extfieldsofstudy: Medicine Chemistry

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Valorization of spent black tea by recovery of antioxidant polyphenolic compounds: Subcritical solvent extraction and microencapsulation

Abstract Spent black tea (SBT), waste remaining after producing tea beverages, is potentially an underutilized source of antioxidant phenolic compounds. This study evaluated the integrated processes of subcritical solvent extraction of polyphenols from SBT followed by microencapsulation to improve the stability of obtained extract. Optimization of extraction conditions was carried out by response surface methodology for the best recovery of antioxidant phenolic compounds. Two variables [temperature (°C) and ethanol concentration (%)] were used to design the optimization model using central composite inscribed. Extraction temperature of 180°C and ethanol concentration of 71% were optimal for the highest yield of total polyphenols (126.89 mg gallic acid equiv./g SBT) and 2,2‐diphenyl‐1‐picrylhydrazyl scavenging activity (69.08 mg gallic acid equiv./g SBT). The extract was encapsulated using pectin, sodium caseinate, and a blend of these compounds (ratio 1:1) as wall materials by spray drying. The wall material significantly influenced (p < .05) encapsulation efficiency, particle size, morphology, thermal stability, crystallinity, and storage stability. The blend of wall materials produced an amorphous powder with the highest phenolic retention (94.28%) in the accelerated storage at 45°C for 40 days. The microcapsules prepared with sodium caseinate were smaller with lowest mean diameter and highest thermal stability than the other types of materials. Obtained microencapsulates have potential use in different food systems to enhance their antioxidant property.

Because of its convenience of consumption, ready-to-drink (RTD) or bottled tea is produced commercially in most parts of the world, in particular, Japan. As a consequence, a large amount of spent black tea (SBT), the residues after manufacturing this tea product, is generated annually (Kondo, Hirano, Kita, Jayanegara, & Yokota, 2018). These used tea leaves mostly become waste with only a small percentage used as feedstock or turned to compost (Sagar, Pareek, Sharma, Yahia, & Lobo, 2018). As the conditions used for tea brewing are mild, a significant amount of polyphenols with a high antioxidant power is retained in SBT (Abdeltaif, SirElkhatim, & Hassan, 2018). Hence, utilizing food manufacturing waste such as SBT is a sustainable and economically attractive way to recover antioxidant phenolic compounds but an efficient method for extracting polyphenols from SBT has not yet been developed and tested.
Several techniques have been investigated for extracting phytochemicals from food waste (Sagar et al., 2018;Yanagida, Shimizu, & Kimura, 2005). Of these techniques, subcritical solvent extraction (SSE) is a greener and faster method (Munir, Kheirkhah, Baroutian, Quek, & Young, 2018), which uses a pressurized liquid kept below its critical point (374°C for water) and above its boiling point (100°C for water). These conditions allow fluids to remain in a liquid state due to the applied pressure and it creates low polar water with equivalent to organic solvents at ambient temperature (Shimizu, Ushiyama, & Itoh, 2019;Zhang, Baroutian, Munir, & Young, 2017;Zhang & Wolf 2019). This technique facilitates rapid extraction without the loss or changing the chemical integrity of thermolabile compounds (Essien, Young, & Baroutian, 2020). Combining subcritical water with an organic solvent such as ethanol and methanol has also been used to improve the yield, extraction time and solubility of compounds (Kwon & Chung, 2015;Pronyk & Mazza, 2009). Response surface methodology (RSM) is commonly used for optimizing the process parameters for the extraction of phytochemicals. This is a useful mathematical and statistical tool for defining the effect of independent variables and their interactions on a particular response, such as the yield.
However, effectiveness of polyphenols mainly depends on their stability, bioactivity, and bioavailability. The unsaturated bonds in the molecular structure of polyphenols make them vulnerable to oxidants, light, and heat, thus reducing their activity (Kailaspathy, 2015).
Therefore, protecting phenolic compounds by encapsulation following their extraction would be a better way to maintain the structural integrity of polyphenols until their industrial application. The microencapsulation of phenolic extracts not only preserves them but also produces a powdered product that is convenient for food application. At present, spray drying is the most widely used technique for the microencapsulation of polyphenols and other heat labile compounds because of its short thermal contact time, cost-effectiveness, and suitability for industrial application.
As well as the technique used for microencapsulation, selecting a coating or wall material is also crucial for efficient spray drying (Ushiyama & Shimizu, 2018). Of the different types of wall material, polysaccharides and protein agents are commonly used either alone or in combination because of their distinct properties. Pectin is a polysaccharide with strong film-forming, gelling, and binding abilities. Its ability to form stable dispersions at low concentrations facilitates microencapsulation by spray drying (Rehman et al., 2019).
Sodium caseinate is the salt of casein, a major milk protein fraction.
Generally, milk proteins act as effective film-formers and emulsifiers while polysaccharides act as filler materials (Augustin & Oliver, 2014).
Ultimately, encapsulated SBT would exhibit important properties that would facilitate the shelf life of polyphenols because the coating materials can act as a barrier against adverse environmental conditions. Therefore, the objective of the present study is to develop a method for recovering phenolic compounds from SBT by integrating the processes of SSE optimization and the subsequent encapsulation of SBT using different wall materials by spray drying. The encapsulated powder using pectin, sodium caseinate, and a mixture of these compounds as wall materials will be characterized to evaluate their encapsulation efficiency, morphology and size, thermal stability, crystallinity, and storage stability.

| MATERIAL S AND CHEMI C AL S
Low grown unblended black tea was supplied by Nawa withana Kanda tea factory in Sri Lanka, gallic acid by Sigma-Aldrich Distilled water was used in all the experiments. All other chemicals and solvents used were analytical grade.

| Preparation of spent black tea
Black tea leaves (20 g) were brewed in 1,000 ml of boiling water (100°C) for 6 min. The infusion was then filtered using a tea strainer, and the residue was dried in an air-drying oven at 45°C overnight.

| Subcritical solvent extraction
Subcritical solvent extraction was performed using an organic synthesizer (Chemi-station PPV 3000, Tokyo Rikakikai Co. Ltd, Tokyo, Japan) with an agitator and an 11-ml reactor with a maximum temperature and pressure of 200°C and 5 MPa, respectively. For each experimental run, 0.5 g of SBT was mixed with 10 ml of solvent at a solid: solvent ratio of 1 g: 20 ml. The extraction reactor was filled and then purged three times with nitrogen gas to remove the atmospheric oxygen present in the reactor vessel, and then, an initial pressure of 2.0 MPa was applied. The heating control was adjusted to obtain the desired temperature, which was then maintained for 10 min. During extraction, the agitation speed was kept at 17 g to prevent any local overheating and to increase the mass transfer. The extraction process was conducted in at various ethanol concentration (0%-100%) and temperature (100°C-180°C) ranges, based on the RSM design given in Table 1. After the extraction, the reactor was immediately cooled by placing it in a container of cold water. The extracts were filtered through filter paper (6 μm) under vacuum after the vessel pressure reached the initial pressure, and then, the filtrate was lyophilized. The lyophilized powder of SBT extract was stored at 4°C until further analysis.

| Determination of total phenolic content
The TPC of the dried extract was measured colorimetrically using the Folin-Ciocalteu (FC) method described by Dranca and Oroian (2016) with little modifications. Briefly, the dried extract obtained was diluted with a dilution factor of 100, and then, a 1.0-ml aliquot of the extract in triplicate was transferred into a test tube and mixed thoroughly with 5.0 ml of FC reagent diluted 1:10 with distilled water.
After keeping for 3 min, 5.0 ml of sodium carbonate (7.5%, w/v) was added and mixed. The mixtures were then allowed to stand for 1 hr in the dark before measuring the absorbance using a UV-Vis spectrophotometer (JASCO V-560, JASCO corporation, Tokyo, Japan) at 756 nm against the blank. Gallic acid was used as the standard for preparation of the standard curve (7.812-250 μg/ml, R 2 = .998). The TPC values were expressed as milligrams of gallic acid equivalent/g (dry weight) material (mg GAE)/g SBT).
DPPH scavenging capacity was expressed as milligrams of gallic acid equivalent/g (dry weight) material (mg GAE)/g SBT).

| Response surface methodology design
The study was conducted as a two-factor full factorial experiment with the influence of two independent variables (temperature and ethanol concentration) on the responses (total phenolic content and DPPH scavenging capacity) being evaluated ( Table 2). The CCI design consisted of 13 experiments using 5 centers, 4 axial, and 4 factorial points. The experimental data obtained were fitted to a second order polynomial model of the form: where y is the predicted values of TPC or DPPH scavenging capacity; x i , the coded levels of the design variables (Temperature and ethanol concentration); b o , a constant; b i , the linear effect; b ii , the quadratic effect; and b ij , interaction effects.
The statistical significance of differences between the mean values of variables was determined at the 5% probability level (p < .05), and the data were analyzed by ANOVA. Minitab 19.1.1 software (Minitab Inc., State College, PA, USA) was used to generate the surface plots and the optimized conditions. All assays for characterizing the SBT extract were performed in triplicate.

| Preparation of feed solutions
The coating materials (3 g) were dissolved in 100 ml of distilled water at 90°C and then stirred until a clear dispersion was achieved. Three coating materials were evaluated as follows: 100% pectin (PE), 100% sodium caseinate (SCN), and a 50%:50% combination of pectin and sodium caseinate (PE + SCN). The prepared PE solution was kept at room temperature while the SCN and PE + SCN solutions were kept in a refrigerator overnight to allow complete hydration to occur. The next day, SBT extract concentrated by a rotary evaporator (N-1210 and SB-1300 water bath, EYELA Tokyo Rikakikai Co., Ltd, Tokyo, Japan) was added dropwise to the prepared biopolymer solutions heated to 40°C with magnetic stirring at 21.5 g for 20 min. The prepared feed solutions were sonicated for 20 min then homogenized (HERACLES-16g, Koike Precision Instruments, Tokyo, Japan) with stirring for 30 min before further processing. The feed solution contained 20 g of the carrier solution and 1 g of the concentrated SBT extract. All the prepared feed solutions were then spray dried.

| Measurement of viscosity
Before spray drying, the viscosity of all feed solutions was measured using a Sine-wave Vibro Viscometer SV-10 (A&D Co. Ltd., Tokyo, Japan). All measurements were carried out at room temperature.
Each experiment was performed three times, and the average value was taken as the final value.

| Spray drying conditions
The liquid feeds were spray dried using a laboratory scale spray dryer OSK 55MO102 (Osaka Seimitsu Kikai Co. Ltd, Osaka, Japan).
The values of the operational parameters established for the drying process were as follows: solid concentration, 3% (g/g); inlet air tem-

| Encapsulation efficiency
The encapsulation efficiency (EE%) of the powders was calculated as: where TPC is the total phenolic content, and SPC is the surface phenolic content (Kaderides & Goula, 2019).
TPC was determined by dissolving 10 mg of the sample in 4 ml of ethanol and methanol followed by thorough agitation and sonication for 40 min to completely break down the microencapsulates. Then, the solution was filtered through a 0.45-μm filter. SPC was measured by washing a powder sample (10 mg) into a filter paper (0.45 μm) using 4 ml of ethanol and methanol.

| Morphology and particle size analyses
The morphology of the particles was examined using scanning electron microscopy (SEM, JSM-6301F, JEOL Ltd., Tokyo, Japan) at a beam voltage of 10 kV and a working distance of 39 mm. From the micrographs, the particle diameter was calculated using ImageJ open-source software (imagej.net). For measuring the size distribution, 100 particles were counted.

| Thermogravimetric analyses
Thermogravimetric analyses (TGA) were carried out using a

| Crystallinity of powders
The crystallinity of the encapsulated phenolic extract was evaluated using a Rigaku Rint-Ultima III X-ray diffractometer (Rigaku Corp.) with Cu-Kα radiation generated at 40 kV/40 mA at a wavelength of 0.154187 nm. The scanning range was 5-40° 2θ at a speed of 2° 2θ/min. The degree of crystallinity was calculated as described by Ahmadian, Niazmand, and Pourfarzad (2019): where CD is the degree of crystallinity (%); I net , the crystalline intensity of peaks; and I total , the overall intensity.

| Accelerated storage stability study
Glass vials containing three types of microencapsulated powders loaded with SBT extract (20 mg) were stored in an incubator at 45°C for 40 days. After given periods (0, 20, and 40 days), samples were collected from each batch, and then, their retained phenolic content was determined.

| Statistical analysis
Statistical analyses were carried out using Minitab 19. The results were reported as the mean value of three repeated experimental data.  (Table 3).
using water extraction to be low, or possibly water alone cannot cleave hydrogen bonds (Miralai, Khan, & Islam, 2008). However, ethanol can precipitate polysaccharides and expel them from the solution (Xu et al., 2014). The interactive effect of two variables also showed a positive effect on the TPC. The polynomial equation obtained for the apparent phenolic content was as follows:

| Effect variables on antioxidant activity
The antioxidant activity was determined by the DPPH assay. Like

| Optimization of extraction process and experimental validation
The SSE was optimized to yield an extract with a high content of phenolic compounds and a high antioxidant activity. A graphical optimization based on the effect of the two factors on the responses was conducted using the highest desirability level ( Figure 3). This shows that under the optimal conditions (180°C temperature and 71% ethanol concentration), a TPC of 126.89 mg GAE/g SBT and DPPH activity of 69.08 mg GAE/g SBT can be obtained. These optimal conditions were used later for validation, and the results obtained for TPC (127.15 ± 1.67 mg GAE/g SBT) and DPPH activity (71.31 ± 3.40 mg GAE/g SBT) were very close to those predicted. Under the optimal conditions, the phenolic content and antioxidant activity of the raw and spent black tea were compared (Figure 4). The results showed that only 33% of the antioxidant phenolic compounds had been lost during tea brewing while the remaining 67% could be recovered using the optimized conditions for SSE. The optimal values of TPC from SSE in the present study were considerably higher than that (91.06 mg GAE/g SBT) reported in a previous study that used the maceration-mediated liquid-liquid extraction of SBT (Mukhtar, Mushtaq, Akram, & Adnan, 2018).

| Particle size analyses
The particle size and size distribution are important characteristics of powder that can affect their storage and handling. To evaluate the effect of the type of wall material, the average particle diameter and size distribution were measured (Table 4). This revealed that the type of wall material significantly influenced the diameter of the particles (p < .05). The standard deviation values of the powder diameter  (Table 4). The solution with pectin possessed the highest viscosity and sodium caseinate the lowest. The particles with the mixture of wall materials exhibited the narrowest size distribution (1.45 μm) so were of a more uniform size than particles coated with the other two wall materials. This could have been caused by the heating of the pectin-sodium caseinate blend during its preparation, thus decreasing the polydispersity index by forming a more compact and dense particulate structure F I G U R E 3 Optimization plot for the responses of total phenolic content (TPC, mg GAE/ g SBT) and antioxidant activity (DPPH, mg GAE/ g SBT)

F I G U R E 4
Comparison of the extract from raw black tea (RBT) and spent black tea (SBT) obtained under optimal conditions (180°C temperature and 71% ethanol concentration)

TA B L E 4
Effect of different wall materials on the viscosity of the feed solutions before spray drying, average particle diameter, and encapsulation efficiency of spray dried spent black tea powders through re-arranging the pectin molecules on the sodium caseinate surface (Liang & Luo, 2020).

| Encapsulation efficiency (EE%)
The encapsulation efficiency of SBT powder with coatings varied between 60.06% and 81.55%, thus confirming the successful entrapment of phenolic compounds within the wall materials (Table 4). The results also revealed that the type of coating agent used for encapsulation had an important role in retaining phenolic compounds in the carrier matrix (p < .05). The best result was achieved by the sample with SCN + PE (1:1 mass/mass). Pectin offers the advantageous as a protective carrier and its capability of interacting with hydrophobic molecules (Rehman et al., 2019). However, carbohydrates such as pectin mostly lack the interfacial functionality (Livney, 2010). Hence, it is better to combine polysaccharides with surface active biopolymer-like caseinate to achieve successful encapsulation (Hogan, McNamee, O'Riordan, & O'Sullivan, 2001). Nevertheless, milk protein such as sodium caseinate can bind with polyphenols, especially with catechin, with this mechanism being promoted by a preheating treatment (Haratifar & Corredig, 2014;Shpigelman, Israeli, & Livney, 2010).

| Morphology
SEM was used to observe the microstructure of the encapsulated SBT extracts ( Figure 5). Figure

| Thermal stability
The thermogravimetric (TG) and derivative thermogravimetric (DTG) curves obtained from microparticles allow the visualization of their thermal degradation behavior (Figure 6a). All the tested samples ex- This was possibly because, with its flexible and disordered structure, it is less sensitive to changes in temperature (McClements, 2018). Figure 6a shows that the rate of weight loss during decomposition was lowest in the particles coated with a combination of wall materials possibly because of the enhanced electrostatic interaction between pectin and sodium caseinate (Chang et al., 2017). Pectin is an anionic polysaccharide that can interact with casein mainly through electrostatic, steric, or covalent interactions.

| Crystallinity
The XRD patterns of the encapsulated SBT extracts coated with three polymer mixtures are shown in Figure 7. The results of XRD were evaluated after smoothing by using Match! Software (Crystal Impact, Bonn, Germany). Normally, a crystalline fraction diffracts X-ray coherently according to Bragg′s law to give a sharp peak while an amorphous fraction diffracts incoherently to give a diffuse halo (Chung, 1999). Thus, in Figure 7, the XRD patterns with their wide in the particles coated with a mixture of wall materials. These results agreed with XRD results in previous studies on pectin (Hosseinnia, Khaledabad, & Almasi, 2017) and sodium caseinate (Pan, Zhong, & Baek, 2013). Forming of an amorphous structure enhances the rate of dissolution and solubility compared with a crystalline material thereby increasing the bioavailability of the active material (Kanaujia, Poovizhi, Ng, & Tan, 2015).

| Accelerated storage stability
The stability of the encapsulated SBT extract was evaluated Values represent the mean (n = 3). Different letters in each coating material indicated a statistically significant difference (p < .05) between time points composite inscribed design could be used for the subcritical solvent extraction of phenolic compounds in spent black tea, and the subsequent microencapsulation could enhance the stability of the polyphenols. Therefore, antioxidant phenolic compounds can be effectively recovered from spent black tea a food manufacturing waste product with subsequent microencapsulation turning it into valuable food ingredient.

ACK N OWLED G M ENTS
Author gratefully acknowledges the help of Prof. Kiyonori Takahashi, Research Institute for Electronic Science, Hokkaido University for granting the use of the X-ray diffraction facility. The TGA analysis was performed using TG-DTA analyzer at the Global Facility Centre of Hokkaido University, and the scanning microscopy was provided by the Electron Microscope Laboratory in the Research Faculty of Agriculture, Hokkaido University. We thank Philip Creed, PhD, from Edanz Group (www.edanz editi ng.com/ac) for editing a draft of this manuscript.

CO N FLI C T O F I NTE R E S T
The authors declare that they have no conflict of interest.

E TH I C A L A PPROVA L
Ethical approval is not required for this research.

DATA AVA I L A B I L I T Y S TAT E M E N T
The research data are not shared.


provenance: AgriculturalAndFoodSciences,2019-2020-Agricultural And Food Sciences-2020.jsonl.gz.gz

text:
Pruning for Winter Crop Production in High Density Guava Plantations – A Review

Guava (Psidium guajava L.) commonly known as “Poor Man‟s Apple” or “Apple of Tropics” is one of the major fruit crop of Subtropical and tropical climatic regions. The center of origin of the crop is believed to be Tropical America, extending from Mexico to Peru. In the early 17th century the Portuguese introduced this plant to India (Singh, 1995) and today India is one of the major producers of Guava. Guava belongs to the family „Myrtaceae‟ and is having chromosome number 2n = 22. The

Introduction
Guava (Psidium guajava L.) commonly known as "Poor Man"s Apple" or "Apple of Tropics" is one of the major fruit crop of Subtropical and tropical climatic regions. The center of origin of the crop is believed to be Tropical America, extending from Mexico to Peru. In the early 17th century the Portuguese introduced this plant to India (Singh, 1995) and today India is one of the major producers of Guava. Guava belongs to the family "Myrtaceae" and is having chromosome number 2n = 22. The Genus Psidium consists of more than 150 species but only Psidium guajava is commercially exploited. The common guava is diploid but many of natural and artificial triploids 2n = 33 and aneuploids do exist.

ISSN: 2319-7706 Volume 9 Number 5 (2020)
Journal homepage: http://www.ijcmas.com High density meadow orcharding and crop regulation has made an important breakthrough for optimization of the guava fruit production. The land rates are hiking day by day and there is need for early returns from the invested capital, so it became a worldwide trend to plant trees on a high planting density and to manipulate tree architecture by canopy management to control growth pattern, shape of the plant and increase the fruit production. In guava, three distinct flowering seasons were identified in different parts of India. Flowering occurs on the current season's growth, even though the crop is available around the year. The rainy crop is of inferior quality, infested with fruit flies and do not keep well, whereas the winter crop produces a better fruit quality, fetch a premium price and has a long shelf life. Hence rainy season crop is regulated to next winter season by pruning. Pruning at different timings have shown different effects on the growth, flowering and yield parameters of the crop. It"s essential to standardize the pruning time in order to get crop with higher returns.

Present status
Among guava producing countries, India ranks 1 st in area and production followed by China and Thailand. The highest productivity of 15.8 tones/ha had been recorded in Brazil. In India, guava is the 5 th most important fruit in production after Banana, Mango, Citrus and Papaya. It occupies an area of 2.61 lac hectares with annual production of 36.4 lacs MT and productivity of 13.94 MT/ha in India. Though it is grown successfully all over the country but Uttar Pradesh, Bihar, Madhya Pradesh and Maharashtra are leading producers. Among Indian states Uttar Pradesh ranks 1 st both in area (49 thousand ha) and production (9.19 lacs MT) alone while Punjab accounts for the highest productivity 22.46 MT/ha. (Anonymous, 2017) Composition Guava is an excellent source of ascorbic acid, dietry fibre, pectin and minerals. The composition of guava fruits varies widely with cultivars, stage of maturity and season. The predominant sugars are fructose (59%), glucose (36%) and sucrose (5%) (Mahor et al., 2014). Fructose is the principal sugar in the green ripe fruits while fully ripe fruits contain higher amount of sucrose.

Importance of pruning
Pruning in guava is pre-requisite for the better growth and yield of fruits because it bears on current season growth and flowers appear in the axils of new leaves. In Guava there are three distinct flowering seasons: Spring (Ambebahar), Rainy (Mrigbahar) and autumn (Hasthabahar) with the corresponding rainy, winter and spring harvesting cycles. After 7-8 years guava plants owe to excess vegetative growth and intermingling of the branches on the lower half of the tree canopy resulting in unfruitfulness, as the fruitful bud becomes blind and there is a decline in the yield with sub-optimal fruit quality. Pruning on bearing trees leads to the formation of new shoots, avoid overcrowding of branches, removal of criss-cross branches, diseased branches as well as water sprouts and root suckers. Hence pruning is considered as an important practice especially in meadow orchards where restriction of vegetative growth is essential for maintenance of plant canopy at a desirable height influencing the vigor, productivity and quality of the fruits to encourage new shoot emergence after harvest. Several workers have reported increase in vegetative and qualitative attributes of guava as a result of pruning at different periods. Therefore pruning levels and time of pruning in guava under high density conditions has achieved a greater importance.

Plant height
Lian (2019)  However Kumar and Rattanpal (2010) reported no significant effect of the pruning time on the plant height and found it maximum in unpruned plants. These results were supported by the findings of Lal (1992) and Anez (1998) where unpruned plants were recorded with maximum increase in plant height

Shoot length
When ten years old plants of Lalit guava planted at as spacing of 6 x 6 m were pruned at different times Meena (2016) recorded maximum increase (24.32 cm) in the shoot length in May pruned plants while minimum increase (10.88 cm) in control followed by pruning in June (17.12 cm). The results of this experiment were in line with the findings of Dhliwal (2004), Anez (1998) and Mohammad (2002) who reported maximum increase in the shoot length with pruning in May.
Lian (2019) revealed that the shoot length had been significantly influenced by the difference in the time of pruning. The highest shoot length (23.67 cm) was recorded in mid-May pruned plants which were at par with mid-April pruning (23.11 cm) and lowest (20.89 cm) in mid-June pruned plants.

Contrary
to the above findingsNikumbhe(2017) reported highest increase in the shoot length in unpruned plants (120 cm) followed by pruning in May (86.50 cm) while lowest in the September pruned plants (67.50 cm).

Plant spread
Sah (2017) recorded the highest increase in plant spread with three-leaf pair pruning in comparison to full-shoot pruning during June, as the net photosynthetic area gets reduced for some time with full shoot pruning resulting in reduced plant spread. Likewise, Singh (2007) studied the response of guava (cv. Allahabad Safeda) plants to different rejuvenation periods. The plants were pruned leaving four scaffold branches per leaf from April to June. The plant spread was observed to be significantly affected with different rejuvenation periods. The plant spread was lowest in May and highest in June pruned plants.
However, Meena (2016) recorded highest increase in the plant spread with pruning in May, when 10 years old plants of Lalit guava were subjected to pruning at different timings (April, May and June). Similar results were obtained by Lian (2019) on L-49 guava plants.

Time of flowering
It is a general tendency to grow new shoots after pruning as soon as possible, but, the response was different as per the time and severity of pruning. Meena (2016) reported that the control plants flowered very late (51.33 days) while pruning treatments triggered early blooming. Plants pruned at 45 cm during April flowered at an interval of 32.67 days after pruning, while over 45 days in other pruning treatments. Dhaliwal and Kaur (2003) observed an early flowering with an increase in the severity of pruning, while delayed pruning resulted in late flowering, reduction in flowering age and flowering percentage. Basu (2007) studied the effect of different times of pruning on the cropping behavior of 11 years old L-49 guava. Pruning in the month of April resulted in early flowering (i.e. 51 days after pruning) while delayed flowering when pruned in July (66 days after pruning).
However, Singh (2001) subjected 15 years old plants of Allahabad Safeda and Sardar guava to pruning at varying timings and observed that pruning in May resulted in flowering from July-September while, February pruned plants flowered during April.

Fruit set
The interaction effect of the intensity and time of pruning was studied by Ali (2014) on 10 years old guava plants. The fruit set was recorded to be maximum during both the rainy (73%) and winter (75%) seasons with pruning of 10 cm of shoots in May. Likewise Lian (2019) observed a sudden decrease in percent fruit set with increase in the intensity of pruning. The maximum fruit set (83.33%) was reported by pruning of 50% of the shoot length in mid-May while lowest with pruning of 75% shoot length during mid-June. The reproductive growth was observed to be increasing with light pruning while severe pruning favors vegetative growth.
Contrary to the above findings Brar (2007) observed the fruit set to be positively correlated with the increase in the severity of pruning during both the rainy and winter seasons, when eleven years old Sardar guava plants were pruned in May at three different pruning intensities (15, 30 and 45 cm). The results of this observation were parallel to the findings of Singh (2011) and Lotter (1990) who reported an increment in the percent fruit set with the increase in the severity of pruning.
However, Boora et al., (2016) observed that reduction in the fruit set during the rainy season crop is necessary to improve the fruit set during the winter crop.

Fruit yield
After the pruning of plants in MayBagchi et al., (2008) observed an increase in the concentration of proline, peroxidase, polyphenol oxidase, tryptophan and reduction of phenolics in the different plant parts, leading to increased flowering and fruiting during the winters and contributing higher yield per plant.

Fruit size
The fruit size was observed to be significantly affected with the time of pruning when, Sah (2017) subjected seven years old plants of guava growing under meadow orcharding to half shoot pruning at different timings. The fruit length was recorded maximum in the plants pruned during June while, maximum fruit width in April pruned plants.
Severe pruning was reported to have a marked effect on the fruit size. Adhikari (2015) reported that the maximum size was obtained the fruit produced on the plants pruned at 30 cm level of pruning during mid-May.
Likewise, Basu (2007) recorded maximum fruit size in the fruits produced on the plants pruned in May.

Fruit weight
As a result of pruning the number and area of leaves increases causing an increase in the number of photosynthates and lead to increase in the fruit weight during the winter season crop (Singh et al., 2001). The fruit weight was observed to be increasing with the enhancement of pruning severity and delay in the time of pruning Adhikari (2015). Pruning at the level of 30 cm in early-May produced heavier fruits in the rainy season while during winters heavier fruit were produced with pruning at 30 cm level in mid-May. Similar trend was observed by Basu (2007) when eleven years old plants of Sardar guava were pruned keeping 4 scaffold branches in the May.
However, Das (2018) reported that therewas no significant effect of the pruning treatments on the fruit weight of the rainy and summer season crops for three continuous years except for the winter season crop. The plants pruned in May yielded fruits with maximum weight during the winters in all the three years.

Biochemical parameters
The fruits produced in the winter season were found to be rich in biochemical aspects than rainy season crop, as the higher crop loads lead to the drainage of food reserves hiking the competition for food reserve among the fruits. Aswathy(2017) subjected guava plants to pruning at different timings and observed that fruits produced in winter were superior in quality than the rainy season crop. The plants pruned in May produced fruits with highest TSS (11.11°B) and acidity (0.28 %) while total sugars and ascorbic acid content was recorded maximum in plants pruned during mid-April. These results were parallel to the findings of Ali (2014), Sahar et al., (2014), Sah (2017) and Shiranal (2018).
A significant increase in the TSS and acidity was recorded by Parmar (2019) with increase in the severity of pruning during May while, an inverse trend in the total sugars and Ascorbic acid content. The pruning at 50 % level in May produced fruits with TSS (11.46°B), acidity (0.79%), Ascorbic acid (177.2 mg/100 g) and total sugars (7.37 %) while fruits from 25 % pruned plants were recorded with TSS (11.08°B), acidity (0.76 %), Ascorbic acid (180.2 mg/100 g) and total sugars (7.84 %). These results were in line to the findings of Balamohan (2019), Kumar (2010), Basu (2007), Lal (2002), Kindo (2005) and Sah (2013). This increase in the sugars and ascorbic acid might be due to the impact of high temperatures during flowering, fruit formation and maturation, leading to the degradation of polysaccharides into simple sugars by various metabolic processes, conversion of organic acids into the sugars and the reduction of moisture content as suggested by Lakpathi et al., (2013).

Time of maturity
Singh (2001) observed that the fruits on the plants pruned in the May start getting mature by the mid-November and the first harvest was obtained on the last week of November. The harvesting spam was concentrated from November to January. However fruits on the plants pruned in the June matured in the end of December. Likewise, Singh et al., (2015) recorded fruit maturity 136 days after the pruning in the month of May. Tiwari (2018) subjected the plants of Allahabad Safeda to pruning of 50 % of shoot in the August then harvesting was done in January-February. Similarly when thirteen years old plants of guava were subjected to pruning at 1/3 rd length in the month of April, the fruits got matured by the first October and harvesting was continued till November.
On the basis of the above findings it can be concluded that selection of the optimum time of pruning is a major operation as whole the cropping, flowering, fruit quality and harvesting pattern can be manipulated by choosing the right time of pruning according to the climatic conditions and adaptation of the crop to the climatic conditions.


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Parasitic Contamination of Fresh Vegetables in Open Air Markets of Aksum, Ethiopia

Background: Intestinal parasitic diseases are the foremost causes of illness in humans, responsible for signicant morbidity and mortality. Recently, there have been so many reports related to a foodborne illness linked to consuming fresh vegetables, especially in developing countries. The study was aimed to assess the parasitic contamination level of fresh vegetables collected from open air markets in Aksum town, Tigray region, Ethiopia. Method: A total of 384 samples belonging to six vegetable types were collected randomly from local markets in Aksum town. Samples were washed in saline solution followed by sedimentation for 24 hours.After that the supernatant was decanted leaving the sediment. The sediment was then transferred to a centrifuge tube and centrifuged at 3000 revolution per minute for 5minutes. Finally, the supernatant was decanted and the sediment was examined under light microscope for protozoan cyst, helminthes ova, and larvae Results: The overall prevalence of parasitic contamination was 57.3%. The Cabbage was the most frequently contaminated (79.7%) vegetable succeeded by Spinach (75%), Lettuce (73.4%), Potato (46.9%), Green pepper (37.5%), and tomato (31.2%). Among the parasites detected, Taenia spp. proglottides/egg was the most frequent contaminant with 41.1% prevalence rate followed by Enetrobius vermicularis egg (20.3%), Hookworm spp.(19.5%), Fasciola spp. egg (17.2%), Giardia lamblia cyst (18.5%), Entamoeba spp. cyst(15.9%), Strongyloides stercoralis larva (12.5%),Ascarislumbricoidese gg (9.4%), and Cryptosporidium spp.Chai-square analysis showed that the type of fertilizer used, educational status of the vendors, status of wash before display, and type of vegetable they produce was statistically and signicantly associated with the prevalent parasite forms detected (P < 0.001). Conclusion: This study identied a high prevalence of fresh vegetables contaminated with medically important parasites, thus

Background
Intestinal parasitic diseases are the foremost causes of illness in humans, responsible for signi cantmorbidity and mortality. It is estimated that 3.5 billion people are affected, and 350 million people are ill as a result of these infections, the majority being children 28,29 . Recently, there have been so many reports related to a foodborne illness linked to consuming fresh fruits and vegetables especially in developing countries 30 . These foodborne diseases which lead to serious health and economic problems are mainly caused by Protozoan and Helminthes parasites 1 , 7 . Vegetables are vehicles for the transmission of parasites if contaminated as a result of different associated factors related to planting, collecting, transportation, showcase chain, and improper treatment at domestic 3,4 . Untreated wastewater irrigation, the use of raw manure as fertilizer, improper on-harvest handling and hygienic conditions during preparation are factors that determine the level of contamination 2 .
Studies showed that the numbers of tourists who are visiting Aksum are increasing from time to time, and the number of Hotels and restaurants is increasing to meet the increasing ow of tourists 36 . It is a fact that fresh vegetables are a common diet in Ethiopia particularly in Aksum during the fasting period. It is also the commonly prepared food item in hotels and restaurants.
Hence, the rate of intestinal parasitic infection from the utilization of contaminated vegetables may increase particularly in case the vegetables are inappropriately washed. Thus monitoring and evaluating on the level of parasitic contamination is important to control intestinal parasitic infection in the area. However to our knowledge there is no published data on the status of parasitic contamination of vegetables in and around Aksum. Therefore, this study was conducted to assess the parasitic contamination status of vegetables and associated risk factors in Aksum town, Ethiopia.

Materials And Methods
Study area and period.
The study was carried out in Aksum town, in northern Ethiopia approximately 1010 km from Addis Ababa, Ethiopia's capital. In the town, the majority of populations (88.3%) are Orthodox Christian followers 35 . Since the area in and around Aksum town is suitable to grow fruits and vegetables many farmers are engage in producing a variety of vegetables .The vegetables are supplied from the surrounding villages directly by the farmers. Customers purchase the vegetables either from the farmers or retailers. This study was conducted from March 1 to March 23, 2019, which is the fasting period for the Orthodox Christian followers living in the town.

Sample collection and parasitological Examination
To conduct the study a random sample of 384, 64 from 6 distinct vegetable types namely; Lettuce (Lactuca sativa), Cabbage (Brassica Oleracea), Spinach (Spinacia oleracea), Pepper (Capsicum annum), Potato (Solanumtuberosum) and Tomato (Lycopersicon esculentum) was purchased from the multiple vendors in Aksum markets. Just one vegetable from a single vendor was collected. Each vegetable sample was placed in an isolated plastic sack and labeled with a code and date of collection. Samples were transported to the Biology laboratory of Aksum University for parasitological analysis. The samples were prepared for examination within three hours of collection time.Each vegetable sample was cuted into pieces and weighted to 200 grams.
The chopped samples were washed separately using 1LNormal saline to detach the parasite stages (Ova, cyst, and larvae) of helminths and protozoa. After overnight sedimentation of the washing solution, 15 ml of the sediment was then transferred to a centrifuge tube using a sieve to remove undesirable matters. To concentrate the parasite stages, the tube was centrifuged at 3000 rpm for ve minutes (using Labtech, Model.No.AC 2305 centrifuge). After centrifugation, the top water was decanted carefully leaving about ve milliliters of the sediment. The sediment was then agitated gently to redistribute the parasite stages.
Finally, Lugol (Iodine) stained slides were prepared from each sample sediment and examined under a light microscope for protozoa cyst, helminths ova, and larvae using × 10 and × 40 objectives 15,7 . Besides, Modi ed ZiehlNeelson staining technique was employed to identify Oocysts of Cryptosporidium, Isospora beli and Cyclospora spp. The parasite stages were identi ed based on morphological details as described by Souls 32 .
To identify the factors associated with vegetable contamination, vendors were asked to answer questions about the possible source of contamination for vegetables including; their level of education, the fertilizer type used, and the status of the wash of the vegetables before display and the type of vegetable they produced to identify the factors related to the prevalence of parasites.

Data analysis
The data were analyzed through descriptive prevalence and count statistics. The onesample proportion test was also used to assess the signi cance of vegetable contamination prevalence. To identify the factors related to the prevalence of parasites in vegetables, a Chai-square analysis was used. This is achieved using the statistical software of SPSS version 20 and 95% con dence.
Result From a total of 384 samples screened for parasites,220(57.3%) had at least one and at most seven parasites detected ( Table 1).
Out of 384, 71 vegetable samples were contaminated by only one parasite and 52 vegetable samples were contaminated by two parasites. However, there were only six vegetable samples with seven parasite contamination. The highest prevalence of parasites was detected in cabbage (Brassica oleracea) (79.7%) succeeded by spinach (Spinacia oleracea) (75%), and lettuce (Lactuca sativa) (73.4%). Tomato (Solanum tuberosum) with 31.2% prevalence is the least contaminated vegetable. The onesample proportion test on the contaminated vegetables indicated that the prevalence is statistically signi cant with all Pvalues less than 5%.
As shown in Table 2, the average prevalence of parasites contamination in the variety of vegetable samples was 57.3%. Nine different kinds of parasite forms were detected from the vegetables examined. Its prevalence, however, showed a considerable variation. Particularly, the most frequently observed was Taenia spp. proglottides/egg (41.1%) followed by Enetrobius vermicularis egg (20.3%) and Hookworm spp. egg (19.5%). Besides, Fasciola Spp.egg, Giardia lamblia cyst, and Entamoeba spp.cyst showed considerable decrement namely, 17.2%, 18.5%, and 15.9% respectively. However, Strongyloides stercoralis larva, and Ascaris lumbricoides egg was the least frequently detected parasitic forms with 12.5% and 9.1% prevalence rates, respectively.
The distribution of the parasites on fresh vegetables ( Table 2) also showed that Taenia spp. was the most prevalent of all parasites in all vegetable samples with a prevalence rate of 60.9% in Spinach, 56.2 in Cabbage, 45.3% in Lettuce, 37.5% in Potato, 26.6% in Green pepper, and 20.3% in Tomato.Next to Taenia saginata the second frequently detected parasite in Lettuce was, Hookworm spp. (31.25%) followed by G.lamblia(29.7%).
In Cabbage, G. lamblia, and Hookworm spp. egg/larvae were detected as second and third most prevalent parasites with 26.6% and 25% prevalence rate respectively. In spinach E. vermicularis egg and Fasciola spp.was the second (28.1%) and third frequently detected parasites. On the other hand, G. lamblia cyst was the second prevalent parasite (17.2%) in Green pepper samples succeeded by E. vermicularis, Hook worm spp.which was observed in 12.5% each.
In tomato samples G .lambilia cyst and Cryptosporidium spp.Oocyst.were recorded as the second most prevalent parasite stagess with a 10.9% prevalence each followed by E. histolytica, Hook worm spp., and E.vermicularis which were detected 6% each. The onesample proportion test result showed that all of the parasite detection proportions were found to be statistically signi cant with Pvalues less than 5% ( Table 2).
The chaisquare analysis showed that the type of fertilizers used, the educational status of the vendors, the type of vegetable they produce, and the status of wash before display was found statistically and signi cantly associated with the prevalent parasite contamination (P < 0.001)( Table 3).   The average prevalence is higher compared to some studies reported in Ethiopia 9, 10 , where they reported, 54.4% and 32.41% prevalence rate respectively but lowers than 2 which reported 57.8%. Moreover, compared to the study from Sudan 20 , Libya 21 , Iran 13 , and Jordan 23 , the prevalence rate is highest. But it is lower than the comparable study reported from Ghana 16 , Nigeria 23 , Kenya 33 and Iran 33 that showed prevalence rates of 57.5%, 76%, 75.9%, 79%, and Dahmar city, Yemen's highest rates (100%) 26 .
The geographic position and type and number of samples examined, detection methods, different techniques employed in the laboratory, water types used for irrigation, post-harvesting methods and the type of water used for the wash of vegetables may be attributed to this variation 8,21,20 .
Cabbage (79.7%), followed by spinach (75%) and lettuce (73.4%) were among the most commonly contaminated vegetables of the vegetables examined (Table 1). This difference in contamination rates between the vegetables could be due to shape and sizedifferences 34 . In particular, Cabbage and Spinach have larger and uneven surfaces which make the parasites attach more easily to the surface. This study is consistent with 15 reports that leafy vegetables are contaminated highly in Hanoi, Vietnam in comparison to root and fruit vegetables. Beside, Spinach's rough surface and leaf fold can retain dirt which cannot be removed easily 15 .
The smooth surface of green pepper and tomato, by contrast, can reduce the parasite attachment rate 34  Strongyloides stercoralis larvae were the seventh detected parasite with a prevalence rate of 12.5% ( Table 2).The nding of this study is similar to the previous report from Jimma, Ethiopia (21.9%) 2 . The low prevalence rate was reported in Awash, Ethiopia (6.2%) 10 , and Kogi state, Nigeria (6.06%) 3 . The Highest prevalence rate was reported in two markets in Ghana (36.4%) 16 . Ascaris lumbricoides egg was identi ed as the eighth detected parasite stage (9.1%)contaminating fresh vegetables in this study ( The presen study showed that the type of fertilizer used,the status of wash before display, the typeof vegetable they produce, and the educational status of the vendors was statistically and signi cantly associated with the contamination level of the vegetables (P < 0.001)( Table 3). This nding is consistent with previous reports by 9, 24 who revealed a high prevalence rate of parasites detected from raw manure fertilized vegetables in Mekelle city, Ethiopia, and Amman, Jordan respectively. This result is also in agreement with 34 who showed the high prevalence of vegetables contamination in Tercha town, southern Ethiopia was associated with the type of vegetables they produce, status of wash, and education status of vendors. In this study, the high prevalence of contamination was detected in washed vegetables than unwashed ones. This result is contrary to 7 who reported a high prevalence rate of parasites detected from vegetable samples unwashed before display in Arbaminch, Ethiopia. The high prevalence of washed vegetables in the present study may be due to the use of wastewater to wash the vegetables.

Conclusion
The highest levels of fresh vegetables contaminated with intestinal parasite stages were identi ed. The most frequently detected parasite forms were, in particular, Taeniasaginataproglottides /eggs and Enterobiusvermiculariseggs. The Cabbage was among the most contaminated vegetables screened for parasites. The high incidence of parasite contamination was statistically and signi cantly related to the type of fertilizer used; status of wash before display, vendor education status and vegetable variety they produce. In terms of transmission of the intestinal parasites to men, contaminated fresh vegetables can play an important role in Aksum markets. Therefore it is highly recommended, for instance, to avoid the use of raw manure as fertilizer, and standard washing before using raw vegetables, to reduce the risk of intestinal parasitic infections from contaminated vegetables.

Ethics approval
Ethical clearance was obtained from Aksum Universi thical review board.The purpose of the study was explained to the vendors of vegetables prior to data collection, and verbal consent was obtained from every study participant.


extfieldsofstudy:
Environmental Science
Computer Science

text:
Using Artiﬁcial Neural Networks and Remotely Sensed Data to Evaluate the Relative Importance of Variables for Prediction of Within-Field Corn and Soybean Yields

: Crop yield prediction prior to harvest is important for crop income and insurance projections, and for evaluating food security. Yet, modeling crop yield is challenging because of the complexity of the relationships between crop growth and predictor variables, especially at the ﬁeld scale. In this study, an artiﬁcial neural network (ANN) method was used: (1) to evaluate the relative importance of predictor variables for the prediction of within-ﬁeld corn and soybean end-of-season yield and (2) to evaluate the performance of the ANN models with a minimal optimized variable dataset for their capacity to predict corn and soybean yield over multiple years at the within-ﬁeld level. Several satellite derived vegetation indices (normalized di ﬀ erence vegetation index—NDVI, red edge NDVI and simple ratio—SR) and elevation derived variables (slope, ﬂow accumulation, aspect) were used as crop yield predictor variables, hypothesizing that the di ﬀ erent variables reﬂect di ﬀ erent crop and site conditions. The study identiﬁed the SR index and the slope as the most important predictor variables for both crop types during two training and testing years (2011, 2012). The dates of the most important SR images, however, were di ﬀ erent for the two crop types and corresponded to their critical crop developmental stages (phenology). The relative mean absolute errors were overall smaller for corn compared to soybean: all of the 2011 corn study ﬁelds had errors below 10%; 75% of the ﬁelds had errors below 10% in 2012. The errors were more variable for soybean. In 2011, 37% of the ﬁelds had errors below 10%, while in 2012, 100% of the ﬁelds had errors below 20%. The results are promising and can provide yield estimates at the farm level, which could be useful in reﬁning broader scale (e.g., county, region) yield projections. on a tile-drained for national

Introduction
The ability to predict crop yield prior to harvest is important for crop income and insurance projections, as well as for evaluating food security at local to global scales. Furthermore, models that spatially predict within-field yields can be used to characterize the environmental and management

Study Area
The study was conducted on a tile-drained experimental watershed in eastern Ontario, Canada (45.26 N, 75.18 W; Figure 1). Corn (Zea mays L.) and soybean (Glycine max L.) are the two dominant crops in this region of Canada and in 2016, Ontario accounted for~60% of national grain corn acreage and~50% of national soybean acreage [20,21]. Corn and soybean are the two primary livestock and cash crops grown in the experimental watersheds. These crops are typically planted in May and harvested in September to October. In the study area, both crops were planted at the same time every year. Crop row spacing was around 50 cm for soybean and 60 cm for corn. Soybean and corn plant densities were generally 354,000 and 64,200 plants/ha, respectively. Fall moldboard plowing and spring cultivation using chisel-style implements were the main tillage practices employed on the site. Fertilizer generally consisted of the broadcast application of granular urea prior to planting and a granular starter application. Fertilizer rates were generally~170 kg N/ha for corn and~3 kg N/ha for soybean, depending on soil and crop requirements. More details about the study area and agriculture practices-including tiling systems-are described by Sunohara et al. [22].
Remote Sens. 2020, 12, x FOR PEER REVIEW 3 of 33 The study was conducted on a tile-drained experimental watershed in eastern Ontario, Canada (45.26 N, 75.18 W; Figure 1). Corn (Zea mays L.) and soybean (Glycine max L.) are the two dominant crops in this region of Canada and in 2016, Ontario accounted for ~60% of national grain corn acreage and ~50% of national soybean acreage [20,21]. Corn and soybean are the two primary livestock and cash crops grown in the experimental watersheds. These crops are typically planted in May and harvested in September to October. In the study area, both crops were planted at the same time every year. Crop row spacing was around 50 cm for soybean and 60 cm for corn. Soybean and corn plant densities were generally 354,000 and 64,200 plants/ha, respectively. Fall moldboard plowing and spring cultivation using chisel-style implements were the main tillage practices employed on the site. Fertilizer generally consisted of the broadcast application of granular urea prior to planting and a granular starter application. Fertilizer rates were generally ~170 kg N/ha for corn and ~3 kg N/ha for soybean, depending on soil and crop requirements. More details about the study area and agriculture practices-including tiling systems-are described by Sunohara et al. [22].

Field Data
Corn and soybean yield data were obtained from cooperating farmers for the years 2011, 2012 and 2016, matching the available satellite images. The data were collected during harvest using a John Deere combine harvester equipped with a GREENSTAR™ Yield Monitor System (John Deere, Moline, Illinois). Post-processing was done using Field Operations Viewer (version 2003.11.01; MapShots, San Francisco, California) and Yield Editor (Version 2.0.7; USDA-ARS Cropping Systems and Water Quality Research, Columbia, Missouri) and resulted in a dense point dataset with yields expressed in kg/ha. Points with yield values equal to the mean ± three times the standard deviation were considered outliers and eliminated. The summary statistics of corn and soybean yields for 2011, 2012 and 2016 are given in Table 1 and Figure 2a,b. The field yield data was used to train and test the ANN models.

Field Data
Corn and soybean yield data were obtained from cooperating farmers for the years 2011, 2012 and 2016, matching the available satellite images. The data were collected during harvest using a John Deere combine harvester equipped with a GREENSTAR™ Yield Monitor System (John Deere, Moline, Illinois). Post-processing was done using Field Operations Viewer (version 2003.11.01; MapShots, San Francisco, California) and Yield Editor (Version 2.0.7; USDA-ARS Cropping Systems and Water Quality Research, Columbia, Missouri) and resulted in a dense point dataset with yields expressed in kg/ha. Points with yield values equal to the mean ± three times the standard deviation were considered outliers and eliminated. The summary statistics of corn and soybean yields for 2011, 2012 and 2016 are given in Table 1 and Figure 2a,b. The field yield data was used to train and test the ANN models.

LIDAR and Topographic Derivatives
Based on the reported importance of micro-topographic attributes on crop growth properties [18,19,23], we included Lidar-derived variables to represent the micro-topography of the fields over this low topographic relief study area. Lidar data were collected over the experimental site by GeoDigital (Sandy Springs, GA) in November 2011, and a 1-m spatial resolution digital terrain elevation dataset was derived from the Lidar point cloud using LAStools (rapidlasso GmbH, Gilching, Germany). Slope (Figure 3), aspect (not shown) and flow accumulation ( Figure 4) were derived from the elevation dataset using the spatial analyst ArcGIS toolset within Advangeo ® Prediction (v 2.0 for ArcGIS 10.1; Beak Consultants GmbH, Freiberg, Germany). elevation dataset was derived from the Lidar point cloud using LAStools (rapidlasso GmbH, Gilching, Germany). Slope (Figure 3), aspect (not shown) and flow accumulation ( Figure 4) were derived from the elevation dataset using the spatial analyst ArcGIS toolset within Advangeo ® Prediction (v 2.0 for ArcGIS 10.1; Beak Consultants GmbH, Freiberg, Germany).  The flow accumulation variable was calculated as the number of cells that flowed into each downslope cell ( Figure 4). Higher relative flow accumulation numbers indicate areas of concentrated flow and can be used to determine stream channels, swales or depressions. Cells with a value of 0 indicate local topographic highs with no upstream catchment area, such as knolls and ridges (see https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst). The flow accumulation variable was calculated as the number of cells that flowed into each downslope cell (Figure 4). Higher relative flow accumulation numbers indicate areas of concentrated flow and can be used to determine stream channels, swales or depressions. Cells with a value of 0 indicate local topographic highs with no upstream catchment area, such as knolls and ridges (see https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst).

Optical Remote Sensing Derived Data
A total of 18 RapidEye images (5 m spatial resolution) were collected over the three study years ( Table 2). The RapidEye constellation consists of five satellites that are equipped with identical sensors and located within the same orbital plane. This setting allows the constellation to acquire high-resolution, large area image data on a daily basis (PlanetLabs Inc., https://www.planet.com/).  All images were georeferenced to the universal transverse Mercator coordinate system UTM 18N (with the world geodetic system-WGS-84 ellipsoid) and atmospherically corrected using ATCOR2 (PCI Geomatica v10.3, PCI Geomatics enterprises Inc, Ontario, Canada).
Considering the plethora of static and transient factors that can affect end-of-season yield in a field, we chose a set of satellite-derived vegetation and site spectral indices (Table 3) to reflect plant nitrogen content, crop health (normalized difference vegetation index-NDVI and red edge NDVI-NDVIre) and crop canopy structure (Simple Ratio-R). Table 3. Summary of spectral indices and their respective equations and references.

Spectral Indices Equation Reference
Normalized difference vegetation index (NDVI)

ANN Model
A review study on MLAs listed ANNs as one of the most successful MLAs for yield prediction, together with other methods like support vector regression, M5 prime regression trees, k-nearest neighbor and deep learning [24]. Our study used a parsimony approach to explore the importance of predictor variables and the performance of ANN models with a minimal variable set, in predicting crop yield within fields.
All predictor variables were clipped to the study area and the final spatial resolution of the model was set to the spatial resolution of the spectral indices from RapidEye (5 m). The 5-m resolution was considered via long-term field observations [22], to adequately capture most important micro-topographic features at the study site. A summary of all predictor variables used in the ANN analysis for each year is given in Table 4. We used the Advangeo ® Prediction Software, which uses a multilayer perceptron (MLP) ANN method to predict either classes or numeric values. Advangeo ® Prediction comes preloaded with defaults that have proven effective for many different applications [25]. In this study, we parsimoniously applied default parameters. An overview of the model is given in Figure 5. The input layer receives the controlling parameters and the neurons of the hidden layer(s) and the output layer processes the weighted signals from the neurons of its previous layer and calculates an output value applying an activation function. The default parameters were: network topology (fully connected input layer and one hidden layer with three hidden neurons), activation function (for hidden and output layers: sigmoid with a steepness of 0.5), learning algorithm (derivative of back propagation algorithm), weight initialization ("initialize" algorithm of Widrow and Nguyen) and predefined stop parameters (number of epochs = 100 and mean square error -MSE border = 0.001). The input data consisted of different spatial layers of vegetation indices (SR, NDVI, NDVIre), Lidar derivatives (slope, aspect, flow accumulation), and crop type (corn, soybean). All the data were normalized to a scale of 0 to 1 (linear scaling) for use in the ANN model. The results were scaled back to the original data scales for error analysis.
The ANN model was trained and tested using 2011 data (fields were divided into 50% training vs. 50% testing), after which it was applied to 2012 and 2016 data. The same was done for 2012, where the model was trained and tested using 2012 data, and applied to 2011 and 2016 data. For each training year (2011, 2012), three main model scenarios were explored: (1) the network was trained with yield data from corn and soybean combined for the prediction of the within-field yield of the two crops, (2) the network was trained with soybean yield data only for the prediction of within-field soybean yield and (3) the network was trained with corn yield data only for the prediction of within-field corn yield (Table 5). The input layer receives the controlling parameters and the neurons of the hidden layer(s) and the output layer processes the weighted signals from the neurons of its previous layer and calculates an output value applying an activation function. The default parameters were: network topology (fully connected input layer and one hidden layer with three hidden neurons), activation function (for hidden and output layers: sigmoid with a steepness of 0.5), learning algorithm (derivative of back propagation algorithm), weight initialization ("initialize" algorithm of Widrow and Nguyen) and predefined stop parameters (number of epochs = 100 and mean square error − MSE border = 0.001). The input data consisted of different spatial layers of vegetation indices (SR, NDVI, NDVIre), Lidar derivatives (slope, aspect, flow accumulation), and crop type (corn, soybean). All the data were normalized to a scale of 0 to 1 (linear scaling) for use in the ANN model. The results were scaled back to the original data scales for error analysis.
The ANN model was trained and tested using 2011 data (fields were divided into 50% training vs. 50% testing), after which it was applied to 2012 and 2016 data. The same was done for 2012, where the model was trained and tested using 2012 data, and applied to 2011 and 2016 data. For each training year (2011, 2012), three main model scenarios were explored: (1) the network was trained with yield data from corn and soybean combined for the prediction of the within-field yield of the two crops, (2) the network was trained with soybean yield data only for the prediction of within-field soybean yield and (3) the network was trained with corn yield data only for the prediction of within-field corn yield (Table 5). Table 5. Overview of the model scenarios and number of pixels and fields used in error analysis 1 .

Analysis
The relative importance of the predictor variables for each modeling scenario was determined through connection weights and Garson's algorithm [26]. Both methods are available in the Advangeo ® Prediction Software. For the model performance analysis, we used ANN models based on the predictor variables with the highest connection weights.
The performance of the models was evaluated by calculating the relative mean absolute error (RMAE) for fields using the following equations: where MAE field is the Mean Absolute Error of the field in kg/ha. Predicted Yield is the result from the ANN model and Observed Yield is yield data obtained from farmers. n is the number of pixels within a field.
where RMAE field is in % and Mean Yield Field is the average observed yield of the field, in kg/ha. The RMAE was calculated for each field based on its number of pixels (Table 5). For each model, the RMAEs were then summarized to present the number of fields within each RMAE range.
The RMAEs were compared with the coefficient of variation (CV) of the observed yield to assess the magnitude of the errors. The CV was calculated as ((Standard deviation/average) * 100 Spearman's rank rho correlation coefficient was also reported to describe the relationship between predicted and observed crop yield. The correlation analysis was done for each model based on the number of fields.

Relative Importance of Predictor Variables
Over 20 predictor variables (Table 6) were evaluated for their relevant importance for field-scale crop yield prediction. The results showed two variables with consistently high connection weights and Garson values for both crops: the SR indices and the slope ( Table 6, Appendix A: Table A1).
The dates of the images, however, varied between crop types and between years. In the wetter year (2011), for example, the corn ANN model was optimized with only two relatively early SR images: one from June 27 (~V6, plant has six leaf collars, vegetative development stage) and one from July 23 (~VT-R1, Table 7). The soybean model used two late-season images from August (R5-R6, Table 7), one image from June 27 (~V3, plant has three leaf collars, vegetative development stage) and the slope data. In the drier year (2012: cumulative May-August rainfall of~205 mm), the images were similar for the two crops with the highest weights for July images (July 11: R1 for soybean; July 18: V11 for corn). Late season images were also used in the model: August 18 for both crops (R5-R6 for soybean, R4-R5 for corn) and August 04 for corn (~R3). The slope and flow accumulation were important for soybean (Table 6, Appendix A: Table A1). Table 6. Overview of the input predictor variables and the most important predictor variables according to Garson's algorithm and connection weights. Negative and positive signs for the most important predictor variables indicate the direction of their relationship with yield.

Yield Training Data Input Predictor Variables Most Important Predictor Variables Used in Final Optimized Models
Corn and Soybean (  The difference in image dates between the two crops is related to critical development stages of each crop type (Table 7). Corn is susceptible to water and nutrient deficiencies around the V12 and R1 stages [27], stages that closely match the image dates from both 2011 (July 23) and 2012 (July). Soybean is more susceptible to water and nutrient deficiencies around the reproductive stages R1, R4 and R6 [27]. These stages also match the image dates closely in 2011 (August 12 and 19) and 2012 (August 18). Both Garson's algorithm and the connection weights showed the highest weights for images acquired close to the critical crop development stages given in Table 7.

Predicted within-Field Corn and Soybean Yields
Crop-specific models that were trained and tested in the same year (PM004, PM006, PM019 and PM021 in Table 5, Figure 6a,d, Figure 7a-c) performed best according to the error analysis. In 2011, 100% of the corn fields had errors below 10%, while 42% of the soybean fields had errors below 15% (58% of the fields had errors > 20%). The observed soybean yield had high variability in 2011 where the average CV was 29% and about 15% of the fields had CV values below 10% (85% of the fields had CV > 20%, Figure 2). Observed corn yield variability was low in 2011 (CV < 10%). It is likely that the prediction error patterns reflected the observed yield variability in 2011. In 2012, both crop models performed well, as 75% of the corn fields had errors below 10% (87.5% of the corn fields had errors below 20%) and 100% of the soybean fields had errors below 20%.   only for crop-specific models that were trained and tested in the same year). Performance results were consistent with approaches performed at the field [12] and regional scales [14]. Fiezal et al. [12] reported R 2 values between 0.05 and 0.77, with the best results for an ANN model to estimate corn yield based on the red wavelength reflectance values (16 images between May and September). Kaul et al. [14] evaluated the performance of ANN models for the prediction of yield at the state, regional and local level and reported R 2 values between 0.37 and 0.99 for corn, and between 0.66 and 0.91 for soybean. At these larger scales, Kaul et al. [14] identified the importance of weekly rainfall data. Rainfall can be considered uniform over the fields in our study area. At this scale, our results showed the importance of variables related to the canopy and surface structure, the SR index and slope, respectively. These structural variables contain implicit information on spatial patterns of soil, water and nutrient distributions [23], which can ultimately affect crop yield. In 2011, for example, the ANN models underestimated soybean yield in most of the eastern fields of the study area. The eastern fields have more variability in their micro-topography, as shown in the flow accumulation and slope maps (Figures 3 and 4). The year 2011 was wetter than 2012 and 2016, which means that there was a greater mix of waterlogged and dry areas within the eastern fields (based on their higher variability in micro-topography). This may have affected the yield, but was not captured by the ANN models. The error analysis showed low performance of the ANN models that were trained on both crop types combined (Models PM001 and PM015 in Table 5). Most of the test fields had errors above 20% for soybean and above 10% for corn ( Figure 6). Models that were trained and tested in 2011 or 2012 were also used with predictor variables from 2011, 2012 or 2016. The prediction accuracy of these models was low, with RMAEs above 40% for most of the fields in all models ( Figure 6; Appendix A: Table A1).
The overall weaker performance of the ANN models that were trained and tested in one year, and then applied on other years could be attributed to: (i) differences in phenological development stages: the final models were developed based on images of specific dates. Even though the images of the application years mostly matched the model dates closely, actual crop development stages may not necessarily match. (ii) differences in image dates: 2016 image dates, for example, differed by up to 24 days from the image dates from the trained models, the largest errors were also observed in this year. (iii) differences in environmental and weather conditions: 2012, for example, was much drier than 2011.
Previous research [14,28] showed the importance of rainfall data during the growing season. Kaul et al. [14] suggested including weekly rainfall data as predictor variables for corn and soybean yield prediction, as monthly data were found inadequate for effective crop yield prediction. For field-scale studies, future studies can include the land surface water index (LSWI) or radar-derived soil moisture as a proxy for canopy and surface wetness.
Overall, the results indicate that the ability to predict within-field yield accurately during a growing season will partially depend on the ability of predictor variables to reflect: 1.
the crop type: future studies should explore potential variables for the development of crop-independent models or create crop-specific models.

2.
the canopy and surface wetness: information about water availability should be included (weekly data if possible). 3.
critical crop development stages: satellite images that match the critical crop stages should be included, which means that the phenological stages should be measured or estimated. 4.
field micro-topography: slope was an important indicator overall; future research should explore the inclusion of micro-topography metrics derived from slope data. 5.
differences in planting, spacing and management practices: the transferability of the models will depend on the homogeneity of crop management factors.

Conclusions
This study analyzed the relative importance of remotely sensed predictor variables for the estimation of crop yield and evaluated the performance of ANN MLP models for within-field corn and soybean yield predictions. Knowing which variables affect the end-of-season yield variability within a field is important for precision agriculture applications and decisions on agriculture practices. As most current studies within this domain have focused on large-scale approaches, this study brings information that contributes to methodological advancements and decision making at the farm level.
The results showed a greater importance of the structural variables (SR, slope, flow accumulation) and crop phenology for the prediction of end-of-season yield within corn and soybean fields.
When the models were trained and tested in the same year, the corn-specific model performed better than the soybean model: most of the corn fields had RMAEs lower than 15% for both years. For soybean, 42% of the fields had RMAEs lower than 20% in 2011 and 100% of the fields had RMAEs below 20% in 2012. The accuracy of the models reflected the measured yield variability: fields with a high measured yield variability also had large model errors.
Appendix A   Table A1. Overview of the optimized prediction models and the predictor variables used in testing and application years.


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Assessment of Cytological Effects of Food Preservative Potassium Metabisulphite to Allium cepa

With the unprecedented development of the food industry, food preservatives have gained a leading role in food processing. In this study, investigations were carried out to assess the cytological effects of potassium metabisulphite (PMB) to onion (Allium cepa), one of the most used plants for determining the cytotoxic and genotoxic effects of different chemicals. Meristematic roots of A. cepa were treated with PMB solutions in different concentrations, ranging from 10 mg/l to 35 mg/l for 6, 12 and 24 h alongside an untreated control. The quantified parameters for the different concentrations of the PMB were mitotic index (MI %), mitosis phases index (PI %) and total abnormalities index (TAI %) meaning chromosomal aberrations and nuclear abnormalities. The results indicated that PMB reduced MI in A. cepa with increasing the concentrations and time exposure as compared with the untreated control. Thus, at concentration of 10 35 mg/l PMB, these values were reduced from 8.34% to 4.81% (6 h); 7.18% to 2.35% (12 h) and 4.12 to 1.43 (24 h). Also, the TAI value increased with increasing PMB concentrations and time: 0.51% to 9.98% (6 h), 7.11% to 19.84% (12 h) and 17.79 to 41.21 (24 h). The types of abnormalities induced by PMB in A. cepa meristematic cells were micronucleus, C-metaphase, star anaphase, stickiness, laggards, fragments, binucleated cells and pulverised nucleus. These alarming findings indicate the cytotoxic and genotoxic effect of PMB to A. cepa and suggest necessity to adopt more natural alternatives for food preservation in the future.

Introduction
For centuries before the medieval period, and for centuries afterward, human beings in all parts of the world used a variety of methods to preserve foods for later consumption [1]. Due to the very high demand for processed food products, the demand for substances to facilitate the processing of the raw material, the preservation over a longer period of the food products has increased. At the beginning of the 19th century, a rapid rise in the use of chemical additives has been observed [2].
Food additives, marked with E letter-due to the alignment with the European Union norms [3] can be identified in most processed food products. They are used in all branches of the food industry. Regulation (EC) No 1169/2011 of the European Parliament and of the Council provide food information that shall pursue a high level of protection of consumers' health and interests by providing a basis for final consumers to make informed choices and to make safe use of food, with particular regard to health, economic, environmental, social and ethical considerations [3].
The usefulness of food additives is that they maintain the quality and safety of the products for a longer period of time, maintain or improve the taste of the products, ensure the control of the acidity and alkalinity of the products, maintain the consistency of the products, the aroma or colour of the food, etc. [4].
Preservatives, one of the categories of food additives, protect food by slowing down the damage caused by bacteria, fungi, yeast and air. A preservative is defined as any substance which is capable of inhibiting, retarding, or arresting, the growth of micro-organisms, of any deterioration of food due to micro-organisms, or of masking the evidence of any such deterioration [5].
Food safety is a global problem, and a large number of consumers worldwide face a variety of food safety risks each year [6]. Scientific food management is an important tool to avoid severe health issues [7] [8] [9]. Food safety affects consumer food choice in ways that are different from other dimensions of quality [10]. The use of chemical preservatives is regulated by maximum permitted levels. These amounts vary between countries. When the food additives are given to organisms in excessive amounts, they may cause toxic reactions [11] [12] [13].
Certain food additives may have harmful effects and may lead to headache, nausea, weakness, difficulty in breathing [14] or gastrointestinal, dermatologic and neurologic adverse reactions [15] [16].
Potassium metabisulphite (PMB) also known as potassium pyrosulphite or potassium disulphite is a white crystalline powder with a sulphur odour and is chemically very similar to sodium metabisulphite, with which it is sometimes used interchangeably. The use of PMB is not universally accepted [17] but is widely used in the wine industry, and in food it is found in many types of biscuits and cakes. It can also be found in fast food and pickles, dried fruits, etc.
This study aimed to estimate the cytotoxic and genotoxic potential of PMB

Microscopic Preparations
After each exposure time, the meristematic roots were cut with sharp blade, fixed in Carnoy fixative (ethyl alcohol:glacial acetic acid 3:1) and hydrolysed in 1N hydrochloric acid (HCl) for 5 min. The roots tips were stained using 5 ml of Schiff Reagent for 45 min at room temperature and then were crushed in drop of 2% acetocarmine. The microscopic preparations were performed by squash technique. For this purpose, the slide was placed and coverslip on a double layer of paper towel, then paper was folded over the coverslip and squash down on the coverslip with a strong vertical pressure, using thumb. The pressure was applied to squash the root tip into a single cell layer. Five replicates were made for each concentration.
The microscopic slides were examined at 1000× magnification.

Statistical Analyses
Minimum 1000 cells were scored in the roots tip for each concentration. The

Mitotic Index and Mitosis Phases Index
Treatment of the meristematic roots of A. cepa with PMB showed a strong mitodepressive effect even in lower concentrations ( Table 1). The

Cytological Abnormalities
Genotoxic effect of PMB to A. cepa was manifested by the appearance of several types of cytological abnormalities (chromosomal aberrations and nuclear anomalies) ( Table 2). Their percentages increased as the concentration of the PMB and the exposure time increased when compared with the control. The main types of abnormalities induced by PMB in A. cepa meristematic cells were micronucleus, C-metaphase, star anaphase, stickiness, laggards, fragments, binucleated cells and pulverised nucleus, aspects highlighted on the microscopic images through arrows. Thus, changes that occur at the chromosome or nuclear level are indicated ( Figure 1). The most common type of abnormality identified was micronucleus followed by stickiness.
E. Rosculete et al.   Figure 1. Some cytological abnormalities identified in meristematic cells of A. cepa exposed to different concentration of PBM food preservative: cells with C-metaphase indicated by arrow (a) (b); micronucleus indicated by arrows (c) (d); sticky metaphase (bigger arrow) with lost chromosomes (smaller arrow) (e); laggard (arrow above) and fragments (arrow below) in disturbed metaphase (f); cell with star anaphase indicated by arrow (g); binucleated cell indicated by arrow (h) and pulverised nucleus indicated by arrows (i).

Discussion
In the present investigation, we studied the cytotoxic and genotoxic effects of E. Rosculete et al.
PMB food preservative in A. cepa root-tips using MI (%) and TAI (%) as the toxicological endpoints. The mitotic index decreased considerably in the different treatments. It showed the mitodepressive activity of PMB. Cytological studies revealed statistically significant (p < 0.05) inhibition in mitotic index with an increase in concentration of the PMB when compared with the control. These findings are in agreement with other studies which show the mitodepressive effect of different preservatives in A. cepa cells [11] [24]. This could be due to a movement made slower of cells from synthesis phase to mitosis phase of the cell cycle as a result of PMB exposure and its potential to interfere with intracellular components of A. cepa. As some authors suggest, decrease in mitotic index might be due to inhibition of DNA synthesis [25] or a blocking in the G2-phase of the cell cycle, preventing the cell from entering mitosis [24].
MI is an acceptable measure of cytotoxicity in all living organisms [26] [27]. The cytotoxicity level can be determined by the decreased rate of MI. In this respect, a decrease of MI below 22% in comparison to controls can have a lethal impact on the organism [28], while a decrease below 50% usually has sublethal effects [29]. In present study, the percentage of anaphase and telophase was decreased when compared with the control groups after the all PMB concentrations and exposure time. This may attribute to the blocking of cell division by the PMB food preservative at the end of the metaphase stage and suggests delaying cell cycle.
Generally speaking, for genotoxicity assessment the magnitude of genetic risk to organisms by environmental agents/ chemicals under a specified level of exposure must be determined [30]. Some authors consider that the plant system can detect the genotoxicity and cytotoxicity more quickly than animal bioassays being optimal to environmental monitoring in general [31].
In our study, cytotoxic and genotoxic effects of PMB on A. cepa meristematic roots were indicated at all tested concentrations and all exposure times. All PMB treatments significantly decreased the mitotic activity and increased the frequency of total abnormalities (chromosomal aberrations and nuclear anomalies). In terms of the frequency, the most identified abnormalities were micronuclei in cell interphase. Hamedo and Abdelmigid (2009) considered that micronucleus formation is one of the most economical and most effective ways in determining genotoxicity of different chemicals [32]. Also, the micronucleus assay is recognized as one of the most successful and reliable assays for genotoxic carcinogens that act by causing genetic damage [33] [34] or the most prevalent biomarker of chromosomal defects induced by genotoxic agents [35]. An individual predisposition to diseases is correlated with micronucleus incidence [36] [37]. A variety of genotoxic agents may induce micronucleus formation leading to cell death, genomic instability, or cancer development [35] [38]. A micronucleus may arise from a whole lagging chromosome or an acentric chromosome fragment detaching from a chromosome after breakage which does not integrate in the daughter nuclei [39]. Pollution of the environment with various chemical American Journal of Plant Sciences agents increases the frequency of micronucleus formation in both plants and animal cells, increasing the ecotoxicological risk [40] [41]. In accordance with Gömürgen (2005), such genetic damages like micronuleus may have clastogenic effect of their inducers [23]. Micronuclei were considered as an indication of a true mutation effect [42] thus, the high percentage of the micronuclei induced by PMB on A. cepa cells suggest their mutagenic effect. Therefore, the appearance of micronuclei in meristematic cells of A. cepa may suggest caution in the consumption of foods which containing PMG food preservative.
Chromosome stickiness may result from entanglement of chromatin fibers, which fall to condense properly in preparation for cell division [43] [44] [45]. Stickiness can cause serious changes in nucleic acids physico-chemical properties or chromatin condensation of the nucleus [46]. Also, sticky chromosomes can cause loss of genetic material and subsequently, the cell-division process occurs irregularly, with some chromosomes not adhering to the assembled chromosomal complex and being lost during the cell cycle. Laggard's chromosomes induction is due to delayed termination of chromosome end or because of failure of chromosomal movement and attributed to the spindle apparatus [46] [47].
A growing number of consumers are aware of the potential negative health effects of chemical preservatives, which has prompted the food industry to find natural products used and developed as alternatives [48]. Concerns about synthetic preservatives have caused increasing interest in natural antioxidants [49]. Some study showed that natural antimicrobial extracts from hot peppers are rich in antioxidant and antiradical compounds [49]. Also, the mixture of natural antibacterial extracts could be used as food treatment to extend the shelf-life of pre-cut carrots by two days without affecting their sensory properties [2].
A. cepa is one of leading vegetable crops in the world and the allelopathic potential and biological characteristics of this species have been deeply investigated. Allelopathy refers to the beneficial or harmful effects of one plant on another plant through releasing of biochemical from plant parts [50] [51] [52] [53]. Plants have the ability to synthesize chemical compounds (active principles), their active properties being correlated with the biochemical mechanisms of human metabolism [54] and this must be taken into account in the future when processing and preserving food. Depending on plant type and bacterial strain, essential oil derivatives could have a high antibacterial activity and they can behave as natural preservatives [2].
The processing of some foods like the fresh-cut fruits includes key preservation techniques, namely the use of packaging with modified atmosphere that can be improved with the addition of antimicrobial and antioxidant agents allowing, in combination with low storage temperature, an extended shelf life, at the same time preserving their nutritional and organoleptic proprieties [55]. From this point of view, the use of edible coatings supplemented with essential oils can constitute in the future a promising approach to improve both preservation and safety of fresh-cut fruits [55].
As some authors stated, if food preservatives is judiciously used, they are an important group of substances that help to raise the keeping quality, the nutritional value and the aesthetic qualities of foods [56] [57]. On the other hand, technological processes that are used for food preservation may affect the functional, nutritional, and biological properties of food proteins [58]. In the future, new preservation technologies will have the purpose not just working to increase longevity; they will work also functioning to sustain the same qualities of the food that make it desirable in the first place [55]. The physical characteristics and chemical composition of the food will no longer be compromised during the preservation process [55].

Conclusion
All present results revealed the predominance cytotoxic and genotoxic effect of PMB on A. cepa, which were concentrated and time-dependent. Our study about PMB food preservative showed that it cannot assess the actual level of risk for consumer's health. However, disturbed mitosis and the large number of cytological abnormalities identified in meristematic cells of A. cepa exposed to action of this food preservative can be a warning sign and suggest necessity to adopt more natural alternatives for food preservation in the future. We intend to test other usual chemical preservatives in terms of their cytological effects on meristematic cells of plants.