Corn harvesting time based on growth curve and antioxidant enzyme activity

ABSTRACT

China has a large population and is also a big agricultural country. Food security is an important strategic issue related to China’s national economy and people’s livelihood. Corn is one of the main food crops in China, its yield and quality are closely related to China’s growing grain demand. Improper field harvest, failure to dry out in time, and qualitative changes during the storage process will lead to yield loss. In this study, the issue of timely corn harvest, the growth curve, and the antioxidant enzyme activity of corn kernels were measured throughout the harvest process. The mathematical models of the change of moisture content, dry weight, and antioxidant enzyme activity were established, and the optimal harvest period was determined. The results showed that the moisture content of corn kernels decreased with the increase in harvest time. With the increase in harvest period, the dry weight of corn kernels accumulated in the early stage, when the dry weight of corn kernels entered the ripening period on October 3rd, the accumulation of dry weight tended to be a maximum and no longer increased. After that, there was even a decrease in dry weight which was called hidden loss, the trend of four varieties dry weight was different. The antioxidant enzyme activities of POD (peroxidase), CAT (catalase), and SOD (superoxide) of corn kernels were tended to rise first and then decline, there were significant differences between different varieties. The optimal harvest period for each corn variety was determined according to the change of moisture content, dry matter weight, and antioxidant enzyme activity.

KEYWORDS:

• Corn kernels
• growth curve
• antioxidant enzyme activity
• mathematical models
• timely harvest

Introduction

Corn is one of the main food crops in China, its yield and quality are closely related to China’s growing grain demand, and it plays an important position in China’s agricultural production and national economy^[1]. Due to the high internal water in the newly harvested corn, it is easy to mold and deteriorate during the process of storage and transportation, which seriously affects the quality and brings great economic losses.^[2] Improper field harvest, failure to dry out in time, and qualitative changes during the storage process are all important reasons for the reduction of corn production, and the problem of postpartum loss of corn cannot be ignored. Whether the crops are harvested in time has a very important impact on their yield and quality.^[3] Under normal circumstances, farmers harvest corn in the fields too early or not timely, which will obviously leads to corn production reduction and quality decline,^[4,5] resulting in the reduction of real processing production due to the existence of hidden losses and dominant losses.^[6–8]

The study of Guo Shuquan^[9] showed that the suitable harvest period of silage corn was very important, and the high-quality silage raw material was the material basis for making good silage feed. The nutritive value of silage, in addition to the kinds and varieties of raw materials, was also directly affected by the harvest time. Timely harvest could obtain higher harvest yield and higher quality nutritional value. Zhang Jisheng et al.^[10] explored the effect of different harvest periods on rice growth during the heading stage. The study showed that the rice spike rate increased significantly, but the protein and starch content decreased. The harvest time of one week had no significant impact on the rice yield, but it had a great impact on quality and aroma of rice. So the timely harvest had a great impact on the quality of rice. In 2017, Liu Houqing et al.^[11] found that timely harvesting and drying of rice had an important impact on the taste of rice. The extension of the harvest period would increase the deterioration degree of food taste and the rice yield when rice milling. Timely harvest was an important prerequisite to ensure the yield and quality of rice.

The study of Dong Weixin et al.^[12] showed that the strong ability of SOD and POD enzyme activity to remove reactive oxygen species had a positive effect on the improvement of corn kernels quality. In 2013, Wang Yongjun et al.^[13] found that the increased enzyme activities of SOD, POD, and CAT were an important reason to reduce the degree of corn kernels aging. In 2018, the results of Rui Penghuan et al.^[14] showed that high-temperature stress at grain filling stage significantly reduced the contents of POD, CAT, SOD, and soluble protein in maize leaves, resulted in the destruction and the dynamic imbalance of the protective enzyme system, eventually affected the normal growth and development of maize. The results of Shabeer Ahmed et al.^[15] showed that low concentration salt treatment would improve the antioxidant enzyme activity such as SOD, POD, and CAT the damage of reactive oxygen species to the body decreased and repaired itself. When the concentration increased, SOD enzyme activity gradually decreased, and the body’s own regulating ability was destroyed.

In 2021, Xu Xuanwen^[16] designed an APP for rice maturity detection based on machine vision and image recognition. It was simple to operate, cost-effective, and wide adaptability, which could quickly distinguish the maturity of rice during the harvest period and played a rapid auxiliary role in agricultural production. In 2021, Wu Wenfu et al.^[6,7] found and proposed 7.16% of hidden loss due to improper rice management. In order to effectively reduce and improve rice quality, based on 5T management method (5T management divides the whole life cycle of rice into different growth time intervals, namely five periods: harvesting period (T₁), field period (T₂), drying period (T₃), warehousing period (T₄), storage period (T₅) to complete the process management), they proposed 5T wisdom farm management mode, constructed the wisdom farm system mode and architecture, and developed the rice wisdom farm information system. The system was promoted and tried with high-quality rice in Jilin Province, China. Through the traditional rice after harvest treatment loss analysis and through the implementation of 5T management method after loss and production research, Zhang Na et al.^[8] showed that in the process of 5T management, through the factors of crop production period for strict control could increase the yield and economic benefits by avoiding harvest hidden losses caused by improper management in the harvest and subsequent process.

There are many previous studies on the growth curve and antioxidant enzyme activity of maize, but few studies have been used them to determine the optimal harvest period of maize. The main purpose of this paper is to determine the growth curve of corn kernels and the change of antioxidant enzyme activity during the whole harvest process and to determine the optimal harvest date of corn.

Materials and methods

Materials

The corn varieties were selected in this experiment were XY335, FM985, JD31, KX3564, which were produced in Jilin Academy of Agricultural Sciences, China.

Equipment

The Electrothermal thermostatic blast dryer (DHG-9125A, Shanghai Yiheng Technology Co., Ltd., China), MRI analyzer (NMI20, Suzhou Niumag Analysis Instruments Co., Ltd., China), low-speed desktop large capacity centrifuge (RJ-TDL- 40B, Wuxi Ruijiang Analytical Instrument Co., Ltd., China) and UV-2550 ultraviolet spectrophotometer (TCC-240A, Shimadzu, Japan) were used in this study.

Reagents

The glacial acetic acid (99.5% acetic acid content, Shenyang Huizhong Physical and Chemical Products Factory, China), physiological saline (Liaoning Minkang Pharmaceutical Co., Ltd., China), phosphate buffer (0.1 mol/L 7≤ pH ≤ 7.4, Jiangbiao Testing Technology Co., Ltd., China), POD kit (Nanjing Jiancheng Bioengineering Institute, China), CAT kit (Nanjing Jiancheng Bioengineering Institute, China) and SOD kit (Nanjing Jiancheng Bioengineering Institute, China) were used in this study.

Determination of the growth curve

The test sampling time was from September 1, 2021, to October 17, 2021. After daily sampling, the moisture content of corn kernels was determined by low-field nuclear magnetic resonance (NMR) technology, and the thousand grain weight and dry matter weight were calculated. This method is simple to operate, accurate and reliable results, fast testing, testing process without chemical reagents, saving testing costs, to ensure the maximum safety of operators. The principle of low-field nuclear magnetic resonance (NMR) measurement of moisture content is to use the NMR phenomenon of hydrogen atoms in water to measure water content. The nuclear spin of the hydrogen atoms in water has a weak magnetic moment, when applied magnetic field, the nuclear magnetic moment of hydrogen atoms will produce resonance signal. The water sample is placed in a low-field NMR instrument, the hydrogen atoms in the water sample are put into a resonance state by the alternating magnetic field generated by the transmission coil. At this point, the NMR signal will be detected through the detection coil and converted into the corresponding electrical signal. By processing and analyzing the obtained NMR signal, the frequency and intensity of the nuclear resonance signal of hydrogen atom in water sample can be obtained. Compared with the NMR signal of the standard sample with different water content in known water sample, the water content of the water sample to be measured was obtained. According the signal strength of the sample moisture to be tested was linearly related to the mass, the correspondence between the signal intensity and the water mass of the sample was determined by calibration. So that the moisture content of the test sample could be obtained.^[17]

The quality of the samples used for calibration are shown in Table 1. Because the individual sample point was deviated from the calibration curve, 3 points were removed. The calibration curve is shown in Figure 1, where the correlation coefficient is 0.9992.

Figure 1. Calibration curve.

Table 1. Quality of calibration samples.

Number	1	2	3	4	5	6	7
Undried sample quality (g)	13.8477	12.9603	12.1555	15.1465	15.4252	15.1816	14.9427
Drying sample quality (g)	9.8986	9.9152	9.5975	8.9705	9.4672	9.1487	9.2550

The moisture content was measured three times repeatedly, as described above. The thousand grain weight was calculated according to Equation (1)(1) $m_1=\frac{m_t\times 1000}{N}$(1) ^[18]:

(1) $m_1=\frac{m_t\times 1000}{N}$(1)

where m₁ is the thousand grain weight, g; m_t is the quality of the intact grain, g; N is the number of intact kernels.

The dry matter weight is calculated according to Equation (2)(2) $m_0=\frac{m_1\times (100-{\omega }_{H_{2}O})}{100}$(2) ^[18]:

(2) $m_0=\frac{m_1\times (100-{\omega }_{H_{2}O})}{100}$(2)

where m₀ is the dry matter weight, g; m₁ is the thousand grain weight, g; ω_H2O is the moisture content, g·g^-1.

Determination of the antioxidant enzyme activity

At stable air temperature (the room temperature was about 20°C), 300 g of a sample, which was selected randomly from the laboratory samples and impurities were removed. Samples were crushed to powder by using a microplant grinder (the sample could pass through a 60 mesh standard sieve with an aperture of 0.30 mm.), and packed in polypropylene bags and stored at 4°C until further use. On the basis of reference,^[19] according to the POD, CAT, and SOD kits provided by Nanjing Jiancheng Biology Company, each sample was tested three times in parallel.

Results and discussion

Moisture content

The moisture content is fundamental for the physiological and biochemical reactions of corn kernels.^[20] Figure 2 shows the moisture content of various corn seeds harvested at different harvest periods varied significantly. After 28 days, the decline rates of corn kernels moisture were significantly accelerated, and there were significant differences between different varieties, which were specifically manifested as XY335 > K×3564 > JD31 > FM985.

Figure 2. Moisture content curves of corn kernels.

Thousand grain weight

Figure 3 shows with the increase in harvest time, the change of thousand grain weight of different corn varieties varied greatly, among which XY335 thousand grain weight increased the largest and K×3564 thousand grain weight increased the smallest. During the whole harvest process, the thousand grain weight XY335 increased slowly to 294.667 g (about 25 days), that of FM985 increased to 272.067 g (about 27 days), then began to decrease. The thousand grain weight JD31 increased to 289.717 g (about 28 days), then began to decrease, and the rise and decline stage fluctuated greatly. The thousand grain weight K×3564 increased slowly to 260.648 g (about 26 days), then began to decrease, after 37 days, the thousand grain weight dropped sharply by a large extent.

Figure 3. Thousand grain weight curves of corn kernels.

Dry matter weight

Figure 4 shows the dry weight of corn kernels accumulated with the increase in harvest period. During the whole harvest period, the dry weight of XY335 fluctuated greatly, that of FM985 increased gradually at the initial harvest, and fluctuated greatly after 31 days. The dry weight of JD31 showed a steady upward trend, that of K×3564 shows an upward trend, but it fluctuated greatly and increased little. From the perspective of the increase in dry matter weight of the four maize varieties, during the whole harvest process, the largest increase in dry weight of JD31 was 90.662 g, and the smallest increase in dry weight of K×3564 was 41.898 g. After 33 days, the dry weight of corn kernels entered the ripening period, the accumulation of dry weight tended to be a maximum and no longer increased. After that, there was even a decrease in dry weight which was called hidden loss, there were significant differences between different varieties.

Figure 4. Dry matter weight curves of corn kernels.

POD enzyme activity

According to Figure 5, the overall POD enzyme activities of four corn varieties were increased first and then decreased with the increase in harvest time.^[21] The POD enzyme activity of XY335 increased to 332.199 U/g (about 30 days), that of JD31 increased slowly to 442.199 U/g (about 34 days), and then began to decrease. The POD enzyme activity of FM985 increased to 327.299 U/g (about 28 days), then began to decrease, and after that increased slightly. The POD enzyme activity of K×3564 increased to 312.199 U/g (about 27 days), then began to decrease, and the whole process fluctuated greatly. The POD enzyme activity of corn kernels decreased, which indicated that the kernels begin to age, and the ability of the corn kernel cells to remove the peroxide was reduced. When the aging reached a certain extent, it would cause cells damage and irreversible, then resulting in a reduced viability of corn kernels.^[22]

Figure 5. POD enzyme activity curves of corn kernels.

CAT enzyme activity

As can be seen from Figure 6, CAT enzyme activities changed significantly with the increase in the harvest period, and the overall activities were increased first and then decreased, and the CAT enzyme activities of four varieties varied greatly. The CAT enzyme activity of XY335 basically showed a straight upward trend, reached 187.397 U/g (about 27 days), that of JD31 increased slowly to 74.715 U/g (about 28 days), then began to decline, and the fluctuation was not obvious during the whole harvest period. The CAT enzyme activity of FM985 increased to 157.234 U/g (about 26 days), that of K×3564 increased slowly to 98.861 U/g (about 28 days) and then began to decrease. The CAT enzyme activity of corn kernels increased first, indicated that the ability of cells to remove hydrogen peroxide increased and corn kernels vitality increased. With the increase in the harvest time, the CAT enzyme activity was decreased, indicated a diminished ability of the cells to remove hydrogen peroxide, corn kernels vitality decreased, and aging degree increased.^[22,23]

Figure 6. CAT enzyme activity curves of corn kernels.

SOD enzyme activity

According to Figure 7, the overall SOD enzyme activities of four varieties increased first and then decreased as the harvest time increased. The SOD enzyme activity decreased, indicated that the cells antioxidant ability and vitality increased. The SOD enzyme activity of XY335 increased to 423.226 U/g (about 27 days) and then began to decrease, the fluctuation amplitude was not obvious during the decline process. The SOD enzyme activity of FM985 increased to 417.728 U/g (about 31 days), then began to decrease, and after that increased slightly. The SOD enzyme activity of JD31 increased slowly to 439.207 U/g (about 31 days), that of K×3564 increased to 430.637 U/g (about 29 days), and then began to decrease. The SOD enzyme activity decreased as the harvest time increased, which indicated that the cells’ own resistance regulation ability decreased when the kernels aging to a certain extent, beyond their own regulatory range.^[22,24] Different corn varieties had different resistance regulation ability. The SOD enzyme activities of XY335, FM985, and JD31 declined rapidly in the late harvest, indicated that the antioxidant regulation abilities of them in the later harvest were low.

Figure 7. SOD enzyme activity curves of corn kernels.

Mathematical model

Mathematical model of growth curve

The Regression models of moisture content and dry matter weight of four corn kernels are shown in Tables 2 and 3. Due to the large fluctuation range of dry matter weight in a certain period time, the data were first slid and averaged. Then, the regression analysis of the model was performed.

Table 2. Regression models of moisture content.

	Nonlinear regression model	R^2
XY335	y = 0.001t^3-0.051t^2 + 0.794t + 37.724(1 ≤ t ≤ 44)	R^2 = 0.930
FM985	y = 0.001t^3-0.028t^2 + 0.465t + 39.748(1 ≤ t ≤ 44)	R^2 = 0.880
JD31	y = −9.615 × 10^−5t^3-0.042t^2+0.755t + 37.616(1 ≤ t ≤ 44)	R^2 = 0.859
KX3564	y = −4.737 × 10^−5t^3-0.016t^2 + 0.217t + 41.074(1 ≤ t ≤ 38)	R^2 = 0.941

Table 3. Regression models of dry matter weight.

	Nonlinear regression model	R^2
XY335	y1 = 154.480·1.016^0.411t(1 ≤ t ≤ 19) y2= 176.243·0.996^0.275t(20 ≤ t ≤ 44)	R1^2 = 0.976 R2^2 = 0.991
FM985	y1 = 147.140·1.026^0.295t(1 ≤ t ≤ 13) y2 = 151.120·1.009^0.324t(14 ≤ t ≤ 44)	R1^2 = 0.920 R2^2 = 0.962
JD31	y1 = 136.092·t^0.060(1 ≤ t ≤ 17) y2 = 127.517·1.020^0.631t(18 ≤ t ≤ 30) y3 = 166.419·1.008^0.353t(31 ≤ t ≤ 44)	R1^2 = 0.931 R2^2 = 0.949 R3^2 = 0.988
KX3564	y1 = 145.988·1.012^0.323t(1 ≤ t ≤ 20) y2 = -0.001t^3 + 0.110t^2-4.845t + 216.893(21 ≤ t ≤ 38)	R1^2 = 0.964 R2^2 = 0.978

From the above models, R² of each model is high, indicating that the fitting degree of each model is good. The actual value and the predicted value fitting degree are high, which can better predict the moisture content and dry matter weight change trend of each corn variety in the harvest process.^[25]

Mathematical model of enzyme activity

Due to the large fluctuation range of enzyme activity in a certain period time, the data was first slid and averaged. Then, the regression analysis of the model was performed. The regression models of enzyme activity are shown in Tables 4–6. From the above models, R² of each model is high, indicating that the fitting degree of each model is good. The actual value and the predicted value fitting degree are high, which can better predict the enzyme activities change trend of each corn variety in the harvest process.^[25]

Table 4. Regression models of POD enzyme activity.

	Nonlinear regression model	R^2
XY335	y1 = 96.783·1.105^0.860t(1 ≤ t ≤ 13) y2 = 0.019t^3-1.754t^2 + 46.581t-93.121(14 ≤ t ≤ 44)	R1^2 = 0.851 R2^2 = 0.904
FM985	y1 = 139.812·1.054^0.698t(1 ≤ t ≤ 23) y2 = -0.038t^3 + 4.768t^2-193.474t + 2723.610(24 ≤ t ≤ 44)	R1^2 = 0.932 R2^2 = 0.945
JD31	y1 = 0.015t^3-0.972t^2 + 21.301t + 146.693(1 ≤ t ≤ 19) y2 = 181.794·1.044^0.635t(20 ≤ t ≤ 29) y3 = 0.287t^3-32.461t^2 + 1197.160t -14,070.595(30 ≤ t ≤ 44)	R1^2 = 0.978 R2^2 = 0.946 R3^2 = 0.986
KX3564	y1 = -0.041t^3 + 0.973t^2-0.349t+180.395(1 ≤ t ≤ 20) y2 = 398.893·0.950^0.622t(21 ≤ t ≤ 38)	R1^2 = 0.988 R2^2 = 0.915

Table 5. Regression models of CAT enzyme activity.

	Nonlinear regression model	R^2
XY335	y1 = 20.602·1.067^1.499t(1 ≤ t ≤ 22) y2 = -0.045t^3+5.027t^2-188.776t + 2384.672(23 ≤ t ≤ 44)	R1^2 = 0.851 R2^2 = 0.904
FM985	y1 = -0.033t3 + 1.408t2-11.731t + 48.892(1≤t≤18) y2 = 710.781·0.913^1.011t(19 ≤ t ≤ 31) y3 = 668.930· 0.951^1.540t(32 ≤ t ≤ 44)	R1^2 = 0.932 R2^2 = 0.945 R3^2 = 0.986
JD31	y1=27.801·1.084^0.513t(1 ≤ t ≤ 21) y2=0.009t^3-0.864t^2+24.278t-153.433(22 ≤ t ≤ 44)	R1^2 = 0.978 R2^2 = 0.946
KX3564	y1 = -0.024t^3 + 0.782t^2-4.860t + 23.632(1 ≤ t ≤ 19) y2 = -1.250t^2 + 57.220t-589.710(20 ≤ t ≤ 29) y3 = 1880.951·0.940^2.236t(30 ≤ t ≤ 38)	R1^2 = 0.988 R2^2 = 0.915 R3^2 = 0.986

Table 6. Regression models of SOD enzyme activity.

	Nonlinear regression model	R^2
XY335	y1 = 274.312·1.049^0.445t(1 ≤ t ≤ 18) y2 = 557.321·0.958^0.355t(19 ≤ t ≤ 44)	R1^2 = 0.936 R2^2 = 0.931
FM985	y1 = -0.069t^3 + 1.570t^2-5.232t + 302.580(1 ≤ t ≤ 17) y2 = 190.067·1.050^0.618t(18 ≤ t ≤ 24) y3 = 0.019t^3-2.071t^2 + 70.703t-402.548(25 ≤ t ≤ 38)	R1^2 = 0.971 R2^2 = 0.943 R3^2 = 0.948
JD31	y1 = 0.314t^3-4.468t^2 + 15.300t + 358.584(1 ≤ t ≤ 10) y2 = 334.762·1.028^0.378t(11 ≤ t ≤ 23) y3 = 856.290·0.976^1.189t(24 ≤ t ≤ 31) y4 = 571.018·0.955^0.299t(32 ≤ t ≤ 44)	R1^2 = 0.896 R2^2 = 0.971 R3^2 = 0.956 R4^2 = 0.925
KX3564	y1 = 351.813·1.016^0.306t(1 ≤ t ≤ 25) y2 = 655.462·0.953^0.389t(26 ≤ t ≤ 44)	R1^2 = 0.948 R2^2 = 0.971

Identification of the optimal harvest period

One week starting with 97% of the maximum value of the dry matter weight^[26,27] and one week starting with the maximum value of enzyme activity were determined as the optimal harvest period.^[12] The optimal harvest periods of four corn varieties are shown in Table 7. According to Table 7, the optimal harvest period for XY335 corn kernels was 10 days (9.27–10.6), the optimal harvest period for FM985 corn kernels was 14 days (9.26–10.9), the optimal harvest period for JD31 corn kernels was 12 days (9.28–10.9), the optimal harvest period for K×3564 corn kernels was 12 days (9.27–10.8).

Table 7. Optimum harvest period of four corn varieties.

Harvesting time Variety Index	XY335	FM985	JD31	KX3564
Moisture content (g·g^−1)	9.29 – 10.6	10.5 – 10.9	9.28 – 10.8	10.2–10.8
Dry matter weight (g)	9.27 – 10.3	10.3 – 10.9	10.3 – 10.9	10.2 – 10.8
POD enzyme activity (U/g)	9.30 – 10.6	9.28 – 10.4	10.4 – 10.10	9.27 – 10.3
CAT enzyme activity (U/g)	9.27 – 10.3	9.26 – 10.2	9.28 – 10.4	9.28 – 10.4
SOD enzyme activity (U/g)	9.27 – 10.3	10.1 – 10.7	10.1 – 10.7	9.29 – 10.5

Conclusion

In this study, the growth curve and the antioxidant enzyme activities of corn kernels were measured throughout the harvest process and the optimal harvest period was determined. The results showed that the moisture content of corn kernels were decreased with the increase in harvest time, the thousand grain weight of the corn grains were first increased slightly and then decreased. With the increase in harvest period, the dry weight of corn grain accumulated. After 33 days, the dry weight of corn kernels entered the ripening period, the accumulation of dry weight tended to be maximum and no longer increased. After that, there was even a decrease in dry weight which was called hidden loss. The overall enzymatic activities of POD, CAT, and SOD of corn grains were tended to rise first and then decline, and the difference between the different varieties was more significant. The mathematical models of the change of moisture content, dry weight, and antioxidant enzyme activities were established. The optimal harvest period for each corn variety was determined according to the change of moisture content, dry matter weight, and antioxidant enzyme activity. The optimal harvest period for XY335 corn kernels was from September 27 to October 6, and FM985 corn kernels was from September 26 to October 9. The optimal harvest period for JD31 corn kernels was from September 28 to October 9, and of K×3564 corn kernels was from September 27 to October 8.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the the Youth Science Fund for National Natural Science Foundation of China [32102034].