Sweet potato starch noodle enriched with purple sweet potato anthocyanins: Improving anthocyanin retention and noodle quality by adding citrus peel pectin

Abstract

Due to chemical instability, anthocyanins easily degrade during processing and cooking. This study investigated whether citrus peel pectin (CP) could increase purple sweet potato anthocyanin (PSPA) retention in sweet potato starch noodles. Particle size analysis, ζ-potential measurements, and small-angle X-ray scattering confirmed that CP formed nanoscale complexes with PSPA, which showed heterogeneous multilayer adsorption on CP fitting the Freundlich isotherm model. Adding 0.5% and 3% CP significantly increased PSPA retention from 37.82% to 48.75% and 52.24%, respectively. CP and PSPA altered starch structure and noodle gel network, modifying texture and tensile properties without affecting cooking yield or loss. PSPA reduced hardness and chewiness of noodle. Notably, 0.5% CP combined with PSPA maximized tensile force and extensibility of cooked noodles. This study suggests CP effectively elevates PSPA retention via complex formation, with 0.5% CP balancing PSPA retention and noodle quality, offering insights for improving pigmented starch products with natural polysaccharides.

Highlights

• •Citrus peel pectin (CP) increased the retention of PSPA in sweet potato starch noodle.
• •Purple sweet potato anthocyanins (PSPA) and 0.5% CP exhibited a nanoscale complex.
• •0.5%CP-PSPA interaction showed multilayer adsorption fitted with Freundlich isotherm.
• •AP0.5 demonstrated comparable starch noodle quality to CTRL, with improved color.
• •PSPA and CP enlarged the pores in the microstructure of sweet potato starch noodles.

1. Introduction

Purple sweet potato (Ipomoea batatas L. Lam) is a member of the family Convolvulaceae and is commonly cultivated worldwide with high yield and consumed as human food (Tang et al., 2018). According to Food and Agriculture Organization of the United Nations (2025), 90,930,337.83 tons of sweet potatoes were produced in a 7,215,012-ha area worldwide. Purple sweet potato is consumed in different ways, such as boiling, baking, or processing into flour and starch to use as an ingredient in starch noodles, pasta, cereal-based foods, bakery products (bread, cookies, crackers), and snacks (Maqsood et al., 2025). Purple sweet potato contains essential macronutrients and health-beneficial components, including starch, dietary fiber, and bioactive phenolic compounds (Li, Xiao, et al., 2019). Purple sweet potato is rich in anthocyanin, which has gained popularity in the food industry and among consumers due to its natural pigment, water solubility, and antioxidant effects that protect against various health issues (Huang et al., 2021).

As a food additive, purple sweet potato anthocyanin (PSPA) is widely used in the formulation of foods, such as 3D-printed ready-to-eat starch gels (Huang et al., 2024) and jelly drinks (Tensiska et al., 2017). However, PSPA loss and degradation have been observed in food processing, such as steaming, boiling, microwave, drying, extrusion, and other novel techniques, due to its instability. Mulyawanti et al. (2018) found pigment loss during the cooking process of purple sweet potato pasta due to anthocyanin degradation by an increase in oxidation reactions. Therefore, techniques were applied to enhance anthocyanin retention, including high-pressure processing to fruit samples (Marszałek et al., 2017), steam-blanching (Mahmudatussa'adah et al., 2019), ultrasound-pretreatment, and ohmic-heat treatment to purple sweet potatoes (Karacabey et al., 2023). Besides these techniques, various types of polysaccharides, hydrocolloids, and proteins are also utilized to enhance the stability of anthocyanins, such as chitosan, sodium carboxymethylcellulose-pectin, and xanthan gum (Herrman et al., 2022; Kraithong & Rawdkuen, 2020; Liu et al., 2023).

Previous studies utilized pectins to increase the stability of anthocyanins and reported positive results. For instance, Liu et al. (2024) found that applying sunflower pectin significantly increased the retained anthocyanin from 22.3% to 31.5% and from 43.4% to 52.1% after heating treatment. Moreover, Liu et al. (2023) observed that sunflower pectin remarkably reduced the thermal degradation of purple sweet potato anthocyanin and improved the half-life of anthocyanin at pH 5. In another study, water-soluble blueberry pectin fractions significantly decreased free anthocyanin content at pH 4.5, binding with anthocyanin (Lin et al., 2016). The studies suggested that this binding between pectin and anthocyanin was formed via non-covalent interactions, such as hydrogen bonds and electrostatic interactions (Liu et al., 2023; Liu et al., 2024).

Starch noodles have been produced and consumed as a traditional staple food in Asian countries for thousands of years. Starch noodles are elastic, have a high shelf life, and have less cooking loss (Wang et al., 2015), which provides better mouthfeel and yield. Moreover, starch noodles are preferred by celiac patients and people who are sensitive to gluten. However, starch noodles demonstrate limited visual appeal and low functional and nutritional value, which are important for consumer acceptance. Research interest has increased in starch noodles to improve the nutritional and other qualities of noodles (Wang et al., 2015), and pigmented noodles are gaining popularity. PSPA could contribute to starch noodles to enhance sensory appeal, consumer acceptance, and nutritional functionality.

To date, despite the well-documented stabilizing effect of pectin on anthocyanin retention in previous studies, the application of purple sweet potato anthocyanins (PSPA) in starch noodles via citrus peel pectin (CP) remains underexplored. The retention of PSPA in starch noodles continues to pose significant challenges due to cooking-induced degradation and poor incorporation into the noodle matrix. We hypothesize that CP enhances PSPA retention through the formation of CP-PSPA complexes, driven by their opposite charges. These complexes not only improve anthocyanin retention but also enhance the color properties, structural integrity and cooking quality of the starch noodles. This study focused on unraveling CP-PSPA complex formation to improve anthocyanin retention in purple sweet potato starch noodles, while also evaluating the synergistic effects of PSPA and CP on the noodle properties. Our findings would provide experimental evidence for enhancing anthocyanin retention and advancing the functionalization of anthocyanins in starchy food products.

2. Materials and methods

2.1. Materials

Sweet potato starch (denoted as SPS, 84.4 g/100 g carbohydrate, food grade) was purchased from Chongqing Zhongweitang Organic Food Co., Ltd. (Zhongxian, Chongqing, China). Anthocyanin from purple sweet potato (PSPA) ($E_{1\mathrm{cm}}^{1\%}$= 100, Lot number: 20240315) was purchased from Qingdao Pengyuan Kanghua Natural Source Co., Ltd. (Qingdao, Shangdong, China). Citrus peel pectin (CP, Galacturonic acid ≥74.0%, dried basis, P9135) was obtained from Sigma-Aldrich (St. Louis, MO, USA). Anhydrous ethanol (99.7%, reagent grade) and hydrochloric acid (AR, 36–38% w/w) were provided by Chuandong Chemical (Group) Co., Ltd. (Chongqing, China). Other chemicals used were of analytical grade and were used as received.

2.2. Characterization of the binding behavior of PSPA to CP in solutions

The CP concentrations of 0.5% (w/v) and 3.0% (w/v) were selected to represent relatively low and high CP ratios, respectively, according to preliminary experiments and previous studies investigating pectin-anthocyanin interactions.

2.2.1. Preparation of CP-PSPA complexes

To prepare CP stock solutions, 0.2 g and 1.2 g of CP were individually dissolved in 20 mL distilled water at 50 °C in a water bath, yielding 1% (w/v) and 6% (w/v) solutions, respectively. These samples were centrifuged at 9000 ×g and 20 °C for 20 min (for subsequent SAXS analysis only) to obtain clear supernatants. Subsequently, 5 mL of each CP stock solution (1% and 6%, w/v) was diluted with 5 mL distilled water to create samples labeled P0.5 and P3.0, respectively. Separately, 0.2 g PSPA was dissolved in 20 mL distilled water to prepare a 1% (w/v) PSPA stock solution. This stock was diluted (5 mL + 5 mL distilled water) to obtain a 0.5% (w/v) solution labeled ANT. CP-PSPA complexes (AP0.5 and AP3.0) were formed by mixing 5 mL of the PSPA stock solution (1%, w/v) with 5 mL of the CP stock solutions (1% and 6%, w/v), respectively. All prepared solutions (Supplementary Table S1) were incubated at 50 °C for 10 min before ζ-potential and particle size analysis.

2.2.2. Measurements of ζ-potential and particle size of CP-PSPA complexes

Once the solutions P0.5, P3.0, ANT, AP0.5, and AP3.0 (Section 2.2.1) were prepared, the particle size and ζ-potential were measured immediately with a Malvern Laser Particle Size Analyzer (Nano ZS90, Malvern Panalytical Ltd., Malvern, UK) at 25 ± 0.1 °C under the following conditions: equilibration time, 20 s, number of runs, 4, run duration, 4 s. Samples were in their natural pH levels (pH (ANT) = 3.28, pH (AP0.5) = 2.96, pH (P0.5) = 2.96, pH (AP3.0) = 2.61, pH (P3.0) = 2.53).

2.2.3. Small-angle X-ray scattering analysis (SAXS) of CP-PSPA complexes

SAXS analyses were performed on BL16B1 beamline at the Shanghai Synchrotron Radiation Facility (Shanghai, China). The solutions (P0.5, P3.0, ANT, AP0.5, and AP3.0) were exposed to the incident X-ray beam (λ = 0.124 nm) with exposure time of 40 s. The distance from the sample to the detector was 1900 mm. The scattering data were collected using a Pilatus 2MF detector at 25 °C with an active surface area of 288.8 × 253.7 mm (length × width) and a pixel size of 172 μm. The SAXS pattern was analyzed by the Fit2D software (European Synchrotron Radiation Facility, Grenoble, France). The scattering vector q = 4π sin θ/λ, where 2θ was the scattering angle, and λ was the X-ray wavelength. The scattering vector (q) ranged from 0.122 nm⁻¹ to 5.374 nm⁻¹.

2.2.4. PSPA/CP adsorption study

The adsorption isotherm was determined following methods from references (Koh et al., 2020; Liu et al., 2023) with modifications. Briefly, PSPA solutions (0.2–1%, w/v) in citrate buffer (25 mM, pH 4.0) were analyzed using a microplate reader (Epoch2, BioTek Instruments, Vermont, USA) to generate a calibration curve (y = 6.0364x - 0.3043, where y - absorbance and x - PSPA concentration, R² = 0.9969). A 1% (w/v) CP stock solution and a 4% (w/v) PSPA stock solution were prepared by dissolving 0.2 g CP (db) or 0.8 g PSPA (db) in 20 mL citrate buffer (25 mM, pH 4.0), respectively, with heating at 50 °C until complete dissolution. PSPA working solutions (0.2–4%, w/v) were obtained by diluting the PSPA stock solution with citrate buffer. For the adsorption experiment, 2.5 mL of each PSPA working solution (0.2–4%, w/v) was mixed with 2.5 mL CP stock solution (1%, w/v), yielding 5 mL PSPA-CP solutions (0.1–2% PSPA, w/v). These mixtures were incubated at 50 °C for 1.5 h and then at 4 °C for 24 h. After centrifugation (14,000 ×g, 4 °C, 20 min; Cence H1850R centrifuge, Hunan Xiangyi, Hunan, China), the absorbance was measured at 520 nm using the microplate reader. Control samples (0.1–2% PSPA, pH 4.0) were prepared without CP. The calibration curve was used to convert absorbance values to concentrations. The bound anthocyanin content was calculated by Eq. (1). Freundlich isotherm was applied to fit the data by the following Eq. (2):

$$\text{Bound anthocyanin content}=C_0-C$$ (1)

where the C₀ was the concentration of anthocyanins in the control group, and the C was the concentration of free anthocyanins in the experimental group (with CP).

$$\Gamma =K_F.C^{1/n}$$ (2)

Γ was the adsorbed anthocyanin (mg/mg pectin), C was the concentration of free anthocyanin in the experimental group (mg/mL), K_F was the Freundlich constant, and n was the heterogeneity factor or adsorption intensity.

2.3. Rapid visco analysis (RVA)

The pasting properties of sweet purple potato starch samples with adding CP, PSPA or both (SPS, P0.5, P3.0, ANT, AP0.5, AP3.0) were determined using a Rapid Visco Analyzer (RVA-Tec Master, Perten Instruments AB, Stockholm, Sweden). Samples (3 g dry base) mixed with 25 mL distilled water were poured into aluminum canisters. The test began with a 10 s initial paddle speed of 960 r/min to prevent clumping, followed by 160 r/min throughout the test. Temperature conditions: held at 50 °C for 1 min, heated to 95 °C over 2.5 min, cooled to 50 °C in 4 min, and held for 2 min. Pasting parameters recorded in this test included peak viscosity (PV), trough viscosity (TV), final viscosity (FV), breakdown (BD), setback (SB), and pasting temperature (PT).

2.4. Preparation of sweet potato starch noodles

Sweet potato starch noodles (Supplementary Table S2) were prepared according to Li, Ye, et al. (2019). Firstly, 60 g of distilled water was mixed with 27 g of sweet potato starch (with/ without CP, PSPA, or both) to make a starch slurry (starch-to-water ratio: 27:60, w/w). The slurry was poured into an aluminum tray (21.0 cm × 16.5 cm) and spread evenly. The tray was heated by floating on the surface of a boiling water bath (95 ± 2 °C) for 15 s and subsequently immersed in boiling water (95 ± 2 °C) to cook for 3 min. Secondly, the tray was placed on the surface of the cooling water (15–20 °C) to cool down for approximately 1 min. The formed starch gel was carefully lifted off from the tray and covered with plastic wrap and placed overnight in the refrigerator at 4 °C. Thirdly, the starch gel was cut into uniform strips with dimensions of 1.5 mm (thickness) × 10 mm × 170 mm and dried in a constant temperature and humidity incubator (BSC-150, Shanghai Boxun Industry & Commerce Co., Ltd. Medical Equipment Factory, Shanghai, China) at 60 °C and 40% relative humidity for 5 h. The dried starch noodles were stored in a desiccator until further use. The moisture content of dried sweet potato starch noodles was determined according to the National Standard of the People's Republic of China (GB 5009.3–2016).

2.5. Determination of cooking properties of sweet potato starch noodles

10 g of sweet potato starch noodles were cooked in 400 mL of boiling water (1:40, w/v) for 15 min. The cooked noodles were placed in cool water (20–24 °C) for 30–40 s. The cooked noodles were weighed. The cooking water and rinse water were dried in a forced-air drying oven (DHG-9203 A, Shanghai Yiheng Scientific Instruments Co., Ltd., Shanghai, China) at 105 °C for 5 h. After drying, the samples were taken out and weighed. The cooking loss and cooking yield were calculated by using the following Eqs. (3), (4) (Tan et al., 2018):

$$\text{Cooking loss}\left(\%\right)=\frac{\left(\text{dried weight of the cooking water}\right)}{\left(\text{weight of uncooked noodles}\right)}\times 100$$ (3)

$$\text{Cooking yield}\left(\%\right)=\frac{\text{Weight of cooked noodles}}{\text{Weight of uncooked noodles}}\times 100$$ (4)

2.6. Measuring the color of dried and cooked sweet potato starch noodles

The photographs of the sweet potato starch noodles were taken by a digital camera (Canon 80D, Canon Inc., Tokyo, Japan) under standard lighting with a white background.

The color of dried and cooked sweet potato starch noodles was characterized by a colorimeter (WR-18, Wave Optoelectronics, Shenzhen, China). The colorimeter was adjusted using a white standard plate (L* = 92.12, a* = 0.37, b* = −0.06). L*, a*, and b* values were recorded. WI (whiteness index), the color difference (ΔE), hue angle (h*; basic color), and chroma (C*; color intensity or saturation) were calculated according to Eqs. (5), (6), (7), (8), respectively (Kraithong & Rawdkuen, 2020; Shiau et al., 2023).

$$\mathrm{WI}=100-\sqrt{{\left(100-L^{\ast }\right)}^2+{\left(a^{\ast }\right)}^2+{\left(b^{\ast }\right)}^2}$$ (5)

$$\mathrm{\Delta }E=\sqrt{{\left(\mathrm{\Delta }{L}^{\ast }\right)}^2+{\left(\mathrm{\Delta }{a}^{\ast }\right)}^2+{\left(\mathrm{\Delta }{b}^{\ast }\right)}^2}$$ (6)

$$h^{\ast }=\mathit{Ta}{n}^{-1}\left(\frac{b^{\ast }}{a^{\ast }}\right)$$ (7)

$$C^{\ast }=\sqrt{{\left(a^{\ast }\right)}^2+{\left(b^{\ast }\right)}^2}$$ (8)

where ΔL*, Δa*, and Δb* were the differences in lightness, redness, and yellowness, respectively.

2.7. Determination of anthocyanin retention in dried and cooked sweet potato starch noodles

2.7.1. Extraction of anthocyanins from the noodles

The dried noodles and cooked noodles (freeze-dried prior to use) were cut into ∼1 mm fragments with laboratory scissors, then ground intermittently (15 s on / 30 s off, 3 cycles) in a high-speed grinder (Supor-SHM2005, Zhejiang Shaoxing Supor Household Products Co., Ltd., Shaoxing, China) to avoid anthocyanin overheating degradation. The powder was sieved through 100-mesh sieve for use. 2.00 g (db) of sieved powder was mixed with 5 mL pre-chilled 25% ethanol (1:2.5, w/v; pH 3.3, acidified with 0.1 M HCl, freshly prepared), vortexed 30 s for full dispersion, and then extracted in a light-shielded shaking incubator (ZWY-2102C, Shanghai Zhicheng Analytical Instruments Manufacturing Co. Ltd., Shanghai, China) at 35 °C, 180 rpm for 2 h. After 15 min centrifugation at 9000 ×g (4 °C), the supernatant was collected. The residue was repeatedly extracted with 5 mL pre-chilled acidified ethanol until the supernatant absorbance at 520 nm was below 0.005 (typically 3–4 cycles). The combined supernatants were diluted to 25 mL with acidified ethanol for subsequent measurement.

2.7.2. Determination of anthocyanin retention rate

The anthocyanin content was determined by using the method of Shiau et al. (2023). 1 mL of the sample (from Section 2.7.1) was mixed with 4 mL of HCl-KCl buffer (0.025 M, pH 1.0) and sodium acetate buffer (0.4 M, pH 4.5), respectively. The samples were measured for absorbance at 520 nm and 700 nm with a microplate reader (Epoch2, BioTek Instruments, Inc., Vermont, USA). The total anthocyanin content of samples was calculated by Eq. (9):

$$\mathit{TAC}\left(\mu \mathrm{g}/\mathrm{g},\mathit{db}\right)=\frac{A\times F\times M_w\times 1000\times V}{\epsilon \times x\times W}$$ (9)

where A = [(A_520nm – A_700nm) _pH1.0 - (A_520nm - A_700nm) _pH4.5]; F: dilution factor; M_W: 449.2 g/mol of cyanidin-3-glucoside; V: extract volume (mL); ε (molar extinction coefficient of cyanidin-3-glucoside): 26,900 L/mol; x: path length of cuvette (cm); W: sample weight (g); and db: dry weight basis.

2.8. Characterizing physical properties of dried sweet potato starch noodles

The sweet potato starch noodles were ground into powder by a grinder (Supor-SHM2005, Zhejiang Shaoxing Supor Household Products Co., Ltd., Zhejiang, China) and sieved through a 100-mesh sieve, followed by equilibration at room temperature for 24 h for further analyses, including XRD, ATR-FTIR, SAXS, DSC, and RVA.

2.8.1. SAXS

The test conditions were applied as described in Section 2.2.3. 1 g of the powdered sample and SPS were mixed with 1 mL of distilled water to prepare a slurry (50%, w/v), respectively. Then, the slurry was placed into a sample cell and measured with exposure times of 60 s. The scattering vector (q) ranged from 0.122 nm⁻¹ to 5.374 nm⁻¹.

2.8.2. X-ray diffraction (XRD)

The crystalline structures of powdered samples were determined using an X-ray diffractometer (X'Pert³ Powder, Malvern Panalytical B.V., Almelo, the Netherlands) under the tube pressure of 40 kV (Cu-Kα, λ = 1.54 nm) and tube flow of 40 mA. Signals were recorded in a range of diffraction angles (2θ) from 4° to 40° at a scanning speed of 2°/min (Yang et al., 2021). The relative crystallinity (RC, %) was calculated by MDI-Jade 6.5 software (Materials Data Inc., CA, USA) for the starch and the noodles according to the following Eq. (10):

$$\mathrm{RC}\%=\frac{{\sum }_{i=1}^n{A}_{\mathit{ci}}}{A_t}\times 100\%$$ (10)

where A_ci represents the area of the crystalline peak with index i, and A_t is the total area of the XRD pattern.

2.8.3. Attenuated total reflectance–fourier transform infrared spectroscopy (ATR-FTIR)

Regarding the reported method by Yang et al. (2021), with some modifications, SPS and the noodles were analyzed by an FTIR spectrometer (Spectrum100, PerkinElmer, Inc., MA, USA) equipped with an ATR sensor. The samples were analyzed with 32 scans at a resolution of 4 cm⁻¹ in the wavenumber range of 600–4000 cm⁻¹. Then, OMNIC software (version 9.2.86, Thermo Fisher Scientific Inc., MA, USA) was used to deconvolve the acquired data with a bandwidth of 40 cm⁻¹ and an enhancement factor of 1.9.

2.8.4. Differential scanning calorimetry (DSC)

The thermal behaviors of SPS and sweet potato starch noodles were measured using a differential scanning calorimeter (DSC4000, PerkinElmer, Inc., MA, USA). 5 mg of powdered noodle and 15 μL deionized water were mixed and equilibrated at room temperature overnight before analysis. Then, the samples were placed in an aluminum DSC pan and scanned from 25 °C to 100 °C at a heating rate of 10 °C/min. The onset temperature (T_o), peak temperature (T_p), conclusion temperature (T_c), and gelatinization enthalpy (ΔH) were calculated by using the PerkinElmer Pyris software (Ma et al., 2024).

2.8.5. RVA of the ground noodle samples

Pasting parameters of purple sweet potato starch noodles were determined using a Rapid Visco Analyzer (RVA-Tec Master, Perten Instruments AB, Stockholm, Sweden). According to the Standards of the People's Republic of China (NY/T 1753–2009), 3 g of the powdered sample was mixed with 35 mL of distilled water to prepare a suspension. Then, the suspension was poured into an aluminum canister. The test conditions were applied as described in Section 2.3.

2.9. Characterizing physical properties of cooked sweet potato starch noodles

2.9.1. Microstructures

Dried purple sweet potato starch noodles (2 g) were cooked in boiling water (200 mL, 100 °C) without a lid for 15 min. The cooked samples were immediately transferred to an −80 °C refrigerator (DW-86 L626, Qingdao Haier Special Electric Appliance Co., Ltd., Shangdong, China) and freeze-dried for 48 h using a freeze-drier (Scientz-10 N/C, Ningbo Scientz Biotechnology Co., Ltd., Zhejiang, China). The samples were manually broken to obtain a cross-section, then cut transversely with a sharp blade. The sample was adhered to an aluminum stub with conductive double-sided adhesive tape. Before observation, the samples were coated with a thin layer of gold (approximately 10 nm) using a sputter coater (Quorum Q300TD, Quorum Technologies Ltd., Lewes, United Kingdom) for 50 s with sputter current of 15 mA. Images of the microstructure were obtained using a field-emission scanning electron microscope (Sigma 360, Carl Zeiss, Leicester, United Kingdom) under an accelerating voltage of 2.0 kV with 500 × magnification.

2.9.2. Textural analysis

Prior to analysis, dried noodles were cooked in boiling water (100 °C) for 15 min and soaked in cool deionized water (15–20 °C) for 30 s. Texture profile analysis (TPA) was performed using a texture analyzer (TA.XT Plus, Stable Micro Systems, Surrey, UK) with a P/36 R probe, following method of Hu et al. (2023) with modifications. TPA parameters were set as follows: pre-measurement rate, test rate, post-measurement rate, trigger force, and compression ratio at 1.0 mm/s, 1.0 mm/s, 1.0 mm/s, 5.0 g, and 50%, respectively. Hardness (g), adhesiveness (g·s), springiness, cohesiveness, gumminess, chewiness, and resilience were recorded. Tensile properties were analyzed using the same texture analyzer equipped with a tensile testing device (TA. XT Plus, Stable Micro Systems, Surrey, UK). Parameters included pre-test rate, test rate, post-test rate, trigger force, and drawing distance set to 5.0 mm/s, 1.0 mm/s, 10.0 mm/s, 5.0 g, and 80.0 mm, respectively. Each tensile analysis was repeated at least four times.

2.10. Statistical analysis

All experiments were performed in triplicate or more, with data presented as mean ± standard deviation. Statistical analysis was conducted using one-way ANOVA with Tukey's post hoc test (p < 0.05) via SPSS 27.0 (IBM Corp., Chicago, IL, USA). Independent-sample t-test (p < 0.05) was conducted to determine the differences between two groups. OriginPro 2025 (OriginLab Corp., Northampton, MA, USA) was used for plotting, spectral analysis, and Pearson correlation analysis of sweet potato starch noodle properties.

3. Results and discussion

3.1. Binding of PSPA to CP in aqueous solutions

3.1.1. ζ-potential and particle size of CP-PSPA complexes in solutions

Fig. 1A and B showed the particle size and zeta potential of solutions at their natural pH. ANT exhibited a particle size of 439.13 nm, while P0.5 and P3.0 had sizes of 716.63 nm and 1722.67 nm, respectively. When citrus peel pectin (CP) was mixed with purple sweet potato anthocyanin (PSPA), the particle sizes were 645.83 nm (denoted as AP0.5) and 1481.33 nm (denoted as AP3.0), respectively. No significant differences in particle size were observed between CP solutions (P0.5/P3.0) and their PSPA complexes (AP0.5/AP3.0) (p > 0.05).

Fig. 1.

Particle size (A), zeta potential (B), raw SAXS patterns (C), Lorentz-corrected SAXS plots (D) of citrus pectin (CP) and purple sweet potato anthocyanin (PSPA) solutions. (E) Binding ratio of 0.5%/3.0% (w/v) CP to PSPA (bars with different lowercase letters, p < 0.05). (F) CP-PSPA Freundlich isotherms (pH 4.0, 4 °C, 24 h) vs. free PSPA. Symbols: ▲ replication 1, ■ replication 2, ● replication 3, ▾ replication 4. Samples: P0.5 (0.5% CP), P3.0 (3% CP), ANT (0.5% PSPA), AP0.5 (0.5% PSPA+0.5% CP), AP3.0 (0.5% PSPA+3% CP). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

The average particle size reflects colloidal stability, with larger sizes indicating instability due to increased gravitational separation (Herrman et al., 2022). The elevated particle size of P3.0 and AP3.0 likely stems from larger-scale structures formed via hydrogen bonding, hydrophobic interactions, and electrostatic forces between CP and CP-PSPA (Xing et al., 2023). Similarly, Xing et al. (2025) observed that rhamnogalacturonan-I-anthocyanin complexes (1000–2500 nm) exhibited smaller particle sizes than homogalacturonan-anthocyanin complexes (3000–4000 nm), attributing this difference to the branched structure of rhamnogalacturonan-I promoting intramolecular binding and aggregation.

ζ-Potential reflects sample stability. P0.5 and AP0.5 showed significantly higher ζ-potentials (−14.57 mV and − 12.03 mV, respectively) than other samples (p < 0.05), while increasing CP concentration decreased ζ-potential (p < 0.05). P3.0, AP3.0, and ANT exhibited similar low ζ-potentials (p > 0.05). ζ-Potentials near ±30 mV indicate strong electrostatic repulsion and high stability, whereas values close to 0 mV suggest instability (Herrman et al., 2022). The ζ-potential shift from −14.57 mV (P0.5) to −12.03 mV (AP0.5) with increasing CP concentration (0.5–3.0%, w/v) might result from aggregation, reducing surface area and altering surface charge intensity. Elmizadeh et al. (2024) reported that pectin had pK_a of 3.5, and pH > pK_a enhanced negative charge and stability. In our study, P3.0 (pH 2.53) and P0.5 (pH 2.96) had pH values below pK_a, with P0.5's slightly higher pH potentially explaining its higher ζ-potential. In contrast, prior studies linked increasing pectin concentration to more negative ζ-potentials (Elmizadeh et al., 2024). Our results suggested PSPA introduced positive charge, neutralizing CP's ζ-potential, with 0.5% CP yielding greater stability (−12.03 mV) than 3% CP. These results also suggest that pH conditions might play a critical role on CP-PSPA interactions; therefore, the behavior of these complexes under different pH conditions should be further investigated for broader food applications.

3.1.2. SAXS analysis of CP-PSPA complexes in solutions

SAXS analysis (Fig. 1C-D) revealed interactions between CP and PSPA molecules in solution. In the low q range (0.01–0.23 nm⁻¹), samples P3.0 and AP3.0 exhibited higher scattering intensity than P0.5, AP0.5, and ANT, indicating stronger intermolecular aggregation. ANT showed a significantly smaller radius of gyration (R_g, 1.62 nm) than CP-loaded samples (p < 0.05). Among these, P3.0 (2.21 nm) had a higher R_g than P0.5 (2.17 nm), suggesting increased aggregation. Conversely, AP0.5 (2.26 nm) had a higher R_g than AP3.0 (2.18 nm) (p > 0.05), likely due to CP-PSPA interactions forming more compact complexes. R_g reflects structural dimension and aggregation state of the polymer chain, with smaller values indicating greater structure compactness (Du et al., 2025). The observed trends highlighted the role of CP in modulating PSPA aggregation through molecular interactions.

3.1.3. Adsorption isotherm

The binding capacity of PSPA to 0.5% (w/v) and 3.0% (w/v) CP was determined after 24 h incubation at 4 °C and pH 4.0 (Fig. 1E). No significant difference in the bound anthocyanin ratio was observed between AP0.5 (28.91 ± 0.80%) and P3.0 (28.08 ± 1.35%) (p > 0.05). It indicated that the binding sites between pectin and anthocyanin had been fully saturated at the concentration of 0.5%. Therefore, selecting 0.5% CP for subsequent adsorption isotherm analysis not only ensures the full progress of the binding reaction, but also avoids the excessive viscosity interference of high-concentration CP on the isotherm test, which makes the test results more accurate and reliable. Furthermore, AP0.5 exhibited a smaller particle size and a higher absolute zeta potential value than AP3.0 (Section 3.1.1), indicating improved colloidal stability. Therefore, 0.5% CP was selected for the adsorption isotherm analysis.

The Freundlich isotherm (Fig. 1F) was employed to model the interaction between bound anthocyanin (mg/g pectin) and free anthocyanin concentration (mg/L), based on four replicate measurements. The data showed that bound anthocyanin increased with free anthocyanin concentration, but failed to fit the Langmuir model, which assumes monolayer adsorption without intramolecular interactions (Du et al., 2025). In contrast, the Freundlich model accounts for multilayer adsorption on heterogeneous sites, providing insights into surface heterogeneity and adsorption intensity via the parameter 1/n. Values of 1/n < 1 indicate high affinity, 1/n > 1 low affinity, and 1/n = 1 non-reversible adsorption (Gao et al., 2013). A higher 1/n value further reflects greater surface heterogeneity.

The good fit of the Freundlich model (R² = 0.903) suggested heterogeneous multilayer adsorption in the PSPA-CP system. The 1/n value (1.313) indicated higher adsorption intensity and lower surface heterogeneity compared to microporous resins (K_F = 1–6.19 L/g, 1/n = 1.85–1.67) used in prior PSPA adsorption studies at 30 °C (Liu et al., 2007). This enhanced intensity likely stems from strong PSPA-CP interactions. However, our results differ from anthocyanin-chitosan/alginate bead studies (25 °C) (Pinheiro et al., 2021), where chitosan (K_F = 0.51 mg/g, 1/n = 1.11) and alginate (K_F = 36.7 mg/g, 1/n = 0.21) exhibited higher adsorption intensity or lower heterogeneity, respectively. In contrast, Koh et al. (2020) found the Langmuir model better described anthocyanin adsorption to blueberry pectin (K_L = 0.0016–0.0021). These comparisons implied that CP's adsorption behavior aligned with other polymers despite source-dependent variations.

3.2. Effects of CP and PSPA on pasting properties of sweet potato starch

As shown in Fig. 2, the pasting curve of SPS exhibited a typical profile, with a distinct breakdown phase following peak viscosity. The incorporation of CP, PSPA, or their combination primarily influenced peak viscosity (PV), trough viscosity (TV), and final viscosity (FV), while having a negligible effect on pasting onset. Pasting properties of the tested samples are summarized in Table S3. Among these, the control sample (CTRL) and ANT showed no significant differences (p > 0.05). The PV of sweet potato starch was 5749.0 cP, which was not significantly affected by PSPA addition (ANT). However, CP addition significantly reduced PV. This reduction was attributed to pectin's higher water-binding capacity compared to starch (Mahmood et al., 2017), which inhibited starch granule swelling and reduced leached amylose in the aqueous phase. Similarly, Ma et al. (2019) reported a decrease in PV of corn starch from 1219 cP to 1134 cP with 0.5% (w/w) pectin due to suppressed granule swelling. In contrast, higher pectin concentrations (2.0%–10.0% w/w) increased PV (1261 cP to 1696 cP) through pectin's thickening effect in the continuous phase, unlike our observation with 3% (w/w) CP. This discrepancy might stem from pectin source differences. Trough viscosity (TV) reflects granule disintegration under shear and heating. While TV was marginally affected by PSPA (ANT) and 0.5% CP (P0.5) (p > 0.05), it significantly decreased with 3.0% CP (P3.0) and the combined addition of CP and PSPA (AP3.0). Meanwhile, FV was significantly impacted by the addition of 3.0% pectin (P3.0 and AP3.0) (p < 0.05), whereas SB remained unaffected. Witczak et al. (2014) noted that 1%–2% (w/w) pectin reduced FV from 1204.3 to 976.3 mPa·s, likely due to CP's viscous structure after cooking, which resisted shear stress during cooling.

Fig. 2.

RVA pasting profiles of sweet potato starch and starch-anthocyanin-pectin mixtures. Samples: CTRL (native starch), ANT (starch +0.5% PSPA), P0.5 (starch +0.5% CP), P3.0 (starch +3% CP), AP0.5 (starch +0.5% PSPA+0.5% CP), AP3.0 (starch +0.5% PSPA+3% CP).

3.3. Effects of CP and PSPA on the cooking properties of dried starch noodles

3.3.1. Cooking loss

Table 1 shows cooking loss data for sweet potato starch noodles. All samples in this study had low cooking loss (0.99–1.72%), outperforming rice noodles (8.30%) and other starch noodles (2.74–7.41%) reported in previous studies (Wang, Fan, Ding, Ge and Zhou, 2015, Yang, Zhang, Shan and Chen, 2021). No significant differences were found between control noodles and those with CP, PSPA, or both. However, AP3.0 (1.72%) had higher cooking loss than P0.5 (0.99%) (p < 0.05), likely due to CP-PSPA interactions disrupting starch-stabilizing hydrogen bonds (He et al., 2021). Contrary to a wheat noodle study reported by Shiau et al. (2023), where pigment concentration didn't affect cooking loss, our results align with the observation of Sun-Waterhouse et al. (2013) i.e., pectin addition increased cooking loss in pasta by weakening dough networks. The low cooking loss in our study confirmed sweet potato noodles' excellent cooking quality.

Table 1.

Cooking parameters, anthocyanin retention, texture, and tensile properties of cooked sweet potato starch noodles.

	CTRL	P0.5	P3.0	ANT	AP0.5	AP3.0
Cooking properties
Cooking loss (%)	1.08 ± 0.15ab	0.99 ± 0.29a	1.37 ± 0.20ab	1.59 ± 0.13ab	1.35 ± 0.32ab	1.72 ± 0.23b
Cooking yield (%)	321.66 ± 2.06a	318.87 ± 16.96a	327.39 ± 18.15a	349.03 ± 13.79a	336.21 ± 22.40a	344.09 ± 35.61a
Anthocyanin retention (%)	–	–	–	37.82 ± 4.74a	48.75 ± 1.33b	52.24 ± 4.28b
Textural parameters
Hardness (g)	5367.78 ± 769.47c	4299.63 ± 217.86b	5610.85 ± 441.01c	3090.91 ± 301.59a	3736.29 ± 582.51ab	4770.89 ± 300.84bc
Adhesiveness (g.sec)	−139.87 ± 33.54ab	−114.42 ± 26.62bc	−188.89 ± 46.33a	−77.73 ± 20.35bc	−77.43 ± 3.22c	−90.65 ± 14.18bc
Springiness	0.89 ± 0.07ab	0.73 ± 0.05a	0.93 ± 0.02b	0.83 ± 0.10ab	0.78 ± 0.14ab	0.96 ± 0.02b
Cohesiveness	0.84 ± 0.05a	0.88 ± 0.05ab	0.88 ± 0.03ab	0.87 ± 0.04ab	0.91 ± 0.01ab	0.94 ± 0.04b
Chewiness	4076.09 ± 994.94b	2750.52 ± 239.98a	4614.45 ± 371.94b	2232.12 ± 361.60a	2688.76 ± 824.30a	4323.27 ± 89.77b
Resilience	0.37 ± 0.03a	0.34 ± 0.04a	0.32 ± 0.01a	0.40 ± 0.08a	0.39 ± 0.02a	0.38 ± 0.04a
Tensile Properties
Force (g)	417.67 ± 26.42d	331.75 ± 26.09c	153.10 ± 10.13a	448.33 ± 30.43d	533.46 ± 37.93e	266.12 ± 24.61b
Extensibility (mm)	29.05 ± 3.40a	21.11 ± 1.74b	19.84 ± 1.84b	22.27 ± 3.16b	31.74 ± 4.34a	21.64 ± 2.39b

— not applicable. CTRL: starch noodle, P0.5: starch noodle with 0.5% pectin, P3.0: starch noodle with 3.0% pectin, ANT: starch noodle with 0.5% anthocyanin, AP0.5: starch noodle with 0.5% anthocyanin and 0.5% pectin, and AP3.0: starch noodle with 0.5% anthocyanin and 3.0% pectin. ^a-d values in the same row with different lowercase letters are significantly different (p < 0.05).

3.3.2. Cooking yield

Table 1 presents the cooking yields of sweet potato starch noodles. No significant differences were observed among the samples (p > 0.05). However, all exhibited higher cooking yields (318.87–349.03%) compared to instant noodles with hydrocolloids (297.26%, 270.32%, 281.11%, and 228.79% for guar gum, hydroxypropylmethyl cellulose, carboxymethyl cellulose, and xanthan gum, respectively) (Sutheeves et al., 2020).

3.4. Effect of CP on color characteristics and PSPA retention in sweet potato starch noodles

The dried and cooked sweet potato starch noodles were photographed using a digital camera (Fig. 3A). While samples CTRL, P0.5, and P3.0 exhibited typical starch noodle coloration, samples ANT, AP0.5, and AP3.0 displayed purple-to-pink hues due to the natural pH of PSPA and CP solutions (3.28, 2.96, and 2.61, respectively). Additionally, cooked PSPA-containing noodles appeared lighter in color, indicating color loss during processing. To quantify this effect, color retention experiments and measurements were performed. As shown in Table 1, the retention rate of PSPA in sweet potato starch noodles after cooking was 37.82% for sample ANT. However, adding 0.5% (w/v) or 3% (w/v) CP significantly increased PSPA retention to 48.75% and 52.24%, respectively. This enhancement was likely due to non-covalent interactions between PSPA and CP, such as electrostatic attraction and hydrogen bonding (Liu et al., 2023).

Fig. 3.

(A) Digital photos of dried and cooked starch noodles; (B) 500× SEM cross-sections of cooked starch noodles. Samples: CTRL (blank starch noodle), P0.5 (0.5% CP), P3.0 (3% CP), ANT (0.5% PSPA), AP0.5 (0.5% PSPA+0.5% CP), AP3.0 (0.5% PSPA+3% CP).

Table S4 summarizes the color properties of dried and cooked starch noodles. CP addition increased the L* (lightness) of dried noodles (p < 0.05), with 3% CP further enhancing L* in cooked noodles, indicating improved exterior structure. PSPA-loaded samples showed reduced L* but increased a* (redness), with a* loss indicating PSPA degradation during cooking. Notably, CP co-added samples (AP3.0) retained higher a* after cooking, suggesting CP stabilized PSPA via co-pigmentation (Liu et al., 2023). PSPA reduced b* (blueness/yellowness) in dried noodles, while CP increased b* (p < 0.05), likely due to its color intensity. However, b* remained unchanged in cooked samples. CP also elevated whiteness index (WI) in dried noodles, with 3% CP yielding higher WI than control. Hue values (0°–360°) ranged from purple to yellow, with ANT showing the lowest hue and 3% CP (AP3.0) significantly increasing chroma (saturation) in cooked starch noodles (p < 0.05). Color difference (∆E) between dried and cooked noodles was lowest for control (CTRL), while PSPA and CP additions significantly increased ∆E (p < 0.05).

3.5. Effects of CP and PSPA on the physical properties of dried sweet potato starch noodles

3.5.1. The moisture contents of sweet potato starch noodles

The moisture content of sweet potato starch noodles was measured as follows: CTRL, 12.31 ± 0.07% d; P0.5, 11.60 ± 0.05% b; P3.0, 12.15 ± 0.11% cd; ANT, 11.30 ± 0.17% a; AP0.5, 11.92 ± 0.11% c; AP3.0, 12.08 ± 0.09% cd.

Adding 0.5% (w/w) CP or PSPA reduced moisture content compared to the control (CTRL). For CP, the result was likely due to CP-starch interactions. PSPA's effect might arise from interactions between anthocyanin hydroxyl groups and starch, reducing free hydroxyl groups available for moisture binding, as observed by Mary et al. (2020) in starch films. In specific, Mary et al. (2020) found that 4% (w/w) butterfly pea flower anthocyanin decreased moisture content from 15.63% to 12.78% (p < 0.05). In contrast, PSPA- and CP-combined samples (AP0.5 and AP3.0) showed higher moisture content than ANT, attributed to CP's water-holding capacity (Mahmood et al., 2017) and enhanced water absorption/retention via non-covalent CP-PSPA interactions (Liu et al., 2023).

3.5.2. Effects of CP and PSPA on lamellar structure and fractal characteristics of dried starch noodles

SAXS analysis revealed the nanoscale structure of sweet potato starch and noodles. The SAXS pattern (I(q) vs q, Fig. 4A) showed native starch had a scattering peak at q = 0.627 nm⁻¹, corresponding to a semi-crystalline lamellar structure with 10.03 nm thickness (Bragg equation, Table 2). After noodle-making, peak intensity decreased and shifted to q = 0.293–0.309 nm⁻¹, indicating reduced lamellar order (Lan et al., 2017). The d_Bragg (from I(q)q² vs q, Fig. 4B) increased significantly from 10.03 ± 0.00 nm to 20.46 ± 0.00 nm (p < 0.05), suggesting decreased crystallinity and increased amorphous regions due to amylose chain rearrangement during retrogradation (Jiang et al., 2024). CP and PSPA addition did not alter d_Bragg values (p > 0.05).

Fig. 4.

Raw SAXS patterns (A), Lorentz-corrected (B), Guinier (C), and double-logarithmic (D) SAXS plots of dried starch noodles. Samples: CTRL (blank starch noodle), P0.5 (0.5% CP), P3.0 (3% CP), ANT (0.5% PSPA), AP0.5 (0.5% PSPA+0.5% CP), AP3.0 (0.5% PSPA+3% CP).

Table 2.

Structural and thermal properties of sweet potato starch and sweet potato starch noodles.

	SPS	CTRL	P0.5	P3.0	ANT	AP0.5	AP3.0
Structural properties
dBragg (nm)	10.03 ± 0.00a	20.46 ± 0.00bc	20.16 ± 0.42b	20.31 ± 0.22b	21.43 ± 0.00c	20.46 ± 0.00bc	21.10 ± 0.46bc
Rg (nm)	12.41 ± 0.59b	5.46 ± 0.03a	5.37 ± 0.03a	5.33 ± 0.18a	5.39 ± 0.05a	5.43 ± 0.02a	5.19 ± 0.01a
Dm	1.93 ± 0.22b	1.29 ± 0.02a	1.30 ± 0.05a	1.37 ± 0.06a	1.35 ± 0.00a	1.33 ± 0.02a	1.32 ± 0.01a
R1047/1022	0.58 ± 0.03ab	0.63 ± 0.04bc	0.63 ± 0.01bc	0.68 ± 0.11c	0.61 ± 0.04bc	0.64 ± 0.05bc	0.53 ± 0.05a
R1022/995	0.84 ± 0.06a	0.85 ± 0.04a	0.88 ± 0.03a	0.86 ± 0.12a	0.89 ± 0.06a	0.87 ± 0.07a	0.85 ± 0.11a
RC (%)	30.06 ± 0.79d	13.91 ± 0.37ab	19.21 ± 0.33c	15.41 ± 1.59b	14.20 ± 0.68ab	15.29 ± 0.94b	12.33 ± 0.95a
Thermal properties
To (°C)	68.0 ± 0.21a	52.94 ± 1.46a	53.31 ± 7.86a	52.98 ± 4.58a	53.04 ± 7.84a	56.83 ± 2.06a	54.29 ± 7.69a
Tp (°C)	74.53 ± 0.05b	63.55 ± 0.01a	64.88 ± 1.73a	63.13 ± 2.22a	65.23 ± 0.21a	64.62 ± 0.89a	64.03 ± 3.34a
Tc (°C)	82.19 ± 3.04b	70.37 ± 0.50a	71.33 ± 0.47a	70.52 ± 0.21a	71.16 ± 1.03a	67.12 ± 2.65a	71.24 ± 2.81a
∆H (J/g)	1.98 ± 0.82b	0.52 ± 0.44ab	0.40 ± 0.31ab	0.51 ± 0.24ab	0.41 ± 0.34ab	0.23 ± 0.13a	0.47 ± 0.39ab

d_Bragg, the periodic thickness of starch lamella; R_g, radius of gyration; D_m, mass fractal dimension; R_1047/1022, the ratio of absorbance at 1047 cm⁻¹ to 1022 cm⁻¹; R_1022/995, the ratio of absorbance at 1022 cm⁻¹ to 995 cm⁻¹; RC, relative crystallinity; T_o, onset temperature; T_p, peak temperature; T_c, conclusion temperature; ΔH, gelatinization enthalpy. SPS: sweet potato starch; CTRL: starch noodle, P0.5: starch noodle with 0.5% pectin, P3.0: starch noodle with 3.0% pectin, ANT: starch noodle with 0.5% anthocyanin, AP0.5: starch noodle with 0.5% anthocyanin and 0.5% pectin, and AP3.0: starch noodle with 0.5% anthocyanin and 3.0% pectin. ^a-d values in the same row with different lowercase letters are significantly different (p < 0.05).

The radius of gyration (R_g) of SPS was 12.41 ± 0.59 nm, calculated from the Guinier plot (ln(I(q)) vs q², Fig. 4C) at low q (0.01–0.2 nm⁻¹), reflecting the size of the dense domains (Yan et al., 2025). R_g was significantly lower in CTRL (5.46 ± 0.03 nm) than in SPS (12.41 ± 0.59 nm) (p < 0.05), likely due to structural disorder from heat and moisture during gelatinization. No significant differences in R_g were observed between CTRL and noodle samples (p > 0.05).

Fractal dimensions were determined from the slope of the double-logarithmic curve (q = 0.14–0.25 nm⁻¹, Fig. 4D). Mass fractal dimension (D_m) was calculated as D_m = −α (−3 < −α < −1), indicating increasing structure density with higher D_m (Jiang et al., 2024). SPS exhibited a significantly higher D_m (1.93 ± 0.22) than CTRL (1.29 ± 0.02) (p < 0.05), suggesting a denser structure disrupted by heat during gelatinization, leading to a loose arrangement (Xu et al., 2021). No significant differences in D_m were observed between CTRL and other noodle samples (p > 0.05).

3.5.3. Effects of CP and PSPA on crystalline structure of dried starch noodles

The native SPS displayed C-type crystallinity, characterized by distinct diffraction peaks at 5.6°, 15°, 17.2°, 18°, and 23.2° (2θ) (Fig. 5A). Similarly, sweet purple potato starch noodles exhibited C-type crystallinity with peaks at 5.6°, 15°, 17°, 22°, 24°, and a prominent peak at 19.6° (Fig. 5B). The latter peak is attributed to the formation of endogenous amylose-lipid complexes, indicative of V-type structures, likely resulting from stable single helical chains and lipids interacting during noodle processing. Notably, the addition of CP, PSPA, or both did not alter the crystallinity patterns of the starch noodles.

Fig. 5.

(A-B) XRD patterns of sweet potato starch (SPS) and starch noodles; (C-D) FTIR and deconvolved FTIR spectra of SPS and noodles; (E-F) DSC profiles and RVA pasting curves of dried noodles. Samples: CTRL (blank), P0.5 (0.5% CP), P3.0 (3% CP), ANT (0.5% PSPA), AP0.5 (0.5% PSPA+0.5% CP), AP3.0 (0.5% PSPA+3% CP).

The relative crystallinity (RC) of native SPS was 30.06%. After noodle-making, RC decreased significantly in all samples (p < 0.05) (Table 2), indicating a less ordered re-crystallized structure and reduced complete crystal formation during retrogradation (Yang et al., 2021). This likely resulted from heat, moisture, and mechanical processing during noodle making. P0.5 showed significantly greater RC (19.21%) than CTRL (13.91%) (p < 0.05), attributed to pectin-promoted retrogradation and amylose reorganization (Lu et al., 2025). In contrast, P3.0 had significantly lower RC than P0.5 (p < 0.05), possibly due to reduced water mobility impairing starch retrogradation (Mahmood et al., 2017). Adding PSPA alone did not significantly alter RC compared to CTRL, but AP0.5 and AP3.0 exhibited significantly lower RC than P0.5 and P3.0, respectively (p < 0.05). This result could be due to the CP-PSPA complex disrupting amylose-based gel networks, consistent with the findings on reduced crystallinity in grape seed proanthocyanidins-potato starch complexes (Zhang et al., 2020).

3.5.4. Effects of CP and PSPA on the short-range ordered structure of dried starch noodles

As shown in Fig. 5C, SPS and sweet potato starch noodles exhibited similar FT-IR characteristic bands. The broad absorption at 3000–3600 cm⁻¹ corresponded to O—H groups in hydrogen-bonded starch molecules, with the band broadening attributed to overlapping N—H absorption and O—H frequency shifts due to intramolecular/intermolecular hydrogen bonding. CP addition enhanced infrared absorption in this region compared to the control (CTRL), with AP3.0 showing the most pronounced band, likely due to non-covalent interactions among PSPA, CP, and starch, including hydrogen bonds (Liu et al., 2024). Bands at 2930 cm⁻¹ (anti-symmetrical CH₂ stretching) and 1642 cm⁻¹ (amorphous region absorption) were observed, alongside a band around 1415 cm⁻¹ assigned to C-O-O stretching (Zhang et al., 2020). To analyze short-range ordered structure, spectra in 1200–900 cm⁻¹ were deconvoluted (Fig. 5D). Bands around 995 cm⁻¹ and 1047 cm⁻¹ were linked to starch crystallinity and molecular order, while 1022 cm⁻¹ corresponded to amorphous/disordered regions (Mutungi et al., 2011; Yang et al., 2021). The ratios R_1047/1022 and R_1022/995 reflected crystalline order and molecular disorder, respectively (Mutungi et al., 2011). As shown in Table 2, no significant differences in R_1047/1022 were observed between SPS and CTRL (p > 0.05), or among most starch noodle samples (except AP3.0). AP3.0 exhibited a lower R_1047/1022 value, indicating that CP-PSPA interactions hindered starch chain alignment during starch noodle formation. Additionally, R_1022/995 showed no significant differences among SPS and all noodle samples (p > 0.05).

3.5.5. Effects of CP and PSPA on thermal properties of dried starch noodles

The DSC curves of SPS and sweet potato starch noodles are shown in Fig. 5E. Both SPS and all noodle samples displayed a single endothermic peak. The SPS peak was sharp and intense, whereas noodle peaks were broader and of lower intensity, likely due to reduced crystalline order requiring less thermal energy for gelatinization.

Thermal properties are summarized in Table 2. The onset (T_o), peak (T_p), and conclusion (T_c) temperatures reflect the melting of starch microcrystals with varying thermal stability, while enthalpy change (ΔH) corresponds to the energy required for double-helix and crystalline region melting (Ma et al., 2024). T_o among SPS, CTRL, and other noodle samples showed no significant differences (p > 0.05). However, SPS exhibited significantly higher T_p (74.53 °C) and T_c (82.19 °C) than CTRL (T_p: 63.55 °C; T_c: 70.37 °C) (p < 0.05). Processing-induced disruption of crystalline structure and formation of short-chain starch molecules led to a decrease in T_p and T_c of noodles (Fang et al., 2025). The addition of CP or ANT alone did not significantly alter T_p or T_c versus CTRL (p > 0.05).

ΔH values were comparable between SPS and most noodle samples, except for AP0.5, which showed a significantly lower ΔH (0.23 J/g) than SPS (1.98 J/g) (p < 0.05). This observation is partially supported by the reduced relative crystallinity of AP0.5, while the unchanged short-range order parameters from FTIR indicate that the thermal behavior cannot be fully explained by molecular order variations. It suggests that specific interactions between starch and the low-concentration (0.5% w/w) CP-PSPA complex may reduce ΔH. In contrast, a high CP concentration (3% w/w) might promote CP self-aggregation over interaction with amylose.

3.5.6. Effects of CP and PSPA on pasting properties of dried starch noodles

RVA analysis was conducted to assess viscosity behavior, which reflects water absorption and amylose leaching during cooking and correlated with gel network formation and amylose rearrangement. As shown in Fig. 5F, pasting curves of sweet potato starch noodles were characterized and altered by CP, PSPA, or their combination. The curves clustered into three distinct groups. The first group (CTRL) showed a gradual, less pronounced viscosity increase likely due to limited amylose leaching and particle swelling (Ma et al., 2024). It would be resulted from its relatively intact structure, as observed in SEM images (Fig. 3B). The second group (P3.0 and AP3.0, containing 3% w/w CP) exhibited a marked viscosity rise versus CTRL. This was attributed to water-absorption capacity of CP (Mahmood et al., 2017) and CP-amylose interactions that disrupted structure, and promoted swelling and enhanced the pasting profile. The third group (PSPA and 0.5% w/w CP samples) presented the highest pasting curves, surpassing both CTRL and 3% CP groups. This was associated with nanoscale CP-PSPA complexes formed via non-covalent interactions, which increased the effective surface area for water contact.

3.6. Effects of CP and PSPA on the physical properties of cooked sweet potato starch noodles

3.6.1. Effects of CP and PSPA on microstructures of cooked starch noodles

Fig. 3B shows cross-section images of cooked and freeze-dried sweet potato starch noodles. All samples exhibited porous structures due to freeze-drying-induced water evaporation, but pore morphology varied with CP and/or PSPA addition. The control (CTRL) had a denser structure than other samples, while ANT displayed larger, irregularly shaped pores, likely due to disrupted starch gel networks from limited amylose-amylose interactions. Similarly, Ma et al. (2021) reported that anthocyanin from black soybean coats reduced the three-dimensional network of wheat noodles. CP addition (0.5% and 3%, w/w) further increased pore size versus CTRL, with 3% CP showing the largest and most irregular pores. Gelatinized starch formed a gel network via amylose/amylopectin association, whereas hydrocolloids created a distinct network by interacting with amylose and low-molecular-weight amylopectin during gelatinization (Srikaeo et al., 2018). The type and concentration of hydrocolloids critically influenced the starch gel network, which in turn governed noodle properties. Srikaeo et al. (2018) observed that carboxymethyl cellulose, xanthan gum, and guar gum disrupted the dense structure of rice noodles compared to controls.

3.6.2. Effects of CP and PSPA on the texture properties of cooked starch noodles

The textural properties of sweet potato starch noodles are presented in Table 1. Hardness significantly (p < 0.05) decreased with PSPA addition compared to the control (CTRL), likely due to PSPA disrupting the starch gel network by inhibiting amylose re-association via hydrogen bonding, resulting in a softer structure. Moreover, the hardness significantly decreased from 5367.78 g (CTRL) to 4299.63 g when supplemented with 0.5% (w/w) CP (p < 0.05). By contrast, the 3% (w/w) CP group showed no significant difference in hardness versus the CTRL group (p > 0.05). These different results suggesting a concentration-dependent effect of CP. Increasing polysaccharide concentration might enhance intermolecular entanglement and binding sites, potentially elevating hardness (Mahmood et al., 2017). AP3.0 exhibited higher hardness than ANT (p < 0.05), whereas 0.5% CP addition did not alter hardness compared to ANT. Adhesiveness was similar between ANT and CTRL (p > 0.05), and unaffected by adding 0.5% or 3% CP versus CTRL (p > 0.05). However, P3.0 (−188.89 g·s) showed significantly higher adhesiveness than P0.5 (−114.42 g·s) (p < 0.05). It was attributed to increased surface free water from CP hydration, creating a sticky surface during probe withdrawal. Springiness did not differ significantly between CTRL and other noodles (p > 0.05), but P0.5 (0.73) differed from P3.0 (0.93) (p < 0.05). This higher springiness in P3.0 might stem from its more ordered crystalline structure. Cohesiveness was similar among samples except AP3.0, which had higher cohesiveness (0.94) than CTRL (0.84) (p < 0.05), likely due to enhanced intermolecular entanglement and cross-linking (Mahmood et al., 2017). Chewiness significantly decreased with PSPA versus CTRL (p < 0.05). P0.5 (2750.98 g·s) had lower chewiness than CTRL (4076.09 g·s), while P3.0 showed no difference versus CTRL (p > 0.05). AP3.0 (4323.27 g·s) exhibited higher chewiness than ANT (2232.12 g·s) (p < 0.05), reflecting the energy required to chew noodles to a swallowable state (Ma et al., 2021). Resilience, indicating elastic response to deformation, showed no significant differences among samples (p > 0.05).

3.6.3. Effects of CP and PSPA on the tensile properties of cooked starch noodles

Tensile testing evaluated the impact of PSPA and CP on sweet potato starch noodle stretching properties. Force and extensibility data are presented in Table 1. No significant differences were observed between the control (CTRL) and ANT (p > 0.05), but P0.5 (331.75 g) and P3.0 (153.10 g) exhibited significantly lower tensile force than CTRL (417.67 g) (p < 0.05). Additionally, 3% (w/w) CP reduced tensile force to 266.12 ± 24.61 g in PSPA-loaded noodles versus ANT (448.33 ± 30.43 g), while AP0.5 showed higher force (533.46 g) than ANT (p < 0.05). Extensibility was significantly lower in ANT (22.27 ± 3.16 mm) than CTRL (29.05 mm), with P0.5 (21.11 mm) and P3.0 (19.84 mm) also reduced versus CTRL (p < 0.05). However, 0.5% (w/w) CP increased extensibility to 31.74 mm compared to ANT (p < 0.05). CP likely enhanced water absorption, yielding a looser noodle structure (Mahmood et al., 2017). Conversely, CP-PSPA interactions (e.g., van der Waals forces, hydrogen bonds, electrostatic interactions) between starch-CP and PSPA-CP might elevate force and extensibility (He et al., 2021; Liu et al., 2023). Lower crystallinity, as indicated by our XRD data (Table 2), could reduce tensile strength and extensibility (Kraithong & Rawdkuen, 2020). Supporting this, Kraithong and Rawdkuen (2020) observed that 0.4% (w/w) xanthan gum decreased extruded red Jasmine rice flour noodle tensile strength from 65.45 g to 53.19 g.

3.7. Correlation between the structure and properties of sweet potato starch noodles

Pearson correlation analysis was performed to evaluate relationships between microstructural properties and starch noodle quality (Fig. 6). The periodic thickness of starch lamellae (d_Bragg) was positively correlated with cooking loss (r = 0.82, p < 0.05) and cooking yield (r = 0.91, p < 0.05), suggesting that lamellar structure influences water interaction and component retention during cooking. A negative correlation was observed between R_1022/995 (indicative of molecular disorder) and chewiness (r = −0.89, p < 0.05). Higher R_1022/995 values corresponded to a more disordered starch structure and weaker gel network, resulting in lower chewiness. This finding aligned with Ma et al. (2024), who reported a similar negative correlation between R_1022/995 in sweet potato starch and noodle chewiness.

Fig. 6.

Correlation between structural and physicochemical parameters of starch noodles. R_1047/1022 (1047/1022 cm⁻¹ absorbance ratio), R_1022/995 (1022/995 cm⁻¹ absorbance ratio), d_Bragg (starch lamella periodic thickness), R_g (radius of gyration), D_m (mass fractal dimension), T_o (onset temperature), T_p (peak temperature), T_c (conclusion temperature), ΔH (gelatinization enthalpy), L* (lightness).

4. Conclusion

In this study, citrus peel pectin (CP) was confirmed to enhance purple sweet potato anthocyanin (PSPA) retention in sweet potato starch noodles while maintaining noodle quality. This result was attributed to the complex formation between CP and PSPA, supported by evidence from particle size and zeta potential measurements and small-angle X-ray scattering analysis. PSPA exhibited heterogeneous multilayer adsorption on CP, which was well-fitted by the Freundlich isotherm model. Notably, AP0.5 (PSPA and 0.5% CP) formed a nano-scale complex, and its noodle demonstrated improved PSPA retention, with overall quality comparable to the control. Moreover, 3% CP ‌brought no extra functional benefits to justify its high addition levels, although CP concentration influenced certain noodle quality attributes. Using a lower CP concentration is advantageous from an industrial perspective by reducing CP consumption while maintaining comparable adsorption performance. This finding provides nutritional and industrial significance in terms of the anthocyanin enrichment and the color quality enhancement of starch noodles, which are valuable for consumer acceptance and industrial implementation. Nevertheless, this study has certain limitation. Specifically, CP–PSPA interactions are evaluated only at their natural pH, which may restrict the generalization and application of the complex across food products with different pH environments. To further promote PSPA retention and improve the quality of the PSPA-loaded starch noodles, several points could be considered in future research, such as (1) strengthening the CP-PSPA binding interface through polysaccharide structure tailoring and/or physical treatments (e.g., ultrasound-assisted complexation) to improve anthocyanin encapsulation efficiency; and (2) improving the textural properties of PSPA-loaded starch noodles via cross-linking techniques (e.g., Ca²⁺ cross-linking) to mitigate cooking-induced softening.

CRediT authorship contribution statement

Sevenur Sarıkaya: Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation. Filiz Altay: Writing – review & editing, Supervision, Conceptualization. Guohua Zhao: Supervision, Resources, Project administration. Fayin Ye: Writing – review & editing, Supervision, Resources, Methodology, Funding acquisition, Formal analysis, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A. Supplementary data

Supplementary material

Data availability

Data will be made available on request.