Process optimization of ultrasound-assisted betalain extraction from beetroot waste stalks

Introduction

The rapid growth of the food processing industry has resulted in the generation of substantial quantities of agro-industrial residues, including peels, pomace, seeds, leaves, and stalks. These by-products are often discarded despite being rich sources of valuable bioactive compounds such as phenolics, dietary fibre, carotenoids, and natural pigments (Saini et al., 2025) [8] . Their improper disposal contributes to environmental pollution, whereas their valorization offers an opportunity to produce high-value functional ingredients in line with the principles of the circular bioeconomy and sustainable food processing. Beetroot (Beta vulgaris L.) is a widely cultivated root vegetable recognized for its high nutritional value and antioxidant potential. During harvesting and processing, considerable amounts of stalks and leaves are removed and treated as waste, although they contain appreciable quantities of betalain pigments. Betalains are water-soluble, nitrogen-containing pigments classified into betacyanins, which impart red-violet colour, and betaxanthins, which produce yellow-orange colour (Azeredo, 2009) [2] . Besides their colouring properties, betalains possess antioxidant, anti-inflammatory, antimicrobial, and anticancer activities, making them attractive natural alternatives to synthetic food colourants. The increasing consumer preference for clean-label foods and concerns regarding the safety of synthetic dyes have accelerated research on natural pigments. Beetroot-derived betalains are already used as natural colourants in foods; however, commercial extraction mainly utilizes edible root s, increasing production costs. In contrast, beetroot waste stalks represent an inexpensive and underutilized source of these pigments, offering a sustainable approach for waste valorization while reducing environmental burden. Extraction technology plays a crucial role in determining pigment yield and quality. Conventional extraction techniques such as maceration and Soxhlet extraction are associated with long extraction times, high solvent consumption, and possible thermal degradation of heat-sensitive compounds. In contrast, ultrasound-assisted extraction (UAE) has emerged as an efficient and environmentally friendly alternative (Chemat et al., 2022) [4] .

The technique relies on acoustic cavitation, where the formation and collapse of microscopic bubbles disrupt plant cell walls, enhance solvent penetration, and improve mass transfer, thereby increasing extraction efficiency while reducing processing time and solvent requirements (Ahmadi et al., 2021; Usman et al., 2023) [1,10] . Several factors, including extraction pH, solid-to-solvent ratio, and sonication time, significantly influence the recovery of betalain pigments (Khan et al., 2023) [5] . Optimizing these parameters through conventional onefactor-at-a-time experiments is laborious and fails to account for interactions among variables. Response Surface Methodology (RSM), particularly the Central Composite Design (CCD), is an effective statistical approach for evaluating the combined effects of multiple process variables while minimizing the number of experimental trials (Bhattacharya, 2021) [3] . RSM has been widely applied in food engineering for optimizing extraction processes and developing predictive mathematical models. Although numerous studies have investigated betalain extraction from beetroot roots and other plant materials, limited information is available on the systematic optimization of pigment extraction from beetroot waste stalks using ultrasound-assisted extraction. Therefore, the present study aimed to optimize the extraction of betacyanin and betaxanthin pigments from beetroot waste stalks using UAE and Response Surface Methodology. The effects of pH, solid-to-solvent ratio, and sonication time on pigment yield were evaluated, and optimum extraction conditions were established using a Central Composite Design. This work demonstrates the potential of converting an underutilized agricultural residue into a value-added natural colourant for applications in the food, pharmaceutical, cosmetic, and nutraceutical industries, thereby contributing to sustainable waste management and resource utilization.

Materials and Methods

Raw Material Preparation

Fresh beetroot (Beta vulgaris L.) waste stalks were procured from the local vegetable market of Pantnagar, Uttarakhand, India. The stalks were thoroughly washed under running tap water to remove adhering dirt and foreign matter, and the green leaves were manually separated. The cleaned stalks were dried in a tray dryer at 40 °C for 20 h until a constant weight was achieved. The dried stalks were ground using a laboratory grinder and sieved through a 250 μm (60 mesh) sieve to obtain a uniform powder. The powdered sample was packed in airtight polyethylene zip-lock bags and stored under refrigerated conditions (0-4 °C) until further analysis.

Ultrasound-Assisted Extraction of Betalains

Ultrasound-assisted extraction (UAE) was employed for the recovery of betalain pigments from beetroot waste stalk powder. Five grams of dried powder was mixed with distilled water at the desired solid-to-solvent ratio according to the experimental design. The pH of the extraction medium was adjusted using dilute hydrochloric acid or sodium hydroxide solution. The extraction was carried out using an ultrasonic processor operating at a frequency of 20 kHz, power output of 200 W, and supply voltage of 200 V. The sonication time was varied according to the Central Composite Design (CCD). During ultrasonication, acoustic cavitation generated microbubbles that collapsed violently, causing disruption of plant cell walls and enhancing the diffusion of intracellular pigments into the extraction solvent. Following extraction, the slurry was filtered in two stages. Initially, a muslin cloth was used to remove coarse solid particles, followed by filtration through Whatman No. 46 filter paper. The filtrate obtained was collected and used for pigment analysis.

Determination of Betalain Pigments

The extracted samples were analysed spectrophotometrically for betacyanin (BC) and betaxanthin (BX) contents following the method reported by Maran et al. (2015) [6] . The absorbance of the extract was measured using a UV-Visible spectrophotometer, and pigment concentration was calculated using the respective molecular weight and molar extinction coefficient of each pigment. The results were expressed as mg g⁻¹ dry weight of beetroot stalk powder.

Where, BS = betacyanin or betaxanthin, A = absorption value at 600 nm, L= path length of the cuvette (1 cm) DF = dilution factor, MW represents molecular weight (betaxanthin= 308 g mol-1and betacyanin= 550 g mol-1), ε = molar extinction coefficient (betaxanthin = 48000 L mol-1 cm-1 and betacyanin= 60000 L mol-1 cm-1)

Experimental Design

Optimization of the extraction process was performed using Response Surface Methodology (RSM) based on a threefactor, five-level Central Composite Design (CCD). The independent variables selected were extraction pH (X₁), solid-to-solvent ratio (X₂), and sonication time (X₃), while betacyanin and betaxanthin yields were considered as the response variables.

The CCD consisted of 20 experimental runs, including eight factorial points, six axial points, and six centre points to estimate the experimental error and evaluate model adequacy. The coded and actual levels of the process variables are presented in Table 1. A second-order polynomial model (Equation 1) was used to establish the relationship between the independent variables and the response variables:

where Y is the predicted response, β₀ is the intercept, βᵢ represents the linear coefficients, βᵢᵢ the quadratic coefficients, βᵢⱼ the interaction coefficients, and Xᵢ and Xⱼ are the coded independent variables.

Statistical Analysis

The experimental data were analysed using Design-Expert® software (Version 10.0.1, Stat-Ease Inc., USA). Analysis of variance (ANOVA) was performed to evaluate the significance of the developed models and individual regression coefficients. Model adequacy was assessed based on the coefficient of determination (R²), adjusted R², predicted R², coefficient of variation (CV), lack-of-fit test, and adequate precision. A significance level of p < 0.05 was considered statistically significant.

Optimization and Model Validation

Numerical optimization was carried out using the desirability function approach to determine the optimum extraction conditions for maximum recovery of betacyanin and betaxanthin pigments. The optimized conditions predicted by the model were experimentally validated by conducting triplicate extraction experiments. The experimental pigment yields were compared with the predicted values to verify the reliability and predictive capability of the developed regression models.

Results and Discussion

Optimization of Ultrasound-Assisted Extraction

The extraction of betalain pigments from beetroot waste stalks was optimized using Response Surface Methodology (RSM) with a three-factor Central Composite Design (CCD). A total of 20 experimental runs were conducted to evaluate the combined effects of pH (X₁), solid-to-solvent ratio (X₂), and sonication time (X₃) on the extraction yield of betacyanin (BC) and betaxanthin (BX). The experimental responses obtained under different processing conditions are presented in Table 2. The betacyanin content varied from 1.80 to 6.50 mg g⁻¹, whereas betaxanthin ranged between 1.26 and 4.56 mg g⁻¹, indicating that the extraction parameters significantly influenced pigment recovery. Quadratic polynomial models were developed to establish the relationship between the process variables and pigment yield. The second-order models adequately described the extraction process, confirming the suitability of RSM for optimization of ultrasound-assisted extraction.

Model Adequacy and Statistical Analysis

The adequacy of the developed regression models was evaluated using analysis of variance (ANOVA) shown in Table 3. The quadratic models exhibited high coefficients of determination (R² = 0.9702 for betacyanin and 0.9712 for betaxanthin), indicating that more than 97% of the variation in pigment yield could be explained by the selected process variables. The adjusted R² values (0.9435 and 0.9453) were in close agreement with the corresponding R² values, demonstrating excellent model reliability. Furthermore, the predicted R² values showed good agreement with the experimental observations, confirming satisfactory predictive capability of the developed models.

Effect of Process Variables on Betalain Extraction 3.3.1 Effect of pH

Extraction pH plays an important role in determining the stability and recovery of betalain pigments. Within the investigated range (3.31-6.68), pigment recovery was highest under slightly acidic conditions. The statistical analysis indicated that pH alone had a comparatively smaller effect on pigment extraction than the other variables (p > 0.05). However, its interaction with sonication time significantly influenced betalain recovery (p < 0.01), suggesting that extraction efficiency depends on the combined influence of processing conditions rather than pH alone. Slightly acidic media help maintain betalain stability by minimizing pigment degradation during extraction.

Effect of Solid-to-Solvent Ratio

The solid-to-solvent ratio significantly affected both betacyanin and betaxanthin yields (p < 0.0001). Increasing the ratio from approximately 23 to 50 g mL⁻¹ enhanced pigment extraction due to improved solvent availability and increased concentration gradients between the plant matrix and extraction medium. These conditions promoted efficient mass transfer of intracellular pigments into the solvent (Silva et al., 2021) [9] . However, further increases in solvent volume did not improve pigment recovery, indicating that excessive solvent usage does not proportionally enhance extraction efficiency and may increase processing costs as shown in Figure 1.

Effect of Sonication Time

Sonication time exhibited a significant positive influence on pigment extraction (p < 0.0001). The recovery of both betacyanin and betaxanthin increased with increasing extraction time up to approximately 30 min, after which no substantial improvement was observed Figure 2. This behaviour can be attributed to ultrasonic cavitation, which progressively disrupts plant cell walls and enhances solvent penetration during the initial stages of extraction (Wang et al., 2024) [11] . Prolonged sonication beyond the optimum duration may expose pigments to localized heating and free radicals generated during cavitation, potentially leading to pigment degradation. Therefore, an optimum sonication time is essential to maximize extraction efficiency while preserving pigment stability.

Interaction Effects of Process Variables

Three-dimensional response surface plots illustrated the interaction between extraction variables and their influence on pigment recovery. The interaction between pH and sonication time was statistically significant for both responses, whereas interactions between pH and solid-tosolvent ratio, and between solid-to-solvent ratio and sonication time, were comparatively less significant. The response surfaces revealed that maximum pigment recovery was achieved at intermediate pH values combined with higher solid-to-solvent ratios and longer sonication times. These observations demonstrate the importance of simultaneously optimizing process variables rather than evaluating them individually.

Optimization and Validation

Numerical optimization using the desirability function predicted the optimum extraction conditions as pH 4.7, solid-to-solvent ratio 50 g mL⁻¹, and sonication time 30 min. Under these conditions, the predicted betacyanin and betaxanthin yields were 6.31 mg g⁻¹ and 4.43 mg g⁻¹, respectively. Experimental validation conducted under identical conditions produced values of 6.17 mg g⁻¹ for betacyanin and 4.29 mg g⁻¹ for betaxanthin, closely matching the predicted responses. The small deviation between predicted and observed values confirms the adequacy and reliability of the developed quadratic models for process optimization (Table 4).

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