Next Article in Journal
Evaluation of Scenedesmus dimorphus under Different Photoperiods with Eutrophicated Lagoon Water
Previous Article in Journal
Groundwater Resources of the Transboundary Quaternary Aquifer of the Lake Chad Basin: Towards a Better Management via Isotope Hydrology
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Resources
Volume 12
Issue 12
10.3390/resources12120139
resources-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

First Steps in Developing a Fast, Cheap, and Reliable Method to Distinguish Wild Mushroom and Truffle Species

by
Inês Ferreira
1,
Teresa Dias
1,*,
Juliana Melo
1,
Abdul Mounem Mouazen
2 and
Cristina Cruz
1
1
cE3c—Centre for Ecology, Evolution and Environmental Changes & CHANGE—Global Change and Sustainability Institute, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Bloco C2, 1749-016 Lisboa, Portugal
2
Department of Environment, Faculty of Bioscience Engineering, Ghent University, Coupure Links 653, 9000 Ghent, Belgium
*
Author to whom correspondence should be addressed.
Resources 2023, 12(12), 139; https://doi.org/10.3390/resources12120139
Submission received: 22 September 2023 / Revised: 15 November 2023 / Accepted: 20 November 2023 / Published: 22 November 2023
(This article belongs to the Topic Sustainability in Agri-Food and Forestry Ecosystems)
Browse Figures
Versions Notes

Abstract

:
Wild mushrooms and truffles (MT) are important resources, which can contribute to the socioeconomic sustainability of forestry ecosystems. However, not all wild MT are edible. Fast, cheap, and reliable methods that distinguish wild MT species (including the deadly ones) can contribute to valuing these important forest resources. Here, we tested if wild MT species, and their edibility, could be distinguished based on their aroma profiles (i.e., smellprints). For that, we combined the use of the electronic nose with classification models (linear discriminant analysis (LDA) and partial least squares discriminant analysis (PLS-DA)) to distinguish between 14 wild MT species (including edible and non-edible species) collected in Portugal. The 14 wild MT species could be accurately distinguished using LDA (93% accuracy), while the edible and non-edible species could be accurately distinguished using both LDA and PLS-DA (97% and 99% accuracy, respectively). Keeping in mind that our methodological design’s feasibility was verified using a small sample, the data show the potential of the combined use of the electronic nose with discriminant analysis to distinguish wild MT species and their edibility based on their aromatic profile. Although a larger dataset will be necessary to develop a quick and reliable identification method, it shows potential to be as accurate as the identification performed by mycologists and molecular biology, yet requiring less technical training, and the analyses are cheaper and faster.

1. Introduction

Approximately 148,000 species of fungi have been identified so far. However, it is believed that more than 90% of the fungal species remain unknown, and the total number of fungal species worldwide could reach 2.2 to 3.8 million species [1,2]. Mushrooms and truffles (MT) are the better known fungal species [2]. Mushrooms and truffles both consist of the fruiting body of macrofungi, but mushrooms fruit aboveground while truffles fruit belowground [3]. Indeed, for millennia, humans have included wild MT in their diets, medicinal practices, and ceremonial traditions [4]. In the contemporary era, these natural wonders have evolved into non-timber forest commodities, embodying a vast genetic reservoir that holds profound ecological, sociocultural, economic, medicinal, and biotechnological importance worldwide [5,6,7,8]. In the last decades, we have seen a growing interest in MT for their rich composition and bioactive compounds, which make them a great resource of exciting ingredients for food and nutraceuticals [9,10,11,12,13].
However, not all MT species are safe for human consumption. From the 14,000 MT species identified so far [10], Li and colleagues [14] reviewed 2786 MT species from 99 countries. From that list, most MT species were considered edible, i.e., 79% of the species were identified as edible, and 72% were considered safe for human consumption. An additional 7% of those species required specific pretreatment measures before they could be considered safe or had been associated with allergic reactions in some instances. Furthermore, 17% of those species were categorized as of uncertain edibility due to a lack of conclusive evidence of human safe consumption, while 3% remained unconfirmed due to ongoing debates and differing opinions regarding their edibility and potential toxicity.
Although wild MT constitute a highly esteemed delicacy in specific parts of the world [15,16], they face great skepticism in other regions and, therefore, they are not valued as an important forest resource. The valorization of wild MT has the potential to promote the socioeconomic sustainability of rural communities and forests [17,18]. Supporting the sustainable harvesting and cultivation of these resources can generate income for local people, conserve biodiversity, mitigate climate change [15,19], and contribute to forest ecosystem processes and services [20]. Wild MT are versatile resources with potential applications in medicine, food, cosmetics, and recreation, from developing novel drugs and therapies, to the development of culinary delights and skincare products [9,21,22,23,24]. Their diverse properties offer opportunities for innovation across various industries [20], while highlighting the importance of responsible and ethical use.
Counteracting this skepticism about wild MT in some regions, society has recently and progressively developed a strong interest for wild MT hunting and consumption [25]. The downside of this growing interest in wild MT is the cases of poisoning, which often occur as a common outcome of enthusiastic wild MT gathering and consumption by mushroom enthusiasts that are not highly skilled (i.e., people with insufficient training on wild MT species identification) [26]. As an example, a retrospective study showed that around 94% of the reported mushroom poisoning cases resulted from the consumption of incorrectly identified wild mushrooms [27]. To avoid cases of poisoning, the safe trade of wild MT must rely on the implementation of guidelines and the enactment of legislation that ensures food safety [26]. To bridge knowledge gaps and contribute to wild MT species identification, new identification methodologies and technologies (please see below for some examples) have been developed, further enhancing our understanding and use of these valuable natural resources [28,29,30].
The traditional methods for identifying wild MT species include mainly morphological identification, instrumental analysis (e.g., gas chromatography), and molecular biology approaches [30]. However, developing technologies (e.g., image recognition [31,32], integrating machine learning [33,34,35,36,37]) have been largely applied for wild MT species identification in the last decade. With the globalization of the Internet of Things (IoT), methods to identify wild MT species from field-collection images using a smartphone application have been developed [28,29]. These smartphone identification applications hold promise to aid clinical toxicologists and the general public in accurately identifying wild MT species, but their low accuracy makes them insufficient to distinguish edible wild MT from potentially toxic ones [29]. Similar phenomena are occurring with artificial-intelligence-generated books for foraging and MT identification, which contain inaccurate information such as “taste and smell” as an identifying feature [38]. This could lead amateurs to the incorrect assumption that tasting is an identification method, which can ultimately result in wild MT poisoning. Therefore, it is important to always follow reliable sources, and use highly accurate tools that build greater trust on wild MT species identification.
One of the traits that plays a significant role in identifying wild MT is their odor. In accordance, most field guides for foraging and identifying wild MT include details about the odor alongside the macroscopic description of the flesh. For example, the odor of Agaricus xanthodermus is reported as phenolic, that of fresh Clitocybe odora has a strong anise component, and that of young Amanita phalloides is described as very faint [39]. Although the odor may offer valuable insights into identifying specific wild MT species, these odor descriptions are typically quite vague and generalized. Therefore, such odor identification cannot be regarded as definitive on its own when it comes to wild MT species [40,41]. Nevertheless, this specific trait (i.e., the odor) is beginning to be explored in the identification of wild MT species using technologies such as the electronic nose (or simply e-nose) [30].
The e-nose is a sensitive device that can obtain information about odors—the smellprint. Slight changes in volatile compounds’ odor, composition, or concentration can result in a different sensor response [42]. The e-nose has been used to assess the volatile profiles of mushrooms for several applications in the food industry [43,44], including quality control during postharvest processing [45,46,47], monitoring of the maturation process [48], and assessment of the shelf life and packaging [49,50]. Recently, several studies have investigated the e-nose’s capacity to accurately distinguish different wild MT species by analyzing their volatile profiles [16,40,41,43,44,51,52]. While the findings obtained using the e-nose are promising, a wild MT species identification based on the e-nose requires further research to expand the datasets, standardize the methodology, and explore the integration of machine learning algorithms to maximize its potential in wild MT species identification. Although a few studies have recently focused on this specific question, they covered a small number of wild MT species and genera. Further research is needed to explore a wider range of wild MT species and genera, including those species with similar morphological features, which can easily lead to incomplete or incorrect wild MT species identification.
Therefore, the present study aims to further explore wild MT’s odors as an identification trait by using a fast, cheap, and reliable methodology to analyze their aroma profiles. The feasibility of the methodological design we propose was verified using a small sample, and included: (i) capturing the aroma composition of 14 wild MT species using the e-nose Cyranose-320, and (ii) applying multivariate analysis techniques (principal component analysis (PCA), linear discriminant analysis (LDA), and partial least squares-discriminant analysis (PLS-DA)) to the wild MT aroma profiles to develop classification models able to distinguish the wild MT species, and between edible and non-edible species.

2. Materials and Methods

2.1. Wild Mushrooms and Truffles (MT)

Fourteen wild-growing MT species were collected during field trips in the south and center of Portugal between the autumn of 2022 and the spring of 2023. At least 2 individuals of each species were collected from each site, adding up to a total of 28 samples. After relevant notes of their morphological and ecological features were taken, individuals were placed in separate paper bags and brought to the laboratory in a cooler bag. Specimens were freed from substrate debris at the site and further cleaned in the laboratory. The wild MT samples were kept at 4 °C until analysis in the first 48 h postharvest.
Each wild MT species was identified by an experienced mycologist, based on morphological features, and using standard reference field identification books [53,54,55,56]. When the morphological features were not enough to make a precise identification of the species, the specimens were sent for molecular analysis to confirm their identification. The identification of six wild MT species was confirmed by molecular analysis. Genomic DNA was extracted from 100 mg of each fresh mushroom/truffle. DNA extraction was performed in microtubes with 200 µL of degradation corrosion (Proteinase K at 0.5 mg/mL in 100 mM of Tris-HCl pH 9.0) preheated to 60 °C. The mixture was kept in a dry bath at 60 °C overnight (about 16 h), and initially (in the first 30 min of incubation), they were strongly vortexed for periods of 30 s. DNA extraction was followed by amplification using the forward primer (ITS4 F) 5′ TCCTCCGCTTATTGATATGC and the reverse primer (ITS5 R) 5′ GGAAGTAAAAGTCGTAACAAGG. Polymerase chain reactions (PCR) were carried out in a final volume of 20 µL. The reaction mixture consisted of 10 µL of MyTaq Red Mix 2× (Bioline, Paris, France), 1 µL each of primers F and R (at 10 µM/each), 1 µL of DNA sample, and 7 µL of ultrapure water. The mixture was placed in a Tpersonal cycler (Whatman Biometra, Göttingen, Germany), programmed as follows: initial denaturation at 95 °C for 5 min, then 35 cycles, each consisting of three steps: denaturation at 95 °C for 10 s, annealing at 43 °C for 10 s, and elongation at 72 °C for 60 s, and then a final elongation at 72 °C for 7 min. Aliquots of the PCR reactions were resolved on 0.7% agarose gels stained with ethidium bromide. PCR products were purified by the Zymoclean DNA kit (Zymo Research, Irvine, CA, USA), following the manufacturer’s instructions. Purified PCR products were sequenced in both directions at StabVida (Caparica, Portugal) using the primers previously cited. The obtained sequences were compared with the sequences available from the National Centre for Biotechnology Information (NCBI: http://www.ncbi.nlm.nih.gov (accessed on 20 June 2023) using the BLAST algorithm to identify our wild MT species.
After the identification, each wild MT species’ edibility was attributed based on existing literature. Finally, a literature survey identified other wild MT species that could be morphologically confused with the species we collected.

2.2. Exploring Wild MT’s Odors Using the E-Nose

A Cyranose-320 e-nose (Sensigent, Pasadena, CA, USA) was used to develop a non-destructive, fast, cheap, and reliable method to distinguish between the 14 wild MT species and their edibility. The portable electronic nose (e-nose) Cyranose-320 can rapidly detect and identify samples based on their aroma profile. It is equipped with a nanocomposite sensor array of 32 nanosensors, an internal air sampling pump, and advanced pattern recognition algorithms. The sensor array measures the responses of the nanosensors to the chemical vapors in the air. The pattern recognition algorithms then use these responses to create a “smellprint” of the sample, which is a unique signature that can be used to identify it [42]. Therefore, we specifically used this e-nose to collect volatile profile information of the specimens belonging to the 14 wild MT species and create a smellprint for each species.
Two fresh samples of each wild MT species were analyzed separately. Four grams of each sample were introduced in a 10 mL vial and were incubated for 1 h at room temperature (i.e., 24 °C). The Cyranose-320 was mounted on a tripod, which could be adjusted for inserting the e-nose needle into the vials for headspace reading. Five readings per sample were performed, adding to a total of ten readings per species. The e-nose was coupled to the computer, and PCNose+ software (Version 10.13, Sensigent, Pasadena, CA, USA) was used to set the list of parameter settings of the Cyranose-320 (see Table S1) and data acquisition of the smellprint.

2.3. Smellprints’ Statistical Analyses

2.3.1. Distinguishing the 14 Wild MT Species

To compare each sensor’s response between the 14 wild MT species, we used the Kolmogorov–Smirnov test, a nonparametric test. To evaluate the possibility to distinguish the 14 wild MT species based on their smellprints, a principal component analysis (PCA) was carried out. This helped to identify patterns in the data, and to compare the smellprints of the different species.
Furthermore, we performed a linear discriminant analysis (LDA) to classify the smellprints of the different wild MT for each species. The LDA found linear combinations of the features of a sample to classify them into one of several classes (i.e., the 14 wild MT species). Then, the LDA detected the directions in which the classes were most separated, i.e., the discriminant functions. Finally, each sample was classified (i.e., identified as a wild MT species) by the discriminant function that yielded the highest value. The LDA model performance was evaluated for its accuracy using the following equation:
A c c u r a c y % = T r u e   p o s i t i v e T o t a l × 100
All analyses were performed using the software Microsoft Excel 2019 and XLSTAT-Premium (Version 2021.4.1, Addinsoft, Inc., Brooklyn, NY, USA).

2.3.2. Distinguishing the 14 Wild MT Species According to Their Edibility

To evaluate the possibility of distinguishing edible and non-edible species by their smellprints, two classification models were performed, the LDA and the partial least squares discriminant analysis (PLS-DA). These classification methods were used to test if the groups (edible and non-edible) to which the observations belong are distinct and to reveal the properties of these groups using the e-nose sensor responses as explanatory variables. The LDA was tested as previously described but here, the classes were edible and non-edible. In the PLS-DA, the edibility classification was the dependent variable, and the e-nose sensor data were the independent variables. PLS-DA is a classification method that can simultaneously perform dimensionality reduction and discriminant analysis. PLS-DA is more flexible than LDA because it can handle cases where the classes are not linearly separable. The model performance for the LDA and PLS-DA was evaluated for accuracy, as described above using Equation (1). Multivariate analyses were performed using the software Microsoft Excel 2019 and XLSTAT-Premium (Version 2021.4.1, Addinsoft, Inc., Brooklyn, NY, USA).

3. Results

3.1. Distinguishing the 14 Wild MT Species

Based on the analysis of the morphological features by our experienced mycologist, we undoubtedly identified the following eight wild mushroom species: Cantharellus cibarius, Craterellus lutescens, Craterellus tubaeformis, Cyclocybe cylindracea, Hydnum repandum, Lactarius deliciosus, Pisolithus tinctorius, and Suillus collinitus (Table 1). The six species whose identification was confirmed by molecular analysis were the wild mushrooms Agaricus xanthodermus, Amanita phalloides, Amanita subparvipantherina, Hygrocybe helobia, and Lepista nuda, and the wild truffle Terfezia arenaria. Most of the wild MT species we collected (9 out of 14) were edible, and included the wild truffle Terfezia arenaria and the wild mushrooms Cantharellus cibarius, Craterellus lutescens, Craterellus tubaeformis, Cyclocybe cylindracea, Hydnum repandum, Lactarius deliciosus, Lepista nuda, and Suillus collinitus. The non-edible wild mushroom species included Agaricus xanthodermus, Amanita phalloides, Amanita subparvipantherina, Hygrocybe helobia, and Pisolithus tinctorius. All wild MT species had been reported to be morphologically confused with species from the same genus (e.g., Amanita subparvipantherina can be morphologically confused with Amanita citrina and other Amanita spp.) or from other genera (e.g., Amanita phalloides can be morphologically confused with Agaricus spp., Russula spp., and Thricoloma spp.).
The Cyranose-320 e-nose was able to detect the wild MT smellprints. From the 14 wild MT species analyzed, the truffle Terfezia arenaria was the species that induced higher responses for most of the 32 sensors (Figure 1). The sensors’ responses induced by Terfezia arenaria were different from those induced by the other 13 wild MT species, except for 2 sensors (S30 and S31; Table S1). Furthermore, four edible wild MT species (mushrooms Craterellus lutescens, Lactarius deliciosus, and Lepista nuda, and truffle Terfezia arenaria) induced sensor responses for the 32 sensors different from those induced by the non-edible and deadly mushroom Amanita phalloides. However, the sensors’ responses to the other non-edible wild mushroom species could not be distinguished from those of the edible wild mushrooms.
The first two components of the PCA (based on the response of the 32 sensors for each of the 14 wild MT species) explained 96% of the total variance (PC1 = 90.3% and PC2 = 5.3%) (Figure 2). Despite explaining most of the variance, the PCA model showed overlapping of the smellprints of most wild MT species, with only two species showing a clearly different smellprint: Agaricus xanthodermus and Terfezia arenaria. Finally, the clusters that were formed based on the wild MT species’ smellprints included edible and non-edible species. Therefore, the PCA was not able to clearly distinguish the 14 wild MT species or their edibility.
The LDA classification model presented an overall accuracy of 93% for distinguishing the 14 wild MT species. This overall accuracy level integrates the cases when species were always identified correctly, and those that were identified incorrectly. The LDA classification model correctly identified 9 out of the 14 species analyzed, with 100% accuracy (Figure 3). The species correctly identified by the LDA classification model (i.e., the species where all samples were correctly identified, with no incorrect identifications) included: (i) the edible Cyclocybe cylindracea, Hydnum repandum, Suillus collinittus, and Terfezia arenaria, and (ii) the non-edible Agaricus xanthodermus, Amanita phalloides, Amanita subparvipantherina, Hygrocybe helobia, and Pisolithus tinctorius. It is important to highlight that all non-edible species that we tested were correctly classified (i.e., 100% accuracy).
The LDA classification model, on the other hand, showed misclassifications (i.e., incorrect identifications) for five wild edible mushroom species, with an accuracy of 70% to 90% (Figure 3). In a few samples, the LDA classification model misclassified Craterellus tubaeformis as Cantharellus cibarius (and vice versa), Craterellus lutescens as Lepista nuda, Lactarius deliciosus as Craterellus lutescens, and Lacatrius deliciosus as Lepista nuda (and vice versa). This can be related to the proximity of the smellprints of these species, which was also observed by the overlapping in the PCA (Figure 2). However, although Amanita phalloides, Cyclocybe cylindracea, Hydnum repandum, and Suillus collinitus were also close in the PCA, the LDA correctly identified these species.

3.2. Distinguishing the 14 Wild MT Species’ Edibility

The confusion matrixes for the LDA and PLS-DA models for classifying (i.e., identifying) edible and non-edible species are shown in Figure 4. For both classification models (LDA and PLS-DA), we observed a very high percentage of correct identifications. Using the LDA classification model, we only observed five incorrect identifications of samples, thus reaching 97% accuracy. Using the PLS-DA classification model, we only observed one incorrect identification of samples, thus reaching 99% accuracy (Figure 4). Both classification models (LDA and PLS-DA) were highly accurate in distinguishing the smellprints of edible from those of the non-edible species.

4. Discussion

By combining the use of the e-nose with discriminant analysis, we were able to distinguish 14 wild MT species, and their edibility (i.e., distinguish the edible from the non-edible species), using an accurate, fast, and cheap method. Our study used the highest number of wild MT species in similar studies so far, including wild MT species with similar morphological features, which can easily lead to incomplete or incorrect identifications.

4.1. Fast, Cheap, and Reliable Method to Distinguish Wild MT Species and Their Edibility

The smellprint of the truffle Terfezia arenaria stood out from those of the other wild mushrooms (Figure 1 and Figure 2). Being a belowground fruiting fungus, Terfezia arenaria’s unique aroma plays an important ecological role by mediating this truffle’s communication with below- and above-ground communities [59], including attracting animals that help disperse the truffle spores [60]. Despite the variability in the aromatic profile, this truffle species can have volatile organic compounds that act as a species-specific fingerprint [16,60], which can help explain its distinctive smellprint when compared to that of the wild mushrooms.
Similar phenomena can help explain why the smellprint of the non-edible and lethal wild mushroom Amanita phalloides was so different from that of the nine edible wild MT species of this study (Figure 3). While fifty Amanita spp. have been described as lethal worldwide [61], two Amanita spp. are considered delicacies and of economic interest in the Mediterranean region (Amanita caesarea and Amanita ponderosa) [62]. The use of the e-nose for successfully distinguishing between Amanita mushrooms was demonstrated by Portalo-Calero and colleagues [40,41,51]. These studies obtained an accuracy of 97.7% to 99.9% using multivariate analysis for smellprint classification, and included two lethal species (Amanita phalloides and Amanita verna) and the two edible delicacies Amanita caesarea and Amanita ponderosa [41,51]. The use of electronic devices, such as the e-nose, that accurately distinguish between potentially dangerous wild MT and the safe ones can add an extra layer of safety to wild MT hunting. Improving wild MT’s safe consumption can contribute to the socioeconomic sustainability of forest ecosystems since several of the wild edible MT studied here are highly appreciated and represent an important bio-resource of food and income for rural populations [63]. For example, Cantharellus cibarius, Hydnum repandum, and Lactarius deliciosus are among the wild edible mushroom species authorized for trade and commercialization in at least twelve European Union countries [15], and their international trade has increased in recent years [64].
In accordance with previous studies [43,44,65], the LDA classification model was able to accurately identify the 14 wild MT species by their smellprint (Figure 3), and both the LDA and PLS-DA classification models could accurately distinguish between the edible and non-edible species (Figure 4). These classification models have been largely used for statistical treatment of the volatile compounds of food matrices, and have been showing high accuracy [66,67,68]. For example, the combined use of the e-nose and PLS-DA contributed to the successful identification of filamentous fungal [67] and plant species [66], monitoring product quality during and after production processing [68,69,70,71], product quality [72], and establishing geographic origin [73]. Moreover, comprehensive datasets encompassing a wide range of wild MT species should be established to enhance the accuracy and robustness of the e-nose’s identification capabilities. This would require the collection and analysis of smellprints of a diverse range of wild MT, including rare and lesser-studied species.
Since the incubation temperature has been shown to influence the compounds emitted by the MT samples [16], the fact that we used a fixed ambient temperature (24 °C) for incubating the wild MT samples must have contributed to our method’s accuracy. However, when the temperature or humidity changes (i.e., when identifying the wild MT species in the forest), the e-nose response will drift, which may deteriorate the detection performance. Therefore, in future research, machine learning methods can be considered to design drift compensation methods to promote the intelligent process of e-noses [74,75]. By training algorithms with large datasets of smellprints and corresponding wild MT species, it may be possible to develop automated, real-time identification systems that can identify wild MT species accurately, quickly, and at low costs.
Althought our approach allowed us to accurately distinguish wild MT species and their edibility based on their smellprints, the number of samples we used was limited. The reduced number of wild MT species and specimens we analyzed reflected a reduction in the fruiting of the wild MT due to climate change. Specifically, these wild MT species require high soil water availability for producing their fruit bodies in arid and semi-arid areas (e.g., most of mainland Portugal) [76]. The severe droughts that affected mainland Portugal during the sampling period and previous years [77] negatively impacted the wild MT abundance. Furthermore, wildfires also resulted in a loss of productive forest areas and, consequently, a lower wild MT abundance. Despite our efforts to include more wild MT species and specimens, further research is needed to: (i) increase the number of species (including rare and lesser-studies ones) and specimens, (ii) account for the different phenological phases of the wild MT (e.g., maturation), (iii) assess the importance of geographic origin, and (iv) test product quality.

4.2. Perspectives for Wild MT Identification

One direct application of our approach (combined use of the e-nose and discriminat analysis) is to support accurate wild MT species identification, which can significantly enhance the identification accuracy of mycologists and wild MT enthusiasts. Traditional methods of wild MT identification typically involve time-consuming processes, such as microscopic analysis and chemical reagent tests (Figure 5). Although professional and experienced collectors can avoid the harvest of hazardous and non-edible wild MT, the increasing number of amateurs collecting wild MT increases the risk of poisining [41]. Wild MT identification can include morphological features, instrumental analysis, and molecular biology methods [30] (Figure 5). Morphological identification is a long-established approach, based in fungal taxonomy, requires long training, and greatly benefits from experience. Wild MT species identification by a mycologist is critical to guarantee safe consumption. For example, in Switzerland, a free service is offered to the population to confirm the edibility of self-harvested wild mushrooms to promote the safe and sustainable harvesting of these forest resources [78]. Although the identification of wild MT species by experts (mycologists) is a highly accurate process (i.e., almost 100% accuracy) (Figure 5), we did not find any study demonstrating or quantifying it. On the other hand, there is a limited number of experts because this work’s complexity makes it challenging to find people with a high level of knowledge in this area [79,80]
Sometimes, even expert identification needs support from other methodologies, such as molecular biology. In agreement, during this study, we faced difficulties identifying some species based on their morphological characteristics alone, namely Agaricus xanthodermus (could also be Agaricus silvestris), Amanita subparvipantherina (could also be Amanita citrina), and Hydnum helobia (we could only identify the genus, but not the species). Thus, we performed molecular identification of these species and another three as controls. Therefore, even experienced mycologists may need to use other methodologies to identify (or confirm) wild MT species.
Molecular biology is more expensive than mycologists’ identification (see Figure 5) [81,82] and is mainly applied to fresh or dried fruiting bodies, primary processed products, and deeply processed products [30,83,84,85]. Molecular analysis is a precise method for identifying fungi (accuracy of almost 100%), but its performance can be affected by several factors. For example, (i) it is important to avoid cross-contamination during sample collection and processing [86], (ii) kit-based approaches are recommended to obtain good-quality DNA, but they can be expensive [86], and (iii) the choice of primer is essential for obtaining high-quality PCR products. The ITS region is the most commonly used primer for fungi, but it is not always suitable for all species. If the species of fungus is unknown, it can be difficult to choose the most appropriate ITS primer [80,87].
The three identification techniques (mycologist, molecular biology, and e-nose) have high accuracy but the e-nose stands out due to the ease of the process (simple and with minor material handling) and the short analysis time. On the other hand, both the mycologist and molecular biology require time-consuming expert training, some equipment, time, and costs for the analysis (especially in the case of molecular biology—Figure 5). Therefore, using an e-nose has the potential to become a fast, cheap, and reliable method for wild MT species identification because (i) it is a technique that is easy to learn and training can be completed in a few weeks, (ii) the previously trained e-nose allows fast identification within a few hours, (iii) once the e-nose is purchased, it only requires maintenance costs and, therefore, analysis will be cheap, and (iv) it accurately distinguishes wild MT species and their edibility (Figure 3 and Figure 4). Therefore, this can be a valuable alternative or complementary approach, especially in cases where the morphological features of wild MT do not provide definitive results.
The potential impact of using an e-nose for wild MT identification extends beyond its immediate applications. A better understanding of the volatile profile—smellprint—of different wild MT species can contribute to the knowledge of their biology and ecology. Using tools such as the e-nose can lead to advancements in mycology, food security, and environmental monitoring while improving the socioeconomic sustainability of forest ecosystems. To validate the use of e-noses to identify wild MT and their edibility, it is essential to develop new methodologies and test new models. E-noses have the potential to add value to this forest bio-resource, highlighting the aromatic profile of wild MT that can be of interest to the food industry, and by certifying products and gaining consumers’ trust.
Resources 12 00139 g005
Figure 5. Comparison of methods to identify wild MT species based on their training duration, equipment, time, costs, and accuracy involved in the analysis [78,79,80,81,82,83,84,85,87] and this study. Created with BioRender.com.
Figure 5. Comparison of methods to identify wild MT species based on their training duration, equipment, time, costs, and accuracy involved in the analysis [78,79,80,81,82,83,84,85,87] and this study. Created with BioRender.com.
Resources 12 00139 g005

5. Conclusions

The combined use of the e-nose and discriminant analysis accurately distinguished between 14 wild MT species and their edibility (i.e., between edible and non-edible species). These results suggest that the e-nose could be a valuable tool for wild MT species identification; for example, in cases when the morphological features of wild MT do not provide definitive results.
The e-nose proves to be a fast, cost-effective, and accurate method for wild MT identification, with potential applications in preventing mushroom poisoning. However, since this method is hampered by a small sample size, further research is needed to develop and validate e-nose-based identification methods for a broader range of wild MT species. The potential applications of the e-nose extend beyond identification, including assessing ripeness for harvest and detecting spoilage, suggesting its utility in improving efficiency in wild mushroom harvesting and processing. This study underscores the need for additional research to validate the e-nose’s accuracy and highlights its potential in quality control for the food industry, ultimately enhancing the value of forest bio-resources and consumers’ confidence in these commodities.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/resources12120139/s1, Table S1: Parameter setting of the Cyranose-320; Table S2: Comparison via the Kolmogorov–Smirnov test of the 32 sensors’ responses between the 14 wild species of edible and non-edible mushrooms and truffles.

Author Contributions

Conceptualization, I.F. and C.C.; methodology, I.F. and J.M.; formal analysis, I.F. and J.M.; investigation, I.F., T.D. and C.C.; resources, C.C.; writing—original draft preparation, I.F.; writing—review and editing, T.D., J.M., A.M.M. and C.C.; visualization, T.D.; supervision, A.M.M. and C.C.; project administration, T.D., A.M.M. and C.C.; funding acquisition, I.F. and C.C. All authors have read and agreed to the published version of the manuscript.

Funding

This study was financially supported by: (i) Portuguese funds through Fundação para a Ciência e a Tecnologia (project UIDB/00329/2020, PhD Grant SFRH/BD/131232/2017 to I.F., and researcher contract to T.D.), and (ii) the European Union’s Horizon 2020 Research and Innovation programme SOILdarity, under grant agreement No. 952051.

Data Availability Statement

Data are contained within the article and Supplementary Materials.

Acknowledgments

The authors would like to acknowledge the technical support provided by Filomena Caeiro. The authors would like to thank the three anonymous reviewers for taking the time to assess our manuscript, for their analysis, and for the positive comments that helped improve this manuscript.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

References

  1. Cheek, M.; Nic Lughadha, E.; Kirk, P.; Lindon, H.; Carretero, J.; Looney, B.; Douglas, B.; Haelewaters, D.; Gaya, E.; Llewellyn, T.; et al. New Scientific Discoveries: Plants and Fungi. Plants People Planet 2020, 2, 371–388. [Google Scholar] [CrossRef]
  2. Antonelli, A.; Fry, C.; Smith, R.J.; Simmonds, M.S.J.; Kersey, P.J.; Pritchard, H.W.; Abbo, M.S.; Acedo, C.; Adams, J.; Ainsworth, A.M.; et al. State of the World’s Plants and Fungi 2020; Royal Botanic Gardens: Kew, UK, 2020.
  3. El Enshasy, H.; Elsayed, E.A.; Aziz, R.; Wadaan, M.A. Mushrooms and Truffles: Historical Biofactories for Complementary Medicine in Africa and in the Middle East. Evid.-Based Complement. Altern. Med. 2013, 2013, 620451. [Google Scholar] [CrossRef] [PubMed]
  4. Garibay-Orijel, R.; Caballero, J.; Estrada-Torres, A.; Cifuentes, J. Understanding Cultural Significance, the Edible Mushrooms Case. J. Ethnobiol. Ethnomed. 2007, 3, 4. [Google Scholar] [CrossRef] [PubMed]
  5. Schulp, C.J.E.; Thuiller, W.; Verburg, P.H. Wild Food in Europe: A Synthesis of Knowledge and Data of Terrestrial Wild Food as an Ecosystem Service. Ecol. Econ. 2014, 105, 292–305. [Google Scholar] [CrossRef]
  6. Delic, S.; Ibrahimspahic, A. Value Chain Analysis of Non-Wood Forest Products in Function of Sustainable Development of Forest Resources and Rural Development in Bosnia and Herzegovina. Agric. For. 2017, 63, 277. [Google Scholar] [CrossRef]
  7. Garibay-Orijel, R.; Córdova, J.; Cifuentes, J.; Valenzuela, R.; Estrada-Torres, A.; Kong, A. Integrating Wild Mushrooms Use into a Model of Sustainable Management for Indigenous Community Forests. For. Ecol. Manag. 2009, 258, 122–131. [Google Scholar] [CrossRef]
  8. Frutos, P.; Martínez Peña, F.; Ortega Martínez, P.; Esteban, S. Estimating the Social Benefits of Recreational Harvesting of Edible Wild Mushrooms Using Travel Cost Methods. For. Syst. 2009, 18, 235. [Google Scholar] [CrossRef]
  9. López-Hortas, L.; Flórez-Fernández, N.; Torres, M.D.; Domínguez, H. Update on Potential of Edible Mushrooms: High-Value Compounds, Extraction Strategies and Bioactive Properties. Int. J. Food Sci. Technol. 2022, 57, 1378–1385. [Google Scholar] [CrossRef]
  10. Gopal, J.; Sivanesan, I.; Muthu, M.; Oh, J.W. Scrutinizing the Nutritional Aspects of Asian Mushrooms, Its Commercialization and Scope for Value-Added Products. Nutrients 2022, 14, 3700. [Google Scholar] [CrossRef]
  11. de Román, M.; Boa, E. Collection, Marketing and Cultivation of Edible Fungi in Spain. Micol. Apl. Int. 2004, 16, 25–33. [Google Scholar]
  12. Kalač, P. A Review of Chemical Composition and Nutritional Value of Wild-Growing and Cultivated Mushrooms. J. Sci. Food Agric. 2013, 93, 209–218. [Google Scholar] [CrossRef] [PubMed]
  13. Kalač, P. Chemical Composition and Nutritional Value of European Species of Wild Growing Mushrooms: A Review. Food Chem. 2009, 113, 9–16. [Google Scholar] [CrossRef]
  14. Li, H.; Tian, Y.; Menolli, N.; Ye, L.; Karunarathna, S.C.; Perez-Moreno, J.; Rahman, M.M.; Rashid, M.H.; Phengsintham, P.; Rizal, L.; et al. Reviewing the World’s Edible Mushroom Species: A New Evidence-Based Classification System. Compr. Rev. Food Sci. Food Saf. 2021, 20, 1982–2014. [Google Scholar] [CrossRef]
  15. Ferreira, I.; Corrêa, A.; Cruz, C. Sustainable Production of Ectomycorrhizal Fungi in the Mediterranean Region to Support the European Green Deal. Plants People Planet 2023, 5, 14–26. [Google Scholar] [CrossRef]
  16. Ferreira, I.; Dias, T.; Mouazen, A.M.; Cruz, C. Using Science and Technology to Unveil The Hidden Delicacy Terfezia arenaria, a Desert Truffle. Foods 2023, 12, 3527. [Google Scholar] [CrossRef]
  17. Bonet, J.A.; González-Olabarria, J.R.; Aragón, J.M.D.A. Mushroom Production as an Alternative for Rural Development in a Forested Mountainous Area. J. Mt. Sci. 2014, 11, 535–543. [Google Scholar] [CrossRef]
  18. Brown, M.; McLellan, T.; Li, H.; Karunarathna, S.C. Applied Mycology Can Contribute to Sustainable Rural Livelihoods: Building upon China’s Matsutake Management Initiatives. Environ. Manag. 2018, 61, 263–274. [Google Scholar] [CrossRef] [PubMed]
  19. Field, K.J.; Daniell, T.; Johnson, D.; Helgason, T. Mycorrhizas for a Changing World: Sustainability, Conservation, and Society. Plants People Planet 2020, 2, 98–103. [Google Scholar] [CrossRef]
  20. Niego, A.G.T.; Rapior, S.; Thongklang, N.; Raspé, O.; Hyde, K.D.; Mortimer, P. Reviewing the Contributions of Macrofungi to Forest Ecosystem Processes and Services. Fungal. Biol. Rev. 2023, 44, 100294. [Google Scholar] [CrossRef]
  21. Barros, L.; Cruz, T.; Baptista, P.; Estevinho, L.M.; Ferreira, I.C.F.R. Wild and Commercial Mushrooms as Source of Nutrients and Nutraceuticals. Food Chem. Toxicol. 2008, 46, 2742–2747. [Google Scholar] [CrossRef]
  22. Lu, H.; Lou, H.; Hu, J.; Liu, Z.; Chen, Q. Macrofungi: A Review of Cultivation Strategies, Bioactivity, and Application of Mushrooms. Compr. Rev. Food Sci. Food Saf. 2020, 19, 2333–2356. [Google Scholar] [CrossRef] [PubMed]
  23. Ghorai, S.; Banik, S.P.; Verma, D.; Chowdhury, S.; Mukherjee, S.; Khowala, S. Fungal Biotechnology in Food and Feed Processing. Food Res. Int. 2009, 42, 577–587. [Google Scholar] [CrossRef]
  24. Erjavec, J.; Kos, J.; Ravnikar, M.; Dreo, T.; Sabotič, J.; Sabotic, J. Proteins of Higher Fungi—From Forest to Application. Trends Biotechnol. 2012, 30, 259–273. [Google Scholar] [CrossRef] [PubMed]
  25. Comandini, O.; Rinaldi, A.C. Ethnomycology in Europe: The Past, the Present, and the Future. In Mushrooms, Humans and Nature in a Changing World; Springer International Publishing: Cham, Switzerland, 2020; pp. 341–364. [Google Scholar]
  26. Peintner, U.; Schwarz, S.; Mešić, A.; Moreau, P.-A.; Moreno, G.; Saviuc, P. Mycophilic or Mycophobic? Legislation and Guidelines on Wild Mushroom Commerce Reveal Different Consumption Behaviour in European Countries. PLoS ONE 2013, 8, e63926. [Google Scholar] [CrossRef] [PubMed]
  27. Eren, S.H.; Demirel, Y.; Ugurlu, S.; Korkmaz, I.; Aktas, C.; Güven, F.M.K. Mushroom Poisoning: Retrospective Analysis of 294 Cases. Clinics 2010, 65, 491–496. [Google Scholar] [CrossRef]
  28. Lee, J.J.; Aime, M.C.; Rajwa, B.; Bae, E. Machine Learning-Based Classification of Mushrooms Using a Smartphone Application. Appl. Sci. 2022, 12, 11685. [Google Scholar] [CrossRef]
  29. Hodgson, S.E.; McKenzie, C.; May, T.W.; Greene, S.L. A Comparison of the Accuracy of Mushroom Identification Applications Using Digital Photographs. Clin. Toxicol. 2023, 61, 166–172. [Google Scholar] [CrossRef]
  30. Wei, Y.; Li, L.; Liu, Y.; Xiang, S.; Zhang, H.; Yi, L.; Shang, Y.; Xu, W. Identification Techniques and Detection Methods of Edible Fungi Species. Food Chem. 2022, 374, 131803. [Google Scholar] [CrossRef]
  31. Zhao, H.; Ge, F.; Yu, P.; Li, H. Identification of Wild Mushroom Based on Ensemble Learning. In Proceedings of the 2021 IEEE 4th International Conference on Big Data and Artificial Intelligence (BDAI), Qingdao, China, 2–4 July 2021; pp. 43–47. [Google Scholar] [CrossRef]
  32. Zahan, N.; Hasan, M.Z.; Malek, M.A.; Reya, S.S. A Deep Learning-Based Approach for Edible, Inedible and Poisonous Mushroom Classification. In Proceedings of the 2021 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD), Dhaka, Bangladesh, 27–28 February 2021; pp. 440–444. [Google Scholar] [CrossRef]
  33. Jahan Pinky, N.; Mohidul Islam, S.; Sharmin Alice, R. Edibility Detection of Mushroom Using Ensemble Methods. Image Graph. Signal Process. 2019, 4, 55–62. [Google Scholar] [CrossRef]
  34. Wibowo, A.; Rahayu, Y.; Riyanto, A.; Hidayatulloh, T. Classification Algorithm for Edible Mushroom Identification. In Proceedings of the 2018 International Conference on Information and Communications Technology (ICOIACT), Yogyakarta, Indonesia, 6–7 March 2018; pp. 250–253. [Google Scholar] [CrossRef]
  35. Kousalya, K.; Krishnakumar, B.; Boomika, S.; Dharati, N.; Hemavathy, N. Edible Mushroom Identification Using Machine Learning. In Proceedings of the 2022 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, 25–27 January 2022. [Google Scholar] [CrossRef]
  36. Chumuang, N.; Sukkanchana, K.; Ketcham, M.; Yimyam, W.; Chalermdit, J.; Wittayakhom, N.; Pramkeaw, P. Mushroom Classification by Physical Characteristics by Technique of K-Nearest Neighbor. In Proceedings of the 2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP), Bangkok, Thailand, 18–20 November 2020. [Google Scholar] [CrossRef]
  37. Rahman, H.; Faruq, M.O.; Hai, A.; Bin, T.; Rahman, W.; Hossain, M.M.; Hasan, M.; Islam, S.; Moinuddin, M.; Islam, M.T.; et al. IoT Enabled Mushroom Farm Automation with Machine Learning to Classify Toxic Mushrooms in Bangladesh. J. Agric. Food Res. 2022, 7, 100267. [Google Scholar] [CrossRef]
  38. Milmo, D. Mushroom Pickers Urged to Avoid Foraging Books on Amazon That Appear to Be Written by AI|Fungi|The Guardian. Available online: https://www.theguardian.com/technology/2023/sep/01/mushroom-pickers-urged-to-avoid-foraging-books-on-amazon-that-appear-to-be-written-by-ai (accessed on 3 September 2023).
  39. MushroomExpert.Com. Available online: https://www.mushroomexpert.com/ (accessed on 3 September 2023).
  40. Portalo-Calero, F.; Arroyo, P.; Suárez, J.I.; Lozano, J. Triangular Test of Amanita Mushrooms by Using Electronic Nose and Sensory Panel. Foods 2019, 8, 414. [Google Scholar] [CrossRef]
  41. Portalo-Calero, F.; Arroyo, P.; Melendez, F.; Suárez, J.I.; Lozano, J. Electronic Nose Comparison of the Edible Amanita ponderosa with the Deadly Amanita Verna. In Proceedings of the 2020 IEEE International Symposium on Circuits and Systems (ISCAS), Seville, Spain, 12–14 October 2020. [Google Scholar] [CrossRef]
  42. Sensigents. Available online: https://www.sensigent.com/products/cyranose.html (accessed on 5 June 2023).
  43. Zhou, J.; Feng, T.; Ye, R. Differentiation of Eight Commercial Mushrooms by Electronic Nose and Gas Chromatography-Mass Spectrometry. J. Sens. 2015, 2015, 374013. [Google Scholar] [CrossRef]
  44. Gómez, I.; Lavega González, R.; Tejedor-Calvo, E.; Pérez Clavijo, M.; Carrasco, J. Odor Profile of Four Cultivated and Freeze-Dried Edible Mushrooms by Using Sensory Panel, Electronic Nose and GC-MS. J. Fungi 2022, 8, 953. [Google Scholar] [CrossRef]
  45. Pei, F.; Yang, W.; Ma, N.; Fang, Y.; Zhao, L.; An, X.; Xin, Z.; Hu, Q. Effect of the Two Drying Approaches on the Volatile Profiles of Button Mushroom (Agaricus bisporus) by Headspace GC–MS and Electronic Nose. LWT-Food Sci. Technol. 2016, 72, 343–350. [Google Scholar] [CrossRef]
  46. Chen, D.; Wang, S.; Li, M.; Hao, T.; Lin, S. The Dynamic Changes in Product Attributes of Shiitake Mushroom Pilei and Stipes during Dehydration by Hot Air Drying. J. Food Process. Preserv. 2021, 45, e15648. [Google Scholar] [CrossRef]
  47. Ma, N.; Pei, F.; Yu, J.; Wang, S.; Ho, C.T.; Su, K.; Hu, Q. Valid Evaluation of Volatile Flavor Composition of Fresh and Dehydrated Tuber indicum with Different Drying Methods. CyTA-J. Food 2018, 16, 413–421. [Google Scholar] [CrossRef]
  48. Chilo, J.; Pelegri-Sebastia, J.; Cupane, M.; Sogorb, T. E-Nose Application to Food Industry Production. IEEE Instrum. Meas. Mag. 2016, 19, 27–33. [Google Scholar] [CrossRef]
  49. Song, Y.; Hu, Q.; Wu, Y.; Pei, F.; Kimatu, B.M.; Su, A.; Yang, W. Storage Time Assessment and Shelf-Life Prediction Models for Postharvest Agaricus bisporus. LWT 2019, 101, 360–365. [Google Scholar] [CrossRef]
  50. Gholami, R.; Aghili Nategh, N.; Rabbani, H. Evaluation the Effects of Temperature and Packaging Conditions on the Quality of Button Mushroom during Storage Using E-Nose System. J. Food Sci. Technol. 2023, 60, 1355–1366. [Google Scholar] [CrossRef] [PubMed]
  51. Portalo-Calero, F.; Lozano, J.; Meléndez, F.; Arroyo, P.; Suárez, J.I. Identification of Poisonous Mushrooms by Means of a Hand-Held Electronic Nose. Proceedings 2019, 14, 33. [Google Scholar] [CrossRef]
  52. Zhang, N.; Chen, H.; Sun, B.; Mao, X.; Zhang, Y.; Zhou, Y. Comparative Analysis of Volatile Composition in Chinese Truffles via GC × GC/HR-TOF/MS and Electronic Nose. Int. J. Mol. Sci. 2016, 17, 412. [Google Scholar] [CrossRef] [PubMed]
  53. Henriques, J.L.G. Cogumelos Silvestres de Portugal de Interesse Em Conhecer; Ao Pé das Letras; Livros do Corvo: Vila Nova da Barquinha, Portugal, 2016; ISBN 9789899949812. [Google Scholar]
  54. Llamas Frade, B.; Alfonso, T. Guía de Campo de Los Hongos de La Península Ibérica; Celarayn: León, Spain, 2005; ISBN 848971603X. [Google Scholar]
  55. Mehmood, T.; Raspé, O.; Bhatt, R.P.; Singh, U. First Record of Amanita subparvipantherina (Amanitaceae) from India. Curr. Res. Environ. Appl. Mycol. 2018, 8, 109–117. [Google Scholar] [CrossRef]
  56. Baptista-Ferreira, J. Guia Do Colector de Cogumelos–Para Os Cogumelos Silvestres Comestíveis Com Interesse Comercial Em Portugal. Direção-Geral Agric. Desenvolv. Rural 2013, 43, 89. [Google Scholar]
  57. da Silva, A.P.; Vicente, H.P.; Baptista-Ferreira, J. Guia Do Colector de Cogumelos—Para Os Cogumelos Silvestres Comestíveis Com Interesse Comercial Em Portugal; Direção-Geral Agricultura Desenvolvimento Rural: Lisboa, Portugal, 2013; ISBN 978-989-8539-06-09.
  58. Bhatt, R.P. Wild Edible Mushrooms of Uttarakhand Himalaya: Diversity, Distribution, Nutritive Value and Medicinal Potential; Institute of Himalayan Environment and Development: Uttarakhand, India, 2017.
  59. Ferreira, I.; Dias, T.; Cruz, C. The Potential of Ectomycorrhizal Fungi to Modulate below and Aboveground Communities May Be Mediated by 1-Octen-3-Ol. J. Fungi 2023, 9, 180. [Google Scholar] [CrossRef] [PubMed]
  60. Splivallo, R.; Ottonello, S.; Mello, A.; Karlovsky, P. Truffle Volatiles: From Chemical Ecology to Aroma Biosynthesis. New Phytol. 2011, 189, 688–699. [Google Scholar] [CrossRef]
  61. Cai, Q.; Cui, Y.Y.; Yang, Z.L. Lethal Amanita Species in China. Mycologia 2016, 108, 993–1009. [Google Scholar] [CrossRef]
  62. Batista, T.; de Mascarenhas, J.M.; Mendes, P. Montado’s Ecosystem Functions and Services: The Case Study of Alentejo Central–Portugal. Probl. Landsc. Ecol. 2017, 44, 15–27. [Google Scholar]
  63. De Román, M.; Boa, E. The Marketing of Lactarius deliciosus in Northern Spain. Econ. Bot. 2006, 60, 284–290. [Google Scholar] [CrossRef]
  64. de Frutos, P. Changes in World Patterns of Wild Edible Mushrooms Use Measured through International Trade Flows. For. Policy Econ. 2020, 112, 102093. [Google Scholar] [CrossRef]
  65. Keshri, G.; Challen, M.; Elliott, T.; Magan, N. Differentiation of Agaricus Species and Other Homobasidiomycetes Based on Volatile Production Patterns Using an Electronic Nose System. Mycol. Res. 2003, 107, 609–613. [Google Scholar] [CrossRef]
  66. Gui, X.J.; Li, H.; Ma, R.; Tian, L.Y.; Hou, F.G.; Li, H.Y.; Fan, X.H.; Wang, Y.L.; Yao, J.; Shi, J.H.; et al. Authenticity and Species Identification of Fritillariae Cirrhosae: A Data Fusion Method Combining Electronic Nose, Electronic Tongue, Electronic Eye and near Infrared Spectroscopy. Front. Chem. 2023, 11, 1179039. [Google Scholar] [CrossRef]
  67. Gębicki, J.; Szulczyński, B. Discrimination of Selected Fungi Species Based on Their Odour Profile Using Prototypes of Electronic Nose Instruments. Measurement 2018, 116, 307–313. [Google Scholar] [CrossRef]
  68. Sánchez, R.; Martín-Tornero, E.; Lozano, J.; Boselli, E.; Arroyo, P.; Meléndez, F.; Martín-Vertedor, D. E-Nose Discrimination of Abnormal Fermentations in Spanish-Style Green Olives. Molecules 2021, 26, 5353. [Google Scholar] [CrossRef] [PubMed]
  69. Zhou, Q.; Dai, Z.; Song, F.; Li, Z.; Song, C.; Ling, C. Monitoring Black Tea Fermentation Quality by Intelligent Sensors: Comparison of Image, e-Nose and Data Fusion. Food Biosci. 2023, 52, 102454. [Google Scholar] [CrossRef]
  70. Sánchez, R.; Martín-Tornero, E.; Lozano, J.; Fernández, A.; Arroyo, P.; Meléndez, F.; Martín-Vertedor, D. Electronic Nose Application for the Discrimination of Sterilization Treatments Applied to Californian-Style Black Olive Varieties. J. Sci. Food Agric. 2022, 102, 2232–2241. [Google Scholar] [CrossRef] [PubMed]
  71. Qin, L.; Gao, J.X.; Xue, J.; Chen, D.; Lin, S.Y.; Dong, X.P.; Zhu, B.W. Changes in Aroma Profile of Shiitake Mushroom (Lentinus edodes) during Different Stages of Hot Air Drying. Foods 2020, 9, 444. [Google Scholar] [CrossRef]
  72. Chen, J.; Yang, Y.; Deng, Y.; Liu, Z.; Xie, J.; Shen, S.; Yuan, H.; Jiang, Y. Aroma Quality Evaluation of Dianhong Black Tea Infusions by the Combination of Rapid Gas Phase Electronic Nose and Multivariate Statistical Analysis. LWT 2022, 153, 112496. [Google Scholar] [CrossRef]
  73. Wu, X.; Fauconnier, M.L.; Bi, J. Characterization and Discrimination of Apples by Flash GC E-Nose: Geographical Regions and Botanical Origins Studies in China. Foods 2022, 11, 1631. [Google Scholar] [CrossRef]
  74. Se, H.; Song, K.; Liu, H.; Zhang, W.; Wang, X.; Liu, J. A Dual Drift Compensation Framework Based on Subspace Learning and Cross-Domain Adaptive Extreme Learning Machine for Gas Sensors. Knowl.-Based Syst. 2023, 259, 110024. [Google Scholar] [CrossRef]
  75. Se, H.; Song, K.; Sun, C.; Jiang, J.; Liu, H.; Wang, B.; Wang, X.; Zhang, W.; Liu, J. Online Drift Compensation Framework Based on Active Learning for Gas Classification and Concentration Prediction. Sens. Actuators B Chem. 2024, 398, 134716. [Google Scholar] [CrossRef]
  76. de Aragón, J.M.; Bonet, J.A.; Fischer, C.R.; Colinas, C. Productivity of Ectomycorrhizal and Selected Edible Saprotrophic Fungi in Pine Forests of the Pre-Pyrenees Mountains, Spain: Predictive Equations for Forest Management of Mycological Resources. Ecol. Manag. 2007, 252, 239–256. [Google Scholar] [CrossRef]
  77. IPMA—Monitorização Da Seca Meteorológica. Available online: https://www.ipma.pt/pt/oclima/observatorio.secas/ (accessed on 21 September 2023).
  78. Vapko. Available online: https://www.vapko.ch/index.php/fr/ (accessed on 18 September 2023).
  79. Cassidy, N.; Duggan, E.; Tracey, J.A. Mushroom Poisoning in Ireland: The Collaboration between the National Poisons Information Centre and Expert Mycologists. Clin. Toxicol. 2011, 49, 171–176. [Google Scholar] [CrossRef] [PubMed]
  80. Giusti, A.; Ricci, E.; Gasperetti, L.; Galgani, M.; Polidori, L.; Verdigi, F.; Narducci, R.; Armani, A. Molecular Identification of Mushroom Species in Italy: An Ongoing Project Aimed at Reinforcing the Control Measures of an Increasingly Appreciated Sustainable Food. Sustainability 2020, 13, 238. [Google Scholar] [CrossRef]
  81. Animal Poisons Centre Mushroom Identification Service. Available online: https://animalpoisonscentre.com.au/mushroom-identification-service/ (accessed on 13 November 2023).
  82. Soilvitae. Available online: https://soilvitae.com/ (accessed on 14 November 2023).
  83. Epis, S.; Matinato, C.; Gentili, G.; Varotto, F.; Bandi, C.; Sassera, D. Molecular Detection of Poisonous Mushrooms in Different Matrices. Mycologia 2010, 102, 747–754. [Google Scholar] [CrossRef]
  84. Maeta, K.; Ochi, T.; Tokimoto, K.; Shimomura, N.; Maekawa, N.; Kawaguchi, N.; Nakaya, M.; Kitamoto, Y.; Aimi, T. Rapid Species Identification of Cooked Poisonous Mushrooms by Using Real-Time PCR. Appl. Environ. Microbiol. 2008, 74, 3306–3309. [Google Scholar] [CrossRef]
  85. Zhao, L.; Zhao, X.; Hengchao, E.; Fan, T.; Tian, E.; Wang, C.; Zhang, Y.; Li, X.; Zhao, Z.; Zhou, C. Multiplex TaqMan Real-Time PCR Assay for High-Throughput Identification of Highly Toxic Mushroom Species-Induced Foodborne Poisoning. LWT 2023, 186, 115211. [Google Scholar] [CrossRef]
  86. Tedersoo, L.; Bahram, M.; Zinger, L.; Nilsson, R.H.; Kennedy, P.G.; Yang, T.; Anslan, S.; Mikryukov, V. Best Practices in Metabarcoding of Fungi: From Experimental Design to Results. Mol. Ecol. 2022, 31, 2769–2795. [Google Scholar] [CrossRef]
  87. Badotti, F.; De Oliveira, F.S.; Garcia, C.F.; Vaz, A.B.M.; Fonseca, P.L.C.; Nahum, L.A.; Oliveira, G.; Góes-Neto, A. Effectiveness of ITS and Sub-Regions as DNA Barcode Markers for the Identification of Basidiomycota (Fungi). BMC Microbiol. 2017, 17, 42. [Google Scholar] [CrossRef]
Resources 12 00139 g001
Figure 1. Cyranose-320 e-nose smellprints, integrating the responses of the 32 sensors for each of the 14 wild MT species. The wild MT species were grouped according to their edibility: edible (a) and non-edible (b) species. Each peak represents the response of a different sensor for each species. Each line represents the average response per species (n = 10 replicates). Created with BioRender.com.
Figure 1. Cyranose-320 e-nose smellprints, integrating the responses of the 32 sensors for each of the 14 wild MT species. The wild MT species were grouped according to their edibility: edible (a) and non-edible (b) species. Each peak represents the response of a different sensor for each species. Each line represents the average response per species (n = 10 replicates). Created with BioRender.com.
Resources 12 00139 g001
Resources 12 00139 g002
Figure 2. PCA of the smellprints, integrating the responses of the 32 sensors for each of the 14 wild MT species. The wild MT species are further classified as edible or non-edible. Symbols are the mean (n = 10) per species. Created with BioRender.com.
Figure 2. PCA of the smellprints, integrating the responses of the 32 sensors for each of the 14 wild MT species. The wild MT species are further classified as edible or non-edible. Symbols are the mean (n = 10) per species. Created with BioRender.com.
Resources 12 00139 g002
Resources 12 00139 g003
Figure 3. Overall species identification accuracy of the LDA classification model based on the e-nose smellprints of the 14 wild MT species. Created with BioRender.com.
Figure 3. Overall species identification accuracy of the LDA classification model based on the e-nose smellprints of the 14 wild MT species. Created with BioRender.com.
Resources 12 00139 g003
Resources 12 00139 g004
Figure 4. Edibility identification accuracy of the LDA and PLS-DA classification models based on the e-nose smellprints of the 14 wild MT species. Created with BioRender.com.
Figure 4. Edibility identification accuracy of the LDA and PLS-DA classification models based on the e-nose smellprints of the 14 wild MT species. Created with BioRender.com.
Resources 12 00139 g004
Table 1. List of the wild mushrooms and truffle (MT) species that were collected for this study, with the corresponding class, edibility, location, and species with which they can be morphologically confused.
Table 1. List of the wild mushrooms and truffle (MT) species that were collected for this study, with the corresponding class, edibility, location, and species with which they can be morphologically confused.
Class and SpeciesIdentified byEdibilityLocationMorphologically Confused withReference
Ascomycetes
Terfezia arenaria (Moris) TrappeMolecular biology (100%)EdibleAlentejo, Crato village, in montadoTerfezia leptoderma; Choiromyces gangliformis, Choiromyces meandriformis[53,54,57]
Basidiomycetes
Agaricus xanthodermus Genev. Molecular biology (100%)Not edibleLisboa, Quinta das Conchas, in urban gardenAgaricus spp.[53,54,57]
Amanita phalloides (Vaill. ex Fr.) LinkMolecular biology (99.70%)Not edibleSintra, Parques de Sintra, in pine forestAgaricus spp., Russula spp., Thricoloma spp.[53,54,57]
Amanita subparvipantherina Zhu L. Yang, Q. Cai & Y.Y. CuiMolecular biology (100%)Not edibleLeiria, Carreira village, in mixed woodAmanita citrina
Amanita spp.		[55,58]
Cantharellus cibarius Fr.MycologistEdibleAzambuja, in montado Hygrophoropsis aurantiaca
Omphalatus olearius; Omphalatus illudens		[53,54,57]
Craterellus lutescens (Fr.) Fr.MycologistEdibleLeiria, Carreira village, in mixed woodCraterellus tubaeformis[53,54,57]
Craterellus tubaeformis (Fr.) Quél.MycologistEdibleLeiria, Carreira village, in mixed woodCraterellus lutescens[39,40,44]
Cyclocybe cylindracea (DC.) Vizzini & Angelini 2014MycologistEdibleLisboa, Quinta das Conchas, in urban gardenAmanita spp.[53,54,57]
Hydnum repandum L.MycologistEdibleLeiria, Carreira village, in mixed woodHydnum rufescens, Cantharellus cibarius[39,40,44]
Hygrocybe helobia (Arnolds) BonMolecular biology (99.33%)Not edibleLeiria, Carreira village, in mixed woodHygrocybe spp.[54]
Lactarius deliciosus (L.) GrayMycologistEdibleLeiria, Bajouca village, in mixed woodLactarius spp.[53,54,57]
Lepista nuda (Bull.) CookeMolecular biology (100%)EdibleLisboa, Quinta das Conchas, in urban gardenLepista sordida, Cortinarius spp.[53,54,57]
Pisolithus tinctorius (Mont.) E. FischMycologistNot edibleLeiria, Carreira village, in eucalyptus forestPisolithus spp.; Scleroderma spp.[53,54]
Suillus collinitus (Fr.) KuntzeMycologistEdibleLeiria, Bajouca village, in mixed woodSuillus spp.; Boletus spp.[53,54]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Ferreira, I.; Dias, T.; Melo, J.; Mouazen, A.M.; Cruz, C. First Steps in Developing a Fast, Cheap, and Reliable Method to Distinguish Wild Mushroom and Truffle Species. Resources 2023, 12, 139. https://doi.org/10.3390/resources12120139

AMA Style

Ferreira I, Dias T, Melo J, Mouazen AM, Cruz C. First Steps in Developing a Fast, Cheap, and Reliable Method to Distinguish Wild Mushroom and Truffle Species. Resources. 2023; 12(12):139. https://doi.org/10.3390/resources12120139

Chicago/Turabian Style

Ferreira, Inês, Teresa Dias, Juliana Melo, Abdul Mounem Mouazen, and Cristina Cruz. 2023. "First Steps in Developing a Fast, Cheap, and Reliable Method to Distinguish Wild Mushroom and Truffle Species" Resources 12, no. 12: 139. https://doi.org/10.3390/resources12120139

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop