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Effects of a yeast cell wall product on the performance of broiler chickens and PGC-1α, TLR4, IL-10 and PPARγ genes expression

Abstract The current study was conducted to evaluate the effects of a yeast cell wall product (YCWP) on the performance, the population of Lactobacillus and coliform bacteria and PGC-1α, TLR4, IL-10 and PPARγ genes expression in the intestine of broilers. Dietary treatments were as follows: basal diet (control), control diet + 0.3 g/kg colistin or 0.15, 0.3, 0.45 and 0.6 g/kg YCWP. Feed intake and feed conversion ratio were lower in the YCWP-fed birds compared to the control. Colistin and YCWP fed birds had higher Lactobacillus and lower coliform bacteria counts than control birds (p < .05). Total and IgM antibody against sheep red blood cells was linearly increased by adding the YCWP to the diet (p < .05). Adding the YCWP to the diet linearly increased the caecal concentration of propionate (p < .05). Orthogonal and polynomial contrasts showed that the YCWP significantly improved the villus height, crypt depth, villus height: crypt depth, villus width, and villus surface area (p < .05). The lower concentrations of the YCWP (0.15 and 0.30 g/kg) increased the PPARγ transcription (p < .05), while its upper concentration (0.45 and 0.6 g/kg of diet) did not change the PPARγ gene expression. PGC-1α transcription was increased with 0.15 g/kg of the YCWP supplementation (p < .05). Dietary inclusion of the YCWP decreased the TLR4 gene expression (p < .05) and increased the downstream IL-10 cytokine (p < .05). In conclusion, YCWP improved the broiler’s performance by modifying the gut microflora. Also, YCWP supplementation had an immune regulatory effect via local and systemic responses involving TLR4, IL-10, and PPARγ. Highlights Adding YCWP to the broilers diet did not significantly affect body weight gain, but reduced FCR. Adding 0.45 and 0.6 g of YCWP to the diet, increased the Lactobacillus number and reduced the coliform bacteria number in the ileum. YCWP supplementation increased the production of antibodies (total, IgG, and IgM) against SRBC and developed lymphatic organs. Adding 0.6 g of YCWP to the diet increased villus height and villus surface area and reduced the crypt depth. YCWP supplement had an immune regulatory effect via local and systemic responses involving TLR4, IL-10, and PPARγ.

Introduction
During the last 30 years, scientists have attempted to develop strategies to enhance livestock production, reduce production costs and mitigate the harmful environmental effects. The increase in pathogenic bacteria in the birds' gastrointestinal tract (GIT) could impair growth and feed utilisation. Such pathogenic bacteria may attach to the various parts of the GIT in the poultry, causing considerable damage to the use of antibiotics in commercial production, leading to more research to find the compounds capable of improving the GIT health and immune function in the poultry (Ott et al. 2018).
Today, the use of prebiotics in poultry feeds is widely considered to increase the bird's performance. Prebiotics are defined as the indigestible feeds that can stimulate the growth of one or a limited number of bacteria in the gut, with positive effects on the host (Gadde et al. 2017). The most common prebiotics used in poultry feeds are yeast cell wall products (YCWPs) such as inulin, fructooligosaccharides (FOSs), mannan-oligosaccharides (MOSs), b-glucan, xylooligosaccharides, and galactooligosaccharides (GOSs) (Ricke 2018). For example, MOSs are oligomers made of mannose linked together by b-1, 4 glycoside bonds; they are found in the cell wall of Saccharomyces cerevisiae (Pourabedin et al. 2014). Mannan-oligosaccharides are usually derived from yeast cells, which modulate the immune system and eliminate pathogenic bacteria from the GIT. Mannans present in MOSs can bind to mannose-specific type-I fimbriae in gram-negative bacteria (such as E. coli, Salmonella and Clostridium), preventing them from attaching and colonising the intestinal epithelium (Suresh et al. 2018).
To find out how YCWP is working on the surface of the GIT cells, the present study investigated the expression of genes that are possibly involved in the regulation of metabolism and reaction to the pathogenic bacteria and lipopolysaccharides (LPS) in the epithelial cells under the YCWP treatment. Peroxisome proliferator-activated receptor-c coactivator (PGC-1a) is a transcriptional coactivator characterised as a regulator of mitochondrial biogenesis and function, including oxidative phosphorylation and detoxification of the reactive oxygen species. PGC-1a is highly expressed in the tissues with high-energy demands (Rius-P erez et al. 2020). PGC-1a also regulates the expression of mitochondrial antioxidant genes, which alleviate oxidative injuries and mitochondrial dysfunctions. Dysregulation of PGC-1a exacerbates the inflammatory response, which is commonly accompanied by a metabolic disorder. Following the inflammation, the small levels of PGC-1a downregulate mitochondrial antioxidant gene expression, persuade oxidative stress and promote the nuclear factor kappa B activation. Toll-like receptors (TLRs) is one of the vital plasma membrane receptors that could create connections between luminal pathogens and genes expression of intestinal epithelial cells involved in metabolism such as PGC-1a . TLRs are a class of proteins that play a crucial role in the innate immune system, serving as the first line of the host's response against besetting microbial pathogens (Luo et al. 2014). Various factors can affect TLRs expression and modulate TLRs signalling pathways, leading to the release of endogenous molecules serving as TLR ligands and the responsiveness of innate and adaptive immune systems to combat pathogenic bacteria (Arslan et al. 2011;Napper et al. 2015;Paul et al. 2015). Previous studies demonstrated that TLR4 transmits LPS signals (Yang et al. 1998). However, rearing conditions could modulate the inhibition of the GIT microflora of broilers for the recognition of pathogen-associated molecular patterns to occur by TLRs, enabling the activation of the immune cells in the GIT (Li et al. 2020). Besides, resistance to the pathogens has been shown to be closely correlated with the up-regulation of TLR4, as well as various chemokines and cytokines (Heni et al. 2020;Takizawa et al. 2020). Therefore, PGC-1a acts as a necessary factor connecting metabolic modifications and inflammatory pathways. It is, therefore, an attractive target gene that has considerable benefits for the regulation of the GIT homeostasis under the effect of bacterial populations (Rius-P erez et al. 2020). There is, however, limited information regarding the effects of YCWP on the expression of genes involved in the immune responses of broiler chickens, including peroxisome proliferator-activated receptor c (PPARc), PGC1a, TLR4 and interleukin-10 (IL-10). Based on the previous studies, we hypothesised that the effects of YCWP on the immune system of the broiler chickens could be mediated by regulating the gene expression involved in inflammation and mitochondrial energy metabolism. Therefore, this study aimed to evaluate the effects of adding a YCWP to the broiler diets on the performance, the population of Lactobacillus and coliform bacteria in the ileum, morphology of jejunum, immune system and PPARc PGC1a, TLR4 and IL-10 genes expression, which have not been previously explored.

Birds management and experimental design
This experiment was conducted according to the comprehensive guide of animal welfare, as adopted by FASS (2010). The Animal Policy and Welfare Committee of the Isfahan University of Technology approved all animal care and experimental procedures. This experiment was conducted on a broiler research farm. This broiler research house was equipped to optimise the environmental condition and provide the best animal welfare during the experimental period.
The uniform environmental conditions between all experimental units (floor pen) provided by synchronising of heating systems (floor heating system and warm air tunnels), sidewall and tunnel fans, cooling systems (the cooling pads were located behind sidewall inlets), fogging system (one nozzle was situated in front of each sidewall inlet) and controlled through a computerised ventilation system controller. The temperature was maintained at 33 C for the first two days and then reduced gradually to 22 C until 39 days of age. The light regime was 23 L: 1 D for the first three days and then 20 L: 4 D till the end of the experiment. Chickens were fed ad libitum with the standard mash diets (based on Ross 308 recommendation, Table 1) during the starter (1-10 d), grower (11-24 d), and finisher phases (25-39 d).
A total of 1440 day-old as-hatch broilers (Ross 308) with an average weight of 37.35 ± 0.82 g were used in a completely randomised design with six treatments and ten replicates of 24 chickens. The treatment groups included corn-soy based diet (control), control þ 0.15 g/kg of YCWP, control þ 0.3 g/kg of YCWP, control þ 0.45 g/kg of YCWP, control þ 0.6 g/kg of YCWP, and control þ 0.3 g/kg of colistin antibiotic. In this experiment, YeaMune-UP V R (Pathway Intermediates Company, Seoul, South Korea) was used as a YCWP supplement. The YeaMune-Up is a functional fermented product made from Saccharomyces cerevisiae boulardii; it is rich in b-glucan and mannan-oligosaccharides (MOS). The body weight of each bird was recorded individually on days 10, 24, and 39 of age after 2 hours of fasting. Feed intake was recorded at 10, 24, and 39 d of age. The average daily weight gain, average daily feed intake, feed conversion ratio (FCR), and European efficiency factor (EEF) were calculated accordingly. Also, at 40 days of age, one male bird from each replicate was selected and humanely euthanized by CO 2 for samples collection.

Ileal sample collection for detecting the Lactobacillus and Coliform bacteria
Ileal contents of six chickens from each treatment were used to enumerate Lactobacillus and coliform bacteria at the end of the experiment. One g of digesta was collected (from the last 10 cm of the ileum) and mixed with 9 mL of peptone water broth; 100 mL of the solution was then homogenised in 900 mL phosphate-buffered saline (PBS) and serially (1:10) diluted. The population of Lactobacillus was counted on the De Man, Rogosa, and Sharpe (MRS) agar (Man, Rogosa, and Sharpe agar) and incubated in an anaerobic incubator at 37 C for 48 hours. The coliform bacteria were also counted on the MacConkey agar and incubated at 37 C for 24 hours. The results were expressed as log10 of colony-forming units (CFU) per gram of ileal digesta.

Analysis of cecal short chain fatty acids (SCFAs)
To measure the concentration of short-chain fatty acids in the caecum, ten birds from each treatment were chosen randomly at the age of 40 days; the caecum contents for each were collected and stored at À20 C. One g of caecum contents was suspended in 4 mL of distilling water in a sterile tube. The samples were homogenised and centrifuged at 4,000 Â g for 15 min. One mL of the supernatant was then transferred into an Eppendorf tube and mixed with 250 mL metaphosphoric acid solution. The solutions were then centrifuged again at 4,000 Â g for 15 min. After that, 1 mL of the supernatant was transferred into an Eppendorf tube mixed with 200 mL crotonic acid solution as the internal standard. The solutions were centrifuged again at 4,000 Â g for 15 min. The supernatant was analysed by gas chromatography (GC) for the concentrations of acetate, propionate, butyrate, isobutyrate, valerate, and isovalerate (Zhang et al. 2003).

Analysis of the immune system response
Weighing the lymphatic organs At the end of the experiment, lymphoid organs such as the bursa of Fabricius, thymus, and spleen were separated and weighed individually from ten euthanized chickens per treatment. The relative lymphoid organs weight was calculated and presented as a percentage of live body weight.
Anti-SRBC antibody assay Ten birds from each treatment were injected with 0.5 mL of 5% suspension of sheep red blood cell (SRBC) on days 22 and 29 of age to evaluate the antibody production potential (Seidavi et al. 2017). Blood samples were collected 7 and 14 days after the first injection. The serum of each sample was isolated and frozen at À20 C. Anti-SRBC antibody was measured using the microhemagglutination assay in 96 well microplates, as described by Akhlaghi et al. (Akhlaghi et al. 2013).

Intestinal morphological analysis
For enteric morphology analysis, at the end of the experiment, the jejunal samples were collected. One cm of the jejunal's midpoint from each bird was removed and fixed in 10% buffered formalin. Samples were prepared as described by Ekim (Ekim et al. 2020) for evaluation by optical microscope (Olympus CX31, Tokyo, Japan) and photographed with a digital microscope camera. Crypt depth, height and width of villi, and muscular layers thickness were measured using ImageJ software (Sakamoto et al. 2000;Ekim et al. 2020). The formula for calculating the villus surface area was 2p Â (villus width/2) Â villus height (Prakatur et al. 2019).

Samples preparation and total RNA extraction
At the age of 40 days, 2 cm of the ileum of 3 chickens from each treatment was obtained for gene expression analysis. The collected tissue samples were washed with distilled water and immediately frozen in liquid nitrogen and stored at À80 C until further use. Total RNA was isolated from the intestine tissue samples of the broilers separately using TRIzol (Sinaclon, Tehran, Iran), according to the manufacturer's protocol; RNase-free DNaseI was used to remove DNA contamination. Dried RNA samples were dissolved in DEPC water. RNA integrity was verified by agarose gel electrophoresis and visualisation of the glyceraldehyde 3-phosphate dehydrogenase (GAPDH) expression RNA. The concentration of the RNA samples was determined using Nano Drop-2000 (Thermo, USA).

Real-time quantitative RT-PCR (qRT-PCR) analysis
The expression of four candidate genes, including PPARc, PGC-1a, TLR4, and IL-10, was determined using quantitative reverse transcription-PCR (qRT-PCR, ABI StepOne TM Real-Time PCR System -Thermo Fisher Scientific). The reaction was performed using RealQ Plus 2x Master Mix Green (Amplicon). Primers were designed based on the target genes sequences and blasted with the NCBI Blast Primer; then, they were synthesised commercially (TAG, Copenhagen, Denmark, Table 2). The primers are listed in Table 2. Complementary DNA (cDNA) was synthesised from the total RNA using cDNA synthesis V R RT reagent Kit (Sinaclon). For each sample, 25 ng of cDNA was used as a template in the final volume of 25 lL reaction. The thermal cycling conditions had an initial denaturation step at 95 C for 10 min; this was followed by 40 cycles, including a denaturation step at 95 C for the 30 s, and the annealing and extension step at 60 C for 30 s. The GAPDH gene was used as an internal control. Each experiment was performed in triplicate and repeated three times independently. The triplicate PCRs' cycle threshold (Ct) values were averaged, and the relative quantification of the transcript levels was performed using the comparative 2 ÀDDCT method. The fold change in the target gene, relative to GAPDH, was Table 2. Specific amplification of the gene and internal reference primer.

GAPDH
ÀDCT (a reference sample). Each sample was further amplified without reverse transcription to confirm that no DNA contamination was in the sample.

Statistical analyses
Data were analysed using the GLM procedures of SAS 9.4 statistical software (SAS (Statistical Analysis System) 2009), based on a completely randomised design (CRD). Tukey's test was then used to determine the difference between treatments. The significance was considered at p < .05. Linear and quadratic effects from feeding control, 0.15 g YCWP, 0.3 g YCWP, 0.45 g YCWP and 0.6 g YCWP treatments were analysed using the polynomial contrasts. Additionally, the orthogonal contrast was used in SAS 9.4 (2009) to test the control versus YCWP-supplemented birds.

Growth performance
The effects of adding YCWP to the experimental diets on the performance of the birds during the experimental periods are shown in Table 3. Dietary treatments did not affect ADWG, ADFI, and FCR of the broilers during the starter period (p > .05). The ADWG was similar in different dietary treatments during the grower and finisher periods, but the inclusion of the YCWP linearly decreased ADFI and FCR (p < .05). The same results were found in the overall period, where the lowest FCR was related to the birds fed with 0.45 and 0.6 g/kg YCWP treatments (p < .05). There was no significant difference among experimental diets in terms of EEF during the overall period (p > .05). Contrast analysis also showed that during the grower, finisher and overall periods, adding YCWP to the diets significantly reduced FCR, as compared to the control group (p < .05). Also, with increasing the level of the YCWP in the diet, FCR and ADFI were decreased linearly.

Ileal bacterial population
The effects of dietary inclusion of the YCWP on the microbial population in the ileal content are shown in Table 4. Chickens fed with the 0.45 and 0.6 g of YCWP and colistin had higher Lactobacillus counts in comparison to those fed the control diet (p < .05). Also,  dietary inclusion of 0.45 g/kg of the YCWP reduced the coliform bacteria counts compared to the control treatment (p < .05). The contrast comparison showed that the addition of YCWPs to the diet significantly increased Lactobacillus, while it decreased coliform bacteria than the control birds (p < .05). Also, with increasing the YCWP level in the diet, the population of Lactobacillus was increased, while the population of coliform bacteria was decreased linearly (p < .05).

Immune system responses
Antibody response data (anti-SRBC) was measured as the serum concentrations of total anti-SRBC antibodies, IgG, and IgM (Table 5). In the primary (29 d) and secondary (36 d) responses, the production of anti-SRBC antibodies was not significantly affected by the dietary treatments (p > .05). However, the results of the secondary response showed that those chickens fed with the YCWP had higher levels of anti-SRBC antibodies and IgM, as compared to the control ones (p < .05). Also, the level of anti-SRBC antibodies and IgM was increased linearly with raising the level of the YCWP in the diet (p < .05). At the end of the experiment, the lymphoid organs were weighted; the related data as a percentage of the live body weight are also presented in Table 5. Chickens fed with 0.45 g YCWP had the highest weight of thymus and bursa of Fabricius (p < .05). Besides, the weight of the thymus was increased linearly with raising the level of the YCWP in the diet (p < .05). The spleen weight was similar in different dietary treatments (p > .05).

The concentration of cecal SCFAs
The concentration of SCFAs (acetate, propionate, butyrate, isobutyrate, valeric, isovaleric and total volatile fatty acids) in the caecum on the 40th day can be  seen in Table 6. The SCFAs concentration was not affected by the dietary treatments (p > .05). However, the addition of YCWP increased the propionate concentration, as compared to the control group (p < .05); consequently, it tended to decrease the acetate to propionate ratio (p ¼ .055). Also, the concentration of propionate in the caecum was increased linearly with raising the level of the YCWP in the diet (p < .05).

Histological changes of the jejunum
The results of the assessed histological parameters of the jejunum (villus height, crypt depth, villus width, villus surface area, and muscle thickness) are shown in Table 7 and Figure 1. Adding the YCWP to the diet increased the villus height linearly (p < .05). The highest and shortest villus height was observed in the birds fed with 0.6 g of the YCWP and colistin, respectively (p < .05). The control birds had the deepest crypt, as compared to those fed the diets containing colistin and the YCWP (p < .05). Also, the highest and lowest VH: CD ratio was related to 0.6 g YCWP and control treatments, respectively (p < .05). Furthermore, with increasing the level of the YCWP in the diet, the width of the intestinal villus was increased linearly (p < .05); so, the maximum villus width was observed in the birds receiving 0.45 and 0.6 g of the YCWP, while the lowest villus width was observed in those fed the control, 0.15 and 0.3 g YCWP. Chickens fed with 0.6 g YCWP had the highest villus surface area, while those using colistin and 0.15 g of YCWP had the lowest villus surface area (p < .05). The muscle thickness was similar in treatments (p > .05). According to the results of the contrast analysis, birds whose diet was supplemented with YCWP had a lower crypt depth and higher VH: CD, as compared to those receiving the control diet (p < .05). Also, with the increasing the YCWP level in the diet, VH: CD and villus surface area were increased linearly, while the crypt depth was decreased linearly and quadratically (p < .05).

Gene expression
The results of the gene expression are presented in Figure 2. Feeding birds with 0.15 and 0.3 g of the YCWP increased PPARc transcription, while the inclusion of the YCWP at the level of 0.45 and 0.6 g did not change the PPARc gene expression, as compared to the control birds (p < .05). As demonstrated in Figure  2(B), the PGC1-a mRNA level was increased in the chickens fed with 0.15 g YCWP (p < .05). However, birds fed the diet containing other levels of the YCWP did not change PGC1-a mRNA expression, as compared to the others (p < .05). Colistin did not change PGC1-a gene expression than the control group. The TLR4 gene expression in 0.3 and 0.45 g YCWP-fed birds was significantly lower than that in the control birds (Figure 2(C); p < .05). However, the inclusion levels of 0.15 and 0.6 g YCWP did not change the gene expression of TLR4, as compared to the control birds (p < .05). The effects of the dietary treatments on the gene expression of IL-10 are presented in Figure 2(D). As expected, feeding birds with 0.15 and 0.3 g of YCWP/kg of the diet resulted in the greater transcription of the IL-10 gene. However, at the greater inclusion levels (0.45 and 0.6 g/kg of diet), YCWP supplementation and colistin did not change the IL-10 expression, as compared to the control group (p < .05). The orthogonal contrasts showed that the mRNA for PPARc (p ¼ .021), PGC-1a (p ¼ .000) and IL-10 (p ¼ .012) were elevated in the YCWP group, while TLR4 mRNA (p ¼ .002) was decreased with adding YCWP compared to the control diet ( Figure 3).

Discussion
In this study, the effects of the dietary inclusion of YCWP on the growth performance of the broiler chickens reared under normal physiological conditions were evaluated. prebiotics and YCWP supplementation in the broiler chickens generally improves their performance (Teng and Kim 2018;Ricke et al. 2020); however, in this experiment, YCWP (MOS and b-Glucan) supplementation only improved the FCR of chickens. These results were in agreement with those of Pourabedin et al, reporting that adding MOS to the broilers diet had no significant effect on their daily weight gain. However, in their study, YCWP supplementation did not influence the FCR of 35-d-old broilers, which was in contrast with our findings (Pourabedin et al. 2014). Similar results have also been obtained in the birds fed with MOS and grown under an experimentally controlled condition (Sims et al. 2004). Improving the performance of broiler chickens by dietary supplementation with b-glucans or MOS could be mainly attributed to the modulation of the intestinal microbiota and enhanced innate immune function (Kim et al. 2011). For example, in a recent study, it was reported that adding prebiotics (containing 2.6 Beta LevaFructan 100 gm on 1000 mL distilled water) to the water reduced the adverse effects of Salmonella and increased the performance of broiler chickens during the bacterial challenge (El-Shall et al. 2019). It has also been reported that prebiotics (isomaltooligosaccharide) could significantly improve the growth performance and modulate the broiler chickens' intestinal microbiota when challenged by E.coli O78 (Tarabees et al. 2019). In the present study, the experimental birds were reared in a controlled environment and not exposed to any exogenous bacteria or environmental challenge; this may explain why adding the YCWP to the diets had no effect on the bird's performance in the present study (Wang et al. 2016).
In general, yeast cell wall products and prebiotics modulate the microbial population of GIT by increasing the number of beneficial bacteria such as Lactobacillus and Bifidobacteria and decreasing such pathogenic bacteria as Salmonella and E.coli (Zoumpopoulou et al. 2018). In this experiment, we observed that with increasing the YCWP levels in the diet, the number of Lactobacillus in the ileum was significantly increased, resulting in decreasing the number of coliform bacteria in the ileal content (Table 4). Prebiotics could affect the gut microbiota in several ways: 1. providing the nutrients for the growth of commensal bacteria, 2. inhibiting the growth of pathogenic bacteria by increasing the development of commensal bacteria and creating competitive exclusion between them, and preventing the attachment of pathogenic bacteria to the intestinal epithelial sites (Pourabedin and Zhao 2015;Adhikari and Kim 2017), and 3. binding to the pathogenic bacteria, which possess type-1 fimbriae such as E. coli and Salmonella species. Mannan oligosaccharides have receptor properties for E. coli and Salmonella's fimbriae that can prevent them from attaching to the intestinal epithelium and removing with the digesta flow; MOS can also block the bacterial lectins and reduce the colonisation of these pathogens in the bird's GIT (Fernandez et al. 2002;Teng and Kim 2018).
In this experiment, YCWP had no significant effect on the antibody production against SRBC, as compared to the control group (Table 5). However, the heavier bursa of Fabricius and thymus weight in YCWP-fed birds indicated the improved immune system function. This might be due to a direct interaction between prebiotics and host GIT immune cells, as well as the indirect effects of prebiotics on the immune system by modulating the intestinal microbiota and increasing the beneficial bacteria (Janardhana et al. 2009). Microbiota such as Lactobacillus and Bifidobacterium support the animal's immune system against invasive pathogens by stimulating the GIT immune responses. Prebiotics indirectly strengthen the bird's immune system by stimulating the growth of these bacteria. Also, some YCWPs such as MOS, can interact directly with antigens and increase the immune signals in chickens (Teng and Kim 2018). Generally, prebiotics can strengthen the host immune system in several ways. Firstly, this can be through their effects on the lymphoid tissues of chickens, such as the caecal tonsil, spleen, thymus, and bursa of Fabricius (Table 5). Secondly, this can be realised by enhancing the antibody titres of IgM and IgG in plasma and IgA level in the intestine. Thirdly, it could be due to the enhanced phagocytosis and proliferation of monocytes and macrophages. As the fourth way, increasing the expression of mucin genes in the gut can be noted. Finally, the GIT immune functions could be enhanced (Huang et al. 2015;Adhikari and Kim 2017). In the present study, the lymphatic organs were weighed at the end of the rearing period. The weight of the lymphatic organs can indicate the general condition and health of the birds. In chickens, the bursa of Fabricius and thymus can be regarded as the sites for T and B lymphocytes maturation; therefore, the size and weight of the bursa and thymus can provide important information about the growth and development of the immune system (Grasman 2002). Generally, when the bursa of Fabricius weight to body weight ratio is about 0.2%, it can be concluded that the bird has an immune system with a good performance and health (Sellaoui et al. 2012). In our study, the increase in the thymus and bursa weight could indicate the development of the bird's immune system due to the use of YCWP.
One of the positive effects of YCWP is increasing the production of short-chain fatty acids (specifically propionate, acetate and butyrate) in the caecum by stimulating the growth of gastrointestinal bacteria. However, in the present study, YCWP supplementation had no significant effect on the caecal concentration of SCFAs. According to Table 6, the YCWP could numerically increase the concentration of propionate in the caecum. In addition to the reasons mentioned earlier, the reduction of pathogenic bacteria in the intestine could be attributed to the decrease of pH due to the increased SCFAs in the intestine (Micciche et al. 2018;Kim et al. 2019).
This study showed that adding the YCWP to the diet increased the villus height and reduced the crypt depth ( Table 7). The effect of YCWP on intestinal morphology is probably due to its impact on the GIT microbiota. Some YCWPs, such as MOS, can improve the villi growth and structure in the small intestine and increase the activity of such jejunal enzymes as maltase, aminopeptidase, and alkaline phosphatase (Ricke et al. 2020). It has been shown that adding MOS to the broilers' diets could increase Lactobacillus in the ileum and, consequently, the activity of digestive enzymes in the chickens. Clavijo and Fl orez have also reported that the activity of digestive enzymes is affected by the intestinal microflora. For example, Bifidobacterium and Lactobacillus increase the activity of digestive enzymes such as proteases and lipases (Clavijo and Fl orez 2018). Also, the higher levels of intestinal Lactobacillus in the birds supplemented with MOS resulted in a healthier GIT (Teng and Kim 2018). The intestinal microbiota balance and intestinal morphology, including the villus height, crypt depth and villus height to crypt depth ratio, are the critical indices of gut health and function. Previous studies have also reported that MOS supplementation increases the villus height and surface area, decreases the crypt depth, and up-regulates the gene expression of mucin genes related to mucin secretion (Yang et al. 2008;Baurhoo et al. 2009;Chee et al. 2010). Improved intestinal morphology in MOS-fed chickens may also be due to reducing the population of pathogenic bacteria in the GIT. For instance, some pathogenic bacteria, such as E.coli, can impair the digestive enzymes' secretion in two ways. Firstly, this is done by secreting the microbial proteolytic enzymes that degrade the host digestive enzymes; secondly, this occurs by damaging the intestinal villus and microvillus (Pan and Yu 2014).
Previous reports have also shown that inflammation induces the production of reactive oxygen species (ROS) in the mitochondria and, finally, in the cells. However, almost all cells produce modulators through the expression of genes, which regulate the production of inflammatory factors such as ROS. The uncoupling proteins (UCP) are mitochondrial transporters present in the inner membrane of mitochondria (Ledesma et al. 2002). Some subsets of UCP (UCP-2 and UCP-3) protect the cells against the oxidative damage of superoxide by the mild uncoupling (Brand et al. 2002(Brand et al. , 2004. PGC-1a is the most dominant regulatory protein in mitochondrial biogenesis (Wu et al. 1999). PGC-1a stimulates the nuclear respiratory factor-1 and the mitochondrial transcription factor expression; these, in turn, can lead to up-regulating the expression of nuclear and mitochondrial genes that encode the mitochondrial proteins (Andersson and Scarpulla 2001;Nisoli et al. 2003). As illustrated in Figure 2(B), the dietary supplementation of broiler chickens with the prebiotic at 0.15 g/kg level resulted in the greatest PGC1-a transcription among birds. It has also been reported that the increased UCP could significantly down-regulate the ROS production following fasting (Abe et al. 2006). Also, acute heat stress induces mitochondrial superoxide production in the broiler skeletal muscle through the down-regulation of UCP ). Besides, some previous studies have suggested that PPARc, PGC-1a, and nitric oxide play an important role in mitochondrial biogenesis (Wu et al. 1999;Nisoli et al. 2003). Nisoli et al. (2003) reported that NOS-/-mice had lower PGC-1a mRNA levels than the wild-type ones. It has also been shown that the exposure of chickens to a cold environment results in the prompt up-regulation of the PGC-1a expression (Ueda et al. 2005). Therefore, the up-regulation of PGC-1a may up-regulate such PGC-1a dependent factors as UCPs; these may, in turn, decrease oxidative stress and, at the next level, increase mitochondrial biogenesis.
The family of peroxisome proliferation-activated receptors includes three isoforms: PPARa, PPARb, and PPARc (Berger and Moller 2002). The PPARc is expressed in white and brown adipose tissues and the large intestine. In the molecular epithet, PPARs are a family of ligand-activated nuclear hormone receptors (NRs) belonging to the steroid receptor superfamily. PPARs are nuclear receptor proteins involved in many regulatory functions (Desvergne and Wahli 1999). PPARc is the dominant regulator of the expression of genes encoding proteins that control adipocyte differentiation and fatty acids uptake and metabolism (Kliewer et al. 1997;Rosen et al. 1999). In chickens, PPAR-c plays a crucial role in fat deposition (Sato et al. 2004). It was reported that dietary supplementation of sphingomyelin reduces inflammation in the intestinal epithelial cells through the upregulation of PPARc (Mazzei et al. 2011). many studies reported that PPARc regulates a large number of activities such as glucose and lipid metabolism, cellular proliferation, differentiation, and inflammation (Auwerx 2002;Dubuquoy et al. 2006;Leonardini et al. 2009;Duszka et al. 2016). The PPARc is highly expressed in the GIT; PPARc is expressed in the duodenum and especially in the proximal colon (Mansen et al. 1996;Escher et al. 2001;Harmon et al. 2010). The PPARc transcription and activity are stimulated in the GIT by numerous bacterial metabolites and bacterial by-products such as butyrate (Wachtershauser et al. 2000;Schwab et al. 2007), H2O2 (Voltan et al. 2008), and LPS (Dubuquoy et al. 2003). Expression of PGC-1a mRNA was not different among control, antibiotic group, and birds supplemented with high levels of the YCWP in the ileal tissue (Figure 2(B)). However, we observed a higher level of PPARc mRNA in the birds fed the diet containing 0.15 and 0.3 g/kg YCWP, thus implying that a threshold of PPARc transcription and finally, PPARc protein expression would be needed for triggering the functional roles of PGC-1a. Also, as illustrated in Figure  2(A,B), in the birds fed with 0.45 and 0.6 g/kg YCWP, PPARc and PGC-1a mRNA were the lowest; this might indicate the reduced inflammation in their intestinal cells. Besides, the greater PGC-1a mRNA in 0.15 g/kg YCWP -fed birds reflected the improved metabolism and biogenesis in their intestinal cells. On the other hand, one of the critical roles of PPARc is to prevent lipotoxicity by different mechanisms and to regulate the targets relevant to inflammation (Clark 2002). When a YCWP with a specific function destroys the pathogenic bacteria and their pieces, the endothelial cells prefer to increase the homeostasis instead of activating the anti-inflammation cascade. A previous study showed that PPARc agonists could increase antiinflammatory factors such as adiponectin and reduce tissue necrosis factors (Krishnaswami et al. 2010). Researchers have investigated the effects of PPARc activation on the intramuscular fat in the growing beef bulls, using the oral administration of 2, 4-thiazolidinedione (TZD) to activate PPARc. They have shown that the oral administration of TZD did not affect the carcase quality, but it could decrease the expression of PPAR (a, -d and, -c) in the liver (Arevalo-Turrubiarte et al. 2012). Our results also demonstrated that feeding birds with the YCWP at the levels of 0.45 and 0.6 g/kg decreased the inflammation, indicating that the IL-10 and PPARc expression were similar to control in their ileum; finally, improving metabolism in them (Janani and Kumari 2015). However, as displayed in Table 7 and Figure 1, with increasing the YCWP in the broilers diet, the histological changes of the jejunum were considerably better than those in the control and antibiotic groups. However, we demonstrated that in the lowest level of the YCWP, the PPARc and PGC-1a expression was significantly increased. These all indicate that PPARc and PGC-1a may be involved in the proliferation of the GIT and goblet cells in the crypt depth. Some researchers have also shown that PGC-1a is one of the critical factors in cell proliferation and differentiation (Zheng et al. 2016).
Toll-like receptors, also known as pathogen recognition receptors, are part of the innate immune system (Aderem and Ulevitch 2000). These receptors recognise the molecular patterns of pathogens called microbe-associated molecular patterns, initiate a chain reaction, and stimulate the immune system (Aderem and Ulevitch 2000). Chicken TLR2b4, and 21 are involved in recognising peptidoglycan, lipopolysaccharides, and unmethylated CpG oligonucleotides, respectively (Hoshino et al. 1999;Fukui et al. 2001;Keestra et al. 2010). Yitbarek et al. (2013) showed the greater expression of TLR4, and TLR2b in the bird's spleen and bursa of Fabricius fed with yeast culturesupplementation diets (Yitbarek et al. 2013). They also demonstrated that the dietary supplementation with a yeast culture in chickens significantly increased the TLR21 transcription in their intestine.
However, in the current study, the lower concentration of the YCWP did not restrain the harmful bacteria (Coliform bacteria). But the higher inclusion levels (0.3, 0.45, and 0.6 g/kg of YCWP) resulted in the lower harmful bacteria and finally led to a reduction of TLR4 transcription and inflammation. The linkage between TLR4 and LPS could activate the cascade of the signalling pathway in the inflammation, subsequently increasing the anti-inflammatory cytokine expressions, such as IL-10, which are involved in the immune response and the intrinsic immune system (Chen et al. 2010). The IL-10 is an anti-inflammatory cytokine that acts as a potential factor in decreasing the inflammation and host pathology when diseases occur (Redpath et al. 2014). Dependent protozoan parasites activate IL-10 to diminish the pathogen-damaging host inflammatory responses. Arendt et al. also showed that the Eimeria challenge in broiler chickens increased the IL-10 gene expression (Arendt et al. 2019). Some studies have demonstrated that IL-10 acts as a macrophage-inactivating regulatory cytokine, suppressing the secretion of the pro-inflammatory cytokine and inhibiting the expression of the major histocompatibility class II (MHC II) and co-stimulatory molecules (O'Garra and Vieira 2007). In healthy chickens, the gene expression of IL-10 mRNA has been shown to be greater in the duodenum than in the caecum (Haritova and Stanilova 2012). Yitbarek et al. (2013) also observed the up-regulation of the IL-10 gene only in the ileum of the birds receiving a yeast culture supplemented diet, which can be considered as a local inhibition of the Th1 pathway (Yitbarek et al. 2013). He et al. (2012) have also suggested that the regulation of the macrophage activity by Th1/2 cytokines is crucial to maintain a balanced immunity, providing adequate Th1-mediated protective immunity and preventing the detrimental effects of severe inflammation (He et al. 2012). Therefore, these results indicated that dietary YCWP supplementation at the level of 0.6 g/kg considerably reduced the chronic inflammation in the broilers' GIT; meanwhile, this led to the successful regulation of the metabolism and specifically, mitochondrial biogenesis. In other words, the inclusion of the YCWP in the broiler diets could modulate the local innate immunity in the broiler chickens. Besides, feeding YCWP-supplemented diets for a long period might have decreased the bacterial population, TLR4 responses, and inflammation.

Conclusions
The results demonstrated that dietary YCWP supplementation could positively affect the broilers' gut, epithelial cell differentiation and turnover, and immunomodulation. Furthermore, the YCWP improved intestinal villi development, which could be due to the PPARc gene expression and the increasing population of the beneficial bacteria, specifically Lactobacillus. Supplementation of YCWP regulated pro and anti-inflammatory cytokines via increment of IL-10 and reduction of TLR4 expression systemic pathways. Therefore, YCWP supplementation could have an immune stimulatory effect via the local and systemic responses involving TLRs and cytokines, with implications for enhancing the resistance to the pathogenic microorganisms. Furthermore, because the jejunum is a key organ in controlling the entry of bacteria and other pathogens entering into the GIT, the strong responses of the cytokines observed in the current study warrant further investigation into the role of YCWP in limiting the pathogens' invasion.

Ethical approval
The authors confirm that THEY were followed the animal welfare guide,as adopted by FASS (2010). All animal care and experimentalprocedures were approved by the animal policy and welfare committee of the Isfahan University of Technology.


provenance: AgriculturalAndFoodSciences,2022-2022-Agricultural And Food Sciences-2022.jsonl.gz.gz

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Grain Zinc and Yield Responses of Two Rice Varieties to Zinc Biofortiﬁcation and Water Management

: Zinc (Zn) biofortiﬁcation can improve grain yield and nutritional quality in rice, but its effectiveness is subject to agronomic practices and other factors. In a previous study, the application of Zn to soil enhanced grain Zn in lowland rice in well-drained and waterlogged soil, whereas grain Zn in upland rice increased only in well-drained soil. This new study explores the hypothesis that the application of foliar Zn can enhance grain Zn in upland and lowland rice grown under waterlogged and well-drained conditions. Two rice varieties, CNT1 (wetland rice) and KH CMU (upland rice) were grown in containers in waterlogged or well-drained soil with three Zn treatments (no Zn, soil Zn and foliar Zn). For the soil Zn treatment, 50 kg ZnSO 4 ha − 1 was applied to the soil before transplanting. For the foliar treatment, 0.5% ZnSO 4 (equivalent to 900 L ha − 1 ) was applied at booting and repeated at ﬂowering and milky growth stages. Grain yield in CNT1 was 15.9% higher in the waterlogged than in the well-drained plants, but the water regime had no effect on grain yield in KH CMU. Grain Zn concentration in CNT1 increased from 19.5% to 32.6% above the no Zn control when plants were applied with soil or foliar Zn. In KH CMU, there was an interaction between the water regime and Zn treatment. Application of foliar Zn increased grain Zn by 44.6% in well-drained and 14.7% in waterlogged soil. The results indicate strong interaction effects between variety, water regime and Zn fertilizer application on Zn biofortiﬁcation in rice. Thus, the selection of rice varieties and growing conditions should be considered in order for producers to achieve desirable outcomes from high grain Zn concentrations.

Introduction
Rice grains are inherently low in Zn relative to other cereals [1]. Although the average grain Zn in brown rice of 15 Thai varieties was 29 ± 13 mg kg −1 [2], wetland rice ecotypes can have lower Zn concentrations (19-20 mg kg −1 ) than upland ecotypes (28-31 mg kg −1 ) [3,4]. As rice is a staple food consumed by more than half of the world's population, there is strong interest in boosting the grain Zn concentration for human health. The three approaches used to bio-fortify staple crops are agronomic (e.g., fertilizer application and water management), plant breeding and genetic engineering. Of these, fertilizer application is a practical and effective tool for farmers in all regions as it is rapid and requires fewer resources and experience compared with other strategies [5][6][7]. To achieve Zn-enriched grain with Zn fertilizer, two main factors need to be considered, rice variety and agronomy management [8]. In wheat, adding Zn fertilizer to Zn-deficient soils has long been established as an effective way to maximize grain yield and grain Zn concentration [9][10][11]. However, optimal practices are still being evaluated in rice to achieve desirable outcomes for productivity and premium grain quality [12][13][14]. Grain Zn concentration in rice is influenced by genetic and environmental factors [15]. Factors that reduce the availability of Zn in soil generally decrease grain Zn concentrations [1,7,16]. Thus, Zn fertilization and water management can be effective tools to boost grain Zn in many situations [8,17]. In rice, the response to Zn fertilization methods and growing conditions can differ among varieties [8,18,19]. Farooq et al. [20] compared four Zn application methods in Super Basmati rice grown under flooded and non-flooded fields at two locations in Pakistan. They found that the highest grain Zn concentrations were achieved with foliar Zn application (0.5% Zn) at one site, and with soil Zn application (10 kg Zn ha −1 ) at the second site in both production systems. More generally, foliar Zn application increased grain Zn concentration in brown rice by an average of 25% [19,21]. In addition to the impact of fertilizer Zn and the rice variety, the soil water status also can influence the grain Zn concentration. For example, alternate wetting and drying cycles increased Zn concentration in rice grain compared to continuous flooding [22]. Similarly, Beebout et al. [19] showed that growing rice in well-drained conditions increased Zn concentration in both brown rice (unpolished) and white rice (polished) and attributed this to the higher extractable soil Zn under well-drained conditions than in continuous flooding conditions. In Thailand, Yamuangmorn et al. [23] grew upland and wetland rice varieties in well-drained and waterlogged conditions and found that the addition of the equivalent of 50 kg Zn ha −1 to the soil increased grain Zn by 45% in upland rice in well-drained soil but there was no increase in the waterlogged treatment. In contrast, grain Zn concentrations were enhanced in the wetland variety in both soil water conditions. However, it is unknown whether foliar application of Zn can improve grain Zn concentrations of upland rice in waterlogged soil. Therefore, this paper compares the effectiveness of soil and foliar Zn applications on productivity and grain Zn accumulation in the same wetland and upland rice varieties under well-drained and waterlogged conditions. The hypothesis was that the application of soil or foliar Zn would enhance grain Zn under both waterlogged and well-drained conditions. The information gained from this study will be useful for improving productivity and grain Zn concentration in rice.

Rice Variety and Culture
The large pot experiment was arranged in two (variety) × two (water regime) × three (Zn treatment) factorials in a completely randomized design (CRD) with three independent replications in a total of 36 large pots. The two rice varieties were Chai Nat 1 (CNT1) and Kam Hom Morchor (KH CMU). These varieties were shown in previous studies to belong to different groups with respect to their yield potential, ecotype and grain Zn concentration [2,24]: namely (1) high yielding wetland varieties with low grain Zn (CNT1); and (2) low-yielding upland varieties with high grain Zn (KH CMU). The plants were grown in a glasshouse with two water regimes (waterlogged, well-drained) with three Zn fertilizer treatments (no Zn, soil Zn, foliar Zn) during the wet season (June to September 2018) at Chiang Mai University, Thailand (18 • 47 N, 98 • 57 E). The soil characteristics were sandy loam soil texture (sedimentation method) [25], pH 5.82 (measured in 1:1; soil-water) [26], 1.38% organic matter [27], available phosphorus 35.06 mg kg −1 (Bray II) and 39.28 mg kg −1 exchangeable potassium (NH 4 OAc, pH 7) [28]. The diethylenetriamine pentaacetic acid (DTPA)-extractable Zn concentration was 0.73 mg kg −1 [29]. The average temperature in the open during the cropping season was 27.5 • C with 77.0% relative humidity [30]. Ten-day-old seedlings were transplanted into cement pots of 80 cm in diameter and 50 cm in height. There were twelve plants per pot with a spacing of approximately 20 × 20 cm between plants. For the foliar treatment, 0.5% aqueous ZnSO 4 ·7H 2 O was applied at booting, flowering and milky grain stages using a hand-sprayer, equivalent on a surface area basis to 900 L ha −1 or 13.5 kg ha −1 in total [31,32]. To prevent contamination, the pots were vertically protected with a plastic sheet during spraying. For the soil Zn treatment, 50 kg ZnSO 4 ·7H 2 O ha −1 (11.9 kg Zn ha −1 ), suggested by a previous study [23,33], was incorporated into the soil before transplanting the rice seedlings. For waterlogged soil, the plants were kept in submerged condition with 10 cm of water above the soil surface for the entire period until maturity. For well-drained soil, the pot had a hole for drainage of gravitational water and soil moisture content was maintained at field capacity by supplying water twice daily. All treatments received the equivalent of 125 kg ha −1 of basal 15N:15P:15K fertilizer as four equal split applications at seven days after planting, tillering, booting and flowering stages [34].

Sample Collection and Chemical Analysis
At grain maturity, the plants were harvested and evaluated for yield and yield components (number of tillers plant −1 , number of panicles plant −1 , number of spikelets panicle −1 and percentage of filled grain). Grain yield was measured at 14% moisture content. The unpolished rice grains were oven-dried at 70 • C for 72 h before Zn analysis. Subsamples (20 g) of the unpolished rice were ground for 45 s in a hammer mill (Scientific Technical Supplies D-6072, Dreieich, Germany), dry-ashed at 550 • C for 8 h, the ash was dissolved in HCl (1:1; HCl to deionized water) heated to 120 • C for 20 min and then the final solution was analyzed in an atomic absorption spectrophotometer (Z-8230 Polarized Zeeman, Hitachi, Japan) [35]. Peach (SRM 1547) and soybean leaves were included in each batch as reference materials to validate the method.

Statistical Analysis
Analysis of variance (ANOVA) was conducted to detect any differences in grain yield, yield components and grain Zn concentration between treatments using Statistic 9 (analytical software SX). Data were tested for normality by the normal probability plot method before being analyzed in factorials in CRD. The least significant difference (LSD) test at p < 0.05 was applied to compare the means for significant differences between water regimes and Zn fertilizer treatments.

Grain Yield and Yield Components
The response of grain yield and yield components to rice variety, Zn treatment and water regime is shown in Table 1. Grain yield and yield components differed between the two varieties (p < 0.01). Grain yield of the wetland variety CNT1 was 17% higher than the upland variety KH CMU. The number of tillers and panicles per plant was higher in CNT1, while the percentage of filled grain and the number of spikelets per plant were higher in KH CMU. Grain yield was affected by the water regime, but this depended on the rice variety (p < 0.05), while Zn fertilizer treatment had no effect on the grain yield. In wetland rice CNT1, the grain yield of plants grown in the waterlogged soil was 310.1 g pot −1 , which was 18.9% higher than that of the plants grown in the well-drained soil. By contrast, the grain yield of upland rice KH CMU was not affected by the water regime. The number of panicles per plant and the percentage of filled grain was significantly affected by the Zn treatments. Applying soil and foliar Zn increased the number of panicles per plant by 13.9% and 10.7%, and the percentage of filled grain by 6% and 3%, respectively, in both varieties compared with the no Zn plants. The CNT1 had 89.2% and 79.8% more tillers and panicles per plant, respectively, than KH CMU, regardless of Zn treatments. In addition, there was an interaction effect between rice variety and water regime on the percentage of filled grain (p < 0.01). In CNT1, the percentage of filled grain was 76.5% in the waterlogged soil and this was almost 6.0% higher than in well-drained soil. In comparison, the percentage of filled grain in KH CMU was 82.9% in the well-drained soil which was 5.7% higher than in the waterlogged soil. ns indicates no significant difference, * indicates significant difference at p < 0.05. ** indicates significant difference at p < 0.01, *** indicates significant difference at p < 0.001.

Zn Concentration and Content in Brown Rice
Zinc treatment significantly affected grain Zn concentration in CNT1 (p < 0.05) ( Figure 1A). Soil and foliar Zn treatments increased grain Zn concentration in CNT1 by 19.8% and 32.9%, respectively, compared to the no Zn treatment. Unlike for CNT1, there was a water × Zn interaction in KH CMU on grain Zn concentration (p < 0.05) ( Figure 1B). In well-drained soil, the soil and foliar Zn treatments increased grain Zn concentration in KH CMU by 44.6% and 35.2%, respectively, compared with no Zn plants. Furthermore, in this variety, the application of foliar Zn increased grain Zn concentration to 41.1 mg kg −1 in the waterlogged soil, an increase of 14.7% over the no Zn plants, but the soil Zn treatment had no effect.
contrast, there was a water × Zn interaction in KH CMU on grain Zn content (p < 0.05) ( Figure 1D). Soil and foliar Zn treatments increased grain Zn content by 46.4% and 65.0%, respectively, in plants grown in well-drained soil compared with no Zn plants. However, grain Zn content was not increased in KH CMU in waterlogged soil.

Relationship between Grain Yield and Zn Concentration
There was a weak positive correlation between grain yield and grain Zn concentration in CNT1 (r 2 = 0.55 *), but no such relationship was found in KH CMU (r 2 = 0.07 ns ) (Figure 2). The water regime affected the grain Zn content in CNT1 (p < 0.05) ( Figure 1C) but Zn treatment had no effect on this property. The grain from plants grown in well-drained soil had 21.4% less Zn in comparison with the grain produced in the waterlogged plants. By contrast, there was a water × Zn interaction in KH CMU on grain Zn content (p < 0.05) ( Figure 1D). Soil and foliar Zn treatments increased grain Zn content by 46.4% and 65.0%, respectively, in plants grown in well-drained soil compared with no Zn plants. However, grain Zn content was not increased in KH CMU in waterlogged soil.

Relationship between Grain Yield and Zn Concentration
There was a weak positive correlation between grain yield and grain Zn concentration in CNT1 (r 2 = 0.55 *), but no such relationship was found in KH CMU (r 2 = 0.07 ns ) (Figure 2).

Discussions
The hypothesis that application of foliar Zn can enhance grain Zn in upland and lowland rice grown under waterlogged and well-drained conditions is only partly supported in this study. In KH CMU, the upland rice genotype, foliar Zn application increased grain Zn by 44.6% in well-drained soil and 14.7% in waterlogged soil. In CNT1, the lowland rice genotype, foliar Zn increased grain Zn concentration by an average of 32.8% in both welldrained and waterlogged soil. Additionally, grain yield of CNT1 was affected by water management, whereas there was no effect in KH CMU. The waterlogged CNT1 plants produced 15.9% higher grain yield than the well-drained plants. Even though the application of Zn to CNT1 had no effect on grain yield, it improved grain Zn concentration. However, the magnitude of the response differed with rice variety, especially where foliar Zn was applied. The reduction in grain yield of CNT1 in the well-drained soil was mainly due to the lower percentage of filled grain (Table 1). In contrast, KH CMU was well adapted to both growing conditions as there was no difference in grain yield between treatments. This confirms the earlier responses obtained by Yamuangmorn et al. [23]. Results of the two studies suggest that the two cultivars may differ in their response to the extraction of essential nutrients from well-drained soil. Whilst the mechanisms for nutrient acquisition have so far not been explored in these ecotypes, there are a number of factors that can be considered. These include external changes in the organic matter content and the availability of nitrogen [36], changes in soil pH [18], as well as genetic traits. Experiments have revealed the extent of soil water conditions on changes in soil chemistry [36,37] and total nutrient uptake [38]. It would be interesting to investigate root morphology, nutrient uptake, water and nutrient use efficiencies, and genetic control of grain filling capacity for the two ecotypes to assist in improving rice varieties with high productivity under different water regimes.
Advice on grain Zn biofortification is increasingly being sought by Thai producers and our research shows the importance of addressing variety as well as agronomic practice. The role of genotype, soil Zn availability and Zn fertilization were discussed by [39]. Foliar Zn application has been suggested by many researchers as a promising and effective tool to increase grain Zn concentration as foliar uptake and translocation of Zn occurs

Discussion
The hypothesis that application of foliar Zn can enhance grain Zn in upland and lowland rice grown under waterlogged and well-drained conditions is only partly supported in this study. In KH CMU, the upland rice genotype, foliar Zn application increased grain Zn by 44.6% in well-drained soil and 14.7% in waterlogged soil. In CNT1, the lowland rice genotype, foliar Zn increased grain Zn concentration by an average of 32.8% in both well-drained and waterlogged soil. Additionally, grain yield of CNT1 was affected by water management, whereas there was no effect in KH CMU. The waterlogged CNT1 plants produced 15.9% higher grain yield than the well-drained plants. Even though the application of Zn to CNT1 had no effect on grain yield, it improved grain Zn concentration. However, the magnitude of the response differed with rice variety, especially where foliar Zn was applied. The reduction in grain yield of CNT1 in the well-drained soil was mainly due to the lower percentage of filled grain (Table 1). In contrast, KH CMU was well adapted to both growing conditions as there was no difference in grain yield between treatments. This confirms the earlier responses obtained by Yamuangmorn et al. [23]. Results of the two studies suggest that the two cultivars may differ in their response to the extraction of essential nutrients from well-drained soil. Whilst the mechanisms for nutrient acquisition have so far not been explored in these ecotypes, there are a number of factors that can be considered. These include external changes in the organic matter content and the availability of nitrogen [36], changes in soil pH [18], as well as genetic traits. Experiments have revealed the extent of soil water conditions on changes in soil chemistry [36,37] and total nutrient uptake [38]. It would be interesting to investigate root morphology, nutrient uptake, water and nutrient use efficiencies, and genetic control of grain filling capacity for the two ecotypes to assist in improving rice varieties with high productivity under different water regimes.
Advice on grain Zn biofortification is increasingly being sought by Thai producers and our research shows the importance of addressing variety as well as agronomic practice. The role of genotype, soil Zn availability and Zn fertilization were discussed by [39]. Foliar Zn application has been suggested by many researchers as a promising and effective tool to increase grain Zn concentration as foliar uptake and translocation of Zn occurs without soil chemical constraints [19,40,41]. However, our study highlights the importance of G × E on the effectiveness of biofortification of rice using foliar Zn. The extent of grain Zn loading depends on rice variety and water conditions [32,42]. In a previous report, applying Zn fertilizer to the soil could increase grain Zn concentration in the wetland and upland varieties when grown in both well-drained and waterlogged conditions [17]. Chatzistathis [43] also reported that applying Zn fertilizer when plants were grown in well-drained soil resulted in a higher grain Zn concentration in the unpolished (brown rice) and polished rice than when plants were grown in waterlogged soil. This may be due to the oxygen supply enhancing Zn 2+ uptake in the well-drained soil. In addition, the availability of Zn in well-drained soil can increase with a decrease in soil pH or an increase in redox potential [44]. In waterlogged soil, low redox potential and oxygen (O 2 ) stress may limit soil Zn availability for plants [45]. Furthermore, under continuous flooding, the formation of insoluble forms of Zn such as ZnS and Zn franklinite (ZnFe 2 O 4 ) [37,46], ZnCO 3 [47] and Zn(OH) 2 [48], markedly reduces the effectiveness of Zn fertilizers [49].
The relationship between grain yield and Zn concentration suggests that increasing the grain Zn concentration could enhance the productivity of CNT1, but not KH CMU. Previous reports [32,50,51] indicate the importance of Zn for improved pollen viability and grain fill capacity in some rice varieties. This needs to be further considered when manipulating rice cultivation for maximum yield and grain Zn concentration. The information on Zn biofortification of the two rice ecotypes should be useful for extension workers to better manage rice cultivation for improving grain Zn concentration and stabilizing yield. We now intend to confirm the findings in the field with a greater number of rice varieties for each ecotype.

Conclusions
Improvement of grain productivity and Zn concentration in rice could benefit both rice growers and consumers. The response of rice varieties from different original ecotypes is the key factor underpinning appropriate management of the growing condition and Zn fertilizer application. In this study, the success of biofortification of grain Zn with Zn fertilizer depended on the interaction effects between the rice ecotype, soil water and the method of Zn fertilizer application. Overall, grain yield and Zn concentration can be maximized by growing rice genotypes in either well-drained or waterlogged soil and applying foliar applications of Zn at the appropriate method. Rice producers would benefit from field studies that provide recommendations on the best Zn fertilizer practices for grain Zn concentration in upland and lowland fields.


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Aquaphotomic, E-Nose and Electrolyte Leakage to Monitor Quality Changes during the Storage of Ready-to-Eat Rocket

The consumption of ready-to-eat (RTE) leafy vegetables has increased rapidly due to changes in consumer diet. RTE products are perceived as fresh, high-quality, and health-promoting. The monitoring of the RTE quality is crucial in relation to safety issues. This study aimed to evaluate the maintenance of RTE rocket salad freshness packed under modified atmospheres. A portable E-nose, the electrolyte leakage test (which measures the index of leaf damage—ILD), and NIR spectroscopy and Aquaphotomics were employed. Two trials were carried out, using the following gas mixtures: (A) atmospheric air (21% O2, 78% N2); (B) 30% O2, 70% N2; (C) 10% CO2, 5% O2, 85% N2. Samples were stored at 4 °C and analyzed at 0, 1, 4, 7, 11, and 13 days. ANOVA, PCA, PLS were applied for data processing. E-nose and ILD results identified the B atmosphere as the best for maintaining product freshness. NIR spectroscopy was able to group the samples according to the storage time. Aquaphotomics proved to be able to detect changes in the water structure during storage. These preliminary data showed a good agreement NIR/ILD suggesting the use of NIR for non-destructive monitoring of the damage to the plant membranes of RTE rocket salad.

Introduction
The consumption of ready-to-eat (RTE) leafy vegetables has increased rapidly due to changes that have occurred in the dietary pattern of the consumer. RTE products are perceived as natural, fresh, convenient, high-quality, and health-promoting [1,2]. Rocket (Eruca sativa Mill.) salad is principally consumed as an RTE product, either alone or mixed with other vegetables [3]. It shows different characteristics from other types of salad, as it belongs to the Brassicaceae family. For this reason, it is a source of compounds with biological activity beneficial for human health, such as glucosinolates. Glucosinolates are then hydrolyzed by the enzyme myrosinase with the release of isothiocyanates, which are compounds with antimicrobial activity [4]. Because of its high content of bioactive compounds, different health-promoting functions are attributed to rocket plants [5], but, as with most leafy vegetables, rocket leaves have very high metabolic activity [6], which limits their shelf-life. During storage, the high respiration rate and ethylene production lead to leaf yellowing due to chlorophyll degradation, loss of turgidity, off-flavor development, and general deterioration [7].
Many postharvest technologies, such as refrigeration and modified atmosphere packaging (MAP), are successfully employed to delay the senescence of packed vegetables [8]. These techniques are determinants in preserving visual quality and microbial safety of minimally processed fruit and vegetables during the supply chain [9]. MAP is effective in prolonging the shelf-life of fresh-cut produce by modifying the ratios between gases within the packaging. Successful applications of MAP (with low O 2 and high CO 2 levels) to minimally processed fruits and vegetables have been extensively reported in the literature [2], since low O 2 concentration causes a decrease in respiration, this inhibits the growth of postharvest pathogens, and slows down the deterioration rate. However, very low levels of O 2 may induce anaerobic fermentation with the corresponding accumulation of unpleasant odors, undesirable tastes, and tissue damage [2,8,10]. Moreover, the presence of a very high CO 2 concentration (25%) in the storage atmosphere has been found to be deleterious for fresh-cut artichokes, while only slight beneficial effects were observed at lower concentrations (5 and 15%) [11].
The monitoring of the quality of RTE vegetables is crucial, especially in relation to safety issues [12]. It is essential to evaluate the product quality both during storage and at the end of its shelf-life.
Several techniques are used to monitor the quality of fresh-cut fruit and vegetables during shelf-life, such as the evaluation of texture, respiration rate, pH, microbiological and sensory quality, nutritional and antioxidants status [13]. Among them, the electrolyte leakage test measures the cell membrane permeability, resulting in an index of leaf damage. This method is widely used in the investigation of various stress conditions, such as quality changes during shelf-life in different ready-to-eat products [14][15][16], including rocket salad [3,17]. However, all these methods are generally expensive and time-consuming, require the destruction of the sample, and are not suited to automation.
Among the non-destructive techniques, the electronic nose (E-nose) is a fast and reliable method to evaluate the volatile fingerprint in the headspace of food. According to Gardner and Bartlett [18], the electronic nose is "an instrument which comprises an array of electronic chemical sensors with partial specificity and an appropriate pattern-recognition system, capable of recognizing simple or complex odors". The sensors respond to the whole set of headspace volatiles, giving an idea of the metabolic changes that take place in the headspace of food [19]. More than 70 volatiles have been identified in the headspace of fresh rucola, 20 of which contribute to the leaf aroma [20]. Some typical volatile compounds found in RTE rocket salad are dimethyl sulfide, dimethyl disulfide, dimethyl sulfoxide, and furans derivatives [20,21].
Near-infrared (NIR) spectroscopy, in combination with chemometrics and Aquaphotomics, represents a powerful, rapid, and non-destructive analytical tool to monitor the quality of packaged foods by evaluating the changes occurring during the storage [22].
Aquaphotomics is a recent scientific discipline based on NIR measurements and multivariate spectral analysis that investigates the water-light interactions in biological systems. This approach exploits the fact that changes in the water matrix reflect, as would a mirror, the molecules the water surrounds [23,24]. Aquaphotomics is based on the high sensitivity of water's hydrogen bonds that reflect any change in the aqueous system highlighting perturbations that can be observed, measured, analyzed, and interpreted [24]. According to this approach, the NIR spectra acquired in living systems under various perturbations (temperature, ion concentrations, oxidative stress, illumination, disease, and damage) are characterized by 12 water absorption ranges (6-20 nm width each) in the spectral region of the first overtone of water (1300-1600 nm). Such spectral ranges have been called Water Matrix Coordinates (WAMACs) and labelled Ci, i = 1-12. Within the WAMACs, specific water absorbance bands are related to specific water molecular conformations (water species and water molecular structures) [24]. When a perturbation produces changes at specific water absorbance bands, and when this is determined consistently and repeatedly throughout the Aquaphotomics analysis, these water absorbance bands (WABs) are considered 'activated' by the respective perturbation. The selected WABs are plotted in spider charts, named 'aquagrams', which depict the Water Absorbance Spectral Patterns (WASPs) [24].
This work aimed to evaluate and monitor the changes occurring during the storage of ready-to-eat rocket salad packed under modified atmospheres. The electrolyte leakage test and rapid and non-destructive techniques, such as NIR spectroscopy coupled to Aquaphotomics and a Portable Electronic Nose, were employed.

E-Nose Analyses
In a previous preliminary work [25], the evaluation of the storage of the fresh-cut spring rocket (Trial 1) was carried out only through two non-destructive techniques, electronic nose and NIR spectroscopy.
The E-nose was applied in order to evaluate the evolution of the volatile compounds profile of rocket salad during storage. Preliminary results of Trial 1, only summarized here, highlighted the great influence of three broad-range sensors, W5S, W1S, and W2S, on the characterization of the samples. Such sensors are indicated as sensitive towards a wide range of compounds; however, they possess some selectivity towards specific compounds; respectively, methane, and alcohols for W1S and W2S. These compounds, indeed, indicate anaerobic conditions or fermentation reactions likely characterizing the late storage stages, and the related signals mainly characterized the last checkpoints of all the samples. In particular, C samples showed a peculiar pattern, with an important impact of the three sensors at all sampling points. The E-nose profile of treatment C underwent major changes during the storage, while treatments A and B showed more constant sensor values. Profile B reflected minimal variations occurring inside the rocket bag, suggesting that the B atmosphere was the best in maintaining the product's initial conditions [25].
In the second experiment carried out on second-cut autumn rocket (Trial 2) and fully reported here, the three broad-range sensors were confirmed as the predominant ones in the characterization of the samples. Figure 1 shows the biplot of PCA illustrating the mutual relationships between samples and sensors. The three broad-range sensors were responsible for the positioning in the multivariate space of the longer-stored samples. W1S and W2S greatly described the sample C11 and, to a lesser extent, C7 and A13. W5S mainly characterized the C13 sample. The other sensors, sensitive to aromatic compounds, alkanes, terpenes, and organic sulfur compounds, were located close to the axes intersection and described the short-term stored samples. These findings are in accordance with Mastrandrea et al. [26], who studied the changes in volatile compounds responsible for flavor in wild rocket stored in MAP conditions. They found volatile and aromatic compounds, including sulfur, C6, and C5 compounds, acetaldehyde, isothiocyanate, and thiocyanate derivatives. These compounds are responsible for the typical odor and flavor notes of fresh rocket [26]. Interestingly, A11, B11, and B13 samples clustered close to the fresher samples, indicating that these two active atmospheres played a crucial role in limiting metabolic changes inside the bags during the storage.
In Trial 2, E-nose profiles differed among the treatments (Figure 2a). Samples A and B showed lower sensor values compared to treatment C, which was characterized, in particular, by very high W5S sensor values. Figure 2b reports in detail the trend of the three broad-range sensors. Treatment A showed constant values until t = 11 and then a marked significant increase at t = 13 of all sensors. Treatment B showed significant differences on days 11 and 13 for the W1S and W2S sensors and only at time t = 13 for the W5S. Sample C showed significant differences for all sampling points with maximum values at the last checkpoint, indicating a possible accumulation of a wide range of compounds, such as methane and alcohols, in the package atmosphere. The fact that the three sensors mainly described the samples of treatment C, which is characterized by low oxygen, agrees with the literature data [10,[27][28][29]. Indeed, alcohols, such as ethanol and methanol, have been reported to be released under restricted O 2 conditions [10,27]. Ethanol, for example, is considered a major fermentative metabolite, while methanol is reported to come from enzymatic degradation of pectin by pectin methyl esterase [28]. It can be assumed that in the C treatment, the lack of O 2 for aerobic respiration led to a switch to anaerobic respiration, with a release of alcohols. Increases in fermentative volatiles such as ethanol are reported to negatively affect the sensory properties of the product [30][31][32]. Indeed, Allende et al. [29] reported that products with high respiration rates require high levels of O 2 in the package to maintain their quality.

Electrolyte Leakage Analyses
The present work also shows the results, of both trials, of the electrolyte leakage test. The index of leaf damage (I LD ), measured by an electrolyte leakage test, can be considered a good indicator of the product's freshness [33]. In the first trial (spring mowing, Figure 3a), I LD remained more or less constant in all treatments until day 4. On day 7, it increased significantly only in C treatment (LSD > 0.61). In treatments A and B, the increase in I LD took place only after 10 days. In any case, treatments A and B showed lower I LD values than treatment C until the end of the storage (13 days).
In the second trial (autumn mowing, Figure 3b), the leaves' behavior was slightly different. All the treatments showed an I LD value far higher than in the first trial. I LD values were constant until day 7, after which all the values increased almost exponentially. Moreover, in this case, the C treatment showed the worst performance reaching, at the end of the storage, the highest I LD value (LSD > 2.2). Considering both trials, the low I LD values of the samples in the first days agree with the findings of Luca et al. [16], who reported a low index of leaf damage until day 8 of shelf-life in wild rocket leaves. The higher value of treatment C at the end of the storage period indicates that a modified atmosphere enriched with CO 2 can negatively affect rocket leaves, inducing faster deterioration of the samples. Mastrandrea et al. [34] obtained similar results on rocket leaves under passive modified atmosphere conditions: high CO 2 and low oxygen concentration increased off-odors production and decreased shelf-life.
The different behavior of rocket leaves (higher deterioration in the second trial) can be due to the two different seasons when the mowing took place. Environmental conditions, including temperature, solar radiation, and rainfall, have an important influence on the quality of brassica leafy species [35,36]. Furthermore, rocket is capable of regrowing after mowing, giving under optimal pedo-climatic conditions up to 5-6 cropping in a season [37]. Hall et al. [38] reported that the harvest season and the mowing number affect the leaf water content, with late cuttings and summer cuttings having higher dry weight (lower water content). In the two experiments, two rocket harvests were analyzed-the first cut in the early spring and the last cut in the early autumn. The different harvest season and the cutting number could have affected the leaves' composition, and especially their water content [36,38,39]. For this reason, leaves from plants of the first trial (April) were probably more resistant to deterioration than those of the second trial (October).

NIR Spectroscopy and Aquaphotomics
NIR data of Trial 1, as well as those of E-nose, have recently been presented as a poster [25]. In the present paper, these are reported in more detail and compared with those of Trial 2 and with the electrolyte leakage data. Figure 4 shows the averaged raw NIR spectra of the packed rocket salad samples of Trial 1 (a) and 2 (b). The raw spectra showed broad and overlapped bands related to water absorptions at 960-990 (second overtone -OH stretching), 1150-1170 (combination of the first overtone -OH stretching and OH bending), and 1430-1440 nm (first overtone -OH stretching) [24]. The spectra of leafy vegetables originate from the interaction of the electromagnetic radiation with the compounds that absorb it (chlorophyll, carotenoids, water, cellulose and hemicelluloses, starch, lignin, proteins) and from reflection and internal scattering phenomena of the non-absorbed radiation [40]. The leaf can be modeled as a stack of four layers, each of which owns different physical characteristics and optical properties. The light interacting with the leaf can be subjected to phenomena of specular reflection, absorption, transmission, and scattering [40][41][42]. The difference in vegetable surface, thickness, and structure, as occurs for leaves and petioles, determines a different reflection of the electromagnetic radiation [40]. Furthermore, the growing season is recognized to have an influence on the chemical-physical characteristics of the leaves, such as the amount of dry matter, the leaf area surface, and the thickness [43]. Bonasia et al. [44] reported higher dry matter concentration, higher specific leaf area, and more thickened leaves for rocket salad grown in winter-spring compared to the autumn-winter product. The differences in the sample composition caused by the growing season could have affected the raw spectra of Trial 2, which were more heterogeneous and dispersed than in the first trial. Furthermore, rocket leaves from Trial 2 (October mowing) were more variable in shape and dimensions, while the petioles were thicker than those from Trial 1 (April mowing). This distinct leaf morphology probably caused a change in the arrangement of the layers of leaves and petioles inside the package that gave rise to differences in the optical phenomena in the two systems, resulting in different spectra.
The application of pre-treatments made the peaks narrower and more defined, allowing their easier identification. Figure 4c,d show the detail of the second derivative spectra truncated to 1300-1600 nm in order to examine the first overtone of the water. The presence of wavelength shifts in the analyzed range supported the hypothesis that Aquaphotomics could be helpful to discriminate between the different treatments.

Trial 1
To highlight and enhance the small differences found in the second derivative of the spectra, the normalization was performed on Trial 1 spectra according to Tsenkova et al. [24] ( Figure 5).
The graph allowed the identification of the wavelengths most involved in the characterization of the different samples. In order to identify further characterizing wavelengths, the PCA and PLS models were calculated on the pre-treated spectra.
The explorative PCA explained 98% of the total variance showing samples grouped according to the storage time ( Figure 6): (i) t = 0; (ii) t = 1 samples; (iii) t = 4 samples; (iv) t = 7 to t = 13 samples. Interestingly, sample B at 7 days located together with t = 4 samples. This suggested that the characteristics of the biosystem of the 4 days samples were also maintained for 7 days rocket samples stored in atmosphere B.
According to the loadings plot (Figure 6b), the separation along PC1 between fresh and longer-stored samples was mainly based on a wavelength of 1373 nm, while the separation along PC2 was due to 1453 nm. According to Tsenkova et al. [24], these wavelengths correspond to the absorbance of the free OH stretch and of strongly hydrogen-bonded water, respectively. A preliminary PLS model was built up with pre-treated spectra and the I LD index. Figure 7 shows the scatter plot of the model (Figure 7a) together with the factor loadings ( Figure 7b).    The model showed an R-value in calibration of 0.92 and an R-value in cross-validation of 0.83, with a standard error in cross-validation of 0.30%. The good performance of the preliminary predictive model indicated some relationship between the NIR spectra and the plant membrane damage index. The NIR spectrum of a leaf is indeed strongly influenced by its structure, thickness, and state of hydration, which in turn is an index of plant stress [45]. Aday [46] also found agreement between FT-NIR spectroscopy and electrolyte leakage data on mushrooms treated with electrolyzed water in combination with passive atmosphere packaging.
Based on the common WABs found in normalized spectra and in the PCA and PLS loadings plots, 12 wavelengths inside the WAMACs ranges were selected for the construction of aquagrams. Each wavelength corresponds to the absorption of specific water molecular species. The wavelengths are listed and described in Table 1. The aquagrams of Trial 1 highlighted differences among the samples, giving rise to different WASPs for each sampling time and for each treatment (Figure 8).
Treatments A and C of the Trial 1 showed a shift from left to right up to the fourth day; then, the graph went back to the left. For treatment B, the inversion occurred on the seventh day. This behavior indicates a prevalence of hydrogen-bonded water species and strongly-bound water [47] in the freshly cut rocket. With the progress of the storage, a situation is reached in which free water or weakly hydrogen-bonded water prevails [47]. This point could indicate the loss of freshness of the product. From these premises, it can be assumed that Aquaphotomics estimated the first loss of freshness after 4 days from packaging for the A and C treatments, and after 7 days for the B treatment. This suggested that treatment B was the best at preserving the freshness of rocket salad, in agreement with the E-nose findings.

Trial 2
Based on the characteristics of the second sampling (autumn rocket, October mowing, presence of larger leaves and long petioles), the adequacy of the Aquaphotomics model was verified. The data processing procedure was repeated as for the first trial.
PCA is reported in Figure 9. Again, there was a good separation of the samples according to the days of storage along PC1, which accounted for 98% of the variance (Figure 9a). The group of samples from day 4 was separated from the others along PC2. Interestingly, the B11 sample ranked alongside the first checkpoints, while the C7 sample was superimposed on t = 11/13 samples.
According to the loadings plot (Figure 9b), the separation along PC1 between fresh and longer-stored samples was mainly based on a wavelength at 1366 nm, while the separation along PC2 was due to 1385 and 1521 nm. According to Tsenkova et al. [24], the wavelengths at 1366 and 1385 nm correspond to the absorbance of water solvation shells.
The PLS regression between NIR spectra and I LD data was also tentatively applied to the data from Trial 2. However, in this case, the results were not satisfactory. Better results were achieved after the application of Moving Average smoothing and the first derivative Norris Gap, obtaining R = 0.86 in calibration. However, the results in cross-validation were not as encouraging (R cross-val = 0.54), not making the model useful for practical use. It should be noted that the NIR spectra were acquired on both the sides of intact and sealed salad bags and, therefore, on the whole salad, including both leaves and petioles. Conversely, the I LD data resulted from a destructive analysis carried out exclusively on the leaves. Furthermore, it can be observed from Figure 3b that the samples up to day 7 were characterized by a very low variability of I LD (2.5-3.7), with a detrimental effect on quantitative calibrations [48]. These two aspects could explain the low performance obtained for this model.  The aquagrams were built using the same WABS as in Trial 1 ( Figure 10). The aquagrams of Trial 2 showed a different and opposite profile compared to Trial 1. As previously reported, the presence of plant material with different physiological and metabolic characteristics, due to the different growing and harvesting season [38,39,43,44], might have affected the hydration state of the stored product. Furthermore, morphological variability could have influenced the aquagram trends.
Treatments A and B showed similar profiles, with the fresh product (t0) profile very different from all the others. Consequently, it was difficult to identify a trend as a function of storage time. Fresh samples located on the right side of the chart, showed a prevalence of free water, while the stored samples were more characterized by bound water. The profiles of checkpoints 1-13, on the other hand, looked very similar, and almost overlapped in the region of 1441-1478 nm. This could indicate that the major changes in water occurred immediately after packaging. Major variations occurred at 1416-1428 nm and between 1490-1509 nm. Conversely, treatment C showed different profiles for the various sampling days.
These results suggested building a dedicated series of aquagrams not only as a function of the applied perturbation but also of the variability of the raw material.
This topic should be investigated more in-depth to determine the role of water and interactions with plant tissues during storage.

Materials and Methods
Two experimentations were carried out at the Research Centre for Engineering and Agro-Food Processing of the Council for Agricultural Research and Economics (CREA IT, Milan, Italy). Freshly harvested rocket leaves (Eruca sativa Mill.) were purchased from a local grower in Milan (Italy). The first test was carried out in spring on the first cut rocket (Trial 1), while for the second experiment second cut autumn rocket (Trial 2) was used. The fresh salad was immediately transported to the CREA-IT lab and inspected for impurity and visual defects, discarding damaged and dehydrated leaves. The product underwent washing in mildly chlorinated water (1% sodium hypochlorite solution) for 1 min, followed by rinsing in cold water, and centrifugation to remove the remaining water. Rocket leaves (ca. 60 g) were packed in polypropylene film bags, and each bag was sealed under atmospheric and modified headspace conditions. The following gas mixtures were used: treatment A, atmospheric air (21% O 2 , 78% N 2 ); treatment B, 30% O 2 , 70% N 2 ; treatment C, 10% CO 2 , 5% O 2 , 85% N 2 . Three bags were prepared for each evaluation day for each treatment. All samples were stored at 4 ± 1 • C for 13 days and subjected to NIR, E-nose, and electrolyte leakage analyses at scheduled sampling points: 0, 1, 4, 7, 11, and 13 days for NIR and electrolyte leakage analyses; 1, 4, 7, 11, and 13 days for E-nose analyses.

E-Nose Analyses
The E-nose measurements were performed in triplicate on 3 bags for each treatment through a pierceable Silicon/Teflon disk, using a commercial portable electronic nose (PEN3, Win Muster Airsense Analytic Inc., Schwerim, Germany). The instrument is made up of a sampling apparatus, a detector unit containing the sensor array, and patternrecognition software (Win Muster v.16) for data recording. The sensor array is composed of 10 metal oxide semiconductor (MOS) type chemical sensors: W1C (aromatic compounds), W5S (broad range), W3C (aromatic compounds), W6S (hydrogen), W5C (alkanes, aromatic compounds, less polar compounds), W1S (broad range, methane), W1W (terpenes, organic sulfur compounds, limonene, pyrazine), W2S (alcohols, broad range), W2W (aromatic compounds, organic sulfur compounds), and W3S (methane aliphatic, reacts on high concentrations > 100 mg/kg). The sensor response is given by the ratio between the conductivity response of the sensors to both the sample gas (G) and the carrier gas (G 0 ) over time (G/G 0 ). The E-nose analyses were performed following the conditions reported by Vanoli et al. [49]. For each measurement, the conductivity G/G 0 of the 10 sensors at the time corresponding to the normalized maximum of all signals was taken as the vector of the sensors' signal. The average of the measurements of each replicate was used for statistical analysis. Data were subjected to one-way analysis of variance (ANOVA) for means comparison using Statgraphics ver. 5.1 (Manugistic Inc, Rockville, MD, USA) software package. The means were separated using a Tukey's HSD test, and their statistical significance was determined at 5% (p < 0.05) level. The E-nose data were also analyzed by principal component analysis (PCA) with the Unscrambler software package (v 9.7, Camo, Inondhcim, Norway).

Electrolyte Leakage Test
The index of leaf damage (I LD ) was measured by an electrolyte leakage test. The analysis was performed in triplicate on 3 bags for each treatment. A method modified from Kim et al. [33] was used for ion leakage evaluation. Rocket leaf blades were cut into 1 cm 2 pieces, washed in distilled water, and blotted dry with paper towels. Two grams of the cut samples were placed in a falcon tube containing 25 mL of Milli-Q water and shaken for 1 h in a horizontal shaker at 100 strokes/minute. The initial conductivity (IC) of the solution was measured with a digital conductivity meter (Accumet AR20, Fisher Scientific, Waltham, MA, USA), and the tubes were frozen at −20 • C for one week. Frozen samples were then held at room temperature for 24 h, shaken for 1 h, and final conductivity (FC) was measured. The index of leaf damage was calculated as follows: I LD = (IC/FC) * 100. Differences among averages were calculated by one-way ANOVA (Statgraphics 5.1 statistic software) and a least significant difference (LSD) post-hoc test. All the averages whose difference exceeds the LSD value are statistically significant (p < 0.05).

NIR Spectroscopy
Near-infrared spectra were collected in reflectance mode using the MicroNIR OnSite-W (VIAVI Solutions Italia S.r.l., Monza, Italy) portable spectrometer. Spectra were acquired in the spectral range between 900 and 1600 nm (50 scans; 125 reading points) on 3 bags for each treatment, on both sides of the bags. Ten replicates for each side were collected, for a total of 60 spectra for each treatment. Spectra of PET packaging (#10) were also collected, and the average spectrum was subtracted from each sample spectrum. Before the analysis, the instrument underwent calibration for the black, on the air, and for the white, on the supplied standard tile.

Chemometrics and Aquaphotomics
Chemometric analysis of the NIR data was performed using the Unscrambler software package (v 9.7, Camo, Inondhcim, Norway).
An explorative PCA was applied on spectra pre-processed using the Moving Average smoothing (gap size 15 points) and the first derivative Norris Gap transformation (gap size 21 points) in the range of the first overtone of water (1300-1600 nm).
Predictive NIR models for the I LD index were computed by partial least square (PLS) regression on spectra pretreated with the Standard Normal Variate (SNV) and the second derivative Savitzky-Golay transformation (gap size 11 points).
Spectra were also pretreated according to Tsenkova et al. [24]: the second derivative Savitzky-Golay filter (second-order polynomial fit and 21 points) and Multiplicative Scatter Correction were applied to absorbance spectra to remove potential scatter effects. The transformed spectra were then normalized by applying the following formula: where Aλ is the transformed absorbance, µλ is the mean value of all spectra and σλ is the standard deviation of all spectra at wavelength λ. The normalized spectra were then properly averaged using MS Excel ® . Twelve wavelengths that showed the maximum or minimum values in the normalized spectrum and the most impacting PCA or PLS loadings were chosen. The aquagrams were then built up using the selected wavelengths.

Conclusions
E-nose and I LD results agreed in identifying the B atmosphere as the best for maintaining the freshness of RTE rocket salad in both experiments.
According to E-nose results, the A and B atmospheres confirmed the plausible positive and active role of oxygen concentration in maintaining the freshness of ready-to-eat rocket and minimizing the occurrence of anaerobic fermentations. Treatment C with low oxygen concentration seemed to lead to a switch to anaerobic respiration with a release of alcohols.
Similarly, the index of leaf damage suggested that in the C atmosphere composition, the cell membrane underwent more degradation and damage than in the other treatments.
NIR spectroscopy was useful in observing samples distribution according to the storage time, mainly based on wavelengths corresponding to the absorbance of the water solvation shells. The aquagram proved to be a promising tool for detecting changes in the water structure during storage in atmospheres with different gaseous compositions. For Trial 1 the spider-charts gave rise to different WASPs for each sampling time and treatment, suggesting that treatment B was the best at preserving the freshness of rocket salad, according to the E-nose and I LD results. For Trial 2, aquagrams could not provide a clear interpretation of the changes that occurred inside the bags. However, it was possible to identify a greater variability in the WASPs in the C treatment during the shelf-life, suggesting greater changes in the water structure compared to the A and B atmospheres.
Although the data are very preliminary, the good agreement between the NIR spectra and the I LD data makes NIR spectroscopy a promising technique for non-destructively evaluating the damage status of the plant membrane of the RTE rocket salad during the shelf-life.
NIR coupled with Aquaphotomics should be investigated more in-depth to determine the role of water and interactions with plant tissues during shelf-life in order to confirm these preliminary results.
Some other destructive parameters, such as consistency, fluorescence, ripening index, are under evaluation to confirm these results obtained, using mainly rapid and nondestructive techniques.

Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.

Data Availability Statement:
The data presented in this study are not publicly available due to the privacy related to the Agridigit project.