Extraction, preparation, and carboxymethyl of polysaccharide from <i>Lotus</i> root

YAN, You-Yu; WANG, Qing; SUN, Luo-Hong; ZHANG, Xi-Feng

doi:10.1590/fst.17822

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Food Science and Technology

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Original Article • Food Sci. Technol 42 • 2022 • https://doi.org/10.1590/fst.17822 copy

Extraction, preparation, and carboxymethyl of polysaccharide from Lotus root

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

In this study, we extracted lotus root polysaccharide (LRP) and synthesized carboxymethylated lotus root polysaccharide (CM-LRP) using response surface methodology (RSM). The monosaccharide component of LRP was analyzed by pre-column derivatization high performance liquid chromatography (PCD-HPLC). The polysaccharide was characterized by ultraviolet (UV) spectroscopy scan, Fourier transform infrared spectroscopy (FTIR), circular dichroism spectrometer (CD), and scanning electron microscopy (SEM). It was found that the main monosaccharide component of LRP was glucose, accounting for more than 98% of the total polysaccharide component. RSM analyses revealed that the optimal conditions for CM-LRP synthesis were a reaction duration of 2.22 h, the chloroacetic acid dosage is 2.02 g, and a temperature of 46.87 °C. Under these conditions, the predicted degree of substitution (DS) was determined to be 0.5101. The superoxide anion, ferrous ion and hydroxyl radical antioxidant system were constructed to study the antioxidant activity of LRP and CMLRP. The activity of CMLRP to remove ferrous ions and hydroxyl radicals were significantly stronger than that of LRP, and it also showed a certain concentration-dependent. LRP has a better scavenging ability than CMLRP in scavenging superoxide anion free radicals. Our data revealed that CM-LRP is a promising natural antioxidant with potential value as a food supplement.

Keywords:
Lotus root; polysaccharide; carboxymethyl; antioxidant

Variable	Symbols	Coded levels
	Coded	-1	0	1
Reaction time (h)	A	1	2	3
MCA (g)	B	1.5	2	2.5
Reaction temperature(°C)	C	40	45	50

Run	A	B	C	DS
1	3.00	2.00	40.00	0.4214
2	1.00	1.50	45.00	0.3087
3	1.00	2.00	50.00	0.3843
4	2.00	2.00	45.00	0.5137
5	1.00	2.50	45.00	0.3790
6	2.00	2.00	45.00	0.5112
7	2.00	2.00	45.00	0.5057
8	2.00	2.00	45.00	0.4976
9	3.00	1.50	45.00	0.3839
10	2.00	1.50	40.00	0.3680
11	2.00	2.00	45.00	0.5030
12	3.00	2.50	45.00	0.3786
13	1.00	2.00	40.00	0.4226
14	2.00	2.50	50.00	0.4202
15	3.00	2.00	50.00	0.4767
16	2.00	2.50	40.00	0.4365
17	2.00	1.50	50.00	0.4226

Variables	Sum of squares	df	Mean square	F-value	p-Value
model	0.059	9	6.606E-003	180.52	<0.001***
A	3.445E-003	1	3.445E-003	94.12	<0.001***
B	2.148E-003	1	2.148E-003	58.71	0.0001***
C	3.823E-004	1	3.823E-004	10.45	0.0144*
AB	1.429E-003	1	1.429E-003	39.04	0.0004***
AC	2.190E-003	1	2.190E-003	59.85	0.0001***
BC	1.257E-003	1	1.257E-003	34.34	0.0006***
A²	0.018	1	0.018	480.62	<0.0001***
B²	0.026	1	0.026	719.09	<0.0001***
C²	9.931E-004	1	9.931E-004	27.14	<0.0012***
Residual	2.562E-004	7	3.660E-005
Lack of fit	9.048E-005	3	3.016E-005
Pure error	1.657E-004	4	4.142E-005	0.73	0.5864
Correlation total	0.060	16
$R^{2} = 0.9957$	$R^{2}_{A d j} = 0.9902$	$R^{2}_{P r e d} = 0.9714$	$A d e q p r e c i s i o r = 43.047$

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[1] *Corresponding author: zhangxf9465@163.com