Colorimetric Sensor Array for White Wine Tasting

Chung, Soo‐Il; Park, Tu San; Park, Soo Hyun; Kim, Joon Yong; Park, Seongmin; Son, Daesik; Bae, Young‐Min; Cho, Seong In

doi:10.3390/s150818197

Cited by 28 publications

(17 citation statements)

References 32 publications

(36 reference statements)

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“…The technology has a strong specificity, high sensitivity and is not affected by the humidity characteristics because the color sensitive material used has a broad spectrum. Olfactory visualization technology has been successfully used in gas detection [26] and for liquid foodstuffs, such as coffee [28,29,30], wine [31,32], and vinegar [33,34,35,36]. Therefore, from the technical point of view, this technology is also capable of achieving quantitative analysis of the yeast culture process.…”

Section: Introductionmentioning

confidence: 99%

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

Jiang

Chen

2019

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

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

Jiang

Chen

2019

Sensors

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“…Sensor arrays are widely used in modern scientific research and industrial production [1][2][3]. Depending on the application requirements, sensor arrays can be designed with different geometries, including those that are linear [4], circular [5], planar [6], L-shaped [7], and so on. Rui et al [8] proposed a capacitance-sensor-array-based imaging system to detect water leakage inside insulating slabs with porous cells.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

et al. 2019

Applied Sciences

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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.

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“…Global information can be taken as the ‘fingerprint’ of VOCs, which can be applied for classification and prediction based on appropriate pattern recognition methods. Because of the rapid and low-cost features, E-noses are now widely applied to various food products analysis such as wine [ 11 , 12 , 13 , 14 ], other beverages [ 15 , 16 ], food [ 17 , 18 , 19 ] and fruits [ 20 , 21 ]. In recent years, the development of the E-nose has been driven by the need to design a convenient, portable and specialized system that is available for customized detection.…”

Section: Introductionmentioning

confidence: 99%

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

Wei

Xiao

Wang

et al. 2017

Sensors

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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.

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Colorimetric Sensor Array for White Wine Tasting

Cited by 28 publications

References 32 publications

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

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

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

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

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