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Chemometrics for Multisensor Systems and Artificial Senses

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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: closed (1 July 2022) | Viewed by 29597

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Special Issue Editors

Dr. Larisa Lvova

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Department of Chemical Sciences and Technology, University "Tor Vergata", Via della Ricerca Scientifica, 00133 Rome, Italy
Interests: chemical sensors; multisensor analysis; chemometrics
Special Issues, Collections and Topics in MDPI journals

Dr. Alisa Rudnitskaya

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Guest Editor

CESAM and Department of Chemistry, University of Aveiro, 3810-193 Aveiro, Portugal
Interests: multisensor systems; electronic tongues; electroanalysis; chemometrics; food analysis; environmental analysis; electrochemical sensors and biosensors
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Prof. Dr. Federico Marini

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Guest Editor

Department of Chemistry, Sapienza University of Rome, 00185 Rome, Italy
Interests: chemometrics; analytical chemistry; chemistry; spectroscopy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Multisensor systems–electronic noses and tongues are analytical devices based on an array of partially selective chemical sensors or biosensors and chemometric-based data processing. Called also artificial intelligent sensor systems, electronic noses and tongues have become popular analytical tools during the last two decades, and a wide range of their applications has been put forward, including the classification of samples according to the properties of interest, quantification, process control, and taste and flavor assessment.

As electronic noses and tongues generate nonselective responses, their practical applications rely on advanced data processing, which has become an integral part of these analytical systems today and plays a critical role in assuring the quality and success of conducted analyses.

The data from multisensor systems can be obtained with different transduction methods: electrochemical, optical, etc., and registered with simple, household devices (such as PC, smartphones, tablets). As a result, artificial sensing systems may provide fast and inexpensive analysis through the generation and extraction of reliable chemical information from unresolved complex output signals through chemometric tools.

Moreover, chemometric data processing is employed for various specific purposes, among which how to increase the selectivity of information, to make further modeling less complicated, or to improve predictive accuracy. Different processing methods use different rules and therefore are efficient in extracting information in diverse situations. Parallelly, experimental signals may have a different degree of complexity, ranging from typical chemical signals to images or multidimensional arrays resulting from modern coupled/hyphenated devices. The aim of the present Special Issue is to report recent advances in chemometrics for analytical practice, including progress in sensor materials and multisensor system development, achievements in intelligent signal processing algorithms and methods, an overview of novel measuring techniques, and practical applications.

This Special Issue on “Chemometrics for Multisensor Systems and Artificial Senses” will include but is not limited to the following topics:

Development and applications of chemical sensors and multisensor systems;
Chemometric approaches in multivariate signal processing;
Intelligent data processing algorithms for analytical signal sampling and quantization;
Chemometric feature extraction and separation of overlapping components;
Signal normalization, standardization, optimization, and baseline correction;
Software for signal processing.

New research and ideas for novel chemical sensors and multisensor systems development and application, including signal processing details, are strongly invited to be a part of this Special Issue. We hope to inspire further interest and new research efforts in this exciting area.

Dr. Larisa Lvova
Dr. Alisa Rudnitskaya
Dr. Federico Marini
Guest Editors

If you want to learn more information or need any advice, you can contact the Special Issue Editor Tammy Zhang via <[email protected]> directly.

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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.

Keywords

sensors
multisensor systems
electronic nose (e-nose)
electronic tongue (e-tongue)
chemometrics
intelligent signal processing algorithms

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Published Papers (9 papers)

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Research

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13 pages, 2349 KiB

Open AccessArticle

Optical Multisensor System Based on Lanthanide(III) Complexes as Near-Infrared Light Sources for Analysis of Milk

by Anastasiia Surkova, Andrey Bogomolov, Aleksandra Paderina, Viktoria Khistiaeva, Ekaterina Boichenko, Elena Grachova and Dmitry Kirsanov

Chemosensors 2022, 10(7), 288; https://doi.org/10.3390/chemosensors10070288 - 20 Jul 2022

Cited by 2 | Viewed by 1966

Abstract

Optical multisensor systems are easy-to-use and inexpensive analytical devices. In this work, we propose an optical multisensor system based on the luminescence of Nd(III) and Yb(III) complexes in the near-infrared (NIR) spectral region. The observed emission bands play the role of secondary light sources for further analysis of milk—for the determination of fat content and for the recognition of adulteration. The samples for analysis were prepared by putting a drop of milk upon a thin glass covering the powdered mixture of lanthanide complexes, which were excited by a light-emitting diode (LED) in the ultraviolet region (the maximum intensity at 365 nm). The diffuse-reflectance spectra of samples were acquired in the short-wave NIR range 750–1100 nm using a portable NIR spectrometer. The developed optical system was tested using two sets of milk samples with varying concentration levels of fat and added urea. The obtained spectral data were analyzed using a number of multivariate prediction and classification methods of chemometrics and the results were statistically compared. The regression and classification model performances achieved in this proof-of-concept study illustrate the feasibility of the optical multisensor analysis based on luminescent light sources in the short-wave NIR range, in particular, for their application in the dairy. Full article

(This article belongs to the Special Issue Chemometrics for Multisensor Systems and Artificial Senses)

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13 pages, 5328 KiB

Open AccessEditor’s ChoiceArticle

Digital Detection of Olive Oil Rancidity Levels and Aroma Profiles Using Near-Infrared Spectroscopy, a Low-Cost Electronic Nose and Machine Learning Modelling

by Claudia Gonzalez Viejo and Sigfredo Fuentes

Chemosensors 2022, 10(5), 159; https://doi.org/10.3390/chemosensors10050159 - 26 Apr 2022

Cited by 9 | Viewed by 3635

Abstract

The success of the olive oil industry depends on provenance and quality-trait consistency affecting the consumers' acceptability/preference and purchase intention. Companies rely on laboratories to analyze samples to assess consistency within the production chain, which may be time-consuming, cost-restrictive, and untimely obtaining results, making the process more reactive than predictive. This study proposed implementing digital technologies using near-infrared spectroscopy (NIR) and a novel low-cost e-nose to assess the level of rancidity and aromas in commercial extra-virgin olive oil. Four different olive oils were spiked with three rancidity levels (N = 17). These samples were evaluated using gas-chromatography-mass-spectroscopy, NIR, and an e-nose. Four machine learning models were developed to classify olive oil types and rancidity (Model 1: NIR inputs; Model 2: e-nose inputs) and predict the peak area of 16 aromas (Model 3: NIR; Model 4: e-nose inputs). The results showed high accuracies (Models 1–2: 97% and 87%; Models 3–4: R = 0.96 and 0.93). These digital technologies may change companies from a reactive to a more predictive production of food/beverages to secure product quality and acceptability. Full article

(This article belongs to the Special Issue Chemometrics for Multisensor Systems and Artificial Senses)

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17 pages, 1623 KiB

Open AccessArticle

Grape Cultivar Identification and Classification by Machine Olfaction Analysis of Leaf Volatiles

by Ali Khorramifar, Hamed Karami, Alphus Dan Wilson, Amir Hosein Afkari Sayyah, Anastasiia Shuba and Jesús Lozano

Chemosensors 2022, 10(4), 125; https://doi.org/10.3390/chemosensors10040125 - 29 Mar 2022

Cited by 26 | Viewed by 3294

Abstract

Development of electronic technologies for precise identification of fruit crop cultivars in agricultural production provides an effective means for assuring product quality and authentication. The capabilities of discriminating between grape (Vitis vinifera L.) cultivars is essential for assuring certification of varieties sold in world markets. Machine olfaction, based on electronic-nose (e-nose) technologies, is readily available for rapid identification of fruit and vegetative agricultural products. This technology relies on detection of and discrimination between volatile organic compound (VOC) emissions from plant parts. It may be used in all stages of agricultural production to facilitate crop maintenance, cultivation, and harvesting decisions prior to marketing. An experimental e-nose device was constructed and tested in combination with five chemometric methods, including PCA, LDA, QDA, SVM, and ANN, as rapid, non-destructive tools for identification and classification of grape cultivars. An e-nose instrument equipped with nine metal oxide semiconductor (MOS) sensors was utilized to identify and classify five grape cultivars based on leaf VOC emissions using supervised and non-supervised methods. Grape leaf samples were first identified as belonging to specific cultivar types using PCA analyses, which are non-supervised classification methods, with the first two principal components (PC-1 and PC-2) accounting for 89% of the total variance. Four supervised statistical methods were further tested, including DA, QDA, SVM, and ANN, and provided effective discrimination accuracies of 98%, 99%, 92%, and 99%, respectively. These findings confirmed the suitable applicability of an MOS e-nose sensor array with supervised methods for accurate identification of grape cultivars, which is useful for authentication of vine cultivar types for commercial markets. Full article

(This article belongs to the Special Issue Chemometrics for Multisensor Systems and Artificial Senses)

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17 pages, 3681 KiB

Open AccessArticle

Fast GC E-Nose and Chemometrics for the Rapid Assessment of Basil Aroma

by Lorenzo Strani, Alessandro D’Alessandro, Daniele Ballestrieri, Caterina Durante and Marina Cocchi

Chemosensors 2022, 10(3), 105; https://doi.org/10.3390/chemosensors10030105 - 10 Mar 2022

Cited by 10 | Viewed by 3104

Abstract

The aim of this work is to assess the potentialities of the synergistic combination of an ultra-fast chromatography-based electronic nose as a fingerprinting technique and multivariate data analysis in the context of food quality control and to investigate the influence of some factors, i.e., basil variety, cut, and year of crop, in the final aroma of the samples. A low = level data fusion approach coupled with Principal Component Analysis (PCA) and ANOVA—Simultaneous Component Analysis (ASCA) was used in order to analyze the chromatographic signals acquired with two different columns (MXT-5 and MXT-1701). While the PCA analysis results highlighted the peculiarity of some basil varieties, differing either by a higher concentration of some of the detected chemical compounds or by the presence of different compounds, the ASCA analysis pointed out that variety and year are the most relevant effects, and also confirmed the results of previous investigations. Full article

(This article belongs to the Special Issue Chemometrics for Multisensor Systems and Artificial Senses)

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10 pages, 1009 KiB

Open AccessArticle

Nonlinear Multivariate Regression Algorithms for Improving Precision of Multisensor Potentiometry in Analysis of Spent Nuclear Fuel Reprocessing Solutions

by Nadan Kravić, Julia Savosina, Marina Agafonova-Moroz, Vasily Babain, Andrey Legin and Dmitry Kirsanov

Chemosensors 2022, 10(3), 90; https://doi.org/10.3390/chemosensors10030090 - 25 Feb 2022

Cited by 5 | Viewed by 2803

Abstract

Potentiometric multisensor systems were shown to be very promising tools for the quantification of numerous analytes in complex radioactive samples deriving from spent nuclear fuel reprocessing. Traditional multivariate calibration for these multisensor systems is performed with partial least squares regression—an intrinsically linear regression method that can provide suboptimal results when handling potentiometric signals from very complex multi-component samples. In this work, a thorough investigation was performed on the performance of a multisensor system in combination with non-linear multivariate regression models for the quantification of analytes in the PUREX (Plutonium–URanium EXtraction) process. The multisensor system was composed of 17 cross-sensitive potentiometric sensors with plasticized polymeric membranes containing different lipophilic ligands capable of heavy metals, lanthanides, and actinides binding. Regression algorithms such as support vector machines (SVM), random forest (RF), and kernel-regularized least squares (KRLS) were tested and compared to the traditional partial least squares (PLS) method in the simultaneous quantification of the following elements in aqueous phase samples of the PUREX process: U, La, Ce, Sm, Zr, Mo, Zn, Ru, Fe, Ca, Am, and Cm. It was shown that non-linear methods outperformed PLS for most of the analytes. Full article

(This article belongs to the Special Issue Chemometrics for Multisensor Systems and Artificial Senses)

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17 pages, 4464 KiB

Open AccessArticle

Carbocyanine-Based Fluorescent and Colorimetric Sensor Array for the Discrimination of Medicinal Compounds

by Anna V. Shik, Irina A. Stepanova, Irina A. Doroshenko, Tatyana A. Podrugina and Mikhail K. Beklemishev

Chemosensors 2022, 10(2), 88; https://doi.org/10.3390/chemosensors10020088 - 19 Feb 2022

Cited by 9 | Viewed by 3173

Abstract

Array-based optical sensing is an efficient technique for the determination and discrimination of small organic molecules. This study is aimed at the development of a simple and rapid strategy for obtaining an optical response from a wide range of low-molecular-weight organic compounds. We have suggested a colorimetric and fluorimetric sensing platform based on the combination of two response mechanisms using carbocyanine dyes: aggregation and oxidation. In the first one, the analyte forms ternary aggregates with an oppositely charged surfactant wherein the dye is solubilized in the hydrophobic domains of the surfactant accompanied with fluorescent enhancement. The second mechanism is based on the effect of the analyte on the catalytic reaction rate of dye oxidation with H₂O₂ in the presence of a metal ion (Cu²⁺, Pd²⁺), which entails fluorescence waning and color change. The reaction mixture in a 96-well plate is photographed in visible light (colorimetry) and the near-IR region under red light excitation (fluorimetry). In this proof-of-concept study, we demonstrated the feasibility of discrimination of nine medicinal compounds using principal component analysis: four cephalosporins (ceftriaxone, cefazolin, ceftazidime, cefotaxime), three phenothiazines (promethazine, promazine, chlorpromazine), and two penicillins (benzylpenicillin, ampicillin) in an aqueous solution and in the presence of turkey meat extract. The suggested platform allows simple and rapid recognition of analytes of various nature without using spectral equipment, except for a photo camera. Full article

(This article belongs to the Special Issue Chemometrics for Multisensor Systems and Artificial Senses)

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14 pages, 2410 KiB

Open AccessArticle

Comparison of Various Signal Processing Techniques and Spectral Regions for the Direct Determination of Syrup Adulterants in Honey Using Fourier Transform Infrared Spectroscopy and Chemometrics

by Gerard Dumancas, Helena Ellis, Jossie Neumann and Khalil Smith

Chemosensors 2022, 10(2), 51; https://doi.org/10.3390/chemosensors10020051 - 28 Jan 2022

Cited by 3 | Viewed by 3488

Abstract

Honey consumption has become increasingly popular worldwide. However, the increase in demand for honey has also caused an increase in its adulteration, a deliberate fraud which involves adding of other substances to pure honey for economic purposes. This process not only lowers the quality of honey, but also has potential health risks, including high blood sugar, increased risk of diabetes, and weight gain. Herein, we develop an easy-to-use and direct method of quantifying corn, cane, beet, and rice syrup adulterants in honey using Fourier transform infrared spectroscopy and chemometrics. Various signal processing techniques, including derivatives, moving average, binning, Savitzky–Golay, and standard normal variate using the entire spectral region (3996–650 cm⁻¹) and specific spectral region (1501–799 cm⁻¹), were compared. Optimum results were obtained using first derivative signal processing for both the entire and specific spectral regions. The first derivative signal processing technique garnered the most optimum results using the specific spectral range (1501–799 cm⁻¹) (RMSECV_average = 0.021, RMSEP_average = 0.014, R²_average = 0.859) across all syrup adulterants. An exploratory analysis to assess the utility of this specific spectral region in pattern recognition of samples based on their adulterant content show that this region is effective in discriminating samples according to the presence or absence of honey syrup adulterants. Full article

(This article belongs to the Special Issue Chemometrics for Multisensor Systems and Artificial Senses)

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13 pages, 2936 KiB

Open AccessArticle

Recording the Fragrance of 15 Types of Medicinal Herbs and Comparing Them by Similarity Using the Electronic Nose FF-2A

by Emi Tsuchitani, Mayumi Nomura, Miyuki Ota, Erika Osada, Nobutake Akiyama, Yumi Kanegae, Takeo Iwamoto, Ryuhei Yamaoka and Yoshinobu Manome

Chemosensors 2022, 10(1), 20; https://doi.org/10.3390/chemosensors10010020 - 5 Jan 2022

Cited by 1 | Viewed by 3303

Abstract

Medical herbs have been recognized till now as having different constituents that act on the human body. However, the fragrance of herbs is a complex mixture of odors, which makes it difficult to qualify or quantify the scent objectively on the human sense of smell. In this study, aromas of 15 medicinal herbs were recorded using an electronic nose FF-2A, and their characteristics were compared with aroma samples of wine such as Le Nez du Vin, to determine which wine aromas are similar to each medicinal herb. Thereafter, the aromas of the 15 herbs were standardized to create a reference axis for the aroma of each herb, and the similarity of tea herbs to the reference axis was examined. Additionally, the results were compared with those obtained by gas chromatography-mass spectrometry (GC-MS). In FF-2A, the measured scent is recorded as an absolute value. We succeeded in calculating the similarity of the scents of other herbs with the axes of the scent of each herb by standardizing their scents and creating new axis data. Conversely, although GC-MS is able to identify the components and concentrations of fragrances, an electronic nose can analyze fragrances in a way that is uncommon with GC-MS, such as comparison of similarities between fragrances. Full article

(This article belongs to the Special Issue Chemometrics for Multisensor Systems and Artificial Senses)

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Review

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17 pages, 1169 KiB

Open AccessReview

Electronic Nose and Tongue for Assessing Human Microbiota

by Alessandro Tonacci, Alessandro Scafile, Lucia Billeci and Francesco Sansone

Chemosensors 2022, 10(2), 85; https://doi.org/10.3390/chemosensors10020085 - 17 Feb 2022

Cited by 6 | Viewed by 3353

Abstract

The technological developments of recent times have allowed the use of innovative approaches to support the diagnosis of various diseases. Many of such clinical conditions are often associated with metabolic unbalance, in turn producing an alteration of the gut microbiota even during asymptomatic stages. As such, studies regarding the microbiota composition in biological fluids obtained by humans are continuously growing, and the methodologies for their investigation are rapidly changing, making it less invasive and more affordable. To this extent, Electronic Nose and Electronic Tongue tools are gaining importance in the relevant field, making them a useful alternative—or support—to traditional analytical methods. In light of this, the present manuscript seeks to investigate the development and use of such tools in the gut microbiota assessment according to the current literature. Significant gaps are still present, particularly concerning the Electronic Tongue systems, however the current evidence highlights the strong potential such tools own to enter the daily clinical practice, with significant advancement concerning the patients’ acceptability and cost saving for healthcare providers. Full article

(This article belongs to the Special Issue Chemometrics for Multisensor Systems and Artificial Senses)

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