Monitoring saliva compositions for non-invasive detection of diabetes using a colorimetric-based multiple sensor.

The increasing population of diabetic patients, especially in developing countries, has posed a serious risk to the health sector, so that the lack of timely diagnosis and treatment process of diabetes can lead to threatening complications for the human lifestyle. Here, a multiple sensor was fabricated on a paper substrate for rapid detection and controlling the progress of the diabetes disease. The proposed sensor utilized the sensing ability of porphyrazines, pH-sensitive dyes and silver nanoparticles in order to detect the differences in saliva composition of diabetic and non-diabetic patients. A unique color map (sensor response) was obtained for each studied group, which can be monitored by a scanner. Moreover, a good correlation was observed between the colorimetric response resulting from the analysis of salivary composition and the fasting blood glucose (FBG) value measured by standard laboratory instruments. It was also possible to classify participants into two groups, including patients caused by diabetes and those were non-diabetic persons with a total accuracy of 88.9%. Statistical evaluations show that the multiple sensor can be employed as an effective and non-invasive device for continuous monitoring of diabetes, substantially in the elderly.

An origami-based colorimetric sensor for detection of hydrogen peroxide and glucose using sericin capped silver nanoparticles.

The hydrogen peroxide (H2O2) measurement is considered highly important in industrial wastewater quality assessment, environmental protection, and disease detection. Here, a simple high-performance paper-based sensor is proposed for rapid and in situ detection of H2O2. To this end, 3,3',5,5'-tetramethylbenzidine is embedded in the sensor to act as a color indicator, whose reaction with hydrogen peroxide is catalyzed by a silver nanozyme modified by sericin. The result of the reaction clarified by the appearance of blue color in the sensor detection zone is received by a portable scanner, while also calculating its intensity by image analysis software. This method is sensitive to hydrogen peroxide in the concentration range of 0.5‒240 mg/dL, providing a detection limit of 0.15 mg/dL. The ability of the sensor to determine glucose is also evaluated by adding a layer containing glucose oxidase enzyme to the sensor structure. A desirable response is obtained in the range of 1.0‒160 mg/dL, together with a detection limit of 0.37 mg/dL. Accordingly, the proposed sensor shows satisfactory results compared to clinical methods for monitoring the amount of glucose in biological samples such as serum and saliva.

Visual diagnosis of COVID-19 disease based on serum metabolites using a paper-based electronic tongue.

This study aims to use a paper-based sensor array for point-of-care detection of COVID-19 diseases. Various chemical compounds such as nanoparticles, organic dyes and metal ion complexes were employed as sensing elements in the array fabrication, capturing the metabolites of human serum samples. The viral infection caused the type and concentration of serum compositions to change, resulting in different color responses for the infected and control samples. For this purpose, 118 serum samples of COVID-19 patients and non-COVID controls both men and women with the age range of 14-88 years were collected. The serum samples were initially subjected to the sensor, followed by monitoring the variation in the color of sensing elements for 5 min using a scanner. By taking into consideration the statistical information, this method was capable of discriminating COVID-19 patients and control samples with 83.0% accuracy. The variation of age did not influence the colorimetric patterns. The desirable correlation was observed between the sensor responses and viral load values calculated by the PCR test, proposing a rapid and facile way to estimate the disease severity. Compared to other rapid detection methods, the developed assay is cost-effective and user-friendly, allowing for screening COVID-19 diseases reliably.

Non-invasive detection of COVID-19 using a microfluidic-based colorimetric sensor array sensitive to urinary metabolites.

A colorimetric sensor array designed on a paper substrate with a microfluidic structure has been developed. This array is capable of detecting COVID-19 disease by tracking metabolites of urine samples. In order to determine minor metabolic changes, various colorimetric receptors consisting of gold and silver nanoparticles, metalloporphyrins, metal ion complexes, and pH-sensitive indicators are used in the array structure. By injecting a small volume of the urine sample, the color pattern of the sensor changes after 7 min, which can be observed visually. The color changes of the receptors (recorded by a scanner) are subsequently calculated by image analysis software and displayed as a color difference map. This study has been performed on 130 volunteers, including 60 patients infected by COVID-19, 55 healthy controls, and 15 cured individuals. The resulting array provides a fingerprint response for each category due to the differences in the metabolic profile of the urine sample. The principal component analysis-discriminant analysis confirms that the assay sensitivity to the correctly detected patient, healthy, and cured participants is equal to 73.3%, 74.5%, and 66.6%, respectively. Apart from COVID-19, other diseases such as chronic kidney disease, liver disorder, and diabetes may be detectable by the proposed sensor. However, this performance of the sensor must be tested in the studies with a larger sample size. These results show the possible feasibility of the sensor as a suitable alternative to costly and time-consuming standard methods for rapid detection and control of viral and bacterial infectious diseases and metabolic disorders.

Mask assistance to colorimetric sniffers for detection of Covid-19 disease using exhaled breath metabolites.

According to World Health Organization reports, large numbers of people around the globe have been infected or died for Covid-19 due to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Researchers are still trying to find a rapid and accurate diagnostic method for revealing infected people by low viral load with the overriding goal of effective diagnostic management. Monitoring the body metabolic changes is known as an effective and inexpensive approach for the evaluation of the infected people. Here, an optical sniffer is introduced to detect exhaled breath metabolites of patients with Covid-19 (60 samples), healthy humans (55 samples), and cured people (15 samples), providing a unique color pattern for differentiation between the studied samples. The sniffer device is installed on a thin face mask, and directly exposed to the exhaled breath stream. The interactions occurring between the volatile compounds and sensing components such as porphyrazines, modified organic dyes, porphyrins, inorganic complexes, and gold nanoparticles allowing for the change of the color, thus being tracked as the sensor responses. The assay accuracy for the differentiation between patient, healthy and cured samples is calculated to be in the range of 80%-84%. The changes in the color of the sensor have a linear correlation with the disease severity and viral load evaluated by rRT-PCR method. Interestingly, comorbidities such as kidney, lung, and diabetes diseases as well as being a smoker may be diagnosed by the proposed method. As a powerful detection device, the breath sniffer can replace the conventional rapid test kits for medical applications.

A colorimetric electronic tongue for point-of-care detection of COVID-19 using salivary metabolites.

The monitoring of profile concentrations of chemical markers in saliva samples can be used to diagnose COVID-19 patients, and differentiate them from healthy individuals. Here, this purpose is achieved by designing a paper-based colorimetric sensor with an origami structure, containing general receptors such as pH-sensitive organic dyes, Lewis donors or acceptors, functionalized nanoparticles, and ion metal complexes. The color changes taking place in the receptors in the presence of chemical markers are visually observed and recorded with a digital instrument. Different types and amounts of the chemical markers provide the sensor with a unique response for patients (60 samples) or healthy (55 samples) individuals. These two categories can be discriminated with 84.3% accuracy. This study evidences that the saliva composition of cured and healthy participants is different from each other with accuracy of 85.7%. Moreover, viral load values obtained from the rRT-PCR method can be estimated by the designed sensor. Besides COVID-19, it may possible to simultaneously identify smokers and people with kidney disease and diabetes using the specified electronic tongue. Due to its high efficiency, the prepared paper device can be employed as a rapid detection kit to detect COVID-19.

Paper-Based Optical Nose Made with Bimetallic Nanoparticles for Monitoring Ignitable Liquids in Gasoline.

A paper-based optical nose was fabricated by dropping bimetallic silver and gold nanoparticles on a paper substrate. The nanoparticles were synthesized by both natural (lemon, pomegranate, and orange juices) and chemical (citrate, gallic acid, and ascorbic acid) reducing agents. The performance of the assay was evaluated for identifying gasoline and five ignitable liquids such as diesel, ethanol, methanol, kerosene, and thinner. The interaction of the sensor with sample vapors caused aggregation, consequently changing the color of nanoparticles. The color changes, which were captured by a scanner, represented a specified colorimetric map for each analyte, allowing one to identify the studied fuels. The visual results were confirmed using multivariate statistical analysis such as principal component analysis and hierarchical clustering analysis. Also, partial least-squares regression was used to assist the proposed assay for estimating the amount of studied ignitable liquids as counterfeit species in the gasoline sample. The root-mean-square errors for prediction were 3.4, 2.1, 1.9, 2.0, and 1.7% for diesel, thinner, kerosene, ethanol, and methanol, respectively. Finally, the fabricated sensor indicated high efficiency for the on-site detection of pure industrial gasoline samples from adulterated ones.

Disposable Paper-Based Biosensors for the Point-of-Care Detection of Hazardous Contaminations-A Review.

The fast detection of trace amounts of hazardous contaminations can prevent serious damage to the environment. Paper-based sensors offer a new perspective on the world of analytical methods, overcoming previous limitations by fabricating a simple device with valuable benefits such as flexibility, biocompatibility, disposability, biodegradability, easy operation, large surface-to-volume ratio, and cost-effectiveness. Depending on the performance type, the device can be used to analyze the analyte in the liquid or vapor phase. For liquid samples, various structures (including a dipstick, as well as microfluidic and lateral flow) have been constructed. Paper-based 3D sensors are prepared by gluing and folding different layers of a piece of paper, being more user-friendly, due to the combination of several preparation methods, the integration of different sensor elements, and the connection between two methods of detection in a small set. Paper sensors can be used in chromatographic, electrochemical, and colorimetric processes, depending on the type of transducer. Additionally, in recent years, the applicability of these sensors has been investigated in various applications, such as food and water quality, environmental monitoring, disease diagnosis, and medical sciences. Here, we review the development (from 2010 to 2021) of paper methods in the field of the detection and determination of toxic substances.

Origami paper analytical assay based on metal complex sensor for rapid determination of blood cyanide concentration in fire survivors.

Cyanide-based blood poisoning can seriously damage fire victims and cause death if not detected quickly. Previous conventional methods require laboratory equipment, which are expensive and increase the duration of the analysis. Here, a simple origami based microfluidic device was introduced for point of need detection of blood cyanide concentration in people involved in fire. The device is made of four layers of paper. Each layer was in the size of 1 × 1 cm folded on each other. In this work, the blood sample was acidified by trichloroacetic acid to separate cyanide from methaemoglobin in the form of HCN gas. The produced gas released into borate buffer to recover free cyanide ions which interacted with the Pt complex ([Pt(p-MeC6H4)2(phen)]) used as a receptor in this study. Optimized conditions were applied to have a suitable interaction causing the color of the receptor to change from yellow to colorless. The color changes were recorded by a smartphone, and the sensor response was calculated by the routine image analysis software. The assay was capable of determining cyanide ions at different concentrations in the range of 1.0 to 100.0 µmol L-1. The detection limit of these determination was equal to 0.4 µmol L-1. The assay responses were not affected by the interfering species. As a practical analysis, the proposed sensor was applied to determine cyanide ions in the blood sample of 20 studied fire survivors and 10 controls with high accuracy.

A non-invasive tool for early detection of acute leukemia in children using a paper-based optoelectronic nose based on an array of metallic nanoparticles.

Volatile organic compounds (VOCs) in blood samples can be used as useful biomarkers to diagnose various human diseases. This study describes the potential of a paper-based sensor array for detecting leukemia using blood VOCs. Blood samples were collected from 59 new leukemia cases and 47 healthy cases as a control group. Each blood sample was divided into two parts; one for a laboratory test and the other was used in our study. Samples were mixed with heparin and then transferred to a sterile container, and a sensor was stacked on its cap. This sensor array contains 16 nanoparticles deposited on a sheet of hydrophobic paper in a 4 × 4 array format. Containers were stored in an oven at 60 °C for 4.5 h. Then, the image of sensors was recorded by a scanner and compared to the image before exposing the blood vapor. The sensor responses were subjected to different multivariate statistical methods to develop models that discriminate between control and leukemia samples. The interaction of nanoparticles with the volatile metabolome of blood caused aggregation and consequently changing in the color of nanoparticles. The color changes resulted in a specific pattern for blood samples with leukemia, which is different from those obtained from healthy specimens. The discrimination analysis was approved by pattern recognition methods such as principal component analysis with 97% accuracy. Among 59 patients, the mean age was 6.02 ± 4.55 years (range 1-16 y). The mean total response was 652.83 ± 117.02. The rock curve showed an accuracy of 96% for classifying patients from the control group. The logistic regression model showed that 93.6% of healthy and 93.2% of patients were classified correctly by using this method. These statistics agree with the classification results obtained by principal component analysis. For every 5000-unit increase in platelet count, the chance of leukemia decreased by 9%. Additionally, the chance of being categorized as a patient decreased by 10% for every 20-unit increase in total response. The electronic nose using VOC's of blood is a non-invasive and inexpensive tool for detecting new cases of leukemia with high sensitivity and specificity. Platelet count is an essential para-clinical parameter determining the total response of the sensors. Follow up studies with a larger sample size are warranted to elucidate its clinical applicability.

A paper-based colorimetric sensor array for discrimination of monofloral European honeys based on gold nanoparticles and chemometrics data analysis.

A sensitive and simple nanomaterial based colorimetric sensor array (NBCSA) was developed for discrimination of monofloral honey from various European countries based on three botanical origins as Acacia, Canola and Honeydew. The NBCSA was designed by spotting gold (AuNPs) and silver (AgNPs) nanoparticles synthesized using six different reducing and/or capping agents. The colour intensity of AuNPs represented differential changes when interacting with volatile organic compounds appeared in the headspace of the honey samples. The color difference maps, which are calculated as the difference between color intensity of the sensor before and after exposing to the sample vapors, were used as a fingerprint to discriminate the honey samples based on botanical origin. Classification was achieved utilizing data pre-processing and chemometrics data analysis. Fitting accuracies of 88% and 86% were obtained by partial least squares discriminant analysis and linear discriminant analysis whereas 100% was achieved using support vector machine.

A paper-based colorimetric sensor array for discrimination and simultaneous determination of organophosphate and carbamate pesticides in tap water, apple juice, and rice.

A colorimetric paper-based sensor is proposed for the rapid monitoring of six major organophosphate and carbamate pesticides. The assay was constructed by dropping gold and silver nanoparticles on the hydrophilic zones of a paper substrate. The nanoparticles were modified by L-arginine, quercetin, and polyglutamic acid. The mechanism of sensing is based on the interaction between the pesticide and the nanoparticles. The color of nanoparticles changed during the interactions. A digital camera recorded these changes. The assay provided a unique response for each studied pesticide. This method can determine six individual pesticides including carbaryl, paraoxon, parathion, malathion, diazinon, and chlorpyrifos. The limit of detection for these pesticides were 29.0, 22.0, 32.0, 17.0, 45.0, and 36.0 ng mL-1, respectively. The assay was applied to simultaneously determine the six studied pesticides in a mixture using the partial least square method (PLS). The root mean square errors of prediction were 11, 8.7, 9.2, 10, 12, and 11 for carbaryl, paraoxon, parathion, malathion, diazinon, and chlorpyrifos, respectively. The paper-based device can differentiate two types of studied pesticide (organophosphate and carbamate) as well as two types of organophosphate structures (oxon and thion). Furthermore, this sensor showed high selectivity to the pesticides in the presence of other potential species (e.g., metal ions, anions, amino acids, sugar, and vitamins). This assay is capable of determining the pesticide compounds in tap water, apple juice, and rice samples.Graphical abstract.

Optoelectronic nose based on an origami paper sensor for selective detection of pesticide aerosols.

This study introduces an applicable colorimetric sensor array for the detection of pesticides in the vapor phase. The array consisted of six metal nanoparticles spotted on the piece of filter paper. 3D-origami pattern was used for the fabrication of a paper-based sensor to decrease the effect of the nanoparticles leaching after exposure to analytes. Exposure to pesticide aerosols caused changes in the color of the array due to the aggregation of nanoparticles. These changes provided selective responses to thion pesticides such as malathion, parathion, chlorpyrifos, and diazinon. The sensing assay could also differentiate between aliphatic and aromatic thions and discriminate amine-containing compounds from the other studied analytes. These finding results are clearly confirmed by both visual detection and multivariate statistical methods. The proposed sensor was successfully developed for the quantitative measurement of pesticide aerosols at a very low concentration. The limit of detection of this method determined for malathion, parathion, chlorpyrifos and diazinon were 58.0, 103.0, 81.0 and 117.0, respectively. Moreover, the array could be employed to simultaneously analyze four studied pesticides. The statistcal results confirmed that the method has high performance for concurrent detection of thions as a major air pollutant without the interference of other species.

Structural Elucidation and Ultrasensitive Analyses of Volatile Organic Compounds by Paper-Based Nano-Optoelectronic Noses.

Paper-based optoelectronic noses (OENs) are being developed based on printing of organic and organometallic reagents on hydrophilic substrates that can visualize the odor of volatiles. In this work, we report for the first time the use of nanoparticles for fabrication of novel paper-based OENs, which represent much higher sensitivity and produce simple but discriminant colorimetric signature of volatile metabolomes. This nano-optoelectronic nose (NOEN) system, which is fabricated by dropping of gold and silver nanoparticles (each synthesized by 8 chemical species) on the paper, gives obvious colorimetric signatures for chemicals having individual or combined functional groups. Owning to their ultrasensitivity, these simple devices need very small amounts of analytes. These devices could detect and discriminate 45 volatile organic compounds in 9 chemical families including phenols, alchohols, ketones, aldehydes, amines, acids, esters, arenes, and hydrocarbons. In addition to excellent discrimination ability, this NOEN sensor shows ultrahigh sensitivity such that could determine volatile compounds with detection limits around or lower than 10 ppb. Moreover, it can be combined with multivariate calibration methods for quantitative analysis of a metabolite in a complex mixture.

An optoelectronic tongue based on an array of gold and silver nanoparticles for analysis of natural, synthetic and biological antioxidants.

A colorimetric array, which can discriminate 20 food antioxidants of natural, synthetic and biological groups, is described. It consists of gold and silver nanoparticles that were synthesized using six different reducing and/or capping agents. The function of the array relies on the interaction of the antioxidants with the nanoparticles which causes aggregation or morphological changes. This, in turn, causes a change in the sensors' colors. The array produces a unique combination of colors for each antioxidant. The resulting colorations are determined by recording the absorbances of the arrays at wavelengths of 405, 450, 490 and 630 nm, or by capturing the images with a digital camera. The discriminatory ability of the array is investigated by principle component analysis and hierarchical cluster analysis. The method was applied to quantitative assay of gallic acid, caffeic acid, catechin, dopamine, citric acid, butylated hydroxytoluene and ascorbic acid. The respective limits of detection are 4.2, 13, 53, 6.9, 47, 3.5 and 43 nM, respectively. The simultaneous determination of 5 different antioxidants is achieved utilizing partial least square regression. The root mean square errors for prediction of the test set are 0.0650, 0.0782, 0.811, 0.0206 and 0.135 nM for gallic acid, catechin, butylated hydroxytoluene, dopamine, and ascorbic acid, respectively. This method demonstrates excellent potential for analysis of antioxidants in beverages such as tea and lemon juice. Graphical abstract Schematic of a method for the simultaneous determination of several antioxidants based on changes in the color of gold and silver nanoparticles. The antioxidants cause aggregation and/or morphological changes which can be detected by using both image analysis or by colorimetry.

PC-ANN assisted to the determination of Vanadium (IV) ion using an optical sensor based on immobilization of Eriochorome Cyanine R on a triacetylcellulose film.

More detailed analytical studies of an optical sensor based on immobilization of Eriochorome Cyanine R (ECR) on a triacetylcellulose film have been described to determine Vanadium (IV) ions in some real samples. The sensor based on complex formation between Vanadium (IV) ions and ECR in acidic media caused the color of the film to change from violet to blue along with the appearance of a strong peak appears at 595 nm. At the optimal conditions, the calibration curve showed a linear range of 9.90×10(-7)-8.25×10(-5)mol L(-1). Vanadium (IV) ions can be detected with a detection limit of 1.03×10(-7)mol L(-1) within 15 min depending on its concentration. Also, the working range was improved by using PC-ANN algorithm. The sensor could regenerate with dilute acetic acid solution and could be completely reversible. The proposed sensor was successfully applied for determining V (IV) ions in environmental water and tea leaves.