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Open AccessArticle

Monitoring of MSW Incinerator Leachate Using Electronic Nose Combined with Manifold Learning and Ensemble Methods

and

Chemosensors 2022, 10(12), 506; https://doi.org/10.3390/chemosensors10120506 - 30 Nov 2022

Viewed by 923

Abstract

Waste incineration is regarded as an ideal method for municipal solid waste disposal (MSW), with the advantages of waste-to-energy, lower secondary pollution, and greenhouse gas emission mitigation. For incineration leachate, the information from the headspace gas that varies at different processing processes and might be useful for chemical analysis, is ignored. The study applied a novel electronic nose (EN) to mine the information from leachate headspace gas. By combining manifold learnings (principal component analysis (PCA) and isometric feature mapping (ISOMAP), and uniform manifold approximation and projection (UMAP) and ensemble techniques (light gradient boosting machine (lightGBM) and extreme gradient boosting (XGBT)), EN based on the UMAP-XGBT model had the best classification performance with a 99.95% accuracy rate in the training set and a 95.83% accuracy rate in the testing set. The UMAP-XGBT model showed the best prediction ability for leachate chemical parameters (pH, chemical oxygen demand, biochemical oxygen demand, ammonia, and total phosphorus), with R² higher than 0.99 both in the training and testing sets. This is the first study of the EN application for leachate monitoring, offering an easier and quicker detection method than traditional instrumental measurements for the enforcement and implementation of effective monitoring programs. Full article

(This article belongs to the Special Issue Advanced Real-Time On-Site Sensing Technologies in Food and Environment Analysis)

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Figure 1

Open AccessArticle

Design and Optimization of Electronic Nose Sensor Array for Real-Time and Rapid Detection of Vehicle Exhaust Pollutants

and

Chemosensors 2022, 10(12), 496; https://doi.org/10.3390/chemosensors10120496 - 22 Nov 2022

Cited by 1 | Viewed by 791

Abstract

Traditional vehicle exhaust pollutant detection methods, such as bench test and remote sensing detection, have problems such as large volume, high cost, complex process, long waiting time, etc. In this paper, according to the main components of vehicle exhaust pollutants, an electronic nose with 12 gas sensors was designed independently for real-time and rapid detection of vehicle exhaust pollutants. In order to verify that the designed electronic nose based on machine learning classification method can accurately identify the exhaust pollutants from different engines or different concentration levels from the same engine. After feature extraction of the collected data, Random Forest (RF) was used as the classifier, and the average classification accuracy reached 99.92%. This result proved that the designed electronic nose combined with RF method can accurately and sensitively judge the concentration level of vehicle exhaust pollutants.. Then, in order to enable the electronic nose to be vehicle-mounted and to achieve real-time and rapid detection of vehicle exhaust pollutants. We used Recursive Feature Elimination with Cross Validation (RFECV), Random Forest Feature Selector (RFFS) and Principal Component Analysis (PCA) to optimize the sensor array. The results showed that these methods can effectively simplify the sensor array while ensuring the RF classifier’s classification recognition rate. After using RFECV and RFFS to optimize the sensor array, the RF classifier’s classification recognition rate of the optimized sensor arrays for vehicle exhaust pollutants reached 99.77% and 99.44%, respectively. The numbers of sensors in the optimized sensor arrays were six and eight respectively, which achieved the miniaturization and low-cost of the electronic nose. With the limitation of six sensors, RFECV is the best sensor array optimization method among the three methods. Full article

(This article belongs to the Special Issue Advanced Real-Time On-Site Sensing Technologies in Food and Environment Analysis)

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Open AccessArticle

Real-Time Measurement of Moisture Content of Paddy Rice Based on Microstrip Microwave Sensor Assisted by Machine Learning Strategies

Jin Liu

Shanshan Qiu

and

Zhenbo Wei

Chemosensors 2022, 10(10), 376; https://doi.org/10.3390/chemosensors10100376 - 20 Sep 2022

Cited by 3 | Viewed by 1424

Abstract

Moisture content is extremely imoprtant to the processes of storage, packaging, and transportation of grains. In this study, a portable moisture measuring device was developed based on microwave microstrip sensors. The device is composed of three parts: a microwave circuit module, a real-time measurement module, and software to display the results. This work proposes an improvement measure by optimizing the thickness of paddy rice samples (8–13 cm) and adding the ambient temperatures and the moisture contents (13.66–27.02% w.b.) at a 3.00 GHz frequency. A random forest, decision tree, k-nearest neighbor, and support vector machine were applied to predict the moisture content in the paddy rice. Microwave characteristics, phase shift, and temperature compensation were selected as the input variables to the prediction models, which have achieved high accuracy. Among those prediction models, the random forest model yielded the best performance with highest accuracy and stability (R² = 0.99, RMSE = 0.28, MAE = 0.26). The device showed a relatively stable performance (the maximum average absolute error was 0.55%, the minimum absolute error was 0.17%, the mean standard deviation was 0.18%, the maximum standard deviation was 0.41%, and the minimum standard deviation was 0.08%) within the moisture content range of 13–30%. The instrument has the advantages of real-time, simple structure, convenient operation, low cost, and portability. This work is expected to provide an important reference for the real-time in situ measurement of agricultural products, and to be of great significance for the development of intelligent agricultural equipment. Full article

(This article belongs to the Special Issue Advanced Real-Time On-Site Sensing Technologies in Food and Environment Analysis)

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