**1. Introduction**

With the rapid growth of science and technology, precision components are increasingly in demand in various fields such as aerospace, biomedical engineering, optics, surface engineering and energy. These components not only require tight tolerances and high-quality surface finishes but also require the use of difficult-to-cut materials like high-temperature alloys and hard-brittle materials owing to the physical, mechanical, optical and electronic properties [1]. It is because of the difficultto-cut characteristics, the precision machining of these materials has always been a challenge. For traditional machining methods, there are always some disadvantages, such as high cutting temperature and cutting force, severe tool wear, low efficiency and poor surface quality. To improve the manufacturing quality, tool life and machining efficiency, let the machining cost down, elliptical vibration-assisted machining (EVAM) was proposed. In ultraprecision machining, especially the structured surface with micro-nano features, it requires exceptionally fine and repeatable motions, which makes the piezoelectric actuator a popular candidate for EVAM.

In EVAM, the elliptical vibration cutting (EVC) is a typical method for turning. It has been validated that EVC can improve the machinability of difficult-to-cut materials [2–4]. Meanwhile, the lower cutting force, longer tool life and better machining quality were obtained. In EVC, the vibration of cutting tool or workpiece is usually motivated by external excitation. For elliptical vibration cutting, the locus of cutting tool or workpiece is an ellipse due to the deliberate modulation of vibration devices. According to the principle of vibration, vibration devices can be divided into two types: resonant vibration and non-resonant vibration. For resonant type EVC vibrator, usually consists of an ultrasonic generator, a transducer and an ultrasonic vibration horn. The working frequency is generally above 20 kHz. There are two design schemes for the structure configuration, i.e. patch and sandwich. For the patch type EVC vibrator, two sets of piezoelectric plates are attached to the outer wall of the resonant rod to achieve the same or different modes of resonance. What's more, the Langevin-type transducer is usually adopted in the sandwich-type EVC vibrator. The vibrator can be achieved by adding two or more sets of piezoelectric rings to the resonant rod or coupling two Langevin-type transducers. The ellipse locus of cutting tool can be obtained through the same or different modes coupling in different directions with the nearly same vibration frequency. The working frequency is the resonance frequency. Additionally, the fixed working frequency and vibration parameters, poor dynamic accuracy and open-loop control, heat generation problems are the limitation problems for the resonant vibrator.

For non-resonant type EVC vibrator, usually consists of piezoelectric actuators and a compliant mechanism with deliberate design. Compared with the resonant vibration, the non-resonant type EVC vibrator provides much higher flexibility due to the elastic deformation of compliant mechanism. The vibration parameters like amplitude and frequency are easy to be controlled. The micro-nano motion resulting from elastic deformation of flexure hinge can guarantee the modulation ability and accuracy, which makes it more suitable for the manufacturing of micro-nano structured surfaces.

Generally, compared with workpiece vibration, tool vibration is more popular due to the ease of implementation. In fact, the modulation capability of tools plays a very important role in manufacturing for different materials and structure features. However, the tool modulation capability mainly depends on the performance of the vibrator. Obviously, the non-resonant type EVC vibrator has more advantages than the resonant type EVC vibrator. Up to now, there are many different types of non-resonant EVC vibrators were developed for vibration machining. Therefore, this chapter mainly focuses on the non-resonant type EVC vibrator, aiming to review the working principle of piezoelectric tool actuator, compliant mechanism design, static modeling, kinematic and dynamic modeling, structure optimization and offline testing.


*2.4. Functions of Vocalization in Intraspecies Agonistic Interactions* 2.4.1. Agonistic Function

Adult rats use vocalizations for the regulation of their social life, e.g., for the establishment of dominance hierarchies, during aggressive encounters (mostly with intruders), during sexual behavior, feeding, defending territory, and in other situations requiring significant emotional arousal [75]. In all these situations, the emission of ultrasonic calls plays an important communicative role. Even an overall, rough analysis of all emitted vocalizations in these situations shows significant changes in many acoustic parameters of calls, suggesting that rats emit, at least, some calls specific for a given situation and behavior [124].

Aggressive behavior focused the most attention in research and emissions of 50 kHz and 22 kHz type of vocalizations were recorded during the aggressive attacks and defensive actions of the rats [125,126]. Aggressive/defensive behavior is associated with the highest emotional and autonomic arousal because it may be associated with significant body damage. The general label "aggressive behavior" includes many behavioral patterns, such as threatening, aggressive sideway posturing, directed attack, wrestling, boxing, kicking, punching, jumping, submissive posturing, chasing, and flight, so both offensive and defensive elements. Each of these elements is associated with a potentially different type and combination of emitted vocalizations, which may further differ among different rat strains [127]; however, detailed studies about the role of particular types of calls in these components of behaviors have not been systematically conducted.

The 50 kHz vocalizations are emitted mostly by the attacker and the 22 kHz calls by the defeated rat [127,128]. In the intruder–resident interactions, the intruder emitted mostly 50 kHz vocalizations that were changed to 22 kHz calls after its defeat [128,129]. Audible squeals are frequently emitted during fighting and intermingled with ultrasonic calls. Study of vocalizations emitted in the resident/intruder situation with a wire mesh preventing physical contact confirmed that mostly intruders emitted ultrasonic calls [130]. Interestingly, the ultrasonic calls emitted by the intruder were decreased by systemic morphine, but audible vocalizations were not sensitive to morphine, suggesting that their function is different from ultrasonic calls [130].

The emission of the ultrasonic vocalizations in these offensive/defensive situations plays an agonistic function (from Greek *agonisticos*—combative). This term was first suggested by Scott and Fredericson in 1951, mostly in relation to the complex behavior of rats and mice, and it comprises offensive and defensive groups of behaviors, including withdrawal, avoidance, and escape [25,131,132]. In addition to that, agonistic function includes elements of territorial behavior and defense against intruders.

Rats live in large groups that need space for nesting, hiding, and foraging, and will defend this space against rats from neighboring groups. It was postulated that one of the important functions of the evolution of adult vocalizations was spacing among neighboring animal groups. Vocalizations that can be received from a distance serve this purpose very well [133]. The territories rats defend are rather small and there is no good evidence that rats are defending large and defined boundaries around their living burrows [132]. However, agonistic behavior and relevant calling was shown to play broader functions in controlling population density, group stability, and partner choice [75]. Ultrasonic vocalizations used during territorial defense were not studied in rats, but in a study on mice, several types of calls were demonstrated, which were important in territorial defense [134].

#### 2.4.2. Appeasement Function

Many researchers have observed that the emission of long 22 kHz vocalizations during agonistic encounters by the defeated rat may have an appeasement function, which is widely observed in animal behavior [135]. The emission of these calls would decrease or inhibit further attacks of the aggressive or dominant rat [126,127]. This effect probably does not occur immediately and requires some repeated calling, and the opposite might not be true, i.e., the lack of emission of appeasement calls will not necessarily increase attacks of the aggressor, as it was observed in studies with devocalized rats in which deprivation of ultrasonic signals failed to increase aggressive behavior of the attackers [136]. In this experiment, however, rats could not communicate by any type of calls. Appeasement 22 kHz calls may also be emitted to prevent attack in establishing a dominant–submissive relationship. It was observed that a face-to-face encounter with a dominant rat immediately induced the emission of 22 kHz vocalizations in the submissive rat (usually smaller in size) [137].

It was also suggested that during play behavior (both in juvenile and adult rats), the emission of 50 kHz calls may not only be a play signal but also an appeasement signal that de-escalates agonistic behavior during play and prevents aggressive outcomes, which can happen particularly in playing rats that are unfamiliar to each other [99,138].

Finally, it should be mentioned that audible squealing (sonic threat calls) in young rats that are emitted after bites as pain signals, have been also suggested in the past to have an appeasement effect [58] (p. 126).

#### *2.5. Functions of Vocalization in Reproductive Behavior*

#### 2.5.1. Mating Function

The emission of ultrasonic calls was well studied in the mating and reproductive behavior of rodents. This is a complex and partially ritualized behavior, so the emission of many types of calls were observed. These vocalizations play a mating function, i.e., they contribute to the regulation of the selection of partners, soliciting sexual contact, and initiating copulation. Fifty kilohertz vocalizations are emitted mostly during solicitation and mounting activity, while 22 kHz are emitted by males during the postejaculatory refractory period [73,123]. Male rats emit 50 kHz before successful mating, which may facilitate female responsiveness because females were less responsive when paired with a devocalized male [123,139]. Females also emit ultrasonic calls before copulation that were suggested to play a regulatory role in mating [140,141]. The emission of 50 kHz calls was dependent on female sex hormones because ovariectomized females exhibited few, if any, of the vocalizations [142] and their responses were graded depending on the hormonal condition [143].

Some recent studies, however, could not fully confirm the behavioral role of 50 kHz vocalization during mating [144,145]. Many factors could cause a lack of this response, such as rat strain, too-frequent repetition of tests, rat experience, or stress. Ultrasonic communication during mating is highly dependent on the gonadal status of both partners. For instance, it was found that females produced more 50 kHz ultrasonic vocalizations to intact males than to castrated males but produced similar numbers of calls to both relevant groups of females [146]. Using a playback paradigm, the role of ultrasonic calls in mating was studied in another laboratory, and it was concluded that female rats displayed high levels of social approach behavior in response to the playback of male, 50 kHz ultrasonic vocalizations and did not respond to amplitude-matched white noise [147]. The emission

of 50 kHz calls plays an important role in establishing social proximity [147], and it may be concluded that is important in the regulation of mating behavior.

#### 2.5.2. Social Detachment Function

Male postejaculatory 22 kHz vocalizations are particularly long calls [148]. However, unlike other types of 22 kHz vocalizations, they may have some limited frequency modulation, particularly in the medial and terminal fragments of the call. These calls were postulated to represent a different emotional state of the rat than during the emission of flat-type alarm 22 kHz vocalizations in other situations [149]. The emission of shorter 22 kHz calls than the postejaculatory calls may appear during mating and these calls are associated with unsuccessful intromissions or failed mountings [148], so aversive situations to males.

The postejaculatory 22 kHz vocalizations represent a state of behavioral inhibition with prolonged immobility, a withdrawn or socially depressed state, and an absolute refractory period with "desist-contact" function ([149–152]. During the postejaculatory calling state, rat males do not copulate [153]. It was suggested that the postejaculatory calls have function of keeping the females away [123,150]. It has been observed that experienced females leave the male during the emission of these calls [154]. Contrary to that, the prolonged presence of females together with males increased the duration of the emission of postejaculatory 22 kHz calls [152,155]. This notion that females would avoid male during emission of postejaculatory 22 kHz calls was, however, not always detected [156]. Many factors may influence this behavior and sexual experience is one of them. Nevertheless, the 22 kHz ultrasonic vocalizations in this situation may have a social detachment function or social disaffiliation function, i.e., a role opposite to the affiliative function. This function would be equivalent to the appeasement function in agonistic encounters, discussed above.

Acoustic analysis of the postejaculatory of 22 kHz vocalizations emitted by males during sexual contacts, or intended contacts, revealed that the calls are heterogenous and consist of long flat 22 kHz calls (20–35 kHz range) during the postejaculatory period, and anther class of 22 kHz calls. The other class of calls has higher sound frequency (23–45 kHz range) and some modulated frequency components that were observed during encounters of males with a female in a cage with a physical, perforated barrier, where animals could not have physical contact [157]. This last precopulatory category of calls would be compatible with a negative state of frustration caused by the presence of an inaccessible female, and not as a social detachment function [157] (for more details, see Sections 2.8 and 3.5).


**About the Editor**

**Alessandra Durazzo** was awarded a Master's degree in Chemistry and Pharmaceutical Technology cum laude in 2003, and a PhD in Horticulture in 2010. Since 2005, she has been a Researcher at the CREA-Research Centre for Food and Nutrition. The core of her research is the study of the chemical, nutritional and bioactive components of food, with particular regard to the wide spectrum of substances classes and their nutraceutical features. For several years, she was involved in national and international research projects evaluating several factors (agronomic practices, processing, etc.) that affect food quality, the levels of bioactive molecules and their total antioxidant properties, as well as their possible impact on the biological role played by bioactive components in human physiology. Her research activities address the development, management and updating of bioactive compounds, nutraceuticals, and dietary supplements databases; particular attention is paid to the harmonization of analytical procedures and classification and codification of dietary supplements.

### *Editorial* **New Traits of Agriculture/Food Quality Interface**

**Alessandra Durazzo**

CREA—Research Centre for Food and Nutrition, Via Ardeatina 546, 00178 Rome, Italy<EMAIL_ADDRESS>
#### **1. Introduction**

There is a close link between food and territory. The current challenges can be found in precision agriculture and food metrology from the perspective of monitoring and improving food quality and addressing the promotion of diversity of agroecosystems and diets. The study of Durazzo et al. [1] describes the metrology, agriculture, and food relationship by quantitative literature research analysis as a useful tool for identifying emerging research directions, collaboration networks, and suggestions for more in-depth literature searches.

Research studies describing factors affecting food quality—such as agronomic conditions, post-harvest elicitors, cultivar selection, harvest date, or environmental influences—are described. Sustainable environmental and innovative practices should be promoted.

Hu et al. [2] have compared the grain quality and starch physicochemical properties between Japonica rice cultivars with different contents of amylose, as affected by nitrogen fertilization: the selection of a low amylose content japonica rice cultivar grown without nitrogen fertilizer can reduce the amylose and protein contents, as well as improving the pasting properties, starch retrogradation properties and eating quality of the cooked rice.

Feledyn-Szewczyk et al. [3], by studying the milling and baking quality of spring wheat (*Triticum aestivum* L.) from organic farming, concluded on the basis of the 3-year study results that the most useful variety for organic production is Arabella, followed by Brawura, Izera, Kandela, Katoda, KWS Torridon, Waluta, and Zadra.

Boussahel et al. [4] described phenolic compounds and antioxidant properties of monocultivar olive oils from Northeast Algeria.

Ma et al. [5] showed how reasonable nitrogen fertilizer management improves rice yield and quality under a rapeseed/wheat–rice rotation system.

Jahangirlou et al. [6] showed a case study from the semiarid conditions of Iran on the study of grain quality of maize cultivars as a function of planting dates, irrigation, and nitrogen stress.

Botella et al. [7] studied bioactive compounds of tomato fruit in response to salinity, heat, and their combination: the results show the viability of exploiting abiotic stresses and their combination to obtain tomatoes with increased levels of health-promoting compounds.

Advanced techniques, such as mass spectrometry, infrared, and Raman spectroscopy, in the monitoring and control of foodstuffs for modeling the agrofood system should be considered. Innovative green technologies should be taken into account. Targeting food approaches should be promoted. Chemometrics applications are welcome.

For instance, K ˛edzierska-Matysek et al. [8] proposed a chemometric approach for studying the relationships linking the color and elemental concentrations of blossom honeys with their antioxidant activity.

Guilherme et al. [9] showed a comparative study on phenolics and antioxidant activity of green and red sweet peppers from organic and conventional agriculture: the results confirmed that the production system, as well as the maturation stage affect phenolic content and antioxidant activity, allowing their possible use as maturation–production biomarkers. Moreover, the authors mark how the chemometric approach reveals how

**Citation:** Durazzo, A. New Traits of Agriculture/Food Quality Interface. *Agriculture* **2021**, *11*, 1182. https:// doi.org/10.3390/agriculture11121182

Received: 20 October 2021 Accepted: 10 November 2021 Published: 23 November 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

phenolic composition together with the antioxidant capacities could be used to differentiate the production system and the maturation stage of sweet peppers.

Da Silva et al. [10] studied the effects of camu-camu (*Myrciaria dubia*) powder on the physicochemical and kinetic parameters of deteriorating microorganisms and *Salmonella enterica* subsp. *enterica Serovar Typhimurium* in refrigerated vacuum-packed ground beef: camu-camu powder addition decreases the lipid oxidation of vacuum-packed ground beef, although it influences color features leading to a decreased red hue in the camucamu powder-containing meat. Regarding antimicrobial activity, camu-camu powder does not interfere in *S. enterica ser. Typhimurium* behavior and does not extend the shelf-life of vacuum-packed ground beef based on the concentration of certain spoilage microorganisms, acting only on the kinetic bacterial behavior parameters [10].

Souza et al. [11] showed the physicochemical properties and antioxidant activity of spouted bed dried *Rosmarinus officinalis* extract.

The perspective of Durazzo et al. [12] is focused on antioxidant properties of bee products derived from medicinal plants as beekeeping sources.

It is worth mentioning that the preliminary study of Das et al. [13] evidenced how microbiome analysis of the rhizosphere from wilt-infected pomegranate reveals complex adaptations in *Fusarium* by demonstrating the capabilities of the whole metagenome sequencing approach for rapid identification of potential key players of wilt disease pathogenesis wherein the symptomatology is complex.

The papers included in this Special Issue cover a broad range of interdisciplinary aspects from agriculture and biology, chemistry, and nutrition. It is hoped that this Special Issue will stimulate further research in these areas.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.

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**Acknowledgement**

The views expressed in this manuscript are those of the authors and do not reflect the official policy or position of the Department of the Army, Department of Air Force, Department of Defense, or the U. S. Government.

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*3.3. Radiative Transfer Modelling*

Radiative transfer modelling is a physically based approach that uses physical laws to simulate the interaction of electromagnetic radiation with vegetation (e.g., reflection, transmission, and absorption) [180]. The RTMs simulate vegetation spectra (e.g., leaf reflectance and transmittance) using vegetation biophysical and biochemical properties (e.g., chlorophyll and water contents) in the forward mode, and for inversion of these variables from spectral measurements in the inverse mode [181]. PROSAIL is one of the most widely used RTMs. This model is an integration of the leaf-level PROSPECT model and canopy-level SAIL model and is capable of simulating canopy reflectance using leaf properties (e.g., chlorophyll and water contents), canopy structural parameters (e.g., LAI and leaf angle), and soil reflectance [18].

PROSAIL has also been used in agricultural environments for investigating crop and soil properties. For instance, Casa and Jones [182] inverted PROSAIL and a ray-tracing canopy model with spectroradiometer-measured hyperspectral reflectance data and imaging spectrometer-acquired hyperspectral image data, respectively, for estimating canopy LAI and evaluated factors influencing the estimation accuracy (e.g., the non-homogeneous surface caused by the crop row structure). Richter et al. [98] utilized PROSAIL for estimating LAI, fCover, canopy chlorophyll, and water content from hyperspectral images and compared its performance to other methods (e.g., artificial neural network). Richter et al. [183] applied PROSAIL to investigate similar vegetation variables and analyzed the accuracy and efficiency of this method. Wu et al. [184] examined the sensitivity of vegetation indices to vegetation chlorophyll content using simulated results from the PROSPECT model and suggested

a few well-performed indices. Locherer et al. [74] attempted to estimate vegetation LAI using the PROSAIL model and multi-source hyperspectral images and tested several techniques (e.g., different cost functions and types of averaging methods) used for the inversion process. Yu et al. [37] estimated a range of vegetation phenotyping variables (e.g., LAI and leaf chlorophyll) using hyperspectral imagery and PROSAIL and examined the sensitivity of different spectral ranges to the parameters in the PROSAIL model.

Compared with the regression models discussed in previous sections, the RTMs have been less used in the literature for investigating agricultural features due mainly to their high model complexity and computational intensity. For instance, a wide range of parameters need to be considered in RTM (e.g., chlorophyll, carotenoids, water contents, leaf area index, leaf angles, solar angles, and soil reflectance, along with other parameters, in the PROSAIL model) and the users need to use different techniques (e.g., merit function, look-up table) to facilitate the forward and inversion operations of the model. In addition, it costs much more computing time than the regression models to achieve the predictions of target vegetation variables. However, it is also well known that the regression models tend to be site and time specific and are not readily transferable to other geographical regions or different times over the site [166]. In contrast, RTM is a more transferable approach owning to the fact that it is established based on physical laws and does not require training data for rebuilding the model. In addition, RTM is capable of estimating a range of vegetation properties in one model, while regression models typically can only estimate one variable [36,185].

#### *3.4. Machine Learning and Deep Learning*

Machine learning algorithms, including support vector machine regression (SVM) and RF, are powerful tools for analyzing hyperspectral information since they can process a large number of variables (e.g., spectral reflectance and vegetation indices) efficiently [186]. Machine learning has been widely used in the remote sensing field for estimating properties of ground features or classifying different ground covers [36,114,187]. Researchers have also used different machine learning algorithms and hyperspectral images for agricultural applications. SVM has been a commonly used algorithm in previous research for prediction or classification purposes. For instance, Honkavaara et al. [123] estimated crop biomass using SVM and UAV-acquired hyperspectral imagery. Bostan et al. [51] utilized SVM for classifying different crop types and achieved high classification accuracy. Ran et al. [93] used KNN and SVM classifiers for investigating tillage practices in agricultural fields and compared their performances. RF is another commonly used algorithm for investigating agricultural features with hyperspectral imagery. For instance, Gao et al. [188] successfully classified weed and maize using RF and lab-based hyperspectral images. Using ground-based hyperspectral reflectance data acquired by an ASD spectroradiometer, Siegmann and Jarmer [189] evaluated the performance of RF, SVM, and PLSR for estimating crop LAI and confirmed the good performance of RF. Similarly, using hyperspectral reflectance, Adam et al. [190] attempted to detect maize disease with the RF model. Overall, machine learning models generally have robust performances for investigating agricultural features using hyperspectral imagery.

Deep learning is a subset of machine learning and extends machine learning by adding more "depth" (i.e., hierarchical representation of the dataset) in the model [191,192]. It is a popular approach in recent years for recognizing patterns in remote sensing images and thus for investigating various ground features. Deep learning has been commonly used in the remote sensing field for image classification, such as land cover classification [193–195] and the identification of ground features (e.g., buildings) [196]. Deep learning has also been applied to precision farming to solve complicated issues. Existing studies are, for example, investigating the estimation of crop yield using CNN and multispectral images together with climate data [197], plant disease detection using CNN and smartphone-acquired images [198], crop classification using 3-D CNN and multi-temporal multispectral images [199], and classification of agricultural land cover using deep recurrent neural network and multi-temporal SAR images [200]. Kamilaris and Prenafeta-Boldú [191] reviewed applications of deep learning in agriculture and food production, although not all studies used remote sensing images. Singh et al. [201] reviewed a range of deep learning methods and their applications, specifically in plant phenotyping. Up to now, deep learning has not been well explored for processing and analyzing remote sensing images, especially hyperspectral images, for agricultural applications. Considering the capacity of deep learning for studying feature patterns in images and the rich information in hyperspectral imagery, the integration of the two has a wide range of agricultural applications (e.g., crop classification, weed monitoring, crop disease detection, and plant stress evaluation). Further research in these areas is warranted.

Machine learning or deep learning is capable of processing multi-source and multi-type data [202]. For instance, besides multi-type remote sensing images (e.g., optical, thermal, LiDAR, and Radar), other sources of data, such as weather, irrigation, and historical yield information, can also be incorporated in the modelling process for a possibly better evaluation of targeted agricultural features [203]. Although machine learning and deep learning models are powerful, it is also critical to keep in mind that these models require large-quantity and high-quality training samples to achieve robust performances [202]. Insufficient training datasets or data with issues (e.g., data incompleteness, noise, and biases) may cause undesired model performances.

In summary, different analytical methods (e.g., linear regression, advanced regression, machine learning and deep learning, and RTM) have different levels of complexity, performance, and transferability. More detailed comparisons on these methods are listed in Table 7. Overall, linear regression is the easiest method to use, and its performance is generally acceptable, although this method can be highly influenced by the choice of predictor variables and quality of the sample data. The advanced regression (e.g., PLSR) mostly performs better than the linear regression since it involves multiple variables in the model and is less sensitive to data noise. RTM (e.g., PROSAIL) is capable of producing multiple data products (e.g., chlorophyll, water, and LAI) with reasonably high accuracies. One essential advantage of this method is its high transferability. However, this method has the highest complexity as it requires a wide range of parameters and extensive programming. In terms of machine learning, many algorithms, such as RF and SVM, are well established and mostly performed well in previous studies. Some programming and model adjustments are needed for this method to achieve optimal performance. Deep learning is a relatively new method and is increasingly popular in recent years. Appropriate model design and programming are critical for this approach. It also requires a substantial amount of training data and computing resources to achieve a good model performance.