Classification of rice wine according to different marked ages using a novel artificial olfactory technique based on colorimetric sensor array

Detection of the Inoculated Fermentation Process of Apo Pickle Based on a Colorimetric Sensor Array Method

Apo pickle is a traditional Chinese fermented vegetable. However, the traditional fermentation process of Apo pickle is slow, easy to ruin, and cannot be judged with regard to time. To improve fermentation, LP-165 (L. Plantarum), which has a high salt tolerance, acidification, and growth capacity, was chosen as the starter culture. Meanwhile, a colorimetric sensor array (CSA) sensitive to pickle volatile compounds was developed to differentiate Apo pickles at varying degrees of fermentation. The color components were extracted from each dye in the color change profiles and were analyzed using principal component analysis (PCA) and linear discriminant analysis (LDA). The fermentation process of the Apo pickle was classified into four phases by LDA. The accuracy of backward substitution verification was 99% and the accuracy of cross validation was 92.7%. Furthermore, the partial least squares regression (PLSR) showed that data from the CSA were correlated with pH total acid, lactic acid, and volatile acids of the Apo pickle. These results illustrate that the CSA reacts quickly to inoculated Apo pickle and could be used to detect fermentation.

A paper-based microfluidic sensor array combining molecular imprinting technology and carbon quantum dots for the discrimination of nitrophenol isomers

Paper-based microfluidic analytical devices (μPADs) have recently attracted attention as a rapid test kit owing to their low cost and nonrequirement for external driving pump. However, low accuracy and poor anti-interference ability of μPADs under complex detection condition limit their practical applications. Here, we present a facile way to prepare a novel fluorescence sensor-array μPAD for multi-analyte discrimination based on molecular imprinting technology, and its sensing behavior was studied by using three nitrophenol (NP) isomers (2-, 3-, and 4-NP) as the testing models. Carbon quantum dots (CQDs) emitting blue light were grafted on glass-fiber paper, followed by in-situ modification of three types of molecularly imprinted polymers (MIPs) with 2-, 3-, and 4-NP as template. Each sensing unit on the array showed differential yet cross-reactive binding affinity to NP isomers, resulting in distinct fluorescence quenching efficiency. Thus, precise distinguishment of the three NPs was realized with the MIPs/CQDs/paper-based sensor array. Furthermore, the discrimination ability of the platform was evaluated in mixtures of the NP isomers. Practicability of this apparatus was validated by identification of blind samples and 100% accuracy was achieved. The μPAD has proven to be highly sensitive and accurate, which will serve as an ideal analytical tool in the fields of environment monitoring, disease prognosis, food safety and so on.

Fabrication of a "progress bar" colorimetric strip sensor array by dye-mixing method as a potential food freshness indicator

Colorimetric sensing is a low-cost, intuitive method for monitoring the freshness of food. We prepared a colorimetric strip sensor array by mixing different amounts of bromophenol blue (BPB) and bromocresol green (BCG). As results of NH₃ simulation, the array strip turned from yellow to blue, and the number of blue spots increased with the increasing NH₃, like a progress bar. Although the actual color is quite different, the color-changing trend was consistent with the simulated model calculated by a computer. The progress bar results remained stable under three lighting conditions. Furthermore, in the Cod preservation experiment, the color-changing progress of the strip sensor array is consistent with the simulation and can indicate Cod freshness while providing more distinguish levels. Therefore, a "progress bar" indicator built by this strategy possess the potential of realizing nondestructive, more accurate, and commercially available food quality monitoring through the naked eye and smart equipment recognition.

Advanced applications of chemo-responsive dyes based odor imaging technology for fast sensing food quality and safety: A review

Public attention to foodquality and safety has been increased significantly. Therefore, appropriate analytical tools are needed to analyze and sense the food quality and safety. Volatile organic compounds (VOCs) are important indicators for the quality and safety of food products. Odor imaging technology based on chemo-responsive dyes is one of the most promising methods for analysis of food products. This article reviews the sensing and imaging fundamentals of odor imaging technology based on chemo-responsive dyes. The aim is to give detailed outlines about the theory and principles of using odor imaging technology for VOCs detection, and to focus primarily on its applications in the field of quality and safety evaluation of food products, as well as its future applicability in modern food industries and research. The literatures presented in this review clearly demonstrated that imaging technology based on chemo-responsive dyes has the exciting effect to inspect such as quality assessment of cereal , wine and vinegar flavored foods , poultry meat, aquatic products, fruits and vegetables, and tea. It has the potential for the rapid, reliable, and inline assessment of food safety and quality by providing odor-image-basedmonitoring tool. Practical Application: The literatures presented in this review clearly demonstrated that imaging technology based on chemo-responsive dyes has the exciting effect to inspect such as quality assessment of cereal , wine and vinegar flavored foods, poultry meat, aquatic products, fruits and vegetables, and tea.

A brief review on recent development of multidisciplinary engineering in fermentation of Saccharomyces cerevisiae

Fermentation technology has unprecedented potential to upgrade state-of-art biotechnology and refine the processes used in existing ones, taking into account of complex technical, economic and environmental factors. Given the economic importance and ongoing challenges of biotech sector, multidisciplinary engineering technologies is poised to become an increasingly important tool along with the emergence of modern technology and innovation. This article reviews recent technology advancement in the field of fermentation using Saccharomyces cerevisiae. Interesting research progress has been made by leveraging multiple engineering fields such as electrical engineering, information engineering, electrochemical engineering and new material development, leading to recent development of novel real-time probes (electronic nose technology, analysis of yeast morphology and metabolites, timely control of glucose feed), improved understanding of electro-fermentation (enhanced electronic transfer provision), as well as application of cost-effective and sustainable materials (bioreactor vessel manufactured from textile, and yeast immobilisation support matrix made from abundant natural biomass). To the best of our knowledge, the subject is reviewed for the first time in recent years. Furthermore, this review also constitutes a futuristic S. cerevisiae fermentation process based on the recent advancement discussed.

Rapid discrimination of beer based on quantitative aroma determination using colorimetric sensor array

In this study, 6 beers from Tsingtao Brewery were analyzed by using colorimetric GC-MS and sensor array (CSA). First, forty volatile compounds of six beers, including 16 esters, 10 alcohols, 4 acids and 4 aldehydes, were identified by GC-MS. Beers from the same category were grouped using principal component analysis (PCA) score plot and hierarchical clustering analysis (HCA) dendrogram. Discrimination of the beers was subsequently implemented using a 4 × 4 CSA combined with multivariate analysis. A linear discriminant analysis (LDA) model achieved a 100% recognition rates of the 6 beers. In addition, a partial least square (PLS) model could be used to quantitatively determine ethyl octanoate, phenethyl acetate, isoamyl alcohol and octanoic acid, with correlation coefficients over 0.85 for both the calibration curves of the training and prediction sets. Hence, CSA could be used for rapid and non-destructive determination of beer quality.

Integration of a low-cost electronic nose and a voltammetric electronic tongue for red wines identification

The purpose of this present study was to develop a rapid and effective approach for identification of red wines that differ in geographical origins, brands, and grape varieties, a multi-sensor fusion technology based on a novel cost-effective electronic nose (E-nose) and a voltammetric electronic tongue (E-tongue) was proposed. The E-nose sensors was created using porphyrins or metalloporphyrins, pH indicators and Nile red printed on a C2 reverse phase silica gel plate. The voltammetric E-Tongue with six metallic working electrodes, namely platinum, gold, palladium, tungsten, titanium, and silver was employed to sense the taste of red wines. Principal component analysis (PCA) was utilized for dimensionality reduction and decorrelation of the raw sensors datasets. The fusion models derived from extreme learning machine (ELM) were built with PCA scores of E-nose and tongue as the inputs. Results showed superior performance (100% recognition rate) using combination of odor and taste sensors than individual artificial systems. The results suggested that fusion of the novel cost-effective E-nose created and voltammetric E-tongue coupled with ELM has a powerful potential in rapid quality evaluation of red wine.

Evaluating aroma quality of black tea by an olfactory visualization system: Selection of feature sensor using particle swarm optimization

Aroma is an important index to evaluate the quality and grade of black tea. This work innovatively proposed the sensory evaluation of black tea aroma quality based on an olfactory visual sensor system. Firstly, the olfactory visualization system, which can visually represent the aroma quality of black tea, was assembled using a lab-made color sensitive sensor array including eleven porphyrins and one pH indicator for data acquisition and color components extraction. Then, the color components from different color sensitive spots were optimized using the particle swarm optimization (PSO) algorithm. Finally, the back propagation neural network (BPNN) model was developed using the optimized characteristic color components for the sensory evaluation of black tea aroma quality. Results demonstrated that the BPNN models, which were developed using three color components from FTPPFeCl (component G), MTPPTE (component B) and BTB (component B), can get better results based on comprehensive consideration of the generalization performance of the model and the fabrication cost of the sensor. In the validation set, the average of correlation coefficient (R_P) value was 0.8843 and the variance was 0.0362. The average of root mean square error of prediction (RMSEP) was 0.3811 and the variance was 0.0525. The overall results sufficiently reveal that the optimized sensor array has promising applications for the sensory evaluation of black tea products in the process of practical production.

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.

Evaluating green tea quality based on multisensor data fusion combining hyperspectral imaging and olfactory visualization systems

BACKGROUND

The instrumental evaluation of tea quality using digital sensors instead of human panel tests has attracted much attention globally. However, individual sensors do not meet the requirements of discriminant accuracy as a result of incomprehensive sensor information. Considering the major factors in the sensory evaluation of tea, the study integrated multisensor information, including spectral, image and olfaction feature information.

RESULTS

To investigate spectral and image information obtained from hyperspectral spectrometers of different bands, principal components analysis was used for dimension reduction and different types of supervised learning algorithms (linear discriminant analysis, K-nearest neighbour and support vector machine) were selected for comparison. Spectral feature information in the near infrared region and image feature information in the visible-near infrared/near infrared region achieved greater accuracy for classification. The results indicated that a support vector machine outperformed other methods with respect to multisensor data fusion, which improved the accuracy of evaluating green tea quality compared to using individual sensor data. The overall accuracy of the calibration set increased from 75% using optimal single sensor information to 92% using multisensor information, and the overall accuracy of the prediction set increased from 78% to 92%.

CONCLUSION

Overall, it can be concluded that multisensory data accurately identify six grades of tea. © 2018 Society of Chemical Industry.

Combination of Sensory, Chromatographic, and Chemometrics Analysis of Volatile Organic Compounds for the Discrimination of Authentic and Unauthentic Harumanis Mangoes

This study analyzed the volatile organic compounds (VOCs) of three mango varieties (Harumanis, Tong Dam and Susu) for the discrimination of authentic Harumanis from other mangoes. The VOCs of these mangoes were extracted and analysed nondestructively using Head Space-Solid Phase Micro Extraction (HS-SPME) coupled to Gas Chromatography-Mass Spectrometry (GC-MS). Prior to the analytical method, two simple sensory analyses were carried out to assess the ability of the consumers to differentiate between the Harumanis and Tong Dam mangoes as well as their preferences towards these mangoes. On the other hand, chemometrics techniques, such as principal components analysis (PCA), hierarchical clustering analysis (HCA), and discriminant analysis (DA), were used to visualise grouping tendencies of the volatile compounds detected. These techniques were successful in identifying the grouping tendencies of the mango samples according to the presence of their respective volatile compounds, thus enabling the identification of the groups of substances responsible for the discrimination between the authentic and unauthentic Harumanis mangoes. In addition, three ocimene compounds, namely beta-ocimene, trans beta-ocimene, and allo-ocimene, can be considered as chemical markers of the Harumanis mango, as these compounds exist in all Harumanis mango, regardless the different sources of the mangoes obtained.

Porphyrinoids for Chemical Sensor Applications

Porphyrins and related macrocycles have been intensively exploited as sensing materials in chemical sensors, since in these devices they mimic most of their biological functions, such as reversible binding, catalytic activation, and optical changes. Such a magnificent bouquet of properties allows applying porphyrin derivatives to different transducers, ranging from nanogravimetric to optical devices, also enabling the realization of multifunctional chemical sensors, in which multiple transduction mechanisms are applied to the same sensing layer. Potential applications are further expanded through sensor arrays, where cross-selective sensing layers can be applied for the analysis of complex chemical matrices. The possibility of finely tuning the macrocycle properties by synthetic modification of the different components of the porphyrin ring, such as peripheral substituents, molecular skeleton, coordinated metal, allows creating a vast library of porphyrinoid-based sensing layers. From among these, one can select optimal arrays for a particular application. This feature is particularly suitable for sensor array applications, where cross-selective receptors are required. This Review briefly describes chemical sensor principles. The main part of the Review is divided into two sections, describing the porphyrin-based devices devoted to the detection of gaseous or liquid samples, according to the corresponding transduction mechanism. Although most devices are based on porphyrin derivatives, seminal examples of the application of corroles or other porphyrin analogues are evidenced in dedicated sections.

Discrimination of honeys using colorimetric sensor arrays, sensory analysis and gas chromatography techniques

Aroma profiles of six honey varieties of different botanical origins were investigated using colorimetric sensor array, gas chromatography-mass spectrometry (GC-MS) and descriptive sensory analysis. Fifty-eight aroma compounds were identified, including 2 norisoprenoids, 5 hydrocarbons, 4 terpenes, 6 phenols, 7 ketones, 9 acids, 12 aldehydes and 13 alcohols. Twenty abundant or active compounds were chosen as key compounds to characterize honey aroma. Discrimination of the honeys was subsequently implemented using multivariate analysis, including hierarchical clustering analysis (HCA) and principal component analysis (PCA). Honeys of the same botanical origin were grouped together in the PCA score plot and HCA dendrogram. SPME-GC/MS and colorimetric sensor array were able to discriminate the honeys effectively with the advantages of being rapid, simple and low-cost. Moreover, partial least squares regression (PLSR) was applied to indicate the relationship between sensory descriptors and aroma compounds.

Comparison of different chemometric methods in quantifying total volatile basic-nitrogen (TVB-N) content in chicken meat using a fabricated colorimetric sensor array

PSO-SVMR is an efficient chemometric tool to quantify TVB-N content in chicken.

Colorimetric Sensor Array for White Wine Tasting

A colorimetric sensor array was developed to characterize and quantify the taste of white wines. A charge-coupled device (CCD) camera captured images of the sensor array from 23 different white wine samples, and the change in the R, G, B color components from the control were analyzed by principal component analysis. Additionally, high performance liquid chromatography (HPLC) was used to analyze the chemical components of each wine sample responsible for its taste. A two-dimensional score plot was created with 23 data points. It revealed clusters created from the same type of grape, and trends of sweetness, sourness, and astringency were mapped. An artificial neural network model was developed to predict the degree of sweetness, sourness, and astringency of the white wines. The coefficients of determination (R2) for the HPLC results and the sweetness, sourness, and astringency were 0.96, 0.95, and 0.83, respectively. This research could provide a simple and low-cost but sensitive taste prediction system, and, by helping consumer selection, will be able to have a positive effect on the wine industry.

Advances in artificial olfaction: sensors and applications

The artificial olfaction, based on electronic systems (electronic noses), includes three basic functions that operate on an odorant: a sample handler, an array of gas sensors, and a signal-processing method. The response of these artificial systems can be the identity of the odorant, an estimate concentration of the odorant, or characteristic properties of the odour as might be perceived by a human. These electronic noses are bio inspired instruments that mimic the sense of smell. The complexity of most odorants makes characterisation difficult with conventional analysis techniques, such as gas chromatography. Sensory analysis by a panel of experts is a costly process since it requires trained people who can work for only relatively short periods of time. The electronic noses are easy to build, provide short analysis times, in real time and on-line, and show high sensitivity and selectivity to the tested odorants. These systems are non-destructive techniques used to characterise odorants in diverse applications linked with the quality of life such as: control of foods, environmental quality, citizen security or clinical diagnostics. However, there is much research still to be done especially with regard to new materials and sensors technology, data processing, interpretation and validation of results. This work examines the main features of modern electronic noses and their most important applications in the environmental, and security fields. The above mentioned main components of an electronic nose (sample handling system, more advanced materials and methods for sensing, and data processing system) are described. Finally, some interesting remarks concerning the strengths and weaknesses of electronic noses in the different applications are also mentioned.

Classification of tea category using a portable electronic nose based on an odor imaging sensor array

A developed portable electronic nose (E-nose) based on an odor imaging sensor array was successfully used for classification of three different fermentation degrees of tea (i.e., green tea, black tea, and Oolong tea). The odor imaging sensor array was fabricated by printing nine dyes, including porphyrin and metalloporphyrins, on the hydrophobic porous membrane. A color change profile for each sample was obtained by differentiating the image of sensor array before and after exposure to tea's volatile organic compounds (VOCs). Multivariate analysis was used for the classification of tea categories, and linear discriminant analysis (LDA) achieved 100% classification rate by leave-one-out cross-validation (LOOCV). This study demonstrates that the E-nose based on odor imaging sensor array has a high potential in the classification of tea category according to different fermentation degrees.