Colorimetric Sensor Array for White Wine Tasting

Classification of Strawberry Maturity Stages and Varieties Using Neural Networks Based on Volatile Organic Compounds

Volatile organic compounds (VOCs) are closely associated with the maturity and variety of strawberries. However, the complexity of VOCs hinders their potential application in strawberry classification. This study developed a novel classification workflow using strawberry VOC profiles and machine learning (ML) models for precise fruit classification. A comprehensive VOC dataset was rapidly collected using gas chromatography-ion mobility spectrometry (GC-IMS) from five strawberry varieties at four maturity stages (n = 300) and visualized through principal component analysis (PCA). Five ML models were developed, including partial least squares discriminant analysis (PLS-DA), decision trees, support vector machines (SVM), Xgboost and neural networks (NN). The accuracy of all models ranged from 90.00% to 98.33%, with the NN model demonstrating the best performance. Specifically, it achieved 96.67% accuracy for single-maturity classification, 98.33% for single-variety classification, and 96.67% for dual maturity and variety classification, along with 98.09% precision, 97.92% recall, and 97.91% F1 score. Feature importance analysis indicated that the NN model exhibited the most balanced reliance on various VOCs, contributing to its optimal performance with the broad-spectrum VOC detection method, GC-IMS. Overall, these findings underscore the potential of NN modeling for accurate and efficient fruit classification based on integrated VOC profiles.

An Electronic "Tongue" Based on Multimode Multidirectional Acoustic Plate Wave Propagation

This paper theoretically and experimentally demonstrates the possibility of detecting the five basic tastes (salt, sweet, sour, umami, and bitter) using a variety of higher-order acoustic waves propagating in piezoelectric plates. Aqueous solutions of sodium chloride (NaCl), glucose (C6H12O6), citric acid (C6H8O7), monosodium glutamate (C5H8NO4Na), and sagebrush were used as chemicals for the simulation of each taste. These liquids differed from each other in terms of their physical properties such as density, viscosity, electrical conductivity, and permittivity. As a total acoustic response to the simultaneous action of all liquid parameters on all acoustic modes in a given frequency range, a change in the propagation losses (ΔS12) of the specified wave compared with distilled water was used. Based on experimental measurements, the corresponding orientation histograms of the ΔS12 were plotted for different types of acoustic waves. It was found that these histograms for different substances are individual and differ in shape, area, and position of their extremes. Theoretically, it has been shown that the influence of different liquids on different acoustic modes is due to both the electrical and mechanical properties of the liquids themselves and the mechanical polarization of the corresponding modes. Despite the fact that the mechanical properties of the used liquids are close to each other, the attenuation of different modes in their presence is not only due to the difference in their electrical parameters. The proposed approach to creating a multi-parametric multimode acoustic electronic tongue and obtaining a set of histograms for typical liquids will allow for the development of devices for the operational analysis of food, medicines, gasoline, aircraft fuel, and other liquid substances without the need for detailed chemical analysis.

Taste Sensor Assessment of Bitterness in Medicines: Overview and Recent Topics

In recent decades, taste sensors have been increasingly utilized to assess the taste of oral medicines, particularly focusing on bitterness, a major obstacle to patient acceptance and adherence. This objective and safe method holds promise for enhancing the development of patient-friendly medicines in pharmaceutical companies. This review article introduces its application in measuring the intensity of bitterness in medicine, confirming the achievement of taste masking, distinguishing taste differences between branded and generic medicines, and identifying substances to suppress bitterness in target medicines. Another application of the sensor is to predict a significant increase in bitterness when medicine is taken with certain foods/beverages or concomitant medication. Additionally, to verify the sensor's predictability, a significant correlation has been demonstrated between the output of a bitter-sensitive sensor designed for drug bitterness (BT0) and the bitterness responses of the human taste receptor hT2R14 from BitterDB (huji.ac.il). As a recent advancement, a novel taste sensor equipped with lipid/polymer membranes modified by 3-Br-2,6-dihydroxybenzoic acid (2,6-DHBA), based on the concept of allostery, is introduced. This sensor successfully predicts the bitterness of non-charged pharmaceuticals with xanthine skeletons, such as caffeine or related compounds. Finally, the future prospects of taste sensors are discussed.

Assessment of Bitterness in Non-Charged Pharmaceuticals with a Taste Sensor: A Study on Substances with Xanthine Scaffold and Allopurinol

Taste sensors with an allostery approach have been studied to detect non-charged bitter substances, such as xanthine derivatives, used in foods (e.g., caffeine) or pharmaceuticals (e.g., etofylline). In this study, the authors modified a taste sensor with 3-bromo-2,6-dihydroxybenzoic acid and used it in conjunction with sensory tests to assess the bitterness of non-charged pharmaceuticals with xanthine scaffolds (i.e., acefylline and doxofylline), as well as allopurinol, an analogue of hypoxanthine. The results show that the sensor was able to differentiate between different levels of sample bitterness. For instance, when assessing a 30 mM sample solution, the sensor response to acefylline was 34.24 mV, which corresponded to the highest level of bitterness (τ = 3.50), while the response to allopurinol was lowest at 2.72 mV, corresponding to relatively weaker bitterness (τ = 0.50). Additionally, this study extended the application of the sensor to detect pentoxifylline, an active pharmaceutical ingredient in pediatric medicines. These results underscore the taste sensor's value as an additional tool for early-stage assessment and prediction of bitterness in non-charged pharmaceuticals.

Positively Charged Organosilanes Covalently Linked to the Silica Network as Modulating Tools for the Salinity Correction of pH Values Obtained with Colorimetric Sensor Arrays (CSAs)

Seven increasing levels of water salinity from 0.029 to 0.600 M (as NaCl) were used to investigate the dependence of pH measurement, performed using colorimetric sensor arrays (CSAs), on ionic strength. The CSAs were arrays of sensing spots prepared in the form of sol-gel-embedding Bromothymol Blue (BB) and Bromocresol Green (BCG) in a porous nitrocellulose support. The support was impregnated over the entire thickness (≈100 µm), allowing for the signal (Hue) acquisition on the opposite side to the contact with the sample solution. Three CSAs were prepared, M1, M2, and M3. M1 contained a free cationic surfactant, hexadecyltrimethylammonium p-toluenesulfonate (CTApTs), for modulating the pKa of the indicators. In M2, the surfactant dimethyloctadecyl[3-(trimethoxysilyl)propyl]ammonium chloride (DTSACl) was covalently bonded to the sol-gel. M3 was prepared like M2 but using a larger amount of ethanol as the solvent for the synthesis. The modulation of the CTApTs or the DTSACl concentration enabled the tuning of the pKa. In general, the pKa modulation ability decreased with the increase in salinity. The presence of a surfactant covalently linked to the backbone partially reduced the competitiveness of the anionic species, improving the results. Nevertheless, the salt effect was still present, and a correction algorithm was required. Between pH 5.00 and 12.00, this correction could be made automatically by using spots taken as references to produce sensors independent of salinity. As the salt effect is virtually absent above 0.160 M, M2 and M3 can be used for future applications in seawater.

Multiplexed Chemical Sensing CMOS Imager

A miniaturized and multiplexed chemical sensing technology is urgently needed to empower mobile devices and robots for various new applications such as mobile health and Internet of Things. Here, we show that a complementary metal-oxide-semiconductor (CMOS) imager can be turned into a multiplexed colorimetric sensing chip by coating micron-scale sensing spots on the CMOS imager surface. Each sensing spot contains nanocomposites of colorimetric sensing probes and silica nanoparticles that enhance sensing signals by several orders of magnitude. The sensitivity is spot-size-invariant, and high-performance gas sensing can be achieved on sensing spots as small as ∼10 μm. This great scalability combined with millions of pixels of a CMOS imager offers a promising platform for highly integrated chemical sensors. To prove its compatibility with mobile electronics, we have built a smartphone accessory based on this chemical CMOS sensor and demonstrated that personal health management can be achieved through the detection of gaseous biomarkers and pollutants. We anticipate that this new platform will pave the way for the widespread application of chemical sensing in mobile electronics and wearable devices.

Quantification of phosphatides in sunflower oils using a potentiometric e-tongue

Consisting of two fatty acyl groups, phospholipids are a vital part of vegetable oils and the source of essential fatty acids. Moreover, phospholipids influence oxidative and flavor stability and color evolution of vegetable oils, and their quantification has a significant role in the quality assessment of oils. In this study, we proposed a new highly efficient, affordable, environmentally friendly, and simple approach for the evaluation of phospholipid concentrations based on potentiometric multisensor systems coupled with chemometric data processing. Support vector machines, partial least squares, and multiple linear regressions were used to predict phosphatide concentrations based on potentiometric multisensor system responses. Application of multivariate regression tools yielded the following root mean square errors of prediction: 0.005 mg/100 g of oil in the range 0.0-59.4 mg/100 g for refined oils; 0.008 mg/100 g in the range 0.0-100 mg/100 g for low phosphatide oils and 0.24 mg/100 g in the range 100-2270 mg/100 g for high phosphatide oils. This approach can be considered as a rapid and straightforward method to quantify the phosphatides in sunflower oils.

The Self-Calibration Method for the Vertex Distance of the Elliptical Paraboloid Array

The elliptical paraboloid array plays an important role in precision measurement, astronomical telescopes, and communication systems. The calibration of the vertex distance of elliptical paraboloids is of great significance to precise 2D displacement measurement. However, there are some difficulties in determining the vertex position with contact measurement. In this study, an elliptical paraboloid array and an optical slope sensor for displacement measurement were designed and analyzed. Meanwhile, considering the geometrical relationship and relative angle between elliptical paraboloids, a non-contact self-calibration method for the vertex distance of the elliptical paraboloid array was proposed. The proposed self-calibration method was verified by a series of experiments with a high repeatability, within 3   μ m in the X direction and within 1   μ m in the Y direction. Through calibration, the displacement measurement system error was reduced from 100   μ m to 3   μ m . The self-calibration method of the elliptical paraboloid array has great potential in the displacement measurement field, with a simple principle and high precision.

Monitoring of Cell Concentration during Saccharomyces cerevisiae Culture by a Color Sensor: Optimization of Feature Sensor Using ACO

The odor information produced in Saccharomyces cerevisiae culture is one of the important characteristics of yeast growth status. This work innovatively presents the quantitative monitoring of cell concentration during the yeast culture process using a homemade color sensor. First, a color sensor array, which could visually represent the odor changes produced during the yeast culture process, was developed using eleven porphyrins and one pH indicator. Second, odor information of the culture substrate was obtained during the process using the homemade color sensor. Next, color components, which came from different color sensitive spots, were extracted first and then optimized using the ant colony optimization (ACO) algorithm. Finally, the back propagation neural network (BPNN) model was developed using the optimized feature color components for quantitative monitoring of cell concentration. Results demonstrated that BPNN models, which were developed using two color components from FTPPFeCl (component B) and MTPPTE (component B), can obtain better results on the basis of both the comprehensive consideration of the model performance and the economic benefit. In the validation set, the average of determination coefficient R P 2 was 0.8837 and the variance was 0.0725, while the average of root mean square error of prediction (RMSEP) was 1.0033 and the variance was 0.1452. The overall results sufficiently demonstrate that the optimized sensor array can satisfy the monitoring accuracy and stability of the cell concentration in the process of yeast culture.

Chemically Resistant Perfluoroalkoxy Nanoparticle-Packed Porous Substrates and Their Use in Colorimetric Sensor Arrays

To create printing substrates for colorimetric sensor arrays, chemically resistant membranes are prepared by coating cellulose filter paper with perfluoroalkoxy (PFA) polymer nanoparticles. A water-based fluorothermoplastic polymer dispersion was diluted with an organic solvent that causes weak aggregation of polymer nanoparticles. The resulting solution improved adhesion between the polymer and the cellulose membrane, providing a more mechanically stable substrate. These PFA polymer-coated substrates demonstrated superior chemical resistance against strong alkalines and had relatively uniform nanoporous structures that substantially improved the printability of a colorimetric sensor array. Finally, colorimetric sensor arrays printed on these substrates were evaluated for the detection of four different toxic industrial chemicals (e.g., ammonia, hydrogen sulfide, nitrogen dioxide, and sulfur dioxide) at or below their permissible exposure limits.

Identification of the Rice Wines with Different Marked Ages by Electronic Nose Coupled with Smartphone and Cloud Storage Platform

In this study, a portable electronic nose (E-nose) was self-developed to identify rice wines with different marked ages—all the operations of the E-nose were controlled by a special Smartphone Application. The sensor array of the E-nose was comprised of 12 MOS sensors and the obtained response values were transmitted to the Smartphone thorough a wireless communication module. Then, Aliyun worked as a cloud storage platform for the storage of responses and identification models. The measurement of the E-nose was composed of the taste information obtained phase (TIOP) and the aftertaste information obtained phase (AIOP). The area feature data obtained from the TIOP and the feature data obtained from the TIOP-AIOP were applied to identify rice wines by using pattern recognition methods. Principal component analysis (PCA), locally linear embedding (LLE) and linear discriminant analysis (LDA) were applied for the classification of those wine samples. LDA based on the area feature data obtained from the TIOP-AIOP proved a powerful tool and showed the best classification results. Partial least-squares regression (PLSR) and support vector machine (SVM) were applied for the predictions of marked ages and SVM (R² = 0.9942) worked much better than PLSR.

Assessing White Wine Viscosity Variation Using Polarized Laser Speckle: A Promising Alternative to Wine Sensory Analysis

In this paper, we report measurements of wine viscosity, correlated to polarized laser speckle results. Experiments were performed on white wine samples produced with a single grape variety. Effects of the wine making cellar, the grape variety, and the vintage on wine Brix degree, alcohol content, viscosity, and speckle parameters are considered. We show that speckle parameters, namely, spatial contrast and speckle decorrelation time, as well as the inertia moment extracted from the temporal history speckle pattern, are mainly affected by the alcohol and sugar content and hence the wine viscosity. Principal component analysis revealed a high correlation between laser speckle results on the one hand and viscosity and Brix degree values on the other. As speckle analysis proved to be an efficient method of measuring the variation of the viscosity of white mono-variety wine, one can therefore consider it as an alternative method to wine sensory analysis.

A 3D CFD Simulation and Analysis of Flow-Induced Forces on Polymer Piezoelectric Sensors in a Chinese Liquors Identification E-Nose

Chinese liquors can be classified according to their flavor types. Accurate identification of Chinese liquor flavors is not always possible through professional sommeliers' subjective assessment. A novel polymer piezoelectric sensor electric nose (e-nose) can be applied to distinguish Chinese liquors because of its excellent ability in imitating human senses by using sensor arrays and pattern recognition systems. The sensor, based on the quartz crystal microbalance (QCM) principle is comprised of a quartz piezoelectric crystal plate sandwiched between two specific gas-sensitive polymer coatings. Chinese liquors are identified by obtaining the resonance frequency value changes of each sensor using the e-nose. However, the QCM principle failed to completely account for a particular phenomenon: we found that the resonance frequency values fluctuated in the stable state. For better understanding the phenomenon, a 3D Computational Fluid Dynamics (CFD) simulation using the finite volume method is employed to study the influence of the flow-induced forces to the resonance frequency fluctuation of each sensor in the sensor box. A dedicated procedure was developed for modeling the flow of volatile gas from Chinese liquors in a realistic scenario to give reasonably good results with fair accuracy. The flow-induced forces on the sensors are displayed from the perspective of their spatial-temporal and probability density distributions. To evaluate the influence of the fluctuation of the flow-induced forces on each sensor and ensure the serviceability of the e-nose, the standard deviation of resonance frequency value (SD_F) and the standard deviation of resultant forces (SD_Fy) in y-direction (F_y) are compared. Results show that the fluctuations of F_y are bound up with the resonance frequency values fluctuations. To ensure that the sensor's resonance frequency values are steady and only fluctuate slightly, in order to improve the identification accuracy of Chinese liquors using the e-nose, the sensors in the sensor box should be in the proper place, i.e., where the fluctuations of the flow-induced forces is relatively small. This plays a significant reference role in determining the optimum design of the e-nose for accurately identifying Chinese liquors.

Detection of Organophosphorus Pesticides with Colorimetry and Computer Image Analysis

Organophosphorus pesticides (OPs) represent a very important class of pesticides that are widely used in agriculture because of their relatively high-performance and moderate environmental persistence, hence the sensitive and specific detection of OPs is highly significant. Based on the inhibitory effect of acetylcholinesterase (AChE) induced by inhibitors, including OPs and carbamates, a colorimetric analysis was used for detection of OPs with computer image analysis of color density in CMYK (cyan, magenta, yellow and black) color space and non-linear modeling. The results showed that there was a gradually weakened trend of yellow intensity with the increase of the concentration of dichlorvos. The quantitative analysis of dichlorvos was achieved by Artificial Neural Network (ANN) modeling, and the results showed that the established model had a good predictive ability between training sets and predictive sets. Real cabbage samples containing dichlorvos were detected by colorimetry and gas chromatography (GC), respectively. The results showed that there was no significant difference between colorimetry and GC (P > 0.05). The experiments of accuracy, precision and repeatability revealed good performance for detection of OPs. AChE can also be inhibited by carbamates, and therefore this method has potential applications in real samples for OPs and carbamates because of high selectivity and sensitivity.