**3. Innovative technologies of plant essential oil extraction and quality control**

The conventional extraction methods were heated for a long extraction time, and they depended on extracting solvents from various extraction procedures such as maceration (MA), soxhlet extraction (SE) [7], sonication/ultrasonication extraction (USE) [8], steam distillation (SD) [9], and solid–liquid extraction (SLE) [10]. The bioactive EOCs were destroyed, concentration reduced, lowered down reproducibility and extraction efficiency. These methods had used large content of plant materials and organic solvents, which were the main inefficiencies of natural resource use. The innovative technologies are environmentally friendly for plant EO extraction, constantly being invented and developed for efficient use of various resources. Using high-efficiency and uncomplicated extraction techniques will reduce the production costs of natural resources such as pressurized liquid extraction (PLE) [11], supercritical fluid extraction (SFE) [12], ultrasound-assisted extraction (UAE) [13], microwaveassisted extraction (MAE) [14], pulsed electric field extraction (PEFE) [15], enzyme

assisted extraction (EAE) [16], solvent-free microwave extraction (SFME), and headspace solid-phase microextraction (HS-SPME). They also increase the yield of the bioactive compounds with the high quality of extract.

Application usages of these innovative extraction technologies are interesting alternative ways for enhancing active plant EO properties and efficiencies. The stability and quantity of isolated plant EO can be preserved by encapsulation forms (e.g., droplets, particles, capsules, multilamellar vesicles, active film, and complexes) [17] and polymeric nanoencapsulation forms (e.g., nanocapsules, nanospheres, miscelle, nanogel, liposome, dendrimer, hydrogel, layered biopolymer, mesoporous silica, and nanofiber) [18]. The developed biopesticides products, which based on various encapsulated plant EO techniques (e.g., coacervation, complexation, emulsification, film hydration method, nanoprecipitation, ionic gelation, and spray drying), can slowly and continuously be released to targets under various environmental conditions. According to the literature, many researchers reported that nano-active forms had more efficiency than normal-active forms.

Interesting advances in innovation, electronic nose (E-nose, EN) techniques can be applied for quality control of natural products, especially the volatile organic compounds (VOCs) [19]. The biological olfactory detector system called E-nose sensor technique is based on different electronic aroma detection (EAD) technologies by gas sensors. These are as follows: bulk acoustic wave (BAW), surface acoustic wave (SAW), calorimetric/catalytic bead (CB), carbon black composite (CBC), conductive polymers (CP), electrochemical sensors (EC), fluorescence (FL), metal-oxide semiconductors (MOS), complementary MOS (CMOS), MOS field-effect transistors (MOSFET), micro-electromechanical systems (MEMS), optical fiber live cell (OF-LC), and quartz crystal microbalance (QCM) [20–23]. In addition, E-nose instrument consists of both hardware and software components [24]. They include (1) sensors and chemicals that the specific sensors are designed to convert the chemical information of VOCs into analytical signals; (2) machine learning (ML) algorithms act an information-processing unit such as linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), discriminant function analysis (DFA), stepwise discriminant analysis (SDA), partial least squares regression (PLSR), generalized least squares regression (GLSR), multiple linear regression (MLR), principle component analysis (PCA), support vector machines (SVMs), k-nearest neighbor analysis (KNN), artificial neural networks (ANNs), and genetic algorithms (GA) [25–27], and all pattern-recognition algorithms were processed: data collection, modeling, training, and evaluation; and (3) system performance evaluation, which the results have been calculated through E-nose system evaluation metrics with accuracy, precision, sensitivity, specificity, and F1-score (harmonic mean). These were incorporated with reference-library databases [28] with (4) both sensor types and application of commercially available E-noses.

Applications of E-nose technologies for the development and monitoring control of plant EOs were performed and operated in industrial processes. Rasekh et al. [28] and Rasekh et al. [29], for instance, showed that the developed method of E-nose systems with nine MOSs (MAU-9 MOS E-nose system), and two statistical analyses of LDA and QDA methods were successfully evaluated for quickly identifying and classifying plant EOs derived from fruit and herbal edible-plant sources. The developed E-nose array with statistical methods was shown the discrimination results into two groups of fruits and herbal plant EO types with 100% correct accuracy in both LDA and QDA methods and the classification results of different plant EO sample types with the correct accuracy of LDA (98.9%) and QDA (100%), including tarragon oil (*Artemisia dracunculus* L., Asteraceae), thyme oil (*Thymus vulgaris* L., Lamiaceae), cornmint

#### *Perspective Chapter: Perspectives on Pathogenic Plant Virus Control with Essential Oils… DOI: http://dx.doi.org/10.5772/intechopen.104639*

oil (*Mentha arvensis* L., Lamiaceae), lemon oil (*Citrus limon* L. Burm. f., Rutaceae), orange oil (*C. sinensis* L., Rutaceae), and mango oil (*Mangifera indica* L., Anacardiaceae). Similarly, Okur et al. [30] identified the different six species of mints (family Lamiaceae) by the QCM sensors and digital pattern-recognition algorithms of PCA, LDA, and KNN. The mint species were classified accurately by the statistical methods of PCA (97.2%), LDA (100%), and KNN (99.9%) and include peppermint (*M. piperita* L.), spearmint (*M. spicata* L.), curly mint (*M. spicata* ssp. crispa), horsemint (*M. longifolia* L.), Korean mint [*Agastache rugosa* (Fisch. & C.A.Mey.) Kuntzeand], and catmint (*Nepeta cataria*. L.). Similar results have been reported in the various plant VOCs of edible plant species [31], tomato [32], and apple [33]. Graboski et al. [34] reported that the developable method of carbon nanocomposites (CNC) E-nose system was capable to detect the distinction between the plant EO of clove [*Syzygium aromaticum* (L.) Merr. & L.M.Perry, Myrtaceae], eugenol, and eugenyl acetate. Moreover, Lias et al. [35] found that the E-nose system depicted a strong correlation between sample volume and sensors intensity values to plant EO composition of agarwood. In another study, Wu et al. [36] demonstrated that an ultra-fast gas chromatography (UFGC)-type E-nose system was identified the VOCs of spikenard (*Nardostachys chinensis* Batalin, Valerianaceae) with 94% accuracy. Significantly, the E-nose systems and digital pattern-recognition algorithms were used to classify different plant species and varieties such as garlic (*Allium* spp.) [37], pepper (*Capsicum* spp.) [38], and cucumber [39]. Based on the literature review, E-nose technologies and digital pattern-recognition algorithms are potential and effective safety tools for the rapid detection, identification, verification, and validation of plant EOs of plant materials and commercial plant products as environmentally friendly biopesticides in the strategy and policy of sustainable agricultural management.
