An optimization of the MOS electronic nose sensor array for the detection of Chinese pecan quality

Recent developments of e-sensing devices coupled to data processing techniques in food quality evaluation: a critical review

A greater demand for high-quality food is being driven by the growth of economic and technological advancements. In this context, consumers are currently paying special attention to organoleptic characteristics such as smell, taste, and appearance. Motivated to mimic human senses, scientists developed electronic devices such as e-noses, e-tongues, and e-eyes, to spot signals relative to different chemical substances prevalent in food systems. To interpret the information provided by the sensors' responses, multiple chemometric approaches are used depending on the aim of the study. This review based on the Web of Science database, endeavored to scrutinize three e-sensing systems coupled to chemometric approaches for food quality evaluation. A total of 122 eligible articles pertaining to the e-nose, e-tongue and e-eye devices were selected to conduct this review. Most of the performed studies used exploratory analysis based on linear factorial methods, while classification and regression techniques came in the second position. Although their applications have been less common in food science, it is to be noted that nonlinear approaches based on artificial intelligence and machine learning deployed in a big-data context have generally yielded better results for classification and regression purposes, providing new perspectives for future studies.

Ozone micro-nano bubble water preserves the quality of postharvest parsley

The production and use of ozone micro-nano bubble water (O₃-MNBW) is an innovative technology that prolongs the reactivity of aqueous-phase ozone and maintains the freshness and quality of fruits and vegetables by removing pesticides, mycotoxins, and other contaminants. The quality of parsley treated with different concentrations of O₃-MNBW was investigated during storage at 20 ℃ for 5 d, and found that a ten-minute exposure of parsley to 2.5 mg·L^-1 O₃-MNBW effectively preserved the sensory quality of parsley, and resulted in lower weight loss, respiration rate, ethylene production, MDA levels, and a higher level of firmness, vitamin C, and chlorophyll content, relative to untreated parsley. The O₃-MNBW treatment also increased the level of total phenolics and flavonoids, enhanced peroxidase and ascorbate peroxidase activity, and inhibited polyphenol oxidase activity in stored parsley. Five volatile signatures identified using an electronic nose (W1W, sulfur-compounds; W2S, ethanol; W2W, aromatic- and organic- sulfur compounds; W5S, oxynitride; W1S, methane) exhibited a significant decrease in response to the O₃-MNBW treatment. A total of 24 major volatiles were identified. A metabolomic analysis identified 365 differentially abundant metabolites (DMs). Among them, 30 and 19 DMs were associated with characteristic volatile flavor substance metabolism in O₃-MNBW and control groups, respectively. The O₃-MNBW treatment increased the abundance of most DMs related to flavor metabolism and reduced the level of naringin and apigenin. Our results provide insight into the mechanisms that are regulated in response to the exposure of parsley to O₃-MNBW, and confirmed the potential use of O₃-MNBW as a preservation technology.

Comparative Characterization of Volatile Compounds of Ningxiang Pig, Duroc and Their Crosses (Duroc × Ningxiang) by Using SPME-GC-MS

With the aim to study the flavor characteristics of Ningxiang pigs (NX), Duroc (DC) pigs, and their crosses (Duroc × Ningxiang, DN), electronic nose and gas chromatography-mass spectrometry analysis were used to detect the volatile flavor substances in NX, DC, and DN (n = 34 pigs per population). A total of 120 volatile substances were detected in the three populations, of which 18 substances were common. Aldehydes were the main volatile substances in the three populations. Further analysis revealed that tetradecanal, 2-undecenal, and nonanal were the main aldehyde substances in the three kinds of pork, and the relative content of benzaldehyde in the three populations had significant differences. The flavor substances of DN were similar to that of NX and showed certain heterosis in flavor substances. These results provide a theoretical basis for the study of flavor substances of China local pig breeds and new ideas for pig breeding.

Design and Validation of a Portable Machine Learning-Based Electronic Nose

Volatile organic compounds (VOCs) are chemicals emitted by various groups, such as foods, bacteria, and plants. While there are specific pathways and biological features significantly related to such VOCs, detection of these is achieved mostly by human odor testing or high-end methods such as gas chromatography-mass spectrometry that can analyze the gaseous component. However, odor characterization can be quite helpful in the rapid classification of some samples in sufficient concentrations. Lower-cost metal-oxide gas sensors have the potential to allow the same type of detection with less training required. Here, we report a portable, battery-powered electronic nose system that utilizes multiple metal-oxide gas sensors and machine learning algorithms to detect and classify VOCs. An in-house circuit was designed with ten metal-oxide sensors and voltage dividers; an STM32 microcontroller was used for data acquisition with 12-bit analog-to-digital conversion. For classification of target samples, a supervised machine learning algorithm such as support vector machine (SVM) was applied to classify the VOCs based on the measurement results. The coefficient of variation (standard deviation divided by mean) of 8 of the 10 sensors stayed below 10%, indicating the excellent repeatability of these sensors. As a proof of concept, four different types of wine samples and three different oil samples were classified, and the training model reported 100% and 98% accuracy based on the confusion matrix analysis, respectively. When the trained model was challenged against new sets of data, sensitivity and specificity of 98.5% and 98.6% were achieved for the wine test and 96.3% and 93.3% for the oil test, respectively, when the SVM classifier was used. These results suggest that the metal-oxide sensors are suitable for usage in food authentication applications.

Label-free surface enhanced Raman scattering spectroscopy for discrimination and detection of dominant apple spoilage fungus

Fungal infection is one of the main causes of apple corruption. The main dominant spoilage fungi in causing apple spoilage are storage mainly include Penicillium Paecilomyces paecilomyces (P. paecilomyces), penicillium chrysanthemum (P. chrysogenum), expanded Penicillium expansum (P. expansum), Aspergillus niger (Asp. niger) and Alternaria. In this study, surface-enhanced Raman spectroscopy (SERS) based on gold nanorod (AuNRs) substrate method was developed to collect and examine the Raman fingerprints of dominant apple spoilage fungus spores. Standard normal variable (SNV) was used to pretreat the obtained spectra to improve signal-to-noise ratio. Principal component analysis (PCA) was applied to extract useful spectral information. Linear discriminant analysis (LDA) and non-linear pattern recognition methods including K nearest neighbor (KNN), Support vector machine (SVM) and back propagation artificial neural networks (BPANN) were used to identify fungal species. As the comparison of modeling results shown, the BPANN model established based on the characteristic spectra variables have achieved the satisfactory result with discrimination accuracy of 98.23%; while the PCA-LDA model built using principal component variables achieved the best distinguish result with discrimination accuracy of 98.31%. It was concluded that SERS has the potential to be an inexpensive, rapid and effective method to detect and identify fungal species.

Intelligent Mobile Electronic Nose System Comprising a Hybrid Polymer-Functionalized Quartz Crystal Microbalance Sensor Array

We devised a low-cost mobile electronic nose (e-nose) system using a quartz crystal microbalance (QCM) sensor array functionalized with various polymer-based thin active films (i.e., polyacrylonitrile, poly(vinylidene fluoride), poly(vinyl pyrrolidone), and poly(vinyl acetate)). It works based on the gravimetric detection principle, where the additional mass of the adsorbed molecules on the polymer surface can induce QCM resonance frequency shifts. To collect and process the obtained sensing data sets, a multichannel data acquisition (DAQ) circuitry was developed and calibrated using a function generator resulting in a device frequency resolution of 0.5 Hz. Four prepared QCM sensors demonstrated various sensitivity levels with high reproducibility and consistency under exposure to seven different volatile organic compounds (VOCs). Moreover, two types of machine learning algorithms (i.e., linear discriminant analysis and support vector machine models) were employed to differentiate and classify those tested analytes, in which classification accuracies of up to 98 and 99% could be obtained, respectively. This high-performance e-nose system is expected to be used as a versatile sensing platform for performing reliable qualitative and quantitative analyses in complex gaseous mixtures containing numerous VOCs for early disease diagnosis and environmental quality monitoring.

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.

Application of electronic nose for determination of Slovak cheese authentication based on aroma profile

Electronic nose with sensors is used in many industries and for various applications such as quality control, process monitoring, shelf life evaluation, origin or authenticity assessment. The aim of this work was to investigate the electronic nose with FID detectors applicability for characterization of steamed cheese and for the assessment of steamed cheese quality decay during storage. Samples of smoked and unsmoked steamed cheese varieties from 5 Slovak enterprises concerning different regions of Slovakia were analysed. Data from aroma profiles were processed by statistical technique PCA. Compounds like acetaldehyde, 1-propanal, propanoic acid, ethyl hexanoate, furfural, butan-2-one, isovaleric acid, 1-hexanol or α-pinene were determined as significant flavours in fresh steamed cheese samples. In the current study, no significant differences in aroma profiles between fresh and stored cheese samples were confirmed. Thus, differences in main odour substances composition of steamed cheese varieties, obtained from various producers in several geographic regions of Slovakia, were minor.

A predictive model for the evaluation of flavor attributes of raw and cooked beef based on sensor array analyses

There are currently no standardized objective measures to evaluate beef flavor attributes, especially the comparison between raw beef and cooked beef. Beef flavor attribute is one of the most significant parameters for consumers. This study described a predictive model using a 12-ion-sensor array and sensory properties to evaluate beef flavor attributes based on potential. Then the number of sensors was reduced to six via variance of analysis, and these six sensors were reserved with the saturated calomel reference electrode to constitute a new sensor array. Sensitive flavors of each sensor were selected through multiple comparative analysis. Results showed that the accuracy rate of classifying five basic flavors (acidity, sweetness, bitterness, saltiness, freshness) using the new sensor array was 100%. The processing methods used were based on multivariate statistical methods done with the cluster analysis (CA). Results were compared to sensory evaluation using genetic algorithm (GA). From GA, the accuracy rates of raw and cooked beef were 85.0% and 90.0%, which was consistent with the sensory analysis results. Moreover, reducing the number of sensors could decrease the data dimensionality and detection time. Also raw beef instead of cooked beef could be used in flavor attributes evaluation. This model could become an important method for evaluating beef flavor attributes repeatedly and objectively.

Volatile profiles of fresh rice noodles fermented with pure and mixed cultures

The volatile profiles of fresh rice noodles (FRN) fermented with pure and five commercial mixed cultures were studied by using solid phase micro extraction/ gas chromatography-mass spectrometry, electronic nose, and sensory evaluations. The main volatile compounds of FRN by pure culture included aldehydes represented by nonanal, octanal, and 2,4-Pentadienal, and alcohols represented by hexanol and 1-nonanol. Its aroma profiles showed remarkable changes during the storage time from 0 to 30 h, indicating the reduction in aldehydes and the increase in alcohols and isoamyl alcohol. Significant variations such as the types, relative amounts, and category distributions of volatile compounds were observed in FRN by five mixed cultures. The bacterial compositions of these mixed cultures were quite different, which might be responsible for the significant variations in volatile profiles. Principal component analysis on E-nose data demonstrated that FRN by Culture A, B, and C shared similar flavor, while FRN by Culture D and E possessed different aroma compared to the above three. FRN produced with pure fermentation showed the highest score in sensory evaluation, whereas FRN by mixed cultures indicated rice fragrance, light fragrance, peculiar smell, or foul smell.

Ripeness Prediction of Postharvest Kiwifruit Using a MOS E-Nose Combined with Chemometrics

Postharvest kiwifruit continues to ripen for a period until it reaches the optimal "eating ripe" stage. Without damaging the fruit, it is very difficult to identify the ripeness of postharvest kiwifruit by conventional means. In this study, an electronic nose (E-nose) with 10 metal oxide semiconductor (MOS) gas sensors was used to predict the ripeness of postharvest kiwifruit. Three different feature extraction methods (the max/min values, the difference values and the 70th s values) were employed to discriminate kiwifruit at different ripening times by linear discriminant analysis (LDA), and results showed that the 70th s values method had the best performance in discriminating kiwifruit at different ripening stages, obtaining a 100% original accuracy rate and a 99.4% cross-validation accuracy rate. Partial least squares regression (PLSR), support vector machine (SVM) and random forest (RF) were employed to build prediction models for overall ripeness, soluble solids content (SSC) and firmness. The regression results showed that the RF algorithm had the best performance in predicting the ripeness indexes of postharvest kiwifruit compared with PLSR and SVM, which illustrated that the E-nose data had high correlations with overall ripeness (training: R² = 0.9928; testing: R² = 0.9928), SSC (training: R² = 0.9749; testing: R² = 0.9143) and firmness (training: R² = 0.9814; testing: R² = 0.9290). This study demonstrated that E-nose could be a comprehensive approach to predict the ripeness of postharvest kiwifruit through aroma volatiles.

A Novel Method for Generation of a Fingerprint Using Electronic Nose on the Example of Rapeseed Spoilage

The paper presents application of a new three-parameter method for identification of volatile organic compounds (VOCs) and creation of fingerprints based on the impregnation time (t_IM ), cleaning time (t_CL ), and maximum response ([ΔR/R]_max ) of chemically sensing sensors for detecting spoilage of agricultural commodities. The novelty of this method consists in the use of two additional parameters: an impregnation time and a cleaning time for the first time. An Agrinose built of eight metal oxide semiconductors was used for identification of loss in the rapeseed quality during a short period of storage after harvest. Principal component analysis was applied as a method of data analysis to verify the suitability of the new three-parameter method and visualization of groups of different quality of raw materials. Fourier transform infrared spectroscopy spectra for identification of the infrared bands of fungal polysaccharides and gas chromatography-mass spectrometry analysis of the headspace was applied to describe volatile metabolite contents in reference to the electronic nose technique. The investigations and analyses have demonstrated that the new three-parameter method for determination of volatile compounds ([ΔR/R]_max , t_IM , t_CL ) describes the changes in VOCs more efficiently than the single-parameter approach based only on the maximum sensor response ([ΔR/R]_max ). The proposed method for generation of electronic fingerprints clearly discriminated between rapeseed samples infected with field and storage microflora. Three-parameters method can be useful for quality control in food microbiology and safety, as a rapid method of analysis and detection, including electronic nose sensor technology. PRACTICAL APPLICATION: The use of the proposed method for generation of fingerprints requires no interference with the hardware of the electronic nose but necessitates modification of the software only. This facilitates implementation of the three-parameter method in available devices. This kind of methods and devices can be useful for example in storage process with active ventilation.

Characterization of the Volatile Compounds in Raw and Roasted Georgia Pecans by HS-SPME-GC-MS

Volatile compounds are responsible for the characteristic aroma of raw and roasted pecans. Yet, much is unknown about the specific effects of roasting on pecan volatiles. In this study, the volatiles of raw "Desirable" pecans from Georgia and 3 roasted pecan samples (175 °C for 5, 10, and 15 min) were determined by HS-SPME coupled to GC-MS using stable deuterium-labeled volatiles as internal standards for quantitation. As expected, roasting markedly impacted the volatile profile of pecans: a total of 63 flavor-active compounds were identified in roasted samples, including 9 compounds not detected in raw "Desirable" pecans. Pyrazines, notable indicators of the Maillard reaction, were found only in roasted samples and demonstrated continual increases throughout observed roasting times. Furthermore, it was noted that hydrocarbon derivatives showed substantial increases with roasting, likely a result of the degradation of nonvolatile lipids. The observed changes correspond well to prior sensory investigations concerning the impact of roasting on pecan flavor, and explain increases in intensity for roasted, nutty, buttery, and sweet sensory traits. PRACTICAL APPLICATION: The results of this study document the volatile constituents generated during the roasting of pecans, and this may help formulators, who are trying to develop natural and artificial pecan flavors in new food products.

Determining degree of roasting in cocoa beans by artificial neural network (ANN)-based electronic nose system and gas chromatography/mass spectrometry (GC/MS)

BACKGROUND

Roasting is a critical step in chocolate processing, where moisture content is decreased and unique flavors and texture are developed. The determination of the degree of roasting in cocoa beans is important to ensure the quality of chocolate. Determining the degree of roasting relies on human specialists or sophisticated chemical analyses that are inaccessible to small manufacturers and farmers. In this study, an electronic nose system was constructed consisting of an array of gas sensors and used to detect volatiles emanating from cocoa beans roasted for 0, 20, 30 and 40 min. The several signals were used to train a three-layer artificial neural network (ANN). Headspace samples were also analyzed by gas chromatography/mass spectrometry (GC/MS), with 23 select volatiles used to train a separate ANN.

RESULTS

Both ANNs were used to predict the degree of roasting of cocoa beans. The electronic nose had a prediction accuracy of 94.4% using signals from sensors TGS 813, 826, 822, 830, 830, 2620, 2602 and 2610. In comparison, the GC/MS predicted the degree of roasting with an accuracy of 95.8%.

CONCLUSION

The electronic nose system is able to predict the extent of roasting, as well as a more sophisticated approach using GC/MS. © 2018 Society of Chemical Industry.

Identification of Chinese Herbal Medicines with Electronic Nose Technology: Applications and Challenges

This paper provides a review of the most recent works in machine olfaction as applied to the identification of Chinese Herbal Medicines (CHMs). Due to the wide variety of CHMs, the complexity of growing sources and the diverse specifications of herb components, the quality control of CHMs is a challenging issue. Much research has demonstrated that an electronic nose (E-nose) as an advanced machine olfaction system, can overcome this challenge through identification of the complex odors of CHMs. E-nose technology, with better usability, high sensitivity, real-time detection and non-destructive features has shown better performance in comparison with other analytical techniques such as gas chromatography-mass spectrometry (GC-MS). Although there has been immense development of E-nose techniques in other applications, there are limited reports on the application of E-noses for the quality control of CHMs. The aim of current study is to review practical implementation and advantages of E-noses for robust and effective odor identification of CHMs. It covers the use of E-nose technology to study the effects of growing regions, identification methods, production procedures and storage time on CHMs. Moreover, the challenges and applications of E-nose for CHM identification are investigated. Based on the advancement in E-nose technology, odor may become a new quantitative index for quality control of CHMs and drug discovery. It was also found that more research could be done in the area of odor standardization and odor reproduction for remote sensing.