extfieldsofstudy: Chemistry Medicine

s2fieldsofstudy:
Agricultural And Food Sciences
Chemistry

text:
An acidic-groups detection method and its application to analysis of Chinese humic acid samples

The method of non-aqueous conductivity titration (NACT) of organic weak acids was applied to quickly and accurately determine the phenolic-hydroxyl and carboxyl-groups contents in humic acid. By varying the pH of the humic-acid sample, the concentration of the titrant, and the nitrogen-gas flow rate, the optimal titration conditions were determined to be a sample pH of 4, titrant concentration of 0.05 mol/L, and nitrogen-gas flow rate of 80 mL/min. Applying the detection method to p-hydroxybenzoic acid showed that its phenolic-hydroxyl content was 758.82±111.76 cmol/kg and carboxyl content was 744.44±51.11 cmol/kg. The theoretical phenolic-hydroxyl and carboxyl-groups contents of the p-hydroxybenzoic acid were 723.96 cmol/kg respectively, indicating that the method can accurately quantify the carboxyl and phenolic-hydroxyl groups in the sample. The NACT was used to measure the phenolic-hydroxyl and carboxyl-groups contents in humic acid quickly and accurately. In addition, 29 humic acid samples from 8 provinces of China covering the main humic-acid producing areas were collected and analyzed for acidic-groups content using the reported method.

Introduction
Humic substances are complex and heterogeneous mixtures of polydispersed materials formed in coal, soils, sediments, and natural waters by biochemical and chemical reactions during the decay and transformation of plant and microbial remains (a process called humification) [1]. Humic acid, together with fulvic acid and humins are the major component of humic substances. As the acid-insoluble fractions of humic substances, Humic acid is partially soluble in water and form micelle-like structures in neutral to acidic conditions [2]. Humic acid is polymer compound rich in active groups such as phenolic-hydroxyl, carboxyl, sulfhydryl, and carbonyl [3,4] and is widely used in agriculture, ceramics, oil extraction, and environmental protection as an organic fertilizer, binder, mining aid, water purifier, and heavy-metal remover [5][6][7][8][9][10][11]. Humic acid is mainly found naturally in peat, lignite, and weathered coal in nature [12,13], which composition can vary according to geographical origin, age, climate and biological conditions, making the precise characterization of these substances difficult [14,15]. Humic acid is highly acidic due principally to carboxyl and phenolic-hydroxyl groups. Total acidity of humic acid may range from 1 mol/kg to more than 14 mol/kg. Other functional groups in humic acid include quinone and ketonic carbonyl, amino, and sulfhydryl groups (Fig 1) [13,[16][17][18][19][20][21][22][23]. These groups are responsible for exhibiting good hemostasis, and antidiarrheal functions. Humic acid is widely used in medicine and feed additives [24][25][26], and the phenolichydroxyl and carboxyl-groups contents in humic acid is positively correlated with its biological activity [27,28]. By hydrolyzing the phenolic-hydroxyl and carboxyl esters in humic acid, the free phenolichydroxyl and carboxyl-groups contents can be increased and the biological activity of the humic acid can be improved, so quantitative analysis of these groups in humic acid is key to its quality control. Folin phenol method and calcium acetate method (FPCA), potentiometric titration, NMR, and infrared method are commonly used to study the acidic groups in humic acid [29][30][31][32][33][34][35][36], but some of these methods are complicated, while some have high detection limits and inaccurate measurement results. For example, the FPCA method can be used to determine the concentrations of phenolic-hydroxyl and carboxyl-groups through classical titrations in the laboratory, however, the two different groups cannot be simultaneously determined, resulting in time-consuming work. Furthermore, NMR spectroscopy and infrared method involves the use of complicated equipment and have limiting operation-technology requirements, making it inconvenient for common application in the production and quality control of humic-acid products.
The non-aqueous conductivity titration (NACT) is the most common titrimetric procedure used in pharmacopoeial assays and it is suitable for the titration of very weak acids and very weak bases, which has been used to quantitatively analyze phenolic-hydroxyl and carboxyl groups in lignin [37][38][39]. Because humic acid and lignin have similar molecular structures and NACT can be applied to the measurement of weak organic acids [38], in this study, we attempted to adopt this method to measure the phenolic-hydroxyl and carboxyl-groups contents of humic acid by optimizing the humic-acid sample pH, concentration of the titrant, and  nitrogen-gas flow rate. It was found that it can be used to simultaneously measure the phenolic-hydroxyl and carboxyl-groups contents in humic acid by one titration, which is convenient and quick.

Test materials
Samples were collected from the humic-acid producing area of China, as shown in Table 2 and Fig 2. Qingshuang Chen from Shandong Asia-Pacific Haihua Biotechnology Co., Ltd. and Xue Li from Yinfu (Jinan) Biotechnology Co., Ltd. collected, registered, and numbered the humic acid samples from the two companies. The two companies do not make any requirements on the use of humic acid raw materials. Humic-acid raw materials contain various trace elements and impurities, which would affect the detection accuracy; hence, they are converted into sodium humate by a reaction with sodium hydroxide during the extraction process. Briefly, 50 g humic acid ore powder and 5 g NaOH were mixed in 1000 mL distilled water and stirred at room temperature for 2h. The reaction solution was centrifuged 2800×g for 30 minutes to remove the insoluble precipitates. The supernatant was dried at 105˚C in oven to a constant weight.
The chemical reagents used in this study included: p-hydroxybenzoic acid, potassium hydroxide, hydrochloric acid, acetone, pyridine, and anhydrous ethanol, which were purchased from Sinopharm Chemical Reagent Co., Ltd. and were all of analytical grade (AR). The main instruments used were: a DDSJ-308F conductivity meter with a lightning magnetic DJS-1C platinum black electrode (Shanghai Lei Magnetic Instrument Factory) and an 85-1 magnetic constant-temperature mixer (Changzhou Guohua Electric Co., Ltd.).

Preparation of humic-acid sample
10 g of sodium humate was weighed into 100 mL of 12 mol/L hydrochloric acid, stirred well, and then slowly diluted to 300 mL with distilled water. The mixture was then centrifuged at 2800×g for 5 minutes.
A total of 300 mL distilled water was added to the precipitate after centrifugation with thorough stirring. The mixture was centrifuged at 2800×g for 5 min, and the pH of the supernatant was measured at the same time. This water-washing process was repeated, followed by drying for concentrate analysis (Fig 3).

Principle and process of NACT of phenolic-hydroxyl and carboxyl groups in humic acid
During the titration, OHin the KOH-isopropanol titration solution first reacts with the more acidic carboxyl groups on the humic-acid molecule. After this reaction is complete, the OHcontinues to react with the phenolic-hydroxyl groups. The conductivity change of the solution during the reaction is measured using a conductivity meter.
The specific process included weighing 100 mg of humic acid (dry), placing it in a clean and dry 50 mL round-bottom four-necked flask, adding 40 mL of pyridine/acetone (1:4,v:v) solvent and 1 mL of distilled water, and finally adding 0.41 mL ethanol (99.5%, mL/mL). Nitrogen was flowed in to protect the active groups, while the sample was stirred at 200 rpm. After 5 minutes, the titration was performed with a KOH-isopropyl alcohol solution, with the conductivity instrument used to record the conductivity curve until two equivalence points appeared [37]. The equivalence points M and N were determined using the conductivity curve (Fig 4).
The dotted line is the tangent epitaxial line of the inflection point, and the intersection points M and N are the equivalence points of the carboxyl-and phenolic-hydroxyl groups corresponding to the two inflection points, respectively. Therefore, the carboxyl-groups content A and the phenolic-hydroxyl-groups content P can be calculated using Eqs (1) and (2) on the basis of previous study [39]: where V M and V N are the titrant consumptions (mL) at the equivalence points M and N, m is the sample mass (mg); C is the concentration of titrant(mol/L); 45 and17 are the molar masses of the carboxyl group and the phenolic-hydroxyl group, respectively.

Condition optimization
The sample pH, titrant concentration, and nitrogen-gas flow rate were varied to determine the optimal reaction conditions using the weathered coal from Shanxi province, China ( Table 1).

Validation of the NACT method
To verify the accuracy of the method for the detection of phenolic-hydroxyl and carboxyl groups, 20 mg p-hydroxybenzoic acid was used as the substrate and the measured phenolichydroxyl and carboxyl-groups contents were compared with the theoretical values. Folin phenol method and calcium acetate method (FPCA) were used to determine the concentration of phenolic hydroxyl groups and carboxyl groups, respectively. In Folin phenol method, 5μL humic acid (2.5 mg/mL) was added into 95μL distilled water. The solution was further mixed with 30μL Folin phenol reagent and 70μL sodium carbonate solution (10%, w/v) in the dark. After reaction at 37˚C in the dark for 30 min, the absorbance was measured at 765 nm. p-hydroxybenzoic acid was used as the standard solution to determine  the phenolic hydroxyl groups by Folin phenol method. According to the standard curve, the molar number of phenolic hydroxyl in humic acid was calculated, and then the content of phenolic hydroxyl in humic acid sample was obtained. To measure the carboxyl groups, 1g of humic acid sample was added into 130 mL calcium acetate solution (0.25M) in a flat-bottom flask, which reacted for 2 hours at 100 o C in a water bath with condenser tube. Finally, 0.01M sodium hydroxide solution was used to titrated until the pH increase to 8.2. The content of carboxyl groups in humic acid sample was calculated based on that the molar number of sodium hydroxide consumed in titration was equal to that of carboxyl groups in humic acid.

Statistical analysis
All statistical analyses were performed using SPSS 22.0. A p-value <0.05 was considered a statistically significant result.

Effect of pH and rinsing cycles on conductivity
The titration cannot be directly performed because the Na + ions in the sodium-humate sample would affect the conductivity of the solution. Instead, insoluble humic acid was formed by reacting the sodium humate with excess hydrochloric acid, and the sample was then washed with distilled water to remove Na + , H + , Cl -, etc., to ensure that no free ions were present in the sample. For convenience, the end point of H + removal was identified by measuring the pH of the supernatant of the washing liquid. It can be seen from Fig 5 that after washing 8 times, the pH of the supernatant no longer changed and the pH of humic acid tended to be stable. This indicated that there was almost no free H + and Clremaining in the humic acid sample, so it had reached the requirement for titration. Therefore, in the rest of the study, the samples were washed with 2.4 L of distilled water 8 times to remove free ions. It can be seen from Fig 6 that the pH value of the sample had a great influence on the conductivity. With 4-6 washing cycles (pH<4.0), there were three inflection points in the conductivity curve, which could be because the residual H + and Claffecting the ion concentration in the solution that further affect the conductometric curves. The conductivity curve was more clearly defined after washing 8-10 times (pH�4) as most of the residuals were removed. However, the humic-acid content in the sample was 55±1.2% after 8 washing cycles, and it was 51 ±0.9% after 10 washes, indicating that humic-acid sample loss occurred during washing. As long as the pH is stable, the number of washings should be as low as possible to minimize sample loss, so 8 water-washing cycles was optimal.
Therefore, in order to simplify the analysis process, in future experiments, the number of washing cycles and distilled water consumption need not be measured. Instead, the supernatant pH value of 4.0 could be directly used to determine whether H + in the sample had reached the removal end point.

Effect of titrant concentration on the conductivity curve
The titrant concentration had a strong influence on the conductivity measurements, as shown in Fig 7. When the titrant concentration was 0.03 M, the second equivalence point did not appear. This may be because the concentration of the titration solution was too low and the phenol hydroxyl titration equivalent point cannot be reached. As the volume of the reaction system was limited, we increased the concentration of the titrants. When the titrant concentration was 0.05 M, both the equivalence points appeared. However, when the titrant solution concentration was 0.07 M, the OH-concentration was higher compared to that of the carboxyl content in the sample, so the reaction jumped past the first equivalence point. This made it impossible to calculate the carboxyl content. Therefore, the titrant concentration of 0.05 M was used for the next experimental process.

Effect of nitrogen-gas flow rate on conductivity
Phenolic hydroxyl groups in humic acid were easily oxidized to quinone groups during titration with the increase of pH value [40,41], and the humic acid could be converted to short chain fatty acids such as acetic acid and oxalic acid during the oxidation process [42]. Nitrogen-gas was injected to avoid an undesired oxidation. Fig 8 shows that the nitrogen-gas flow rate had a significant influence on the conductivity curve. The nitrogen-gas flow rate of 40 mL/min and 60mL/min were too low so the reaction did not go to completion or the acidic groups were destroyed by oxidation [41], while the nitrogen-gas flow rate of 40 mL/min and 60mL/min allowed for full reaction of the humic acid. When the nitrogen-gas flow rate was 80 mL/min, the phenolic-hydroxyl-groups and carboxyl-groups contents were measured to be 105.88±17.65 cmol/kg and 60±4.44 cmol/kg respectively, and with the nitrogen flow rate at 100 mL/min, they were 117.65±35.29 cmol/kg and 62.22±8.89 cmol/kg. It can be seen that the measured functional groups contents were very similar. Considering that evaporation of the solvent under a too-high nitrogen-gas flow rate would affect the conductivity, the optimum nitrogen-gas flow rate was identified to be 80 mL/min.

Verify the accuracy of the method using p-hydroxybenzoic acid
Because the acidic-groups content in humic acid is uncertain, the phenolic-hydroxyl and carboxyl-groups contents of the reference compound p-hydroxybenzoic acid were determined to further verify the accuracy of the NACT. The theoretical carboxyl and phenolic-hydroxyl content in p-hydroxybenzoic acid was 723.96 cmol/kg respectively. The equivalence points M and N of the conductivity curve were determined (Fig 9), wherein M is the carboxyl equivalence  point and N is that for phenolic-hydroxyl groups or similar. The carboxyl-groups content A and the phenolic-hydroxyl-groups content P can thus be calculated using Eqs (1) and (2), respectively. In three parallel measurements, the phenolic-hydroxyl and carboxyl-groups contents of p-hydroxybenzoic acid were found to be 758.82±111.76 cmol/kg and 744.44±51.11 cmol/kg. These results indicated that the NACT could be used quickly and accurately to measure the phenolic-hydroxyl and carboxyl-groups contents in p-hydroxybenzoic acid after optimizing the conditions.

Validation of the NACT method by FPCA method
To validate the data from NACT method, FPCA method was further carried out to measure the phenolic hydroxyl groups and carboxyl groups in the purchased humic acid standard ( Fig  10). The result showed that the phenolic hydroxyl groups were 106.26±2.71 and 102.19±4.39 cmol/kg determined by NACT method and by FPCA method, respectively. While the carboxyl groups were 43.93±3.42 and 42.31±2.46 cmol/kg measured by the two methods. The data from the two methods did not show significant difference (phenolic hydroxyl group, p = 0.24; carboxyl group, p = 0.54), which indicated that NACT method is as accurate as previous method.

Measurement of phenolic-hydroxyl and carboxyl-groups contents in humic acid products from different production areas in China
Quantitative analysis of phenolic-hydroxyl and carboxyl groups in humic acid products from different mining areas in China was carried out using the NACT established in this study, with the results shown in Table 2.
China has a vast territory with large geological differences. According to available data, the distribution of humic-acid mines in China is very uneven, and they are mainly found in Inner Mongolia, Shanxi, Ningxia, Xinjiang, Gansu, Heilongjiang, Jiangxi, and a few other provinces. Humic acid is a complex macromolecular substance that is buried in the ground during the movement of the earth's crust and undergoes a combination of physical, chemical, and microbial processes. Therefore, the structure of the humic acid in each region varies because of the differences in the geological conditions. It can be seen in Table 2 that the humic-acid content in the 29 samples varied from 25.1±0.53% to 71.8±0.98%. There was no obvious correlation between the phenolic-hydroxyl and carboxyl-groups contents and the humic-acid content in the 29 samples. For example, in sample H1 with the lowest humic-acid content, the phenolic-hydroxyl and carboxyl-groups contents were 94.12±25.88 cmol/kg and 64.44±9.11 cmol/kg, respectively, while the humicacid content was 71.8±0.98 in sample X6, the phenolic-hydroxyl and carboxyl-groups contents were 82.35±17.06 cmol/kg and 68.89±15.11 cmol/kg. It can be seen that there was no obvious correlation between the humic acid and acidic-groups contents in the results (Fig 11).

Conclusions
For quick and effective determination of the phenolic-hydroxyl and carboxyl-groups contents in humic acid, the measurement of the conductivity curve of humic-acid samples prepared at pH 4 was found to be the most accurate approach. The optimum conditions for the titration process were titrant concentration of 0.05 mol/L and nitrogen gas permeation rate of 80 mL/ min. The NACT was applied to the reference compound p-hydroxybenzoic acid, giving a phenolic-hydroxyl-groups content of 758.82±111.76 cmol/kg and a carboxyl-groups content of 744.44±51.11 cmol/kg, identical to the theoretical phenolic-hydroxyl and carboxyl-groups contents. This method could be used to quickly and accurately measure the phenolic-hydroxyl and carboxyl-groups contents in p-hydroxybenzoic acid, thereby indicating method accuracy and feasibility.
NACT is a new method for measuring the phenolic-hydroxyl and carboxyl-groups contents in humic acid. It is fast, simple, and accurate and facilitates the simultaneous measurement of the phenolic-hydroxyl and carboxyl-groups contents by one titration, which can greatly improve detection efficiency.
The NACT was successfully conducted in this study to determine the acidic-groups content in the samples obtained from the main humic-acid mines in China, demonstrating its applicability for the molecular-structure analysis and functional development of humic acid.


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

text:
REPRODUCTIVE PERFORMANCE OF DAIRY COWS IN RELATION TO GENOTYPE, AGE, PARITY, BODY WEIGHT AND BODY CONDITION SCORE AT RAJSHAHI DISTRICT OF BANGLADESH

Received 17 November, 2020 Revised 16 December, 2020 Accepted 28 December, 2020 Online 12 January, 2021 ------------------------

INTRODUCTION
Bangladesh is an agro-based country. Livestock, an integral part of agriculture, plays an important role to keep the rural economy of Bangladesh viable (Sarma and Ahmed, 2011). About 80% of Bangladesh is directly or indirectly involved in agriculture and livestock farming. The contribution of livestock in gross domestic product (GDP) is about 2.51 % in Bangladesh (DLS, 2016). The importance of livestock production has increased in Bangladesh as witnessed by the growth of the sub-sector over the last few decades and the contribution to employment in the country (FAO, 2005). Among the livestock, cattle are most available and versatile component providing a significant contribution to gross domestic, export products and raw materials for industries in relation to existing integrated agricultural farming system in Bangladesh. So, Livestock of Bangladesh is back bone of rural economy. The main goal of dairy farmers is one calf/cow per year. It plays an important role to promote human health and wealth by supplying animal protein in the form of milk and meat. The cattle population in Bangladesh is currently estimated 25.7 million cattle (http://en.banglapedia.org/ index.php? title=Livestock) and among them in Rajshahi district is approximately 5.35 lakh (personal contact with DLS, Rajshahi; 2015). The urban and peri-urban/rural dairy production system has tremendous potential for development and could play a significant role in minimizing the acute shortage of dairy products in urban centers of any country (Mureda and Zeleke, 2008). Reproduction, health and production i.e. calf and milk production are the principal factors affecting the profitability of a dairy herd. Productive performance is determined to a great extent by the milk yield at first lactation and the farm, followed by the age at first calving (Ilieva and Peeva, 2007). In recent years, the dairy industry agenda in many countries has been dominated by health-related problems (Vacek et al., 2007). One prerequisite for effective health management is the accurate knowledge of factors that affect the health status of a cow, such as age, parity, body weight or health history, and of the relationship between health problems and other economically important traits, such as calf, milk production, reproduction, and length of productive life.
The lifetime productivity of cows largely depends on their individual reproductive performance such as age at puberty, age at first calving and calving interval (Ensminger, 1969). The most important measures of reproductive performance of cows are age at first calving, length of calving interval, and length of cow productive life. The current level of reproductive performance in dairy herds is well below the optimum in most countries (Morton, 2003). Reproductive inefficiency in turn, however, affects dairy herd profitability by decreasing milk yield, the number of replacement heifers produced and by increasing culling rate. Healthy pregnant cows are seldom culled from a dairy management control of reproduction and profitability in dairy herd (Grohn and Rajwala, 2000;Santos, 2007). The differences in management (production) systems and environmental conditions under which cattle are maintained could greatly affect the occurrence of reproductive health problems. To ensure food security of our people we need to improve our livestock production first. Low birth rates and reproductive problems are main problems for growth of this sector. Even an apparently healthy animal may not necessarily be in a state of good reproductive health. Very little work has been done to identify the extent of productive and reproductive performance of dairy cows in Rajshahi district, especially situated in low land and hilly areas. Hence, in order to design relevant dairy development strategies and implement context specific interventions for future development of the dairying at study areas, performance evaluation of existing dairying is important. The scientific information regarding the aforementioned parameters helps as a starting point for further development endeavors in dairy enterprise in the nation. Therefore, the present study was undertaken and designed with the objectives of identification of reproductive performance of cows in relation to breed/genotype, age, parity, body weight and body condition available in selected areas of Rajshahi district of Bangladesh.

Study areas and population
The study areas were selected at the Rajshahi district of Bangladesh. A total of 500 cows selected and data were collected directly from the dairy farms owners/farmers/attendant by using questionnaires during the period from September 2013 to December 2014.

Method of data collection
The data were collected from the selected farms by the researcher every three (3) months interval in year. After collecting the questionnaire, preliminary sorting and checking, data were prepared for analysis.

Description of animals
Cross-bred cattle are mainly Holstein Friesian and Sahiwal. Approximately 20% animals are crossed in Bangladesh. Indigenous cattle are found everywhere of the country has no definite characteristics and constitute about 80% of indigenous cattle population of the country. Their coat color varies from red, grey, white, black or a mixture of them in different proportion.

Grouping of experimental animals
To achieve the goal, animals were grouped according to following considering factors: Breed/genotype: The cow was classified broadly in Indigenous or Local and Cross-bred. Selected cows were further group into their genetic composition. These were

Body condition score (BCS)
Effect of body condition on the reproductive performance and disorders were studied. Cows of different body condition were considered for the study and BCS were determined by Nicholson and Butterworth (1986) with some modified technique. The cows were divided in the following groups considering body conditions: Group I : Poor (n = 108) Group II : Medium (n = 120) and Group III : Good (n = 272) Where, Poor: Heads of vertebral process (HVP) were visible, the pad on tuber coxae and ischiatic tuberosity was thicker than that of the under conditioned animal. Medium: Flank and covering well balanced. The gap between thighs was filled up by fat apparently looks lat not round. Good: The gap between thighs was filled up by fat apparently looks fatty and well-muscled. Reproductive traits of cows studied: The following reproductive traits of dairy cows were studied in this study-Age at puberty: The age at which a heifer first shows the estrous signs and behaviors may be defined as age at puberty. It was measured in month (m). Age at first calving: It is defined as the age when a heifer first calving a newborn calf. It was measured in month (m). Post-partum heat period: It is considered as the interval between date of calving and the date of first insemination or first heat show after parturition. It was calculated in days (d). Calving interval: The number of days between two successful calving of the same cows or the period from one calving to the next was termed as calving interval. It was measured in days.

Data management and analysis
The data obtained from the questionnaire was entered into the Microsoft Excel 2007 and transferred to SPSS version, 17.0. Data were statistically analyzed to calculate the effect of reproductive and productive traits of dairy cows. Various standard statistical procedures ((percentages, mean and chi-square test etc.) had been adopted in this study. The mean and standard error of mean for the reproductive traits were calculated with help of a computer package programme (SPSS) version 17.0. Mean of different traits were then tested by using analysis of Variance (ANOVA) by Duncan Multiple Range Test (DMRT). Data were presented as Mean±SE. P<0.05 was considered as significant. Simple ANOVA was performed considering the age of dairy cows and to observe the significant differences among the mean values, Duncan's multiple range test (DMRT) was performed to observer significant differences of reproductive traits in different breed, genotype, age, parity, body weight etc. of dairy cows.

Effects of breeds/genotypes on reproductive performance of dairy cows
The effects of breeds on reproductive performance of dairy cows are summarized in Table 1 and Figure 1. in cross-bred cows. The higher value of service per conception (1.95 ± 0.05) was observed in crossbred cows and the lower value (1.84 ± 0.08) was observed in local breed cows. There was only significant effect (P<0.05) of breed on post-partum heat period of dairy cows, other reproductive traits were not significant.      The effects of age group on reproductive performance of dairy cows The effects of age group on reproductive performance of dairy cows are presented in Table 3 and Figure  4 and 5. The lower value of age at puberty and age at first calving (24.46 ± 0.53 m and 33.62 ± 0.56 m) were observed in 3 to <5 years of age and the higher (

The effects of parity on reproductive performance of dairy cows
To determine the variation of reproductive parameters of dairy cows due to parity effect are presented in Table 4 and Figure 6. The lower value of almost reproductive traits of age at puberty (25.

The effects of body weight on reproductive performance of dairy cows
The effects of body weight on reproductive performance of dairy cows are presented in Table 5 and

The effects of body condition score on reproductive performance of dairy cows
The effects of body condition score on reproductive performance of dairy cows are presented in Table 6    The reproductive performance of dairy cows in Rajshahi district of Bangladesh Infertility problems are usually overlooked by the farmers and reproductive health management is not ensured. These lead to delayed first calving, long calving interval and poor conception rate, imbalance feed management of dairy cows and calves, unavailability of pasture land, inadequate veterinary services, excessive cost of concentrate and unawareness of farmers in herd health management (Shamsuddin et al., 2001) are burning issues, which seriously affects the farm economy. The breeds of animal is genetically determined that affects production and reproduction. In this study, the higher value of all reproductive parameters was observed in local than cross-bred cows except service per conception. There was only significant effect (P<0.05) of breed on post-partum heat period of dairy cows. In local cows, the present value is lower than that of observed by Rahman and Kalita (2015) which were average age at puberty (31.45± 0.87) months, age at first calving (43.60±0.96) months, calving interval (533.63± 0.87) days, post-partum heat period (210.1±0.19) days and service per conception 2.47±0.11. Jabber and Green (1983) stated an extended age at first calving in Bangladesh similar to the present study which was 36 months. Tesfu Kasa (1996) observed post-partum intervals and days open were 110.4 ± 23.5 and 148.8 ± 61.8 days for Zebu and 97.5 ± 25.1 and 157.8 + 55.5 days for cross-bred cow which were lower than the present study. In comparison, Sarder et al. (1997) reported the reverse the present study that Holstein-Friesian cross requiring the longest time (149 days) to onset of post-partum estrus compared with the local ones (119 days). The author also reported the local cows required fewer S/C (1.4) than the cross-bred (1.8), Khan (1990) found the S/C of local and cross-bred cows was 1.3 and 1.4, respectively which were lower than the present study. The findings of present calving interval were lower than the findings of Halim (1992) Sultana (1995) observed that S/C of local (L)), similar to the present study but S/C of Local x Sahiwal and Local x Friesian were lower than the present study. Ghosh (1995) observed the S/C were 1.6 ± 0.2 and 1.7 ± 0.2, for F x L and SL x L, respectively which were also lower than the present findings. Shamsuddin et al. (2001) reported shorter days open in cross-bred Sahiwal cows than that in cross-bred Friesian and local cows which are almost similar to the present findings. Mondal (1998) 6 days, respectively, in Baghabari milk producing area that are higher than the present study. This difference of reproductive traits among the breeds could be due to effects of the differences in management practice, environmental conditions, little rainfall, malnutrition and inappropriate semen handling and insemination techniques (Arthur et al., 1989).
In the present study, we found age group had significant (P<0.05) effect on age at puberty, age at first calving and had no significant (P>0.05) effect on post-partum heat period, service per conception days open and calving interval. Rahman et al. (1995) found the highest age at puberty in heifers 47.3 ± 0.6 months, age at puberty, age at first calving, post-partum heat period and days open in cows were as 43 to 48 months, 56.3 ± 0.5 months, 360.1 ± 0.5 and 360.6 ± 0.4 days, respectively than the present study. This study was more or less similar to the result of Sarder (2001) where he observed age at puberty, age at first calving, service per conception in heifer, onset of post-partum estrous, service per conception in cows, days open and caving intervals were 27.9 months, 38.7 months, 1.68, 139 days, 1.6, 160 days and 438 days, respectively. Spalding et al. (1975) reported slight increase in the fertility of cows up to 3 to 4 years of age and a decline after 4 years of age. They found a marked decline in fertility in the cow over 7 years of age that are agree with the present study. Shamsuddin (1988) observed lower result of the present study of S/C in cows and heifers which were 1.69 and 1.86, respectively. Coleman et al. (1985) reported that reproductive performance had become poor with the advancement of age. Plasse et al. (1972) reported higher value of calving interval 496 days in old cows and 424 days in young cows than the present study. Environmental condition, nutrition, care and management, suckling and post calving infection on female reproductive tracts may also affect this trait. best performance with regard to onset of ovarian cyclicity recorded by (Pereira et al., 1995). In contrast, Darwash et al. (1996) found that the interval from calving to onset of ovarian activity become progressively longer as the number of parities increased. Body size is an important genetic factor in cattle production. Body condition scores are an excellent indicator of reproductive performance. It is an arbitrary scale for estimating the amount of body fat in cows (Wildman et al., 1982). BCS had a good reflection on the reproduction. Good BCS better reproductive performance. This study observed the potential effect of body weight and body condition score on reproduction. In the present study, >300 kg body weight had better reproductive performance compared to other body weight groups and good body condition cows showed the excellent performance than others groups of cows. Haider (2007) observed that cows having a BCS >3.25 were superior in early insemination (82.4±72 days), days open (105.8±72 days), calving interval (397±62.3 days) and S/C (2.1±2.3) than their counterparts. For conception within 115 days BCS 3.25 (55%) and BCS >3.25 (54%) shown better performance than BCS 1.5-<3 (49%), BCS 3 (52%). The author also revealed that the higher the body condition score, the better were the reproductive performance. The present study also in line with (Haider, 2007), Riberior et al. (1997) and (Wright et al., 1992). A local study by Shamsuddin et al. (2001) stated that body condition score 3.5 or more at AI had shorter interval between calving and conception than with cows having ≤2.5 body condition score that are agree with the present study. The higher reproductive performance of good BCS might be due to better hygienic managemental practice of the farm that reduces the incidence of mastitis and other diseases.

CONCLUSIONS
The current investigation showed a relatively lower productive and reproductive performance of dairy cows at compared with different parameters in Rajshahi district of Bangladesh. The situation demands for further study to determine causes, economic impact and appropriate control strategies under different management systems. This study also clearly indicates that lack of awareness about performance of dairy cow farmers and their importance in the economic viability of their farms. Hence more efforts should be extended towards training, increasing awareness of the animal owners/attendant available veterinary services about these problems and their proper management.


s2fieldsofstudy:
Agricultural And Food Sciences
Biology

text:
Identification and comparison of proteomic and peptide profiles of mung bean seeds and sprouts

The objectives of this study were to analyze and compare the proteomic and peptide profiles of mung bean (Vigna radiata) seeds and sprouts. Label-free proteomics and peptidomics technologies allowed the identification and relative quantification of proteins and peptides. There were 1918 and 1955 proteins identified in mung bean seeds and sprouts, respectively. The most common biological process of proteins in these two samples was the metabolic process, followed by cellular process and single-organism process. Their dominant molecular functions were catalytic activity, binding, and structural molecule activity, and the majority of them were the cell, cell part, and organelle proteins. These proteins were primarily involved in metabolic pathways, biosynthesis of secondary metabolites, and ribosome. PCA and HCA results indicated the proteomic profile varied significantly during mung bean germination. A total of 260 differential proteins between mung bean seeds and sprouts were selected based on their relative abundance, which were associated with the specific metabolism during seed germination. There were 2364 peptides identified and 76 potential bioactive peptides screened based on the in silico analysis. Both the types and concentration of the peptides in mung bean sprouts were higher than those in seeds, and the content of bioactive peptides in mung bean sprouts was deduced to be higher.

Introduction
The mung bean (Vigna radiata) has been widely consumed as one of the most valuable edible legume crop sources in many countries for a long time, such as China, Canada, and the United States [1]. The popularity of mung bean is related to its specific growth characteristics, such as relative drought tolerance and short growth cycle (70-90 days) [2]. Importantly, mung bean contains balanced nutrients and has a high nutritional value [3]. In particular, mung bean seeds are rich in protein (18-32%) and the mung bean protein is more digestible and less allergic than other legume proteins, which indicated it could be an excellent source of protein [4]. The majority of the mung bean proteins were mung bean storage proteins, which mainly consists of globulin and albumin. The compositions and content of mung bean proteins could influence the bioactivity and functionality of mung bean [5]. At present, mung bean proteins were mainly identified by two-dimensional electrophoresis combined with liquid chromatograph-mass spectrometer analyses [6,7]. However, some limitations exist, low abundant, hydrophobic, extreme isoelectric point, and molecular weight proteins are rarely detected, preventing a complete description of the proteome [8]. Approximately 15% of mung bean proteins have not yet been extensively studied until now [1]. Recently, with the development of proteomic technique, label-free proteomics enables efficient and accurate identification and relative quantification of proteins, and it has been commonly used in plant proteomes research [9]. Plant peptides, whether they are extracellular or intracellular, have various physiological functions, such as signaling and defense [10]. Certain bioactivities including angiotensin I-converting enzyme (ACE) inhibitory activity, antioxidant activity, and antibacterial activity have been identified in the peptides of the mung bean protein hydrolysate [11]. To date, only three kinds of mung bean peptides (KDYRL, VTPALR, and KLPAGTLF) have been confirmed to have ACE inhibitory activity [12]. There are limited reports on the comprehensive proteomic and peptide profiles of mung bean.
Mung beans can be eaten cooked, fermented, or ground into flour. Also, mung beans are usually processed into soups or germinated into sprouts [13]. Mung bean sprouts have higher nutritional values and antioxidant activities compared to raw seeds [14]. They have the potential to prevent certain chronic diseases and cancers [15]. During germination, aerobic respiration and biochemical metabolism resulted in the hydrolysis of protein, carbohydrate, and fat in mung beans, as well as the formation of a series of metabolites [16]. The structural proteins are newly synthesized [17], and the mung bean storage proteins are degraded to new peptides, which influence the health claims of mung bean sprouts [13]. However, there is very limited data on the changes in proteins and peptides during the sprouting of mung bean.
Therefore, the objectives of this study were to analyze and compare the proteomic and peptide profiles of mung bean seeds and sprouts. Label-free proteomics and peptidomics technologies allow the identification and relative quantification of proteins and peptides in mung bean seeds and sprouts. Comparative studies on the proteomic and peptide profiles of mung bean seeds and sprouts contribute to clarify the impact of sprouting on the nutrition and function of mung beans. The results could promote a better understanding of the nutrition of mung bean seeds and sprouts, establish a fundamental study for further processing and application of mung beans, and provide comprehensive insights into the various mechanisms of germination in mung bean.

Materials
Mung bean (Vigna radiata) was purchased from Shanxi Dongfangliang Life Science and Technology Co., Ltd (Datong, Shanxi, China), and stored at 4 °C. The mung bean sprouts were prepared as previously described [18], with some modifications. Mung bean seeds were soaked in excess water for 10 h at room temperature (22 ± 2 °C) and then drained. The soaked mung bean seeds were tiled into the germination tray, which then was placed in an artificial climate box to germinate for 5 days in darkness.
The temperature and humidity were set at 22 °C and 80%, respectively. The mung bean sprouts were lyophilized for further analysis.

Extraction of mung bean protein
The mung bean proteins were extracted as described by Wiśniewski et al. [19], with some modifications. Mung bean seeds and sprouts were homogenized in lysis buffer consisting of 4% sodium dodecyl sulfate, 1 mM DL-Dithiothreitol, 150 mM Tris-HCl pH 8.0, and 1% protease inhibitor (Sigma-Aldrich, MO, USA). The homogenate was incubated in boiling water for 3 min and then sonicated on ice. The crude extract was incubated in boiling water again and centrifuged at 16,000×g at 25 °C for 10 min to collect the supernatants. Simultaneously, the BCA protein assay kit (Bio-Rad, USA) was used to determine the protein concentration.

Protein digestion
The protein was digested using the filter-aided sample preparation procedure, as previously described [19]. Briefly, 250 μg protein was mixed with 200 μL UA buffer (8 M Urea, 150 mM Tris-HCl pH 8.0) and centrifuged in a 10 kDa ultrafiltration tube at 14,000×g for 15 min. The precipitate was mixed with 100 μL 0.05 M iodoacetamide in UA buffer and incubated for 30 min at room temperature in darkness. The mixture was then centrifuged at 14,000×g for 10 min and the filter was centrifuged three times with 100 μL UA buffer and twice again using 100 μL 25 mM NH 4 HCO 3 . Finally, the protein suspension was digested with 3 μg trypsin (Promega) in 40 μL 25 mM NH 4 HCO 3 at 37 °C overnight to obtain the peptide filter. The peptide concentration was determined using UV spectroscopy at 280 nm.
The data-dependent top10 method dynamically choosing the most abundant precursor ions from the survey scan (300-1800 m/z) for HCD fragmentation was applied to obtain the MS data. The target value was determined based on the predictive Automatic Gain Control. The dynamic exclusion duration was set as 25 s. Survey scans were acquired at a resolution of 70,000 at m/z 200 and resolution for HCD spectra was 17,500 at m/z 200. The normalized collision energy was 30 eV and the underfill ratio, which specifies the minimum percentage of the target value likely to be reached at maximum fill time, was defined as 0.1%. The instrument was run with peptide recognition mode enabled.

Sequence database searching and data analysis
The original MS data were analyzed using MaxQuant software version 1.3.0.5 and searched against the UniProt Vigna radiata database (35,454 total entries, 20191130). The search parameters were set as previously described [21]. and the label-free relative quantification was carried out in MaxQuant [22]. The abundance of protein was calculated based on the normalized spectral protein intensity (LFQ intensity). The UniProt-GOA database (http://www.ebi.ac.uk/GOA/) was used to annotate the gene ontology (GO) classification consisted of biological process, cellular component, and molecular function. Besides, the protein pathway was searched against the Kyoto encyclopedia of genes and genomes (KEGG) database (http://www.genom e.jp/kegg/).

Peptide profile analysis of mung bean seeds and sprouts
Peptides in mung bean seeds and sprouts were extracted as previously described with some modification [23]. The samples were quickly grounded in liquid nitrogen using a dry ice-cooled mortar and pestle, 5 g of bean powder was then dissolved in extraction buffer containing 1% TFA and 1% plant protease inhibitor (Sigma-Aldrich, MO, USA). Mixed samples were homogenized at 4 °C for 1 h and then sonicated with five short bursts of 6 s followed by intervals of 8 s for cooling on the ice. After that, samples were centrifuged at 10,000g for 20 min at 4 °C and filtered in an Amicon Ultracel 10 kDa Molecular weight (Mw) cut-off centrifuge filter tube (Millipore, MA, USA) by centrifugation (4 °C, 8000g) to remove peptides larger than 10 kDa. The desalting process, mass spectrometry conditions, and the data analysis were the same as the above proteomics analysis, except the peptide separation time was 60 min. Finally, the relative intensity of the peptide was obtained.

Screening potential bioactive peptides
The potential bioactive peptides in mung beans were screened based on the in silico analysis [24]. The potential of the peptide to be biologically active was scored using the Peptide Ranker database (http://disti lldee p.ucd.ie/Pepti deRan ker/). Good solubility is usually a prerequisite to exert the biological activity for peptides, therefore, the water solubility of the peptide is estimated using the Innovagen database (http:// www.innov agen.com/prote omics -tools ). The peptide with an instability index less than 40 is regarded as stable, and an instability index above 40 indicates the peptide may be unstable. Therefore, the ExPASy Prot-Param tool (https ://web.expas y.org/protp aram/) was used to evaluate the stability of the peptide. In this study, peptides with scores > 0.5, good solubility, and instability index < 40 were regarded as the potential bioactive peptides [25].

Statistical analysis
All experiments were carried out at least in triplicate. Independent-samples T-test was performed with the SPSS 19 version and the results were considered significant at P < 0.05. Principal component analysis (PCA) and hierarchical clustering analysis (HCA) were performed on SIMCA-P 14.1 (Umetrics AB, Umea, Sweden) and Multi experiment Viewer version 4.8 (www.tm4.org/ mev/), respectively.

Analysis of the proteomic profiles of mung bean seeds and sprouts
The protein concentration in mung bean sprouts (23.92 mg/mL) was lower than that in mung bean seeds (37.59 mg/mL), which could be due to the fact that the storage proteins were continuously hydrolyzed by the activated mung bean proteases to provide the necessary nutrition for seed germination and seedling growth [15]. It has been reported that the protein content of cowpea, jack bean, dolichos, and mucuna also decreased after germination [26]. A total of 2195 proteins were identified (Additional file 1: Table S1), which were significantly more than the mung bean proteome reported in previous studies [6,27]. The types of proteins increased significantly after sprouting, which was in line with the result of Skylas et al. [27]. During germination, storage proteins are hydrolyzed and de novo synthesis of proteins occurs, which are both necessary for the completion of seed germination [28]. The variation of protein composition and content during germination reflects the balance between hydrolysis and synthesis of proteins. The major mung bean proteins, such as globulin, albumin, and glycinin, were all determined in this study. Globulin and albumin are the main mung bean storage proteins. The three types of globulins consisting of basic type (7S), vicilin type (8S) and legumin type (11S) globulins were all present in the mung bean seeds and sprouts. Moreover, the abundance of the globulins in mung bean seeds and sprouts accounted for 69.35% and 71.25% of the total protein abundance in the respective samples, which could be comparable with the results reported in the previous studies [1]. The greatest number proportion of these two samples was proteins with Mw between 20 and 40 kDa (Table 1), which could be related to the diversity of organelle proteins. For example, fifty-two of them were ribosomal proteins (40S, 60S, and 80S) involved in the formation of ribosomes and there were 43 mitochondrial proteins involved in mitochondrial function. However, the sum of the abundance of the proteins with Mw between 40 and 60 kDa accounted for more than 59% of the total protein abundance, due to the presence of globulins (subunits with molecular masses of 40-52 kDa).

GO and KEGG pathway analysis
GO analysis is a good tool to explain the role of eukaryotic genes and proteins in cells, thereby comprehensively describing the properties of genes and gene products in organisms [29]. The exhaustive overview of the biological process, cellular component, and molecular function involved in all proteins is shown in Fig. 1. The most common biological process of proteins in mung bean seeds and sprouts was the metabolic process, followed by cellular process and single-organism process (Fig. 1a). Together they accounted for over 74% biological functions. A similar result for the key biological processes of germinating pea seed proteins has also been reported by Wang et al. [28]. Seed germination involves a complex coordination of various physiological, metabolic, and cellular processes, as previously described by Das et al. [30]. Dry legumes initially absorb water to activate a series of metabolic processes, accompanied by the reorganization of cellular structure. The top three predominant cellular components consisted of the cell, cell part, and organelle, and more than 62% of the identified proteins were located in them (Fig. 1b). The difference in the various molecular functions of the proteins was extremely obvious (Fig. 1c). The proportion of catalytic activity and binding activity was significantly higher than other molecular functions, which accounted for approximately 82% of all molecular functions. The highest catalytic activity might be related to the presence of large amounts of enzymes in plants. The KEGG pathways of all proteins identified in mung bean seeds and sprouts were analyzed (Fig. 1d). The results intuitively demonstrated that the majority of proteins in mung bean seeds and sprouts were involved in the metabolic pathways, followed by biosynthesis of secondary metabolites, ribosome, carbon metabolism, and biosynthesis of amino acids. Similar phenomena on molecular functions and KEGG pathways were observed in the rice proteins [31].

PCA and HCA analysis
PCA was performed to visually distinguish the proteomic profiles of mung bean seeds and sprouts. There were three extracted principal components with a total variance of 79.9% (Fig. 2a), and the first two principal components (PC1 and PC2) separately accounted for 40.6% and 22.2% of the total variance, respectively. The proteomic profile of mung bean seeds was obviously separated from that of sprouts along PC1 (40.6% variance). To further evaluate the quantitative relationship of proteins among samples, heat-map visualization combined with hierarchical cluster analysis was utilized (Fig. 2b). It was evident that mung bean seeds and sprouts were separated into different clusters. All of the mung bean seed samples were grouped together in one cluster on the left side of the HCA dendrogram, while mung bean sprout samples were clustered on the right side. The normalized protein abundance was then visualised by colour: red-highest and blue-lowest values. Proteins in the right side of the  heat map were omitted due to the limited graphics space, and which were consistent with the order of proteins in Table S1. Both the PCA and HCA results indicated that the proteomic profile varied significantly during mung bean germination.

Analysis of differential proteins between mung bean seeds and sprouts
There were 240 and 277 proteins specific to mung bean seeds and sprouts, respectively. Moreover, there were 1678 consensus proteins identified in mung bean seeds and sprouts (Fig. 3). The differential abundance analysis of 1678 consensus proteins between mung bean seeds and sprouts was performed. Fold changes (Fc) were the specific values of the protein abundance in mung bean sprouts over that in mung bean seeds. Variables with Fc > 2 or < 0.5 and P < 0.05 were considered to be differential as previously described [21]. A volcano plot was then mapped to reflect the specific Fc and P value of each protein (Fig. 4). The green point with log 2 (Fc) < − 1 and −lg (P value) > 1.301 represented the overabundant proteins in mung bean seeds, and the red point with log 2 (Fc) > 1 and −lg (P value) > 1.301 represented the overabundant proteins in mung bean sprouts. Finally, 260 differential proteins were selected between mung bean seeds and sprouts. There were 149 proteins with higher abundance in the mung bean seeds and 111 proteins with higher abundance present in mung bean sprouts.
The GO classification and KEGG pathway of these differentially expressed proteins were analyzed (Fig. 5). Binding (42.95-45.05%) and catalytic activity (40.94-63.06%) were the main molecular functions for the overexpressed proteins in both mung bean seeds and sprouts. More over-expressed proteins in mung bean seeds had  guanyl ribonucleotide binding (22.82%) than those in sprouts (2.70%), while more over-expressed proteins in sprouts owned hydrolase activity (23.42%) compared with seeds (14.77%). Some guanyl ribonucleotide binding proteins, such as mitochondrial Rho GTPase and signal recognition particle receptor subunit beta, are GTPase-activating proteins (GAPs) [32]. GAPs enhance the hydrolysis of GTP during seed germination, thereby accelerating their inactivation, which explains why their abundance decreased significantly during germination [33]. Similarly, the abundance of ADP-ribosylation factor-like protein with guanyl ribonucleotide binding activity in seeds was obviously higher than that in sprouts, which might also be related to its capacity to bind and hydrolyse GTP [34]. The hydrolysis of storage reserves is one of the most important physiological processes during seed germination. Accumulated evidence suggests that the content of hydrolases of various plant seeds gradually increases during germination to induce the degradation of organic macromolecule into soluble substance for other tissues requirement, which is a conserved mechanism of seed germination [35]. Cellular component analysis indicated that the majority of differentially expressed proteins were cell part, cell, intracellular, and intracellular part proteins. The percentage of membrane proteins in the over-expressed proteins in mung bean sprouts (23.42%) was significantly higher than that in mung bean seeds (13.42%). The top three dominant biological processes consisted of metabolic process, cellular process, and organic substance metabolic process. Over-expressed proteins in mung bean seeds only had a more important role in macromolecule metabolic process (20.81%) and cellular macromolecule metabolic process (24.16%) than those in sprouts (14.41% and 19.82%, respectively). The majority of differentially expressed proteins belonged to the metabolic pathways, followed by carbohydrate metabolism and biosynthesis of secondary metabolites. More over-expressed proteins in mung bean seeds (3.36%) were involved in the protein processing in endoplasmic reticulum compared with sprouts (1.80%).
To investigate the individual differential proteins between mung bean seeds and sprouts, the 10 most over-expressed and 10 most under-expressed proteins in mung bean seeds/sprouts are shown in Table 2. The abundance of late embryogenesis abundant (LEA) protein D-34 isoform X2, LEA protein isoform X2, and LEA protein D-34 in mung bean sprouts were 0.005%, 0.024% and 0.038% of the corresponding protein abundance in mung bean seeds. This group of proteins accumulates in seed embryos and protects against water stress and seed dehydration through protein-protein interactions [27]. The abundance of this group of protein significantly decreased during germination due to the loss of seed embryo tissue. Seed biotin-containing protein SBP65 and seed biotin-containing protein SBP65-like were other proteins found to be more abundant in mung bean seeds than the sprouts. It has been reported that this kind of protein in pea had some similarities with LEA protein, including the amino acid compositions and hydrophilic characteristics [36]. During the process of germination, the dehydration tolerance was gradually lost, seed biotin-containing protein SBP65 as the stress response protein, was able to minimize the influence of the loss of dehydration tolerance on seed sprouting [28]. The abundance of lipoxygenases (LOXs) in mung bean sprouts was higher than that in mung bean seeds. LOXs were the key enzymes that promoted seed development during sprouting. The mung bean LOXs had similar biophysical and chemical characteristics to other legumes LOXs [37]. During the germination process, the LOXs were synthesized to degrade the lipid bodies in seeds [38]. The significant increase in the abundance of this type of protein during mung bean germination was consistent with the changes in this kind of protein during soybean and rice germination [39].

The peptide profiles of mung bean seeds and sprouts
A total of 2364 peptides were identified and the detailed information is shown in additional file 2: Table S2. As far as we are aware, no studies have been previously published on the peptide profiles of mung bean seeds and sprouts using a peptidomics approach. There were 1662 and 1795 peptides present in mung bean seeds and sprouts, respectively. The number of peptides increased after sprouting, because the storage proteins were hydrolyzed into peptides under the action of the activated mung bean proteases [15]. The sequences of the identified peptides ranged from 8 to 25 amino acids, the Mw of peptides varied from 762.39 Da to 3241.55 Da and their grand average of hydropathicity (GRAVY) indexes distributed from −3.64 to 2.518. These physicochemical properties were distributed across a relatively wide span, reflecting the peptides identified have a large physicochemical diversity [40], which further indicates that the peptides in mung bean seeds and sprouts were effectively identified. The detailed distribution of the basic physicochemical characteristics of the peptides is shown in Fig. 6. Usually, peptides that consisted of less than 20 amino acids are prone to be absorbed [41], and there was no difference in the proportion of these peptides between mung bean seeds and sprouts (Fig. 6a). The peptides with Mw below 1300 Da in mung bean sprouts were less than those in mung bean seeds (Fig. 6b), which might result from the depletion of small peptides for de novo synthesis of proteins during germination. The peptides with the GRAVY index greater than 0 in mung bean seeds were more than those in mung bean sprouts, and vice versa (Fig. 6c), which indicated that there were more hydrophilic peptides in mung bean sprouts compared with seeds [42]. The hydrophobicity of peptide influences its absorption and bioactivity, therefore, the potential of peptides to exert their nutrition and functionality increased during mung bean germination. The peptides in mung bean seeds were derived from 180 proteins, while those in mung bean sprouts were released from 160 proteins. The occurrence might be related to the hydrolysis of certain peptides into undetectable small peptides during germination. The majority of the peptides in these two samples were originated from beta-conglycinin, beta chain-like (15.97% and 16.44%), followed by glycinin G4 (13.24% and 15.65%). These proteins are typical 7S and 11S storage globulins [17,43], with relatively lower abundance compared with 8S globulins. The result indicated the number of peptides in the sample was independent of the abundance of the parent protein.
A Venn diagram of the peptides determined in the mung bean seeds and sprouts is shown in Fig. 7a. There were 1093 consensus peptides present in these two samples. Besides, there were 569 and 702 peptides specific to mung bean seeds and sprouts, respectively, which intuitively reflected the significant impact of germination on the peptide profile of mung beans. A total of 76 potential bioactive peptides screened is shown in Additional file 3: Table S3. There were 44 and 59 potential bioactive peptides present in mung bean seeds and sprouts, respectively. Their potential bioactive peptides accounted for 2.38% and 2.25% of the total peptide intensity in the respective samples. The peptide concentration in mung bean seeds and sprouts was 2.64 and 4.53 mg/mL, respectively. Therefore, we deduced that there were more bioactive peptides in mung bean sprouts than mung bean seeds, which could explain the more obvious biological activity of the mung bean sprouts after germination [13]. There were 27 consensus potential bioactive peptides present in these two samples, and there were 17 and 32 potential bioactive peptides specific to mung bean seeds and sprouts, respectively (Fig. 7b). Although the bioactivities of these peptides have not been confirmed, they provide directions for the screening and identification of mung bean bioactive peptides.

Conclusion
In this study, quantitative proteomic and peptidomic analyses have provided novel molecular insights into the proteomic and peptidomic patterns of mung bean seeds and sprouts. Quantitative evidence revealed 111 proteins upregulated and 149 proteins downregulated during mung bean seed germination. Bioinformatics analysis indicated that the majority of them belonged to the cell part, cell, and intracellular compartments with binding and catalytic activities, which primarily involved in the metabolic process and cellular process. The results further confirmed that seed germination is mainly accompanied by the activation of metabolic processes and the reorganization of cellular structure. Several proteins, especially the LEA protein family and biotin-containing proteins, decreased significantly during germination, which was associated with their prevention of water stress and seed dehydration. Both the types and concentration of peptides increased after germination. Seventysix potential bioactive peptides were screened based on the in silico analysis, and the content of bioactive peptides in mung bean sprouts was deduced to be higher than that in mung bean seeds. The proteomic and peptide profiles obtained in this study could promote a better understanding of the nutrition of mung bean seeds and sprouts, and provide comprehensive insights into the various mechanisms of germination in mung bean. Further researches will be required to confirm the bioactivities of these potential peptides in vitro and in vivo.


text:
Study of bioaccessibility of cobalt species in berries and seeds by mass spectrometry techniques

In recent years, açai berries (Euterpe Oleracea M.), goji berries (Lycium barbarum L.), bilberries (Vaccinium myrtillus L.), and chia seeds (Salvia hispanica L.) have increased interest worldwide due to their nutritional value and health benefits. In the present study, SEC-ICP-MS and μ-HPLC-ESI-MS/MS were used for the investigation of cobalt speciation and evaluation of its bioaccessibility in these products. Total cobalt content was determined, and açai berries (0.348 ± 0.042 μg g−1) and chia seeds (0.352 ± 0.036 μg g−1) were found as the best sources of this element. Different elution profiles of the extracts of examined berries and seeds obtained with the use of ammonium acetate, Tris-HCl, and SDS suggested that cobalt is bound by different bioligands in each biomatrix. The bioaccessibility of cobalt species was evaluated by SEC-ICP-MS. On the chromatograms of extracts obtained after simulation of gastrointestinal digestion, peaks corresponding to low molecular mass (17.00–1.35 kDa) cobalt complexes were observed. In the case of goji berries, their intensities were significantly higher on chromatograms of gastrointestinal than gastric extract. In enzymatic extracts, different forms of vitamin B12 were identified by μ-HPLC-ESI-MS, including its natural forms—methylcobalamin (Me-Cbl) and adenosylcobalamin (Ado-Cbl).

Introduction
Cobalt is an essential element for plants, animals, and human beings, needed in a small amount in the diet to conduct its daily requirement. As an integral part of the only metal-containing vitamin, B 12 , cobalt plays an important role in some biochemical processes, e.g., synthesis of nucleic acids and amino acids (Adolfo et al. 2016;Stoica et al. 2004). Vitamin B 12 or cobalamin is an essential vitamin for the human organism, stored in the liver. It can occur in different forms but only methylcobalamin (Me-Cbl), hydroxocobalamin (OH-Cbl), and adenosylcobalamin (Ado-Cbl) are its natural forms (Bosle et al. 2016;Kumar et al. 2010). Cyanocobalamin is a man-made form of vitamin B 12 , supplied by food supplements or fortified food, used to prevent and treat low blood levels of this vitamin (Indyk et al. 2017). Speciation analysis of cobalt in food is usually limited to the determination of vitamin B 12 and its derivatives. It is important to mention that different chemical forms of metal may be beneficial or toxic for human organism (Intawongse and Dean 2006).
Studies concerning chemical speciation of elements in food are needed for a more accurate evaluation of biological availability or toxicity of the element in the diet (Koplík et al. 2002a). Usually, analytical methods for trace element speciation are based on the combination of effective separation method (mainly liquid chromatography or electrophoresis) and element-specific detection, most often realized by inductively coupled plasma mass spectrometry (Koplík et al. 2002b;Szpunar and Łobiński 2002).
Size exclusion chromatography (SEC), used in this study, is especially suitable for separation of element species of limited stability, for example in protein-rich matrices, as in the case of Spirulina (Arthrospira platensis G.) (Wojcieszek et al. 2015). The main advantage of SEC can be seen in the simplicity of application (Koplík et al. 2002a;Koplík et al. 2002b). SEC-ICP-MS (inductively coupled plasma mass spectrometry) has been successfully applied in the study of cobalt species in various samples, such as soybean flour (Koplík et al. 2002b), seeds of legumes (Koplík et al. 2002a), goldenberries (Wojcieszek and Ruzik 2016a), noni juice (Rybak and Ruzik 2013), Brazil nuts (Kannamkumarath et al. 2004), eggs (Lipiec et al. 2012;Lipiec et al. 2011), or even compost (Sadi et al. 2002). However, in a great number of already published works, studies of trace elements in food and food supplements are based mainly on the establishment of their total concentration what is not enough in terms of information regarding the fraction that is potentially bioaccessible for the human organism. Only a part of the total content of trace elements present in the food matrix can be absorbed and utilized by the human body (Khouzam et al. 2011). Therefore, beyond the determination of the total content in a food product, studies of metals should also consider their bioaccessibility (Peixoto et al. 2016).
Bioaccessibility includes all physical, chemical, and microbiological processes between chewing in the mouth and precipitation in the intestines in the human body. Knowledge about the bioaccessibility value, described as the amount dissolved in the digestive tract, is necessary to determine the bioavailability value, known as the amount of the substance that reaches the bloodstream (Altundag et al. 2015;Ng et al. 2015).
Vitamin B 12 is well-known to be the sole vitamin that is absent from plant-derived food sources. Foods (meat, milk, eggs, fish, and shellfish) derived from animals are the major dietary sources of vitamin B 12 (Watanabe 2007). Edible plants and mushrooms rarely contain a considerable amount of vitamin B 12 , mainly due to concomitant bacteria in soil and/or their aerial surfaces (Watanabe and Bito 2018). However, some investigation confirmed the presence of B 12 in plant food (Nakos et al. 2016) and show the vitamin B 12 -containing plant-derived food: fermented soybean (0.7-8.0 μg/100 g), broccoli (trace amount), asparagus (trace amount), various types of tea leaves (0.1-1.2 μg/100 g), wild edible mushroom (approx. 0.09 μg/100 g), and edible algae (approx. 63.6 μg/100 g) (Watanabe et al. 2014). Food products chosen for the present study are recently more and more popular, due to their nutritional value and health benefits. They are important sources of proteins, minerals, antioxidants, and vitamins; contain a lot of biologically active compounds; and may reduce the risk of various diseases (Chu et al. 2010;Heinrich et al. 2011;Muñoz et al. 2013;Namiesnik et al. 2013;Wang et al. 2010;Wojcieszek and Ruzik 2016b).
To the best of our knowledge, there are no data reported so far regarding the characterization of cobalt species in berries and chia seeds. Therefore, the aims of the presented study were (i) to determine cobalt content in analyzed samples and evaluate to what extent their daily consumption fulfills the recommended daily intake for this element, (ii) to separate extracted cobaltcontaining species on SEC column coupling with ICP MS detector, (iii) to evaluate the bioaccessibility of cobalt present in analyzed samples by an in vitro digestion method, and (iv) to confirm the presence of vitamin B 12 in extracts obtained after simulation of digestion using μ-HPLC-ESI-MS/MS (μ-high-performance liquid chromatography/electrospray ionization tandem mass spectrometry).

Chemicals and materials
Most of the reagents used during analysis were of analytical reagent grade purchased from Sigma-Aldrich (Sigma-Aldrich, Buchs, Switzerland). The nitric acid of purity for trace metal analysis was purchased from Fluka (Switzerland). The formic acid of LC/MS purity was obtained from Fisher Scientific (Fair Lawn, NJ, USA). Methanol was purchased from POCH (Gliwice, Poland). Pepsin from porcine gastric mucosa and pancreatin from porcine pancreas were of biological grade (Sigma-Aldrich, Buchs, Switzerland). Deionized water (18 MΩ cm) prepared with a Milli-Q system (Millipore Elix 3, Millipore, Saint-Quentin, France) was used throughout. The SEC column was calibrated using the size exclusion standard (BIO-RAD, Warsaw, Poland). The calibration curves were prepared using a solution of Environmental Spike Mix (1000 mg L −1 of Fe, K, Ca, Na, Mg and 100 mg L −1 of Ag, Al, As, Ba, Be, Cd, Co, Cr, Cu, Mn, Mo, Ni, Pb, Sb, Se, Tl, V, Zn, U; matrix 5% HNO 3 ) purchased from Agilent Technologies.

Instrumentation
Samples of extracts were injected on a Superdex200 10/ 300GL (GE Healthcare Life Sciences) exclusion column which was coupled to the ICP-MS instrument by directly connecting the column outlet to the cross-flow nebulizer through a PEEK tubing. Chromatographic separations were performed using Agilent 1100 gradient HPLC pump (Agilent Technologies, Waldbronn, Germany) as the sample delivery system. Buffer solution of 10 mM ammonium acetate (pH 7.4) served as the mobile phase, and the flow rate was fixed on 0.7 mL min −1 . Calibration of SEC column was accomplished with a standard mixture of thyroglobulin (670 kDa) t r = 11.8 min, γ-globulin (158 kDa) t r = 14.8 min, ovalbumin (44 kDa) t r = 21.4 min, myoglobin (17 kDa) t r = 27.3 min, and vitamin B 12 (1.35 kDa) t r = 30.2 min.
An Agilent 7500a ICP MS (Tokyo, Japan) was used as a detection system for the Agilent 1100 HPLC system Wojcieszek and Ruzik Journal of Analytical Science and Technology (2020) 11:26 and as an element-specific detector for quantification of total metal content in analyzed samples. Ni-skimmer was installed in the interface; the position of the torch and nebulizer gas flow was adjusted each day of work. The optimum performance of the apparatus was found with particular attention for the lowest level (≤ 0.2%) of polyatomic oxide interferences-to reduce the possibility of occurrence of cobalt interferences ( 43 Ca 16 O + , 42 Ca 16 O 1 H + and 40 Ar 18 O 1 H + ). The instrument working conditions were optimized daily using a 10 μg L −1 solution of 7 Li + , 89 Y + , and 209 Bi + in 2% HNO 3 , with a dwell time of 0.1 ms for each isotope. μ-HPLC-ESI-MS analysis was performed using Agilent 1200 series chromatograph (Agilent Technology, Waldbronn, Germany) equipped with a binary capillary pump, degasser, autosampler, thermostatically controlled column compartment, and capillary Zorbax SB C18 column coupled to electrospray ionization triple quadrupole mass spectrometer (Agilent 6460 Triple Quad LC/MS, Agilent Technologies, Santa Clara, CA, USA). All the operations, acquiring, and analysis of data were processed by the Mas-sHunter Software (Agilent Technology, USA).
Operational parameters are summarized in Table 1. A Bandelin Sonorex Model 1210 ultrasonic bath (Germany) and MPW Model 350R centrifuge (MPW, Poland) were used for extraction procedures. Microwave digestion with a Speedwave®four Berghof system (Germany) was used for mineralization of samples. A thermostatically controlled water bath (Memmert WB 10, Germany) was used during simulation of digestion.

The samples and sample preparation
Frozen bilberries (Vaccinium myrtillus L., harvested in Poland in 2012) were stored at −20°C until analysis. The lyophilized Brazilian açai berries (Euterpe oleracea Mart.) and chia seeds (Salvia hispanica L.) were obtained from KenayAG, Poland (imported from Brazil and Peru, respectively). The chia seeds and bilberries were lyophilized and ground using agate mortar and pestle until a homogenous powder was formed; the powder was stored at room temperature. The dried goji berries (Lycium Barbarum L.) were obtained from Sante, Poland, (imported from China) and ground in liquid nitrogen with agate mortar and pestle, in the same way with chia seeds and bilberries.

Sample mineralization for determination of the total amount of cobalt
Samples for the determination of total cobalt content were prepared by microwave-assisted mineralization. Three replications of each sample were precisely weighed (c.a. 0.2 g of dry mass) and transferred to PTFE (polytetrafluoroethylene) vessels, and a mixture of 5 mL of 69% HNO 3 and 3 mL of 30% H 2 O 2 was added. Then, the digestion was carried out. After cooling down, obtained solutions were diluted to a final volume of 25 mL with MQ water. Further dilutions were prepared just before ICP-MS analysis with 2% HNO 3 containing yttrium of concentration 10 ng mL −1 used as an internal standard (Połeć-Pawlak et al. 2007). The multi-element standard solution was chosen for calibration. Concentrations of elements were determined by ICP-MS using the external calibration curve. Calibration graphs were obtained for cobalt concentrations within the range 0.1-10.0 ng mL −1 , and they were linear in this range from 0.2 ng mL −1 to 10.0 ng mL −1 with r 2 above 0.998.

Extraction of cobalt species from analyzed samples
For the extraction of cobalt compounds, the following solutions were used: 10 mM ammonium acetate (pH 6.8), 30 mM Tris-HCl (pH 7.4), and 2% sodium dodecyl sulfate (SDS) in water. Samples of analyzed food products (0.05 g of dry powder) were weighted and extracted with 2.0 mL of each solvent for 1 h using an ultrasonic bath. The obtained solutions were next centrifuged for 20 min at 15,000 rpm at 15°C. The final supernatants were filtered with 0.45-μm syringe filter (Sigma-Aldrich, Bellefonte, PA, USA), two first drops were discarded, and only the remaining part of the filtrates was injected on the size exclusion column (Rybak and Ruzik 2013).

In vitro gastrointestinal digestion studies
The in vitro digestion method used in the study was based on that described by Luten et al. (1987), modified and adapted for the samples being studied. Simulation of gastric digestion was initiated by 2.5 mL of pepsin solution (6% w/v pepsin in 0.15 M NaCl, acidified by HCl to pH 1.8) added to 0.05 g of sample. After 15 min of sonication (initial degassing), samples were incubated for 3.5 h at 37°C, followed by centrifugation (20 min, 15,000 rpm, 4°C) and filtration through 0.45-μm syringe filters. For intestinal digestion, 2.5 mL of pancreatin solution was added to the gastric digests (after adjustment to neutral pH) and sonicated for 15 min. Then, mixtures were incubated for 2 h at 37°C, followed by centrifugation and filtration as in the case of gastric digestion. Both supernatants were analyzed by SEC-ICP-MS. In the next step, gastric and gastrointestinal extracts were ultracentrifuged through filters with a molecular mass cutoff of 3 kDa before μ-HPLC-ESI-MS analysis to separate enzymatic proteins from small molecular mass compounds (Howlett et al. 2006).

Results and discussion
The total concentration of cobalt in analyzed berries and seeds The total content of cobalt was determined using ICP-MS, after microwave-assisted digestion of samples. Results were obtained from three independent experiments, and the mean value of metal concentration was calculated. As the interest of this study, the following total amount of cobalt was established in açai berries (0.348 ± 0.042 μg g −1 ), chia seeds (0.352 ± 0.036 μg g −1 ), goji berries (0.046 ± 0.003 μg g −1 ), and bilberries (0.082 ± 0.010 μg g −1 ). The results are in good agreement with the earlier studies reported by other authors in fruits and vegetables (Millour et al. 2011;Rybak and Ruzik 2013).
The RDA (recommended daily allowance) values for cobalt does not exist, there is no evidence that the intake of cobalt is ever limiting in the human diet. Normal daily intake is reported to be in the range of 2.5-3.0 mg per day. Toxicity with cobalt has been reported to occur within the range of greater than 25-30 mg cobalt daily (Hokin et al. 2004;Lauwerys and Lison 1994). However, the RDA value for vitamin B 12 provided by the Commission of the European is 2.5 μg per day. This data was used for evaluation of extent the daily dosage of berries and chia seeds would provide a significant contribution to the diet. The daily dosage of açaí (100-150 g) and chia seeds (40 g) contributes to the normal daily intake of approximately 15% for cobalt which can also occur as Wojcieszek and Ruzik Journal of Analytical Science and Technology (2020) 11:26 Page 4 of 9 a vitamin B 12 , and other berries only 2-3%. According to the results reported in this work, açaí and chia seeds are a good source of vitamin B 12 and could contribute to meeting the B 12 requirements for the human body.
Method validation Relative standard deviations among replicates of analysis of each sample were always lower than 10%. Limit of detection (LOD) and limit of quantification (LOQ) were calculated for standard deviations (SD) of 10 measurements for blank, and it was found to be 0.024 and 0.073 μg L −1 , respectively. The calibration equation achieved by ICP MS for cobalt was y= 8605x −7.853 with a correlation coefficient (r2) of 0.9997. The total concentration of other trace elements in analyzed samples was also determined by authors, and the results are presented in previous studies Wojcieszek et al. 2017;Wojcieszek et al. 2016). The accuracy of the method was evaluated for the cobalt by spiking analyzed samples of chia seeds and berries with the standard of cobalt solution, and the total concentration of elements were carried out.
The appropriate standard solution was added to the three samples, and recovery values were between 75 and 103%.
To get better knowledge about forms of cobalt present in analyzed food samples, extraction of cobalt species and their further characterization using hyphenated techniques has been performed.

SEC-ICP-MS examination of extracts containing cobalt compounds
The species containing cobalt (including the cobalamin forms) were separated according to their sizes and detected by ICP-MS, as described in the experimental section. The chromatographic run time was fixed on 60 min, but no peaks were observed at retention times higher than 31 min. The SEC-ICP-MS elution profiles of cobalt extracted from analyzed samples (Fig. 1) show that Co is mainly bound to compounds that appear between 20 and 30 min of analysis, of molecular masses of 44.0-1.3 kDa, similar to studies describing the presence of cobalt in noni fruit (Rybak and Ruzik 2013) and cape gooseberry (Wojcieszek and Ruzik 2016a). The results obtained for food samples revealed two peaks of cobalt for almost all analyzed extracts, with the most intense one detected in the low molecular mass region, registered after 25 min of the chromatographic process.
On the chromatograms of SDS extracts, also additional peaks were observed, probably corresponding to hydrophobic proteins bound to the investigated element. The peak at 11 min appears on the chromatograms of all analyzed food samples except that obtained for goji berries extract. The retention time is situated in the region of column's exclusion limit corresponding to high molecular weight compounds of at least 500 kDa, that could be explained by the ability of metal ions to form big agglomerates even with low molecular weight ligands. SDS is dedicated to the extraction of hydrophobic proteins that are suspected to create complexes with investigated elements (Połeć-Pawlak et al. 2005). However, it should be mentioned that due to the fact that aqueous solution of SDS was used for extraction, some peaks, especially those appearing in the region of low molecular mass compounds, can also correspond to water-soluble cobalt species.
When extraction was performed with the use of ammonium acetate, on the chromatograms of all analyzed foodstuffs, the peak at 27 min was observed, originated probably from water-soluble cobalt complexes. Column calibration allows finding that this peak corresponds to cobalt complexes with bioligands of molecular mass about 17.0 kDa. The additional peak appeared on chromatograms of all analyzed samples at 25 min, but its peak areas are small, and its separation from other peak is not satisfactory in the case of chia seeds and goji berries. Another small peak registered on chromatograms of chia seeds extract at 21 min is absent on the chromatograms of other foodstuffs.
Tris-HCl was found to be not a very efficient extractant for cobalt complexes from analyzed samples comparing to SDS and ammonium acetate solutions. In general, no peaks were observed in the low molecular mass region of chromatograms, corresponding to cobalt complexes of molecular masses lower than 1.3 kDa.
Additionally, metal recovery from the SEC column was established by ICP-MS (as the ratio of metal injected onto the column to that in the collected eluate) as 92% for cobalt. On the other hand, the recoveries of cobalt from the SEC column obtained for tris extracts of berries and seeds were 94-105%, obtained for SDS extracts were 89-103% and for ammonium acetate extracts about 89-107%, depending on the fruit.
Trying to summarize, SEC-ICP-MS study shows that the greatest number of cobalt species (including the cobalamin forms) are extracted by SDS from bilberries and goji berries, and by ammonium acetate from chia seeds and açaí berries. It leads to the conclusion that the nature of bioligands complexing cobalt in analyzed berries and seeds is different. However, the used technique does not offer more detailed information concerning their chemical identity and this should be searched by another one, e.g., ESI-MS/MS (see below, "Confirmation of the presence of vitamin B12 by μ-HPLC-ESI-MS analysis" section).

Estimation of bioaccessibility of cobalt compounds by SEC-ICP-MS
SEC-ICP-MS analysis of extracts obtained after simulation of gastrointestinal digestion with both pepsin and pancreatin was carried out under the same conditions as described above. On chromatograms of these extracts peak at 11 min (of high molecular mass compounds) was not observed it any case (Fig. 1).
Chromatograms of bilberries and açai berries obtained after enzymatic digestion were almost identical, showing two similar peaks at 26 and 29 min. It suggests that during digestion of these berries, the same (or of very similar behavior) cobalt complexes are formed in the gastrointestinal tract and become available for the human organism. Both peaks correspond to low molecular mass cobalt complexes, 17.00-1.35 kDa, probably its conjugates with digestion products of peptides and proteins present in analyzed berries. A high affinity of cobalt ions to proteins and amino acids has already been reported (Paustenbach et al. 2013), and it was also found that enzymatic protein may interact with cobalt compounds (e.g., cobalamin) and improve their bioaccessibility (Lipiec et al. 2011).
In the case of chia seeds, the same peaks were observed on the chromatograms, but the intensity (and peak areas) of the second one, at 29 min, was significantly less intense (for gastric digestion); gastrointestinal digestion both peaks on the chromatograms are definitively smaller. It leads to a conclusion that chia seeds are not a good source of cobalt compounds accessible for the human body after digestion.
The same two peaks appear on chromatograms of the extracts from goji berries after enzymatic digestion, but in this case, intensity and peak areas of signals of the gastrointestinal extract were significantly higher than those observed for gastric extract.
Concluding the study concerning bioaccessibility of cobalt compounds present in examined berries and seeds, one can find that among them goji berries offer the greatest amount of this element complexes undergoing digestion and potentially available for the human organism (the value of cobalt bioaccessibility is presented in Table 2). The differences among the bioaccessibility-for açaí and goji, the intestinal bioaccessibility is greater than the stomach values and for bilberries and chia, the opposite occurred-can be explained probably by the different other Wojcieszek and Ruzik Journal of Analytical Science and Technology (2020)  fruit ingredients and the occurrence of various complexing factors in the plant.

Sequential extraction of cobalt compounds from goji berries
In previous studies (Fig. 1a, b, c, d), it was observed that exclusively on the chromatogram of SDS extracts from goji berries only one peak is registered. Their behavior in the course of enzymatic digestion also is different than of another examined foodstuffs (chromatograms 1). Additionally, taking into account that this product probably offers more bioaccessible forms of cobalt than others, it was examined with greater care. For more detailed study, sequential extraction has been chosen. It was performed with three solutions used in the following order: (i) 10 mM ammonium acetate (pH 6.8), (ii) 30 mM Tris-HCl (pH 7.4), (iii) 2% SDS. In the first step extraction of low molecular mass, watersoluble compounds of cobalt were expected. The last extraction, with SDS solution, was used to separate cobalt complexes of high molecular mass, probably with hydrophobic proteins. Typically for a sequential procedure, the next extractant was added not to the fresh sample but the residue left after the previous extraction.
On the chromatograms (Fig. 2) of ammonium acetate (1st step) and Tris-HCl (2nd step) extracts of goji berries, one peak in the low molecular mass region was observed. The intensity of the peak registered for ammonium acetate extract was higher, which can suggest, that most of the water-soluble cobalt complexes can be extracted in this step. SDS solution extracts, according to expectations, high molecular mass fractions appeared as one, low intense peak on the chromatogram. Such low intensity of this peak suggests that in goji berries only a small amount of cobalt is bound to compounds of higher masses.
Results of the study, as well as those obtained in SEC experiments, indicate that for further examination of complicated matrices of foodstuffs, it is necessary to elaborate efficient method for characterization of trace amounts of cobalt species, e.g., μ-HPLC-ESI-MS protocol.
Confirmation of the presence of vitamin B 12 by μ-HPLC-

ESI-MS analysis
As it was mentioned in the "Introduction" section, the most important for human organisms cobalt species are cobalamin derivatives and because of it a simple μ- HPLC-ESI-MS method (Wojcieszek et al. 2015) for the identification of vitamin B 12 in bilberries, goji berries, açai berries, and chia seeds, after simulation of gastrointestinal digestion with both pepsin and pancreatin, has been presented. Cobalamin species observed after enzymatic digestion of analyzed samples are presented in Table 2. However, in obtained scan mode mass spectra, the cobalt species were observed only when the positiveion mode was used. Among the examined products, bilberries were found as the richest in cobalt species. The presence of biological forms of cobalamin-methylcobalamin and adenosylcobalamin (also known as coenzyme B 12 )-was confirmed by two peaks observed in the mass spectrum, at m/z 1345 and 790, respectively. The protonated CN-Cbl + ion at m/z 678 and the native CN-Cbl + ion at m/z 1355 corresponding to the cyanocobalamin were observed after gastric digestion. The unexpected presence of cyanocobalamin in bilberries may create a basis for the hypothesis, that plantation of analyzed bilberries could be fortified by vitamin B 12 .
It is important to mention that after the end of the process of gastrointestinal digestion, the signal at m/z 665 was observed in the registered mass spectra, corresponding to cobalamin. It can be concluded that during the simulation of gastrointestinal digestion, the β-ligands (Ado-, Me-or CN-) are probably lost and only one form of cobalamin could be identified.
The comparable conclusion can be drawn in the case of açai berries-in the mass spectrum of their enzymatic extract also signal at 679 (cyanocobalamin) was present. In this foodstuff, also methylcobalamin was found (confirmed by signals at m/z 673 and 1344). Moreover, in the gastrointestinal extract, the signal at m/z 790 could be also observed, corresponding to coenzyme B 12 .
Nevertheless, in enzymatic extracts of chia seeds, two forms of cobalamin could be identified only in gastric one-cyanocobalamin (signal at m/z 678) and adenosylcobalamin (m/z 790). We could not observe cobalt species after the gastrointestinal digestion.
Moreover, in goji berries, only methylcobalamin could be identified after the gastric digestion. Additionally, after the end of the process of gastrointestinal digestion, only methylcobalamin and cobalamin could be observed.

Conclusions
SEC-ICP-MS and μ-HPLC-ESI-MS were found to be useful tools for the examination of cobalt speciation in berries and seeds. Chromatographic profiles (SEC-ICP MS) of extracts obtained with the use of different extraction media (ammonium acetate, Tris-HCl, SDS) allows drawing conclusions dealing with different nature of biologically active compounds binding cobalt. Three-step sequential extraction protocol allows for obtaining high molecular mass cobalt species from berries in the third step, performed with aqueous SDS solution (two first steps remove watersoluble compounds). For the identification of cobalamin (vitamin B 12 -the only form of cobalt being absorbed by the human organism) analogs in examined foodstuffs, the developed μ-HPLC-ESI-MS procedure can be recommended. The elaborated methods may be used as a simple tool for studying metal speciation in various foodstuffs.