Application of the electronic nose to the identification of different milk flavorings

HS-GC-IMS and HS-SPME/GC-MS coupled with E-nose and E-tongue reveal the flavors of raw milk from different regions of China

Milk authentication requires identification of the origin and assessment of the aroma characteristics. In this study, we analyzed 24 raw milk samples from different regions of China by profiling volatile flavors using headspace solid phase microextraction-gas chromatography-mass spectrometry, headspace gas chromatography-ion mobility spectrometry, and intelligent sensory technology (E-tongue and E-nose). The flavor of raw milk in Southern and Northern China had evident differences based on the intelligent sensory technology. However, the differences among the samples from the northeast, northwest, and central regions were not significant. Correlations between milk origin and volatile compounds based on variable importance prediction > 1 and principal component analysis results revealed differential compounds including pyridine, nonanal, dodecane, furfural, 1-decene, octanoic acid, and 1,3,5,7-cyclooctatetraene. Our study findings provided a deeper understanding of the geographical differences in raw milk volatile compounds in China.

Effect of a Dairy Cow's Feeding System on the Flavor of Raw Milk: Indoor Feeding or Grazing

The flavor of fresh, raw milk is considered to be the key to maintaining the quality of dairy products, and is very crucial in affecting a consumer's choice. To better understand the differences in flavor of fresh milk between feeding patterns, we conducted the following study. Twelve Holstein cows reared in pure grazing mode and twelve reared intensively in medium to large farms were selected from the Xinjiang Uygur Autonomous Regions at the same time, and the flavor of their raw milk was analyzed. Aroma profiles and taste attributes were assessed by electronic nose and electronic tongue, respectively, and volatile flavor compounds were characterized and quantified by Headspace-Solid Phase Microextraction/Gas Chromatography-Mass Spectrometry. Thirteen volatile compounds were identified in the indoor feeding pattern and 12 in the grazing; most of them overlapped. W1S, W2S and W5S were the main contributing sensors of the electronic nose for the overall assessment of the aroma profile. Raw milk from grazing had more intense astringency, bitterness, sourness and richness in taste compared to indoor feeding. Different dietary conditions may contribute to a variety of aroma profiles. Oxime-, methoxy-phenyl-, octadecanoic acid, furfural and dodecanoic acid were the key volatile flavor compounds of grazing. Meanwhile, raw milk from indoor feeding patterns was unique in 2-nonanone, heptanoic acid and n-decanoic acid. All three detection techniques were valid and feasible for differentiating raw milk in both feeding patterns, and the compounds were significantly correlated with the key sensors by correlation analysis. This study is promising for the future use of metabolic sources of volatile organic compounds to track and monitor animal feeding systems.

Recent Progress in Amine Gas Sensors for Food Quality Monitoring: Novel Architectures for Sensing Materials and Systems

The increasing demand for food production has necessitated the development of sensitive and reliable methods of analysis, which allow for the optimization of storage and distribution while ensuring food safety. Methods to quantify and monitor volatile and biogenic amines are key to minimizing the waste of high-protein foods and to enable the safe consumption of fresh products. Novel materials and device designs have allowed the development of portable and reliable sensors that make use of different transduction methods for amine detection and food quality monitoring. Herein, we review the past decade's advances in volatile amine sensors for food quality monitoring. First, the role of volatile and biogenic amines as a food-quality index is presented. Moreover, a comprehensive overview of the distinct amine gas sensors is provided according to the transduction method, operation strategies, and distinct materials (e.g., metal oxide semiconductors, conjugated polymers, carbon nanotubes, graphene and its derivatives, transition metal dichalcogenides, metal organic frameworks, MXenes, quantum dots, and dyes, among others) employed in each case. These include chemoresistive, fluorometric, colorimetric, and microgravimetric sensors. Emphasis is also given to sensor arrays that record the food quality fingerprints and wireless devices that operate as radiofrequency identification (RFID) tags. Finally, challenges and future opportunities on the development of new amine sensors are presented aiming to encourage further research and technological development of reliable, integrated, and remotely accessible devices for food-quality monitoring.

Trends in artificial aroma sensing by means of electronic nose technologies to advance dairy production - a review

Controversies surrounding the name and how the electronics nose (e-nose) works have been at the center stage since the advent of the technology. Notwithstanding the controversies, the technology has gained popularity in the sensory analysis of dairy foods, because of its rapid results delivery on product aroma profile or pattern, which can be used to assess quality. This review critically evaluated the advances made in the application of the e-nose or artificial sensory system in the dairy industry, focusing on the evaluation of milk, yoghurt and cheese properties, and the trends and prospects of the technology. Most of the e-nose devices applied in the available scientific publications used sensors such as metal oxide semiconductor sensors (MOS), metal-oxide-semiconductor field-effect transistor (MOSFET), conducting polymers composites and quartz microbalance (QMB), and flame ionization detector FID, in a recent study. Though known for aroma sensing, the technology has been applied to evaluate the shelf life or microbial spoilage and to discriminate dairy products based on the volatile profile composition, as determined by the sensors. In most cases, the limitation of the technology is the inability of it to provide information on the nature of constituting compounds, except in gas chromatography and mass spectrometry-based e-nose systems.

An Outlook of Recent Advances in Chemiresistive Sensor-Based Electronic Nose Systems for Food Quality and Environmental Monitoring

An electronic nose (Enose) relies on the use of an array of partially selective chemical gas sensors for identification of various chemical compounds, including volatile organic compounds in gas mixtures. They have been proposed as a portable low-cost technology to analyse complex odours in the food industry and for environmental monitoring. Recent advances in nanofabrication, sensor and microcircuitry design, neural networks, and system integration have considerably improved the efficacy of Enose devices. Here, we highlight different types of semiconducting metal oxides as well as their sensing mechanism and integration into Enose systems, including different pattern recognition techniques employed for data analysis. We offer a critical perspective of state-of-the-art commercial and custom-made Enoses, identifying current challenges for the broader uptake and use of Enose systems in a variety of applications.

Ozone Treatment as a Process of Quality Improvement Method of Rhubarb (Rheum rhaponticum L.) Petioles during Storage

The aim of the study was to identify the effects of ozone treatment reflected by the microbial, mechanical properties and selected chemical parameters during the storage of rhubarb petioles. For this purpose, after they were harvested, rhubarb petioles were treated with gaseous ozone at concentrations of 10 and 100 ppm, for the duration of 5, 15 and 30 min. Subsequently, the plant material was stored at room temperature for 14 days. After this time, the raw material was subjected to a number of chemical and mechanical tests. It was shown that the rhubarb petioles treated with ozone at a rate of 100 ppm for 30 min were characterized by the lowest loss of water content. It was also found that, compared to the control, most samples subjected to ozone treatment presented better mechanical properties, as well as higher oxidative potential and contents of polyphenols and vitamin C. Based on these findings, it was determined that ozone treatment largely increases storage stability of rhubarb.

An Array of MOX Sensors and ANNs to Assess Grated Parmigiano Reggiano Cheese Packs' Compliance with CFPR Guidelines

Parmigiano Reggiano cheese is one of the most appreciated Italian foods on account of its high nutrient content and taste. Due to its high cost, these characteristics make this product subject to counterfeiting in different forms. In this study, an approach based on an array of gas sensors has been employed to assess if it was possible to distinguish different samples based on their aroma. Samples were characterized in terms of rind percentage, seasoning, and rind working process. From the responses of the sensors, five features were extracted and the capability of these parameters to recognize target classes was tested with statistical analysis. Hence, the performance of the sensors’ array was quantified using artificial neural networks. To simplify the problem, a hierarchical approach has been used: three steps of classification were performed, and in each step one parameter of the grated cheese was identified (firstly, seasoning; secondly, rind working process; finally, rind percentage). The accuracies ranged from 88.24% to 100%.

Discrimination of Two Cultivars of Alpinia Officinarum Hance Using an Electronic Nose and Gas Chromatography-Mass Spectrometry Coupled with Chemometrics

Background: Alpinia officinarum Hance is both an herbal medicine and a condiment, and generally has different cultivars such as Zhutou galangal and Fengwo galangal. The appearance of these A. officinarum cultivars is similar, but their chemical composition and quality are different. It is therefore important to discriminate between different A. officinarum plants to ensure the consistency of the efficacy of the medicine. Therefore, we used an electronic nose (E-nose) to explore the differences in odor information between the two cultivars for fast and robust discrimination. Methods: Odor and volatile components of all A. officinarum samples were detected by the E-nose and gas chromatography-mass spectrometry (GC-MS), respectively. The E-nose sensors and GC-MS data were analyzed respectively by principal component analysis (PCA), the correlation between E-nose sensors and GC-MS data were analyzed by partial least squares (PLS). Results: It was found that Zhutou galangal and Fengwo galangal can be discriminated by combining the E-nose with PCA, and the E-nose sensors S2, S6, S7, S9 were important sensors for distinguishing different cultivars of A. officinarum. A total of 56 volatile components of A. officinarum were identified by the GC-MS analysis, and the composition and content of the volatile components from the two different A. officinarum cultivars were different, in particular the relative contents of 1,8-cineole and α-farnesene. The classification result by PCA analysis based on GC-MS data was consistent with the E-nose results. The PLS analysis demonstrated that the volatile terpene, alcohol and ester components primarily interacted with the sensors S2 and S7, indicating that particular E-nose sensors were highly correlated with some aroma constituents. Conclusions: Combined with advanced chemometrics, the E-nose detection technology can discriminate two cultivars of A. officinarum, with GC-MS providing support to determine the material basis of the E-nose sensors’ response.

Analysis of volatile compounds in fresh sturgeon with different preservation methods using electronic nose and gas chromatography/mass spectrometry

Contamination of microorganisms causes a rapid deterioration in the quality of fresh sturgeon meat, which results in the shortening of the shelf-life and increase in the health risk. In this paper, two preservation treatments based on microbial control were considered. During the chilling storage (0–6 days) period, the sensory analysis and the volatile compound (VOC) evaluation were performed by electronic nose and SPME-GC/MS. Results showed that washing with acidic oxidized electrolyzed water and the addition of ε-PL influences the sensitive VOCs of the fresh sturgeon by inhibiting the spoilage of microbes or introducing the chemical agents like free chlorine and reactive oxygen species. Furthermore, GC/MS analysis detected more than 40 kinds of VOCs, mainly aldehydes and ketones, in the fresh sturgeon during the chilling storage period. The relative content of heptanal, nonanal, and acetophenone increased linearly with the storage time in all the groups, where R² of all the groups was larger than 0.9. However, the content of hexanal and octanal decreased simultaneously. This indicated that the present work discovered the potential biomarkers acting as indicators for rapidly evaluating the quality of sturgeon products.

Contamination of microorganisms caused rapidly quality deterioration of fresh sturgeon meat. During the chilling storage, the sensory analysis and volatile compounds evaluation were well performed by electronic nose and SPME-GC/MS.

Effect of the catalyst on the physical and aroma attributes of interesterified milk fat-vegetable oil blends

Abstract The present study aimed to verify the influence of the catalyst (sodium methoxide and the lipase from Rhizopus oryzae immobilized on a hybrid support - silica-polyvinyl alcohol) on the physical and aroma attributes of the products obtained from the interesterification of milk fat with vegetable oils (soybean and canola). The evaluation was carried out using an electronic nose (e-nose) and a texture analyzer. Interesterification reactions were carried out with binary blends (65% of milk fat and 35% of vegetable oil) using 0.75% sodium methoxide (60 °C for 60 min) and immobilized Rhizopus oryzae lipase at a fixed proportion of 500 units of activity per gram of blend (45 °C for 6 h). The results showed that the e-nose sensors discriminated satisfactorily the aromas of both raw materials and products and were able to distinguish them according to the catalyst used. In relation to the texture, both catalysts generated products with appropriate consistencies with satisfactory plasticity and spreadability at refrigerator temperatures.

Hyperspectral Imaging and Spectrometry-Derived Spectral Features for Bitter Pit Detection in Storage Apples

Bitter pit is one of the most important disorders in apples. Some of the fresh market apple varieties are susceptible to bitter pit disorder. In this study, visible–near-infrared spectrometry-based reflectance spectral data (350–2500 nm) were acquired from 2014, 2015 and 2016 harvest produce after 63 days of storage at 5 °C. Selected spectral features from 2014 season were used to classify the healthy and bitter pit samples from three years. In addition, these spectral features were also validated using hyperspectral imagery data collected on 2016 harvest produce after storage in a commercial storage facility for 5 months. The hyperspectral images were captured from either sides of apples in the range of 550–1700 nm. These images were analyzed to extract additional set of spectral features that were effective in bitter pit detection. Based on these features, an automated spatial data analysis algorithm was developed to detect bitter pit points. The pit area was extracted, and logistic regression was used to define the categorizing threshold. This method was able to classify the healthy and bitter pit apples with an accuracy of 85%. Finally, hyperspectral imagery derived spectral features were re-evaluated on the visible–near-infrared reflectance data acquired with spectrometer. The pertinent partial least square regression classification accuracies were in the range of 90–100%. Overall, the study identified salient spectral features based on both hyperspectral spectrometry and imaging techniques that can be used to develop a sensing solution to sort the fruit on the packaging lines.

Disposable all-printed electronic biosensor for instantaneous detection and classification of pathogens

A novel disposable all-printed electronic biosensor is proposed for a fast detection and classification of bacteria. This biosensor is applied to classify three types of popular pathogens: Salmonella typhimurium, and the Escherichia coli strains JM109 and DH5-α. The proposed sensor consists of inter-digital silver electrodes fabricated through an inkjet material printer and silver nanowires uniformly decorated on the electrodes through the electrohydrodynamic technique on a polyamide based polyethylene terephthalate substrate. The best sensitivity of the proposed sensor is achieved at 200 µm teeth spaces of the inter-digital electrodes along the density of the silver nanowires at 30 × 10³/mm². The biosensor operates on ±2.5 V and gives the impedance value against each bacteria type in 8 min after sample injection. The sample data are measured through an impedance analyzer and analyzed through pattern recognition methods such as linear discriminate analysis, maximum likelihood, and back propagation artificial neural network to classify each type of bacteria. A perfect classification and cross-validation is achieved by using the unique fingerprints extracted from the proposed biosensor through all the applied classifiers. The overall experimental results demonstrate that the proposed disposable all-printed biosensor is applicable for the rapid detection and classification of pathogens.

Creation and imitation of a milk flavour

Flavour plays a crucial role in food and is the most important aspect of milk.

Preparation of gluten free bread enriched with green mussel (Perna canaliculus) protein hydrolysates and characterization of peptides responsible for mussel flavour

Green mussel protein hydrolysates (GMPH) utilization for the enrichment of gluten-free bread followed by characterization of flavour peptides using chromatography and electronic nose techniques have been done. The degree of hydrolysis was carried out in each protease digest, and the higher degree of hydrolysis was observed in pepsin digestion. Gluten-free (GF) bread was formulated by using buckwheat flour (BWF), rice flour (RF) and chickpea flour (CPF) (70:20:10) and GMPH were added in the range of 0-20% in the GF bread for enrichment with GMPH. Radar plot of the electronic nose analysis showed that the sensors P30/2, T30/1 and T70/2 had a higher response to the GF bread and GMPH. Consequently, the peptide sequence was obtained manually by ESI-MS spectra of GMPH (KGYSSYICDK) and F-II (SSYCIVKICDK). Flavour quality was 97% discriminately comparable to the GMPH and F-II fractions. Mussel flavoured GF bread can be included in the celiac diet.

A Novel Feature Extraction Approach Using Window Function Capturing and QPSO-SVM for Enhancing Electronic Nose Performance

In this paper, a novel feature extraction approach which can be referred to as moving window function capturing (MWFC) has been proposed to analyze signals of an electronic nose (E-nose) used for detecting types of infectious pathogens in rat wounds. Meanwhile, a quantum-behaved particle swarm optimization (QPSO) algorithm is implemented in conjunction with support vector machine (SVM) for realizing a synchronization optimization of the sensor array and SVM model parameters. The results prove the efficacy of the proposed method for E-nose feature extraction, which can lead to a higher classification accuracy rate compared to other established techniques. Meanwhile it is interesting to note that different classification results can be obtained by changing the types, widths or positions of windows. By selecting the optimum window function for the sensor response, the performance of an E-nose can be enhanced.

Coupling gas chromatography and electronic nose detection for detailed cigarette smoke aroma characterization

Aroma characterization of whole cigarette smoke samples using sensory panels or electronic nose (E-nose) devices is difficult due to the masking effect of major constituents and solvent used for the extraction step. On the other hand, GC in combination with olfactometry detection does not allow to study the delicate balance and synergetic effect of aroma solutes. To overcome these limitations a new instrumental set-up consisting of heart-cutting gas chromatography using a capillary flow technology based Deans switch and low thermal mass GC in combination with an electronic nose device is presented as an alternative to GC-olfactometry. This new hyphenated GC-E-nose configuration is used for the characterization of cigarette smoke aroma. The system allows the transfer, combination or omission of selected GC fractions before injection in the E-nose. Principal component analysis (PCA) and discriminant factor analysis (DFA) allowed clear visualizing of the differences among cigarette brands and classifying them independently of their nicotine content. Omission and perceptual interaction tests could also be carried out using this configuration. The results are promising and suggest that the GC-E-nose hyphenation is a good approach to measure the contribution level of individual compounds to the whole cigarette smoke.

Discrimination of chicken seasonings and beef seasonings using electronic nose and sensory evaluation

This study examines the feasibility of electronic nose as a method to discriminate chicken and beef seasonings and to predict sensory attributes. Sensory evaluation showed that 8 chicken seasonings and 4 beef seasonings could be well discriminated and classified based on 8 sensory attributes. The sensory attributes including chicken/beef, gamey, garlic, spicy, onion, soy sauce, retention, and overall aroma intensity were generated by a trained evaluation panel. Principal component analysis (PCA), discriminant factor analysis (DFA), and cluster analysis (CA) combined with electronic nose were used to discriminate seasoning samples based on the difference of the sensor response signals of chicken and beef seasonings. The correlation between sensory attributes and electronic nose sensors signal was established using partial least squares regression (PLSR) method. The results showed that the seasoning samples were all correctly classified by the electronic nose combined with PCA, DFA, and CA. The electronic nose gave good prediction results for all the sensory attributes with correlation coefficient (r) higher than 0.8. The work indicated that electronic nose is an effective method for discriminating different seasonings and predicting sensory attributes.

Aging time and brand determination of pasteurized milk using a multisensor e-nose combined with a voltammetric e-tongue

A combined approach based on a multisensor system to get additional chemical information from liquid samples through the analysis of the solution and its headspace is illustrated and commented. In the present work, innovative analytical techniques, such as a hybrid e-nose and a voltammetric e-tongue were elaborated to differentiate between different pasteurized milk brands and for the exact recognition of their storage days through the data fusion technique of the combined system. The Principal Component Analysis (PCA) has shown an acceptable discrimination of the pasteurized milk brands on the first day of storage, when the two instruments were used independently. Contrariwise, PCA indicated that no clear storage day's discrimination can be drawn when the two instruments are applied separately. Mid-level of abstraction data fusion approach has demonstrated that results obtained by the data fusion approach outperformed the classification results of the e-nose and e-tongue taken individually. Furthermore, the Support Vector Machine (SVM) supervised method was applied to the new subset and confirmed that all storage days were correctly identified. This study can be generalized to several beverage and food products where their quality is based on the perception of odor and flavor.

Electronic nose with a new feature reduction method and a multi-linear classifier for Chinese liquor classification

An electronic nose (e-nose) was designed to classify Chinese liquors of the same aroma style. A new method of feature reduction which combined feature selection with feature extraction was proposed. Feature selection method used 8 feature-selection algorithms based on information theory and reduced the dimension of the feature space to 41. Kernel entropy component analysis was introduced into the e-nose system as a feature extraction method and the dimension of feature space was reduced to 12. Classification of Chinese liquors was performed by using back propagation artificial neural network (BP-ANN), linear discrimination analysis (LDA), and a multi-linear classifier. The classification rate of the multi-linear classifier was 97.22%, which was higher than LDA and BP-ANN. Finally the classification of Chinese liquors according to their raw materials and geographical origins was performed using the proposed multi-linear classifier and classification rate was 98.75% and 100%, respectively.

Application of Electronic Nose for Detection of Wine-Aging Methods

This study reports the application of an electronic nose for the identification and classification of red wines aged three different methods. The signals of the different wines detected by the 10 sensors present in the E-nose are significantly different from each other. The response to the signal generates a typical chemical fingerprint of the volatile compounds present in the wines. Principal Component Analysis can be applied for the dimensionality reduction of the collected signal. Since the total contribution rate of the first three principal components is up to 97.27%, different wines can be distinguished from each other by the three principal components. Euclidean distance, correlation analysis, Mahalanobis distance and linear discrimination analysis can offer 100% accuracy for known samples, and the accuracy rate can reach 88.9% for the 18 test samples. In addition, numerous advantages exist compared with sensory analysis in both authentication and quality control of wines.

Food analysis using artificial senses

Nowadays, consumers are paying great attention to the characteristics of food such as smell, taste, and appearance. This motivates scientists to imitate human senses using devices known as electronic senses. These include electronic noses, electronic tongues, and computer vision. Thanks to the utilization of various sensors and methods of signal analysis, artificial senses are widely applied in food analysis for process monitoring and determining the quality and authenticity of foods. This paper summarizes achievements in the field of artificial senses. It includes a brief history of these systems, descriptions of most commonly used sensors (conductometric, potentiometric, amperometic/voltammetric, impedimetric, colorimetric, piezoelectric), data analysis methods (for example, artificial neural network (ANN), principal component analysis (PCA), model CIE L*a*b*), and application of artificial senses to food analysis, in particular quality control, authenticity and falsification assessment, and monitoring of production processes.

Characterization of aroma compounds of Chinese famous liquors by gas chromatography-mass spectrometry and flash GC electronic-nose

Aroma composition of five Chinese premium famous liquors with different origins and liquor flavor types was characterized by gas chromatography-mass spectrometry (GC-MS) and flash gas chromatographic electronic nose system. Eighty-six aroma compounds were identified, including 5 acids, 34 esters, 10 alcohols, 9 aldehydes, 4 ketones, 4 phenols, and 10 nitrous and sulfuric compounds. To investigate possible correlation between aroma compounds identified by GC-MS and sensory attributes, multivariate ANOVA-PLSR (APLSR) was performed. It turned out that there were 30 volatile composition, ethyl acetate, ethyl propanoate, ethyl 2-methyl butanoate, ethyl 3-methyl butanoate, ethyl lactate, ethyl benzenacetate, 3-methylbutyl acetate, hexyl acetate, 3-methyl-1-butanol, 1-heptanol, phenylethyl alcohol, acetaldehyde, 1,1-diethoxy-3-methyl butane, furfural, benzaldehyde, 5-methyl-2-furanal, 2-octanone, 2-n-butyl furan, dimethyl trisulfied and 2,6-dimethyl pyrazine, ethyl nonanoate, isopentyl hexanoate, octanoic acid, ethyl 5-methyl hexanoate, 2-phenylethyl acetate,ethyl oleate, propyl hexanoate, butanoic acid and phenol, ethyl benzenepropanoate, which showed good coordination with Chinese liquor characteristics. The multivariate structure of this electronic nose responses was then processed by principal component analysis (PCA) and hierarchical cluster analysis (HCA). According to the obtained results, GC-MS and electronic nose can be used for the differentiation of the liquor origins and flavor types.

Storage study and quality evaluation of coconut protein powder

Coconut skim milk and insoluble protein are 2 major byproducts in the production of virgin coconut oil. Coconut skim milk was homogenized along with insoluble protein and spray dried to obtain a value-added product, namely, coconut protein powder (CPP). This study deals with the storage study of CPP under different conditions (refrigerated [control], ambient and accelerated). CPP samples were withdrawn periodically at designated intervals of 15 d for accelerated and control, and 30 d for ambient condition. CPP stored at different conditions exhibited marginal moisture uptake (by 0.74 % w/w for control, 0.76 % w/w for ambient, and 1.26 % w/w for accelerated condition) and as a result, had very little effect on the functional properties of the powder. Withdrawn CPP was tested for sensory quality aspects and subjected to instrumental analysis as well. Withdrawn CPP was incorporated as a milk substitute in dessert (Kheer). Quantitative descriptive analysis of the powder and product (Kheer) showed no significant difference in attributes of CPP during the storage period of 2 mo. Electronic nose analysis revealed that CPP samples were not much different with respect to aroma pattern matching, respectively.