Geographical origin of Sauvignon Blanc wines predicted by mass spectrometry and metal oxide based electronic nose

Rapid authentication of red wine by MALDI-MS combined with DART-MS

A simple, rapid and high-throughput approach was developed for authentication of red wine for the first time, by combining spectral results from matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) and direct analysis in real time mass spectrometry (DART-MS). By coupling with orthogonal partial least squares discrimination analysis (OPLS-DA), this approach enabled successful classification of 535 wines from 8 countries, with the correct classification rates of 100% on the calibration set and over 90% on the validation set for almost all countries, and 26 potential characteristic markers selected. Compared to one single technique, this approach allowed detection of more compound ions, and with better fitting and predictive performances. The satisfactory differentiation results of vintages and grape varieties further verified the robustness of the approach. This study demonstrated the feasibility of combining multiple mass spectrometric techniques for wine analysis, which can be extended to other fields or to combinations of other analytical techniques.

Wild Saccharomyces Produced Differential Aromas of Fermented Sauvignon Blanc Must

Nine Saccharomyces strains, previously isolated from vineyards in Southern Brazil, were used as starter cultures in fermentations of Sauvignon Blanc (SB) must at laboratory scale, to study inter-strain differences in aroma profiles. The molecular profiles differentiated the following isolates from the reference strain (SC2048), which is typically used in wine production: 06CE, 11CE, 33CE, 01PP, 12M, 13PP, 26PP, 28AD, and 41PP. Under the same conditions, each of these strains produced different concentrations and combinations of metabolites, which significantly influenced the aroma of the fermented SB must. Volatile compounds such as octanoic acid, diethyl succinate, and ethyl lactate were associated with the strains 26PP, 41PP, 01PP, and 12M, while strains 33CE, 28AD, 13PP, and 06CE were associated with the production of ethyl acetate and 1-hexanol. Strain 06CE produced 592.87 ± 12.35 µg/L 1-hexanol. In addition, the olfactory activity values (OAVs; we considered only values >1) allowed us to evaluate the participation of each compound in the aroma of the final fermented SB. In conclusion, the selected wild strains are promising candidates for improving the regional characteristics of wine.

Use of Multivariate Statistics in the Processing of Data on Wine Volatile Compounds Obtained by HS-SPME-GC-MS

This review takes a snapshot of the main multivariate statistical techniques and methods used to process data on the concentrations of wine volatile molecules extracted by means of solid phase micro-extraction and analyzed using GC-MS. Hypothesis test, exploratory analysis, regression models, and unsupervised and supervised pattern recognition methods are illustrated and discussed. Several applications in the wine volatolomic sector are described to highlight different interactions among the various matrix components and volatiles. In addition, the use of Artificial Intelligence-based methods is discussed as an innovative class of methods for validating wine varietal authenticity and geographical traceability.

E-Nose and Olfactory Assessment: Teamwork or a Challenge to the Last Data? The Case of Virgin Olive Oil Stability and Shelf Life

Electronic nose (E-nose) devices represent one of the most trailblazing innovations in current technological research, since mimicking the functioning of the biological sense of smell has always represented a fascinating challenge for technological development applied to life sciences and beyond. Sensor array tools are right now used in a plethora of applications, including, but not limited to, (bio-)medical, environmental, and food industry related. In particular, the food industry has seen a significant rise in the application of technological tools for determining the quality of edibles, progressively replacing human panelists, therefore changing the whole quality control chain in the field. To this end, the present review, conducted on PubMed, Science Direct and Web of Science, screening papers published between January 2010 and May 2021, sought to investigate the current trends in the usage of human panels and sensorized tools (E-nose and similar) in the food industry, comparing the performances between the two different approaches. In particular, the focus was mainly addressed towards the stability and shelf life assessment of olive oil, the main constituent of the renowned “Mediterranean diet”, and nowadays appreciated in cuisines from all around the world. The obtained results demonstrate that, despite the satisfying performances of both approaches, the best strategy merges the potentialities of human sensory panels and technological sensor arrays, (i.e., E-nose somewhat supported by E-tongue and/or E-eye). The current investigation can be used as a reference for future guidance towards the choice between human panelists and sensorized tools, to the benefit of food manufacturers.

Assessment of Volatile Aromatic Compounds in Smoke Tainted Cabernet Sauvignon Wines Using a Low-Cost E-Nose and Machine Learning Modelling

Wine aroma is an important quality trait in wine, influenced by its volatile compounds. Many factors can affect the composition and levels (concentration) of volatile aromatic compounds, including the water status of grapevines, canopy management, and the effects of climate change, such as increases in ambient temperature and drought. In this study, a low-cost and portable electronic nose (e-nose) was used to assess wines produced from grapevines exposed to different levels of smoke contamination. Readings from the e-nose were then used as inputs to develop two machine learning models based on artificial neural networks. Results showed that regression Model 1 displayed high accuracy in predicting the levels of volatile aromatic compounds in wine (R = 0.99). On the other hand, Model 2 also had high accuracy in predicting smoke aroma intensity from sensory evaluation (R = 0.97). Descriptive sensory analysis showed high levels of smoke taint aromas in the high-density smoke-exposed wine sample (HS), followed by the high-density smoke exposure with in-canopy misting treatment (HSM). Principal component analysis further showed that the HS treatment was associated with smoke aroma intensity, while results from the matrix showed significant negative correlations (p < 0.05) were observed between ammonia gas (sensor MQ137) and the volatile aromatic compounds octanoic acid, ethyl ester (r = -0.93), decanoic acid, ethyl ester (r = -0.94), and octanoic acid, 3-methylbutyl ester (r = -0.89). The two models developed in this study may offer winemakers a rapid, cost-effective, and non-destructive tool for assessing levels of volatile aromatic compounds and the aroma qualities of wine for decision making.

An eNose-based method performing drift correction for online VOC detection under dry and humid conditions

Our recently demonstrated innovative concept of electronic nose (eNose) based on a combination of gas sensors is further tested and benchmarked in the present study. The system is a test bed for gas sensors of different principal technologies distributed within three compartments, which share a compact, very low volume, temperature-controlled sensing chamber. Here, the eNose-based analyser contains three sensing arrays of commercially available semiconducting metal oxide (MOX) gas sensors: one compartment contains 8 analog MOX sensors, while the other two compartments comprise 10 digital MOX sensors. The presented instrument is explicitly tested for the discrimination between mid-range (3-18 ppm) concentrations of different volatile organic compounds (VOCs) including acetaldehyde, acetone, ethanol, ethyl acetate, isoprene and n-pentane under dry and humid conditions, which are all considered relevant gases in future breath diagnostic applications. Since the experiments were performed in periods of time separated by around 20 days, they are affected by drift. For this reason, we explore the opportunity of drift mitigation using methods based on component removal computed by linear discriminant analysis, partial least squares discriminant analysis and direct orthogonalization, which lend themselves to future in-field applications of the developed device and sensing methodology.

Gas Sensors Based on Chemi-Resistive Hybrid Functional Nanomaterials

Chemi-resistive sensors based on hybrid functional materials are promising candidates for gas sensing with high responsivity, good selectivity, fast response/recovery, great stability/repeatability, room-working temperature, low cost, and easy-to-fabricate, for versatile applications. This progress report reviews the advantages and advances of these sensing structures compared with the single constituent, according to five main sensing forms: manipulating/constructing heterojunctions, catalytic reaction, charge transfer, charge carrier transport, molecular binding/sieving, and their combinations. Promises and challenges of the advances of each form are presented and discussed. Critical thinking and ideas regarding the orientation of the development of hybrid material-based gas sensor in the future are discussed.

Evaluation of Aroma Compounds in the Process of Wine Ageing with Oak Chips

Many modern alcoholic beverages are subjected to ageing processes during which compounds extracted from wood contribute decisively to the overall beverage character. Wines represent a perfect example of beverage in which ageing is a crucial technological manufacturing step. During winemaking, producers accelerate chemical changes in wine composition by traditional and alternative methods, such as the use of oak wood barrels and/or oak wood chips. Our research aimed to investigate the overall volatile composition and sensory quality of red wines aged for two timeframes, namely, 1.5 and 3 months, and with two technological variants, i.e., American and French oak wood chips. Red grapes from the Fetească neagră (Vitis vinifera) variety were harvested from a vineyard in the North-East region of Romania. Stir bar sorptive extraction and gas chromatography coupled with mass spectrometry (SBSE-GC-MS) was used to extract minor aromas present in wine samples. The results showed clear differences between wines treated with American and French oak chips. Furthermore, ageing for 3 months increased the concentration of cis-whiskey lactone and guaiacol in American oak-treated wine samples. For wines aged with French oak chips, we observed higher concentrations of furfural, 5-methylfurfural, 4-vinylguaiacol, and trans-whiskey lactone. The increased presence of chemical compounds in wine aged with French oak chips generated prominent smoky, licorice, and toasty aromas, whereas in wines aged with American oak chips, notes of vanilla, toasty, and cacao aromas were noticed. Moreover, red wines aged with American and French oak chips were discriminated by chemometric analysis, which confirmed the evolution of aroma compounds.

Volatome finger printing of red wines made from grapes grown under tropical conditions of India using thermal-desorption gas chromatography-mass spectrometry (TD-GC/MS)

The current study evaluated the key characters of aroma composition in diversified red wines (Cinsaut, Grenache, Cabernet Franc, Petit Verdot, Cabernet Sauvignon, Nielluccio, Tempranillo, Syrah, Merlot and Caladoc). Out of hundreds of volatile compounds 64 compounds were considered for study. Different groups consisting of fatty acids, volatile alcohols, aldehydes, esters, volatile phenols and terpenes were analysed using gas chromatography mass spectrometry coupled with thermal desorption (TD-GC-MS). Among all these diversified classes, alcohols were found as the most dominant group followed by esters and acids whereas aldehydes, phenols and terpenes were found to be minor compounds. Among the varieties, Nielluccio wine recorded highest concentration of total volatile compounds (191.53 mg/L) while, it was least in Caladoc wines (15.45 mg/L). The principal component analysis clearly differentiated Grenache wines based on their relationships between scores and their aroma composition followed by Nielluccio and Cinsuat wines. Out of sixty four compounds, only six aromatic compounds viz. butanediol, isoamyl actate, γ-Terpene, butanol, acetic acid and furfural have satisfying aroma descriptors with floral and fruity nuances and contribute to differentiate the Grenache wines from other varieties which have similar scores in PC1 analysis. The cluster analysis also suggested that the wines in the same group (Cinsaut, Tempranillo and Syrah), (Cabernet Franc, Cabernet Sauvignon, Caladoc and Merlot) and (Nielluccio and Petit Verdot) had similar aroma characterization. Grenache wines were well differentiated from the sub group formed by other red varieties.

Classification of illicit liquors based on their geographic origin using Attenuated total reflectance (ATR) - Fourier transform infrared (FT-IR) spectroscopy and chemometrics

In recent decades vibrational spectroscopy especially infrared (IR) spectroscopy has emerged as a fast, sensitive, and reliable technique in classifying alcoholic beverages based on geographic origin. However, Attenuated total reflectance (ATR) - Fourier transform infrared (FTIR) spectroscopy has not been used in many studies. In the present study, an attempt has been made to classify 75 samples of illicit liquor from different regions of India based on their geographical origin. The samples were scanned in the MIR range of 600-3000cm^-1. It was observed that while using PCA 76% accuracy was obtained and while using LDA 93% accuracy was obtained. The samples of Delhi, Mansa, Patiala, Pathankot, and Fatehgarh Sahib show 100% classification with LDA whereas the samples from Ferozpur and Gurdaspur showed a 75% correct classification. These results point out toward the potential applicability of ATR-FTIR for the classification of alcoholic beverages based on the geographic origin.

Characterization of dried and freeze-dried sea fennel (Crithmum maritimum L.) samples with headspace gas-chromatography/mass spectrometry and evaluation of an electronic nose discrimination potential

Processed samples (air-dried @ 40 and @ 60 °C and freeze-dried) of sea fennel (Crithmum maritimum L.), an autochthonous spice with interesting market potential, were analyzed by headspace gas-chromatography/mass spectrometry and classification capabilities of an electronic nose in discriminating between samples with stepwise forward statistics were evaluated as well. Freeze-drying process was the most preservative in terms of limiting darkening without compromising appearance of the final product, providing weight loss of about 85% and water activity below the limit for mold growth issues. Headspace analysis of samples highlighted the presence of 35 volatiles grouped as terpene hydrocarbons, oxygenated terpenes, sesquiterpen hydrocarbons, phenyl propanoids, not-terpenic aldehydes and not-terpenic ketones. Correlations emerged between selected sensors and some detected volatile organic compounds. Stepwise linear discriminant analysis and simple K-nearest neighbors obtained a 100% overall correct classification rate in cross-validation of the electronic nose in classifying samples, whereas stepwise quadratic discriminant analysis and Naive-Bayes gave 93.3%. The sea fennel could be a new interesting spice to launch in the food market and the electronic nose showed the potential to be used in monitoring the industrial process aimed at extending its shelf-life.

A GC-MS untargeted metabolomics approach for the classification of chemical differences in grape juices based on fungal pathogen

Fungal bunch rot of grapes leads to production of detrimental flavour compounds, some of which are well characterised but others remain unidentified. The current study uses an untargeted metabolomics approach to classify volatile profiles of grape juices based on the presence of different fungal pathogens. Individual grape berries were inoculated with Botrytis cinerea, Penicillium expansum, Aspergillus niger or A. carbonarius. Grape bunches were inoculated and blended with healthy fruit, to provide 10% (w/w) infected juice. Juices from the above sample batches were analysed by GC/MS. PLS-DA of the normalised summed mass ions indicated sample classification according to pathogen. Compounds identified from those mass ion matrices that had high discriminative value for classification included 1,5-dimethylnaphthalene and several unidentified sesquiterpenes that were relatively higher in B. cinerea infected samples. A. niger and A. carbonarius samples were relatively higher in 2-(4-hexyl-2,5-dioxo-2,5-dihydrofuran-3-yl)acetic acid, while P. expansum samples were higher in γ-nonalactone and m-cresol.

Identification of Tequila with an Array of ZnO Thin Films: A Simple and Cost-Effective Method

An array of ZnO thin film sensors was obtained by thermal oxidation of physical vapor deposited thin Zn films. Different conditions of the thermal treatment (duration and temperature) were applied in view of obtaining ZnO sensors with different gas sensing properties. Films having undergone a long thermal treatment exhibited high responses to low ethanol concentrations, while short thermal treatments generally led to sensors with high ethanol sensitivity. The sensor array was used to distinguish among Tequilas and Agave liquor. Linear discriminant analysis and the multilayer perceptron neural network reached 100% and 86.3% success rates in the discrimination between real Tequila and Agave liquor and in the identification of Tequila brands, respectively. These results are promising for the development of an inexpensive tool offering low complexity and cost of analysis for detecting fraud in spirits.

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.

Artemisia umbelliformis Lam. and Génépi Liqueur: Volatile Profile as Diagnostic Marker for Geographic Origin and To Predict Liqueur Safety

Artemisia umbelliformis, commonly known as "white génépi", is characterized by a volatile fraction rich in α- and β-thujones, two monoterpenoids; under European Union (EU) regulations these are limited to 35 mg/L in Artemisia-based beverages because of their recognized activity on the human central nervous system. This study reports the results of an investigation to define the geographical origin and thujone content of individual plants of A. umbelliformis from different geographical sites, cultivated experimentally at a single site, and to predict the thujone content in the resulting liqueurs through their volatile fraction. Headspace solid phase microextraction (HS-SPME) combined with gas chromatography-mass spectrometry (GC-MS) and non-separative HS-SPME-MS were used as analytical platforms to create a database suitable for chemometric description and prediction through linear discriminant analysis (LDA). HS-SPME-MS was applied to shorten analysis time. With both approaches, a diagnostic prediction of (i) plant geographical origin and (ii) thujone content of plant-related liqueurs could be made.

Exploring the Effects of Geographical Origin on the Chemical Composition and Quality Grading of Vitis vinifera L. cv. Chardonnay Grapes

The relationship between berry chemical composition, region of origin and quality grade was investigated for Chardonnay grapes sourced from vineyards located in seven South Australian Geographical Indications (GI). Measurements of basic chemical parameters, amino acids, elements, and free and bound volatiles were conducted for grapes collected during 2015 and 2016. Multiple factor analysis (MFA) was used to determine the sets of data that best discriminated each GI and quality grade. Important components for the discrimination of grapes based on GI were 2-phenylethanol, benzyl alcohol and C₆ compounds, as well as Cu, Zn, and Mg, titratable acidity (TA), total soluble solids (TSS), and pH. Discriminant analysis (DA) based on MFA results correctly classified 100% of the samples into GI in 2015 and 2016. Classification according to grade was achieved based on the results for elements such as Cu, Na, Fe, volatiles including C₆ and aryl alcohols, hydrolytically-released volatiles such as (Z)-linalool oxide and vitispirane, pH, TSS, alanine and proline. Correct classification through DA according to grade was 100% for both vintages. Significant correlations were observed between climate, GI, grade, and berry composition. Climate influenced the synthesis of free and bound volatiles as well as amino acids, sugars, and acids, as a result of higher temperatures and precipitation.

Comprehensive evaluation of direct injection mass spectrometry for the quantitative profiling of volatiles in food samples

Although qualitative strategies based on direct injection mass spectrometry (DIMS) have recently emerged as an alternative for the rapid classification of food samples, the potential of these approaches in quantitative tasks has scarcely been addressed to date. In this paper, the applicability of different multivariate regression procedures to data collected by DIMS from simulated mixtures has been evaluated. The most relevant factors affecting quantitation, such as random noise, the number of calibration samples, type of validation, mixture complexity and similarity of mass spectra, were also considered and comprehensively discussed. Based on the conclusions drawn from simulated data, and as an example of application, experimental mass spectral fingerprints collected by direct thermal desorption coupled to mass spectrometry were used for the quantitation of major volatiles in Thymus zygis subsp. zygis chemotypes. The results obtained, validated with the direct thermal desorption coupled to gas chromatography-mass spectrometry method here used as a reference, show the potential of DIMS approaches for the fast and precise quantitative profiling of volatiles in foods.This article is part of the themed issue 'Quantitative mass spectrometry'.

Determination of Ignitable Liquids in Fire Debris: Direct Analysis by Electronic Nose

Arsonists usually use an accelerant in order to start or accelerate a fire. The most widely used analytical method to determine the presence of such accelerants consists of a pre-concentration step of the ignitable liquid residues followed by chromatographic analysis. A rapid analytical method based on headspace-mass spectrometry electronic nose (E-Nose) has been developed for the analysis of Ignitable Liquid Residues (ILRs). The working conditions for the E-Nose analytical procedure were optimized by studying different fire debris samples. The optimized experimental variables were related to headspace generation, specifically, incubation temperature and incubation time. The optimal conditions were 115 °C and 10 min for these two parameters. Chemometric tools such as hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA) were applied to the MS data (45–200 m/z) to establish the most suitable spectroscopic signals for the discrimination of several ignitable liquids. The optimized method was applied to a set of fire debris samples. In order to simulate post-burn samples several ignitable liquids (gasoline, diesel, citronella, kerosene, paraffin) were used to ignite different substrates (wood, cotton, cork, paper and paperboard). A full discrimination was obtained on using discriminant analysis. This method reported here can be considered as a green technique for fire debris analyses.

Profiles of Phenolic Acids and Flavan-3-ols for Select Chinese Red Wines: A Comparison and Differentiation According to Geographic Origin and Grape Variety

The profiles of phenolic acids and flavan-3-ols for the selected Chinese red wines and the potential of using phenolic acids and flavan-3-ols to differentiate the geographic origin and grape variety of wines from China are investigated in this study. Significant differences and markers could be found according to the geographical origin and grape variety. Through a canonical discriminant analysis a good differentiation was developed according to the geographic origin or grape variety, and the accuracy of the discriminant model was 88.9% and 100%, respectively. According to the phenolic acid and flavan-3-ols profiles of the wine samples and good differentiation in the region and the variety discriminant analysis, minimal fraudulent claims were noted for the Chinese red wines investigated.

PRACTICAL APPLICATION

This study provides some help for the protection of geographical origin and monovarietal wine claims.

Application of an HS-MS for the detection of ignitable liquids from fire debris

In arson attacks, accelerants such as ignitable liquids are commonly used to initiate or accelerate a fire. The detection of ignitable liquid residues at fire scenes is therefore a key step in fire investigations. The most widely used analytical technique for the analysis of accelerants is GC-MS. However, pre-concentration of the ignitable liquid residues is required prior to the chromatographic analysis. The standard method, ASTM E1412, involves passive headspace concentration with activated charcoal strips as a method to isolate the ignitable liquid residues from fire debris and these residues are subsequently desorbed from the carbon strip with solvents such as carbon disulfide. In the work described here, an alternative analytical technique based on an HS-MS (headspace mass spectrometry) has been developed for the thermal desorption of the carbon strips and analysis of different ignitable liquid residues in fire debris. The working conditions for the HS-MS analytical procedure were optimized using different types of fire debris (pine wood burned with gasoline and diesel). The optimized variables were desorption temperature and desorption time. The optimal conditions were 145°C and 15 min. The optimized method was applied to a set of fire debris samples. In order to simulate post burn samples several accelerants (gasoline, diesel, citronella, kerosene, paraffin, and alcohol) were used to ignite different substrates (wood, cotton, cork, paper, and paperboard). chemometric methods (cluster analysis and discriminant analysis) were applied to the total ion spectrum obtained from the MS (45-200 m/z) to discriminate between the burned samples according to the accelerant used. The method was validated by analyzing all samples by GC-MS according to the standard methods ASTM E1412 and ASTM E1618. The results obtained on using the method developed in this study were comparable to those obtained with the reference method. However, the newly developed HS-MS method is faster, safer, and more environmental friendly than the standard method.

The potential of e-nose aroma profiling for identifying the geographical origin of licorice (Glycyrrhiza glabra L.) roots

Licorice roots cultivated commercially in distinct geographical areas such as China, Iran, Italy (Abruzzo, Basilicata, Calabria and Sicily) and Turkey were classified using an artificial olfactive system (e-nose) based on metal oxide semiconductor sensors (MOS). The resultant instrumental data were processed using a multivariate statistical analysis method in order to classify the raw samples according to its origin. The e-nose odourprintings were obtained by a canonical discriminant analysis carried out with the aim of relating the specific data-sets corresponding to whole licorice roots aroma with the e-nose reference dataset. E-nose results were compared to those obtained by SPME/GC-MS. The HS-SPME/GC/MS analysis was used as a control system to check for the actual existence of differences in the chemical composition of sample headspace. These results imply the possibility to use an electronic nose as a tool for a quick, effective and non-destructive authentication of licorice roots.

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.

Diverse applications of electronic-nose technologies in agriculture and forestry

Electronic-nose (e-nose) instruments, derived from numerous types of aroma-sensor technologies, have been developed for a diversity of applications in the broad fields of agriculture and forestry. Recent advances in e-nose technologies within the plant sciences, including improvements in gas-sensor designs, innovations in data analysis and pattern-recognition algorithms, and progress in material science and systems integration methods, have led to significant benefits to both industries. Electronic noses have been used in a variety of commercial agricultural-related industries, including the agricultural sectors of agronomy, biochemical processing, botany, cell culture, plant cultivar selections, environmental monitoring, horticulture, pesticide detection, plant physiology and pathology. Applications in forestry include uses in chemotaxonomy, log tracking, wood and paper processing, forest management, forest health protection, and waste management. These aroma-detection applications have improved plant-based product attributes, quality, uniformity, and consistency in ways that have increased the efficiency and effectiveness of production and manufacturing processes. This paper provides a comprehensive review and summary of a broad range of electronic-nose technologies and applications, developed specifically for the agriculture and forestry industries over the past thirty years, which have offered solutions that have greatly improved worldwide agricultural and agroforestry production systems.

Artificial noses

The mammalian olfactory system is able to detect many more odorants than the number of receptors it has by utilizing cross-reactive odorant receptors that generate unique response patterns for each odorant. Mimicking the mammalian system, artificial noses combine cross-reactive sensor arrays with pattern recognition algorithms to create robust odor-discrimination systems. The first artificial nose reported in 1982 utilized a tin-oxide sensor array. Since then, however, a wide range of sensor technologies have been developed and commercialized. This review highlights the most commonly employed sensor types in artificial noses: electrical, gravimetric, and optical sensors. The applications of nose systems are also reviewed, covering areas such as food and beverage quality control, chemical warfare agent detection, and medical diagnostics. A brief discussion of future trends for the technology is also provided.