Exploring Hyaluronidase and Alpha-Glucosidase Inhibition Activities of the Hydrothermal Extract of Coffee Silverskin Obtained from a Central Composite Design

Abstract

Coffee silverskin (CS), the main by-product of coffee roasting production, contains various valuable bioactive compounds in its chemical compositions. Hydrothermal water extraction (HDTE) is one of the promising techniques for valorizing the organic fraction of CS into functional bioactive ingredients, which can be further exploited in various applications. This study aimed to evaluate the hyaluronidase and α-glucosidase inhibition activities of the CS extracts obtained under optimized water extraction conditions. Process optimization was performed using central composite design response surface methodology (CCD-RSM) with a broader range of extraction temperatures (25, 137.5, and 250 °C), reaction times (5, 38.5, and 72 min), and solid-to-liquid ratios (1:10, 1:80, and 1:150). The highest yield of 39.62% was obtained at 137.5 °C, with a reaction time of 72 min and an S/L ratio of 1:80. The total caffeoylquinic acid contents (T-CQA) were quantified based on the sum of three major isomers, including 3-CQA, 4-CQA, and 5-CQA. The results revealed that the highest T-CQA (2.76 ± 0.20 mg/g CS) was significantly obtained (p < 0.05) by subcritical water extraction (SWE) at 143.2 °C with an S/L ratio of 1:10 and an extraction time of 10.41 min. At such conditions, the total phenolic content (TPC), antioxidant properties (AP), and caffeine were 96.13 mg gallic acid equivalence per gram (GAE/g) CS, 20.85 ± 0.17 mg Trolox equivalence per gram (TE/g) CS, and 10.84 ± 1.25 mg/g CS, respectively. The 50% inhibition capacity (IC50) of hyaluronidase and α-glucosidase inhibition of the CS extracted were 5.00 mg/mL and 9.00 mg/mL, respectively. Our results supported the potential direct or indirect applications of CS, such as hydrothermal CS extract (HDT-CSE), in functional food or drinks. Repurposing CS residue to manufacture new products can efficiently reduce the amount of organic waste in landfills, thus conserving resources and energy and contributing to a lower overall carbon footprint in coffee production.

1. Introduction

Coffee silverskin (CS) is a thin yellow layer around coffee beans separated during roasting [1,2,3]. It was estimated that approximately 2.08 kg of CS is generated per 100 kg of coffee beans, or during roasting; 2.5 kg of CS is generated per 120 kg of roasted coffee in Spain [4]. To enhance the eco-sustainability of the coffee roasting industry, its sole by-product, coffee silverskin (CS), should be utilized before it is discarded as waste. According to the management hierarchy (Figure 1), the most viable options for CS are repurposing, recycling, or recovery. For example, CS has been successfully repurposed as a natural antioxidant in food preservation due to its rich phenolic content [5]. CS is rich in nutrients and bioactive compounds, making it a promising resource for many. CS also contains a wide range of beneficial macronutrients and micronutrients. A review article reported the nutritional value of coffee silverskin, such as being high in fiber (62%) and being a good source of proteins (16.2 to 18.6%), potassium, magnesium, calcium, iron, and vitamin C while being low in fat (2.2%) [6,7]. Bioactive compounds found in CS are melanoidins (17–23%), a product of Maillard reactions during roasting, and phenolic compounds, such as chlorogenic acids or CGAs (15.8%), caffeine (1.0 to 1.4%), tannin (0.02%), and other polyphenols [6,7]. These compositions contribute to many nutritional claims and health-promoting properties such as prebiotic, antioxidant, anti-obesity, anti-diabetic, and anti-inflammatory properties, as well as promoting healthy skin [4], making CS suitable for applications in food, pharmaceuticals, cosmetics, and nutraceuticals [6,8,9,10]. It has also been recycled into bio-based materials such as bioplastics and used to produce activated carbon for water filtration [11,12,13]. Coffee silverskin has a unique biomass composition with 50 to 60% carbohydrates [6], as cellulose (10 to 24%) and hemicellulose (7–17%) [11], suggesting a potential source for biofuel production through pyrolysis or anaerobic digestion [14]. However, its high lignin content (17–31%) poses a limitation for recycling into animal feed. These applications align with the waste management hierarchy, prioritizing resource optimization and environmental sustainability.

Coffee silverskin (CS) extracts are rich in bioactive compounds, primarily caffeine and chlorogenic acids (CGA), which present as 5-CQA, 3-CQA, 4-CQA, and dicaffeoylquinic acids. The extract also contains significant phenolic acids like vanillic, caffeic, and ferulic acids [15,16]. Caffeine and phenolic acids, such as CGA and its isomers, were proven to have an antifungal ability of up to 70% [17], radical scavenging activities in chemical tests such as ABTS, and an anti-diabetic ability by reducing pancreatic oxidative stress and supporting glucose metabolism [18]. Many studies indicated that CS-derived phenolic compounds and phenolic acids in various plant extracts could reduce hyaluronidase and α-glucosidase activities [19,20,21]. By binding to both the active and allosteric sites of these enzymes, phenolics inhibit their function, including the digestion of starch and carbohydrates.

Additionally, these compounds inhibit hyaluronidase, an enzyme that degrades hyaluronic acid, contributing to skin aging and wrinkle formation [22,23,24]. Phenolic compounds in CS can inhibit hyaluronidase, an enzyme that degrades hyaluronic acid, contributing to skin aging and wrinkle formation [24,25,26]. These effects are attributed to the aromatic hydroxyl groups in phenolic compounds, which bind to enzyme-active sites, disrupting their function. As a result, CS extracts show potential in reducing oxidative stress, preventing diabetes, and promoting skin health, further supporting their applications in functional foods and cosmetics.

Efficient extraction is essential to maximize the utilization of medicinal plants and biomedical potential biomass. Conventional extraction methods rely significantly on the extracting power of different solvents, which contribute to high convective mass transfer into the matrix. Hydrothermal extraction (HDTE) and subcritical water extraction (SWE) are solvent-free green extraction techniques that effectively extract polar and less polar compounds. SWE, which uses high temperature (100–374 °C) and pressurized water, is an environmentally friendly alternative that reduces operational time and increases yield. Water’s dielectric constant at elevated temperatures decreases, enabling it to mimic partially polar organic solvents like methanol and ethanol. It is excellent for the safe extraction of coffee and coffee processing by-products (such as spent coffee grounds and husks), where harmless substances are required during the extraction process [27,28,29,30,31].

The extraction process can be efficiently optimized through experimental design, such as response surface methodology (RSM), a cost-effective and time-saving approach that identifies influencing factors to maximize the target (e.g., yield) with fewer experimental runs. Additionally, numerical regression algorithms enable the data to be fitted to mathematical models and to predict values in the examined region of the studied factor levels [13]. Central composite design (RSM-CCD) and Box–Behnken (RSM-BBD) are commonly employed among the experimental design tools [32]. They provide insight into system behavior, explaining the relationship between factors and responses while simultaneously facilitating optimization. BBD requires fewer runs compared to CCD, avoids running under extreme experimental conditions (i.e., reducing the risk of impractical or harmful conditions), and is suitable for mid-range optimization, meaning the optimized values should be predetermined. However, BBD has limited coverage and gives lower precision quality for higher-order models (if any). Similar to BBD, CCD is suitable for fitting quadratic models. Including central and axial points beyond the cube, points can elucidate the curvature of the response and facilitate exploration beyond the predetermined design points. It is also well suited for cases where the curvature of the response is essential, and a quadratic regression model is required [13,32,33]. Hence, RSM-CCD was chosen for the study to assess higher-order terms (quadratic or cubic).

To the best of our knowledge, our paper is the first to explore the use of RSM-CCD in a broader experimental range to optimize the hydrothermal extraction of CS. Also, much research reported the physiological functions of coffee beans, spent coffee grounds, etc. Still, information regarding the antioxidant activities, focusing on the hyaluronidase and α-glucosidase inhibition effects of the HDTE CS extracts, was limited. This study evaluates the antioxidant activities (i.e., anti-glucosidase and anti-hyaluronidase capabilities) of the CS extract obtained from the optimized hydrothermal extraction conditions and investigates the application of CS in drinks.

2. Materials and Methods

2.1. Materials and Chemicals

Coffee silverskin (CS) generated from roasting coffee beans, approximately 80% Coffea arabica and 20% Coffea robusta, was provided by Coffee Passion Co. Ltd., Samut Songkhram Province, Thailand. Upon arrival, the CS samples were blended by a home blender and sieved to sizes ranging from 150 to 500 μm. Then, the samples were kept in a dry and cold place with sunlight avoided. The moisture content of the CS samples was analyzed by a moisture analyzer (Sartorius MA35, Göttingen, Germany) and found to be 9.45 ± 0.52% throughout the study.

Chlorogenic acid (3-(3,4-dihydroxycinnamoyl)quinic acid), trans-5-O-caffeoylquinic acid, 4-O-(3,4-dihydroxycinnamoyl)-D-quinic acid, α-D-Glucosidase from yeast, p-Nitrophenyl α-D-glucopyranoside C12H15NO8, (±)6-hydroxy-2,5,7,8-tetramethylchromane-2-carboxylic acid (Trolox), 4-(dimethylamino) benzaldehyde, 5-methylphenazinium methyl sulfate, and 7,8-dimethyl-10-[(2S,3S,4R)-2,3,4,5-tetrahydroxyphethl]benzo[g]pteridine-2,4-dione (riboflavin), 1,3,7-trimethylxanthine (caffeine), hyaluronic acid sodium salt from rooster combs, hyaluronidase from bovine testes TypeI-S, ~55 kDa 400–100 units/mg solid, and nitro tetrazolium blue chloride HPLC grade, ≥90.0% assay, were purchased from Sigma Aldrich, Singapore. 1,1-diphenyl-2-picryhydrazyl free radical (DPPH) was purchased from Tokyo Chemical Industry, Tokyo, Japan. Other chemicals were analytical grade.

2.2. Hydrothermal Extraction (HDTE) Process

The extraction was performed in a one-pot system using a 450.0 mL high-pressure lab-scale autoclave reactor (Amar Equipment’s Pvt. Ltd., Mumbai, India), as shown in Figure 2. The reactor was outfitted with a variable-speed impeller, a thermometer probe, an external cooling jacket, an interior cooling coil, and a pressure gauge. Additionally, it was linked to data logger software and controllers. The sample mixture was constantly mixed throughout the experiment using the impeller at 600 rpm. According to the design of the experiment (DOE) for RSM-CCD established from the Minitab, a broader range of extraction conditions was mindfully established. The selection of primary variables, i.e., extraction temperature, extraction time, and the solid-to-liquid ratio (S/L ratio), is justified by their significant impact on the efficiency and yield of the process. These parameters directly influence the solubility of the target compounds, diffusion rates, and the overall equilibrium of the extraction performance. Also, focusing on these variables allows for a more streamlined experimental design, reducing complexity while providing critical insight into optimizing the process. This approach balances scientific rigor and practical feasibility in process optimization. Given that antioxidative compounds are susceptible to heat, it is necessary to unveil which extraction temperature, extraction time, and S/L ratio are best to obtain the highest content of those antioxidative compounds of interest. Hence, our study focuses on the green extraction technique and the solvent using the HDTE. The ranges of each variable were set according to literature reviews and the capacity of our equipment.

The extraction was performed at different solid-to-liquid ratios (S/L) ranging from 1:10 to 1:150 g/mL. The extraction temperatures were selected, covering inside and outside the subcritical region, ranging from 25 to 250 °C, and the extraction times ranged from 5 to 72 min. After the extraction, the mixed liquor of CS and water was separated by GF/C Whatman No. 1 filter paper in a Büchner funnel 250.0 mL under a vacuum. The filtrate was used as a sample to quantify TPC, DPPH, and antioxidant activities [34,35,36,37,38,39]. For further analysis, the filtrate was then concentrated at 45 °C using a rotary evaporator (Bushi, Zurich, Switzerland). Finally, the extracts were freeze-dried at −80 °C (Gold Sim Cellular Science, Miami, FL, USA) and stored at –20 °C [40,41,42].

2.3. Experiment Design and Analysis of Variance

The design of the experiment for response surface methodology (RSM) was established based on a central composite design (CCD) by Minitab 16 (State College, PA, USA). Three independent factors were temperature (A, °C), time (B, min), and S/L ratio (C, mL/g).

Table 1. Variables and levels used in the central composite design (CCD).

Symbols	Variables	Levels
		−1	0	+1
A	Extraction temperature (°C)	25	137.5	250
B	Extraction time (min)	5	38.5	72
C	S/L ratio (g/mL)	1:10	1:80	1:150

shows the input variables (A, B, and C) and three different levels (low, center point, and high) for the CCD used in this study. The experiment comprised 20 runs of 23 factorial points and six replicates of the center point, as presented in Table S1. Four selected responses were the yield (%) of the CS extracts, i.e., the TPC, AP, and T-CQA contents. To estimate the optimum condition of each response, a second-order polynomial response surface model is formulated using the following equation [43]:

$$\mathrm{y}={\mathrm{\beta }}_0+\sum _{\mathrm{i}=1}^{\mathrm{k}}{\mathsf{\beta }}_{\mathrm{i}}{\mathrm{X}}_{\mathrm{i}}+\sum _{\mathrm{i}=1}^{\mathrm{k}}{\mathsf{\beta }}_{\mathrm{i}\mathrm{i}}{\mathrm{X}}_{\mathrm{i}}^2+{\sum }_{\mathrm{i}<\mathrm{j}}^{\mathrm{k}}{\mathsf{\beta }}_{\mathrm{i}\mathrm{j}}{\mathrm{X}}_{\mathrm{i}}{\mathrm{X}}_{\mathrm{j}}$$

(1)

where y is the response of interest; β₀, β_i, β_ii, and β_ij are the regression coefficients for the intercept, linear, quadratic, and interaction terms, respectively; and X_i and X_j are the independent variables.

The coded values (−1, 0, +1) simplify the statistical analysis of the design when interpreting interaction effects and quadratic terms. It allows easy comparison across different levels of the factors [31,43]. The uncoded values represent the actual physical condition under which the experiments were conducted, for example, temperature ranges of 25 °C to 250 °C or 5 min to 72 min. The choice of these ranges allows for a comprehensive exploration of the process condition’s lower and upper bounds [40,44,45].

2.4. Determination of CGA Isomers and Caffeine Content in CS Extract by HPLC-UV

This study determined three chlorogenic acid isomers (3-CQA, 4-CQA, and 5-CQA expressed as T-CQA). T-CQA and caffeine were quantified using a high-performance liquid chromatography method performed on a variance system, equipped with a ProStar 310 UV-VIS detector (Agilent Technologies, Palo Alto, CA, USA) described by Ginting et al. [46] and Craig et al. [47]. To determine CGA (5-CQA, 4-CQA, and 3-CQA), trifluoroacetic acid (solvent A) and acetonitrile (solvent B) were used as the mobile phase with a ratio of 90% (A) and 10% (B). The mobile phase and methanol were filtered separately through 0.20 μm filter paper. The flow rate is controlled at 1 mL/min. The operation column is the C-18 AR column (25 cm × 4.6 mm i.d, particle size 5.0 µm), and the wavelength for CGA and caffeine are 326 nm and 272 nm, respectively.

2.5. Determination of Total Phenolic Content (TPC)

The total phenolic content was determined by the Folin–Ciocalteu method [48]. The crude extract was prepared to be 1 mg/mL with 50% methanol. Then, 400.0 L of each crude extract was further diluted 5.5 times with distilled water, and 1 mL of Folin–Ciocalteu (10-fold dilution with DI water) reagent was added. After 3 min, 4.0 mL of 20% (w/v) sodium carbonate was added to the tube. The mixture was kept in the dark for 60 min before the absorbance was measured at 650 nm. The total phenolic content was expressed as mg of gallic acid equivalent (GAE) per g dry weight.

2.6. Determination of DPPH Antioxidant Properties (AP)

The antioxidant activity or radical scavenging activity of plant extract is the measure of the ability of the extract to neutralize free radicals. The radical scavenging activity of the extract was tested by a 2,2-diphenyl-1-picrydraxyl (DPPH) assay, and Trolox was used as a reference standard [49]. An amount of 0.2 mL of extract solution (1.0 mg/mL in DI water) was mixed with 3.8 mL DPPH reagent (0.6 mM in ethanol) in the 15.0 mL test tube. Then, the mixture was incubated in the dark at room temperature for 30 min. For control, the DPPH reagent alone was used. The mixture was measured against the blank (50% ethanol) by UV–Vis spectrophotometer at 517 nm. The percentage of DPPH scavenging is calculated as follows:

$$\mathrm{D}\mathrm{P}\mathrm{P}\mathrm{H} \mathrm{S}\mathrm{c}\mathrm{a}\mathrm{v}\mathrm{e}\mathrm{n}\mathrm{g}\mathrm{i}\mathrm{n}\mathrm{g} \mathrm{a}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{i}\mathrm{t}\mathrm{y} \left(\mathrm{\%}\right)=\mathrm{ }{\displaystyle \frac{{\mathrm{A}}_{\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{ }}-\mathrm{ }{\mathrm{A}}_{\mathrm{s}\mathrm{a}\mathrm{m}\mathrm{p}\mathrm{l}\mathrm{e}}}{{\mathrm{A}}_{\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}}}\times 100$$

(2)

where A_control is the absorbance of the control, and A_sample is the absorbance of different concentrations of CS crude extract.

2.7. Evaluation of In Vitro Anti-Hyaluronidase Activities

The inhibitory activity of the hyaluronidase enzyme of CS crude extract was assessed by determining the amount of N-acetylglucosamine formed from sodium hyaluronate. The assay was described by Narita, et al. (2014) [50]. Prepare 6 of 15.0 mL test tubes for 50.0 μL of different crude extract concentrations (1, 2, 3, 4, 5, and 6 mg/mL). Each test tube follows the same procedure. Add 50.0 μL of 1-S bovine (4.8 mg hyaluronidase dissolved in 0.6 mL of 0.1 mol/L acetate buffer, pH 3.5), then incubate in a water bath at 37 °C for 20 min. Add 100.0 μL of 12.5 mmol/L calcium chloride and incubate the mixture in a water bath at 37 °C for 20 min. The reaction is started by adding 250.0 μL of sodium hyaluronate (1.2 mg/mL) and incubating in a water bath at 37 °C for 40 min. Then, 100.0 μL of 0.4 mol/L sodium hydroxide and 100.0 μL of 0.4 mol/L potassium borate are added to the mixture and incubated in a water bath at 100 °C for 3 min. The last step is to cool down the mixture, then add 3.0 mL of dimethyl benzaldehyde solution (4.0 g of p-dimethyl-amino-benzaldehyde dissolved in 350.0 mL of 100% acetic acid and 50.0 mL of 10 mol/L HCl) and incubate in a water bath at 37 °C for 20 min. The absorbance was measured at 585 nm by PerkinElmer Lambda 35 UV–Vis spectrophotometer. Pure 5-CGA was used as a reference reagent.

$$\mathrm{H}\mathrm{y}\mathrm{a}\mathrm{l}\mathrm{u}\mathrm{r}\mathrm{o}\mathrm{n}\mathrm{i}\mathrm{d}\mathrm{a}\mathrm{s}\mathrm{e} \mathrm{i}\mathrm{n}\mathrm{h}\mathrm{i}\mathrm{b}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \left(\mathrm{\%}\right)={\displaystyle \frac{{\mathrm{A}}_{\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}-{\mathrm{A}}_{\mathrm{s}\mathrm{a}\mathrm{m}\mathrm{p}\mathrm{l}\mathrm{e}}}{{\mathrm{A}}_{\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}}}\times 100$$

(3)

where A_control is the absorbance of the control, and A_sample is the absorbance of different concentrations of CS crude extract.

2.8. Evaluation of In Vitro Anti-α-Glucosidase Activities

The α-glucosidase inhibition assay is conducted to determine the anti-diabetes activity of the crude extract. The analytical assessment was performed as described by Wongnawa et al. [51]. Prepare eight test tubes for each sample concentration and control. Pipette 5.0 mL of 67.0 mM phosphate buffer (pH 6.8) into each test tube and add 0.2 mL of each sample into an individual test tube. Then, add 0.2 mL of glucosidase enzyme (0.15 unit/mL prepared fresh in cold DI water) to each test tube except the control tube (for the control, replace the enzyme with DI water). Next, incubate the mixture in the water bath at 37 °C for 20 min. After 20 min incubation, add 0.5 mL of 10.0 mM p-Nitrophenyl α-Glucoside to every tube. Then, incubate all the mixtures in the water bath at 37 °C for 20 min. Prepare a new set of eight test tubes and take 2.0 mL from each mixture into the new tubes, then add 8.0 mL of 100.0 mM Na₂CO₃ to stop the reaction. Next, measure the absorbance of the mixture using a Perkin Elmer Lambda UV–Vis spectrophotometer at 400 nm. The inhibition of the enzyme is calculated as follows:

$$\mathsf{\alpha }\text{-}\mathrm{g}\mathrm{l}\mathrm{u}\mathrm{c}\mathrm{o}\mathrm{s}\mathrm{i}\mathrm{d}\mathrm{a}\mathrm{s}\mathrm{e} \mathrm{i}\mathrm{n}\mathrm{h}\mathrm{i}\mathrm{b}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \left(\mathrm{\%}\right)={\displaystyle \frac{{\mathrm{A}}_{\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}-{\mathrm{A}}_{\mathrm{s}\mathrm{a}\mathrm{m}\mathrm{p}\mathrm{l}\mathrm{e}}}{{\mathrm{A}}_{\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}}}}\times 100$$

(4)

where A_control is the absorbance of the control, and A_sample is the absorbance of different concentrations of CS crude extract.

2.9. Statistical Analysis

The extraction process of each condition was conducted in triplicate, and the results were indicated as mean ± standard deviation (mean ± SD). The experimental condition of each run was designed using Minitab v16 (Minitab, LLC, State College, PA, USA, Version 16), and the statistical analysis was also performed using Minitab v16. A one-way analysis of variance (ANOVA) was used to test for significant differences between the control and the treated samples at 95% (p ≤ 0.05). The calculations and graphs were conducted in Microsoft Excel.

3. Results

The RSM-CCD examined the independent variables in this study to predict the optimum extraction conditions. The experimental responses of dependent variables Y1 (%yield), Y2 (TPC), Y3 (AP), and Y4 (T-CQA) were determined from the CCD. The result of the coded and uncoded unit of the independent variable with the response of interest, i.e., yield (%), TPC, AP, and T-CQA, is available in Table S1 of the Supplementary Materials. The model analyzed the influencing factors specific to each variable and generated the Pareto charts of the standardized effects (Figure S1).

Table 2. The standardized effects of the yield (%), TPC, AP, and T-CQA.

Standardized Effects (α = 0.05) for the Response of Interest	Significance Factors (Variables)	Insignificance Factors (Variables)
% Yield (Y1)	AA > A > C > CC > B > AC	BB, BC, AB
TPC (Y2)	A > AA > BC > AC	BB, C, B, AB, CC
AP (Y3)	B > A > CC > AB > BB > AA	C, BC, AC
T-CQA (Y4)	BC > B > BB > AA > AC > C	A, AB, CC

Where: AA, BB, and CC refer to square terms of A (temperature), B (extraction time), and C (solid-to-liquid ratio), respectively.

summarizes the standardized effects of the yield (%), TPC, AP, and T-CQA.

Table 3. The p-values of each response to the dependent variables for the SW-CSE generated from the final model.

Parameters	p-Values for Each Response
	% Yield	TPC	AP	T-CQA
Temperature (A)	0.000	0.000	0.000	0.036
Time (B)	0.008	0.363	0.000	0.000
S/L ratio (C)	0.001	0.266	0.679	0.021
Square	0.000	0.025	0.000	0.000
Temperature × Temperature (A ×A)	0.000	0.009	0.008	0.003
Time × Time (B × B)	-	0.035	0.001	0.002
S/L ratio × S/L ratio (C × C)	0.003	-	0.000	-
2-Way Interaction	0.035	0.016	0.000	0.000
Temperature × S/L ratio (A × C)	0.035	0.039	0.000	0.018
Time × S/L ratio (B × C)	-	0.026	-	0.000
Model Summary
R2	99.50%	96.30%	95.16%	92.97%
R2 (adjusted)	99.27%	94.14%	92.33%	88.87%

Where TPC = total phenolic content; AP = antioxidant properties; T-CQA = total caffeoyl quinic acids.

displays the ultimate model for each response, showcasing notably high R² and adjusted R² values.

The individual effects of each variable, i.e., extraction temperature, time, and solid-to-liquid (S/L) ratio, significantly influence extraction yield (Y1), total phenolic content or TPC (Y2), antioxidant activity (Y3), and total chlorogenic acids or T-CQA (Y4). Higher temperatures generally enhance solute solubility and diffusion. However, excesses of such conditions may degrade the thermolabile compounds. A prolonged extraction period promotes diffusion but is vulnerable to the degradation of target compounds due to overexposure. The S/L ratio generally determines solvent efficiency, with low ratios decreasing recovery and excessive ratios diluting the extract. Further discussion on the impact of variables on each response (Y1 to Y4) can be found in Section 3.1, Section 3.2, Section 3.3 and Section 3.4.

3.1. The Effects on the Extraction Yield (%) (Y1)

The yield percentages for all 20 runs (Table S1) ranged from 17.94% to 39.62%. In experimental run 12 (at 137.5 °C, reaction time 72 min, with an S/L ratio of 1:80), the highest yield of 39.62% was achieved, exceeding previously reported values. The yield (%) of the six center points of tests 15–20 (at 137.5 °C, reaction time 38.5 min, with an S/L ratio 1:80) was relatively high, ranging from 37.22% to 38.69%, confirming that the highest yield was obtained at the subcritical water region. As shown in Table 2, the influencing factors on the yield (%) of the hydrothermal CS extracts (HDT-CSEs) are A > AA > C > CC > B > BC > AC, all of which correspond to the final model in Table 3. These findings revealed that the extraction temperature (A) and its square term (AA) considerably affect the TPC. In addition, the phenolic content of the HDTEs is influenced by the interaction of the S/L ratio, extraction temperature, and reaction time. Water becomes subcritical when heated over 100 °C in a pressured system (60 to 75 bar) [52]. Under these conditions, the dielectric properties of water are altered to enhance the solubility of both polar and nonpolar compounds within the CS matrix. Also, sufficient reaction time and water volume are required to achieve appropriate wetting and solubilization of the CS. The mixing intensity produced by the turbine stirrer facilitates the disruption of the cellulosic structure of the CS sample, thereby enhancing water penetration into the CS matrix and promoting the hydrolysis and leaching of soluble chemicals (e.g., polysaccharides, lignin) into the solvent mixture [53].

3.2. The Effects on the TPC (Y2) of the HDT-CSEs

The highest TPC of 83.73 mg GAE/g CS was obtained from experimental run 10 (at 250 °C, reaction time 38.3 min, and with an S/L ratio of 1:80). In comparison, the lowest TPC (4.17 mg GAE/g CS) is from experimental run 3 (at 25 °C, reaction time 5 min, and with an S/L ratio of 1:10. The higher total phenolic content (TPC) values (77.69, 65.05, 64.70, 76.24, and 83.73 mg GAE/g CS) were obtained when using elevated extraction temperature (250 °C) during runs 2, 4, 6, 8, and 10, respectively. These results indicated that high extraction temperatures played a crucial role in increasing the TPC of the HDT-CSEs. The increased temperature of 250 °C reduced the polarity of water, allowing for the more efficient dissolution and solubilization of less polar phenolic compounds. Table 2 presents four key factors influencing the TPC of the HDT-CSE, ranked in the following order: A > AA > BC > AC. The results indicated that extraction temperature (A) under a subcritical level of 250 °C and its quadratic component (AA) are crucial, contributing to a high extraction yield and TPC.

However, the final model (Table 3) revealed that the quadratic term of the reaction time (BB) also significantly contributed to the TPC. Notably, the individual effects of the reaction time (B) and S/L ratio (C) were insignificant, indicating that the selected range of the time and S/L ratio did not cause substantial variation in the TPC of the HDT-CSEs. Nevertheless, the interaction between these factors (BC) significantly impacted the TPC.

3.3. The Effects on the Antioxidant Properties (Y3) of the HDT-CSEs

The antioxidant properties (AP) of the HDT-CSE were assessed using the colorimetric DPPH assay, which measured the extract’s capability to scavenge the purple DPPH radical, resulting in a yellow coloration and expressed as the Trolox equivalent (TE) per unit quantity of CS. The highest AP (14.89 mg TE/g CS) was obtained from experimental run 4 (at 250 °C, reaction time 72 min, and with an S/L ratio of 1:10). In comparison, the lowest activity (2.25 mg TE/g CS) was obtained from experimental run 11 (at 137.5 °C, reaction time 5 min, and an S/L ratio of 1:80). Table 2 reveals six influencing factors on the AP of the HDT-CSE with the following magnitude: B > A > CC > AB > BB > AA. Interestingly, for the antioxidant properties (AP), the primary influencing factors shifted from A and AA, which were significant for yield and TPC, to B and A. Reaction time (B) became the most crucial factor, consequently reducing the individual effect of extraction temperature (A) from the primary to the second position in importance. In addition, the combined effect between reaction time and extraction temperature (AB) and the quadratic term of reaction time (BB) gained greater significance. While the individual influence of the solid-to-liquid ratio (C) was insignificant, its quadratic term ranked third in importance.

Sufficient reaction time and temperature above 140 °C induce the reaction between amino acids and sugars through the Maillard reaction [6], producing the Maillard-derived products (MRP). In addition, the Maillard reaction can also interact with phenolic compounds that might be present in the extract, leading to enhanced stability and antioxidant effectiveness. Some MRP can protect phenolic compounds from degradation, thus contributing further to the overall antioxidant abilities of the extract [9,54,55].

As a relatively high AP was obtained from the experiment at a relatively high extraction temperature with a sufficient reaction time, this phenomenon might relate to the activation energy of the HDT-CSE, which could substantially rise when the temperature was elevated from 25 to 250 °C. These results were consistent with the TPC (Section 3.2), confirming that high temperatures favor the solubilization of the less polar to nonpolar compounds such as tocopherol analog (also called Trolox) [52,56,57]. This result confirmed that the antioxidant properties (AP) of the HDT-CSEs were substantially influenced (p < 0.05) by the interaction of the extraction time and temperature, as evidenced by the extraction period ranging from 5 to 72 min (runs 3 and 4; 7 and 8; 9 and 10; 11 and 12).

3.4. The Effects on the Total Caffeoylquinic Acid or T-CQA (Y4) of the HDT-CSEs

The total content of three chlorogenic acid isomers (3-, 4-, and 5-caffeoylquinic acid) was quantified by HPLC. The overall result obtained from the hydrothermal extraction method is shown in Table S1. The highest total caffeoylquinic acid (T-CQA) of 2.60 mg/g CS was obtained from run 16 (at 137.5 °C, reaction time 38.5 min, and an S/L ratio of 1:80), which was one of the six center points of the experiment design. The lowest T-CQA (0.9 mg/g CS) was acquired from run 3 (at 25 °C, reaction time 72 min, and an S/L ratio of 1:10). The T-CQA content was relatively low when the S/L ratio was 1:150 at any extraction temperature and time. At a high extraction temperature of 250 °C, the T-CQA was notably low compared to that observed at a lower temperature of 137.5 °C, unlike the results seen with TPC and AP. This result indicated that using a relatively high extraction temperature in hydrothermal extraction caused a detrimental effect on the T-CQA content in the CS extract.

Table 2 presents six factors influencing the T-CQA content, ordered by magnitude as follows: BC > B > BB > AA > AC > C. According to the Pareto principle (80/20 rule), these various influencing factors collectively account for approximately 80% of the extract’s T-CQA. Therefore, concentrating efforts on controlling these factors will likely result in a substantial improvement in the T-CQA of the extract. Additionally, it was also noted that extraction temperature alone (A), the interaction between extraction temperature and reaction time (AB), as well as the squared term of the S/L ratio (CC), did not have a significant effect on the T-CQA content of the HDT-CSE (p < 0.05). However, the ultimate model (Table 3) showed that the individual impacts of extraction temperature (A), reaction time (B), and S/L ratio (C) were significant, along with the quadratic term of the reaction time (BB) and the interaction effects between extraction temperature and S/L ratio (AC), as well as between reaction time and S/L ratio (BC). This observation can be attributed to the degradation of chlorogenic acids at relatively high temperatures [57,58,59]. The T-CQA content significantly differed as the reaction time increased from 5 to 72 min under the constant temperature and solid-to-liquid (S/L) ratio conditions. The extraction time (B) and S/L ratio (C) and their interaction effect (BC) were found to be significant factors influencing T-CQA content. From Table S1, the result indicated that prolonged extraction time resulted in lower T-CQA content. Specifically, in experiments 1 and 3, the T-CQA content decreased from 2.3 mg/g CS to 0.9 mg/g CS as the reaction time increased from 5 to 72 min. According to the ANOVA results (see Table 3), the interaction between extraction time and S/L ratio (BC) had the most significant impact on the T-CQA content, with a p-value of 0.000 (p < 0.05).

3.5. Regression Modeling of Hydrothermal Extraction (HDTE) on % Yield, TPC, AP, and T-CQA

Initially, the regression analysis of the full model revealed a very high determination coefficient (R²), ranging from 92.97% to 99.50%. After the statistically insignificant factors (p > 0.05) were excluded, showing only the significant extraction variables (p < 0.05), the adjusted R² was in the range of 88.87% to 99.27%, as presented in Table 3.

Although factors deemed insignificant (with a p-value exceeding 0.05) were excluded from the model, certain insignificant one-way interaction factors remained because their square or two-way interaction proved significant (with a p-value less than 0.05). Eliminating these factors enhances interpretability and clarifies the relationship among variables, which is crucial for optimizing responses. It also lowers the risk of overfitting, ensuring the model captures the truthful underlying patterns rather than extraneous noise, thereby improving its generalizability and predictive accuracy.

The polynomial regression equations of total phenolic content (TPC), DPPH antioxidant properties (AP), and the total caffeoylquinic acid content (T-CQA) of the HDT-CSEs were developed, as presented in

Table 4. Polynomial regression equations for the prediction of % yield, TPC, AA, and total caffeoylquinic acid content (T-CQA) of the SW-CSE.

Responses	Polynomial Regression Equations	R2 (Predicted)	Lack-of-Fit
% Yield	Y1 = Yield = 9.574 + 0.31445 A + 0.02051 B + 0.0487 C − 0.000975 A2 − 0.000286 C2 + 0.000073 A × C	98.67%	0.378
TPC	Y2 = TPC = 5.48 + 0.0989 A + 0.378 B + 0.0263 C + 0.000750 A × A − 0.00645 B2 − 0.000567 A × C + 0.002087 B × C	88.50%	0.292
AP	Y3 = 3.305 + 0.0412 A + 0.1919 B − 0.1259 C − 0.000141 A2 − 0.002219 B2 + 0.000776 C + 0.000416 A × B	83.52%	0.386
T-CQA	Y4 = 2.135 + 0.0084 A + 0.00582 B − 0.00491 C − 0.000029 A2 − 0.000347 B2 − 0.000021 A × C + 0.0000149 B × C	83.84%	0.750

Note: The quadratic terms A × A, B × B, and C × C are replaced with A², B², and C², respectively. TPC = total phenolic content; AP = antioxidant properties; T-CQA = total caffeoyl quinic acids.

. The model’s reliability in predicting extraction conditions for the selected response variables (i.e., yield, TPC, AP, and T-CQA) was evaluated based on the predicted R² values, which were 98.67%, 88.50%, 83.52%, and 83.84%, respectively. As all predicted R² values exceeded 80%, these results confirm that the model provides accurate predictions for the specified responses. For instance, the regression for T-CQA prediction demonstrated an 83.84% confidence level.

In Table 4, the lack of fit is a parameter for assessing how well the model represents the variability in the experimental data. At the same time, p-values are used to evaluate the model’s accuracy. A p-value ≥ 0.05 indicates an insignificant lack of fit, suggesting that the model is highly reliable. For all responses (i.e., yield, TPC, AP, and T-CQA), the p-values for the respective lack of fit are well above 0.05, indicating that the models can reliably predict the optimum conditions and that the developed quadratic polynomial models are accurate and dependable in predicting the responses.

Figure 3 shows contour plots for the effects of the extraction temperature (A), reaction time (B), and solid-to-liquid ratio (C). In the context of process optimization, contour plots are particularly useful for visualizing how changes in the independent variables affect the target response, facilitating the identification of optimal conditions.

Figure 3a–c illustrate the impact of extraction conditions on Y1 (%yield). Plots (a) and (b) indicate that the highest yield (>36%) was achieved at the extraction temperature ranging from 120 °C to 210 °C regardless of extraction time and S/L ratio. The analysis of plots (a), (b), and (c) suggests that the extraction time (X2) and S/L ratio (X3) had minimal influence on the extraction yield, with temperature (X1) being the primary determining factor. Figure 3d–f demonstrate the effect of extraction conditions on Y2 (TPC). The plots (d) and (e) reveal that the highest TPC (>80 mg GAE/g CS) was obtained between 225 °C to 250 °C with extraction times ranging from 5 to 80 min, but the peak was visualized at approximately 40 min. For the S/L ratio, the highest TPC (>80 mg GAE/g CS) was found in a range of 1:10 to 1:150.

Figure 3g–i depict the interaction plot between temperature (X1), time (X2), and solid-to-liquid ratio (X3) on antioxidant activities or AP (Y3). The highest AP values (>12 mg TE/g CS) were primarily observed under the following conditions: extraction temperature ranging from 170 to 250 °C, reaction time ranging from 50 to 70 min, and a solid-to-liquid (S/L) ratio of approximately 1:10. Due to the presence of two concave regions in the contour plot, a minor peak was also detected under similar temperature and time conditions, but with a solid-to-liquid (S/L) ratio of approximately 1:150. Consistent with the trend observed for TPC, AP content increased with rising temperatures from 100 °C to 270 °C. These findings are in agreement with previous studies [60,61].

Figure 3j–l illustrate the effect of extraction conditions on Y4 (T-CQA). The highest T-CQA content (>2.7 mg/g CS) was visualized under the following combination of conditions: extraction temperature ranging from 110 to 170 °C, reaction time from 5 to 17 min, and a solid-to-liquid (S/L) ratio between 1:10 and 1: 20. This result aligns with previous findings, which reported that the 5-CQA was detectable in the subcritical water CS extract at the extraction temperatures of 25, 80, and 180 °C, but began to decompose when the temperature exceeded 210 °C [61].

3.6. Correlation Between Dependent Variables

The Pearson correlation coefficient (r) is used to measure the linear correlation, strength, and direct relationship between two variables. An (r) value between 0.3 and 0.5 indicates a moderate, positive relationship, while values between 0 and −0.3 suggest a weak correlation.

Table 5. Pearson correlation (r) between two dependent variables.

Dependent Variables		Correlation (r)	p-Value
Yield (Y1)	TPC (Y2)	0.40	0.08
AP (Y3)	Yield (Y1)	0.36	0.12
T-CQA (Y4)	Yield (Y1)	−0.23	0.34
AP (Y3)	TPC (Y2)	0.45	0.04
T-CQA (Y4)	TPC (Y2)	−0.13	0.57
T-CQA (Y4)	AP (Y3)	−0.23	0.34

Where TPC = total phenolic content; AP = antioxidant properties; T-CQA = total caffeoyl quinic acids.

demonstrates a positive correlation (r) between TPC (Y2) and AP (Y3), with a p-value of 0.04 (<0.05). This finding aligns with the results presented in Table 5 and Figure 3, which demonstrate that higher levels of TPC are associated with increased AP. TPC and AP content remained stable at elevated extraction temperatures ranging from 100 °C to 250 °C. However, a negative correlation was observed between T-CQA (Y4) and TPC (Y2), as well as between T-CQA (Y4) and AP (Y3), with p-values of 0.57 and 0.34, respectively. Similarly, negative correlations were found between Yield (Y1) and TPC (Y2), Yield (Y1) and AP (Y3), and T-CQA (Y4) and Yield (Y1). These results suggest that T-CQA content is not directly related to TPC and AP content. Unlike TPC and AP, T-CQA content decreases at higher temperatures and longer extraction times (>180 °C, >40 min) due to the decomposition of T-CQA. Therefore, optimizing the extraction condition should focus specifically on maximizing T-CQA content.

The Pearson correlation coefficient (r) was analyzed to optimize the extraction condition for the variables. The analysis revealed no significant correlation between T-CQA, TPC, and AP (See Table 5).

Table 6. Response optimizer of the hydrothermal water extraction conditions on maximizing each variable.

Response	Predicted Value	Optimal Conditions
		Temperature (°C)	Reaction Time (min)	S/L Ratio (g: mL)
Yield (%)	39.6098	169.9091	72.0	1:106.1616
TPC (mg GAE/g CS)	82.0975	250.0	30.7172	1:10.0
AP (mg TE/g CS)	14.2427	250.0	69.2929	1:10.0
T-CQA (mg/g CS)	2.7172	143.1818	10.4142	1:10.0

Where TPC = total phenolic content; AP = antioxidant properties; T-CQA = total caffeoyl quinic acids.

shows the results from the response optimizer of the hydrothermal water extraction conditions on maximizing each variable: % yield, total phenolic content (TPC), antioxidant properties (AP), and total caffeoylquinic acid content (T-CQA). Since chlorogenic acid is this study’s target compound, the optimization focused on maximizing T-CQA content. A response optimizer was employed to determine the optimal combination of input variables to achieve the highest T-CQA content. According to the Minitab optimization tool, the optimal extraction conditions were identified as 143.2 °C, 10.41 min, and an S/L ratio of 1:10, with a predicted T-CQA of 2.72 mg CQA/g CS. The accuracy of the model optimization was validated specifically for T-CQA values (n = 5). The experimental T-CQA value obtained under the optimized extraction conditions was 2.762 ± 0.202 mg TCQ/g CS, which closely matched the predicted values of 2.717 ± 0.114 mg TCQ/g CS. Furthermore, individual response optimization can be generated from the RSM-CCD models (Figure S2).

3.7. The Quantification of Bioactive Compounds in the CSE by HPLC-MS

HPLC-MS further analyzed the optimum CSE to identify the other unknown bioactive compounds. Four major target compounds (i.e., 3-CQA, 4-CQA, 5-CQA, and caffeine) were quantified against their standard calibration curves, as shown in

Table 7. The calibration curve, R² values, and quantification of the target bioactive compounds at the optimized condition.

Compounds	Calibration Curve	R2	mg/g of Dry CS	SD
Caffeine	Y = 25,871x	R2 = 0.9999	10.953	1.14
3-CQA	Y = 5793.5x	R2 = 0.9994	0.783	0.03
4-CQA	Y = 5515.8x	R2 = 0.999	0.745	0.09
5-CQA	Y = 6798.1x	R2 = 0.9999	0.618	0.04

Where: T-CQA = total caffeoyl quinic acids and SD = standard deviation.

. Caffeine is the predominant compound found in the extract, followed by 5-CQA. The caffeine content of the optimal extract was found to be 10.953 ± 1.14 mg/g CS. The chlorogenic acid isomers’ peaks were found at the elution time between 10 and 15 min. 5-CQA appeared at the elution time of 10.760 min, which was 10.44% of the total peak area. 3-CQA and 4-CQA appeared, respectively, at retention times of 14.265 min and 14.645 min. The total content of chlorogenic acid of the three isomers (3-, 4-, and 5-CQA) is 2.762 ± 0.202 mg/g of CS dry weight. Figure 4 shows the chromatogram of bioactive compounds observed in the CS extract. The prominent peak is caffeine, which appeared shortly after 16.108 min. The other six compounds were gallic acid, which was the very first to appear at a retention time of 7.016 min; catechin (at 13.003 min); caffeic acid (at 17.230 min); ferulic acid (at 23.625 min); oxyresveratrol (at 25.195 min); resveratrol (at 30.004 min); ellagic acid (at 31.280 min); and quercetin (at 39.315 min).

3.8. Hyaluronidase Inhibition Activities

Hyaluronic acid (HA) is a big complex molecule with a molecular weight range from 2 × 10⁵ to 10⁷ Da and 2000 to 2500 disaccharides [33]. The complex chemical structure contains plenty of -OH, which could hold onto water. Additionally, it exerts several biological impacts, such as cellular differentiation, embryonic development, inflammatory regulation, wound healing, and viscoelastic properties. HA is naturally produced and presented in the whole human body, constantly and naturally degraded and replaced. Thus, with age and external aggression, the regeneration of HA tends to slow. The depolymerization of HA shortens its polymer structure, which would affect its physio-chemical properties [62]. The majority of HA is the hyaluronidase enzyme. Hyaluronidase is also naturally present in the human body. By slowing down or terminating the enzyme activity, HA degradation is decreased, resulting in a slower aging process. This study studied the hyaluronidase inhibition activity of 3-CQA, 5-CQA, and CS crude extract. This test was performed to determine the capacity of the CS extract to work against aging. And the result shows that the IC50 of 5-CQA, 3-CQA, and CS crude extract are 2.2, 2.8, and 5.00 mg/mL, respectively, as shown in Figure 5. From this study, the test results of IC50 show 5-CQA < 3-CQA < CS crude extract. The smaller IC50 indicates more potent enzyme inhibition, which means that a small amount of inhibitor extract is used in the test.

3.9. Glucosidase Inhibition Activities

The activity of α-glucosidase inhibition was measured by the colorimetric method. CS dried crude extract was evaluated for its α-glucosidase inhibition activity. Chlorogenic acids were found to have high antioxidant activities and were shown to prevent diabetic disease and obesity [63,64]. Phenolic compounds play a significant role in the inhibition of the digestive enzyme in the human digestive tract [65]. Since the total phenolic content in the CS crude extract is high, it is responsible for α-glucosidase enzyme activity. It was observed that the extract obtained from medium-roast CS inhibited enzyme activity up to 96.04% at 25.0 mg/mL, and the 50% inhibition is IC50 = 9.00 ± 1.72 mg/mL. The inhibition of glucosidase enzyme was studied on both pomegranate drinks (one without CS extract and one with CS extract).

4. Discussion

Figure 6 presents a schematic diagram of the hydrothermal extraction of CS in a one pot system and six key findings of our study. Extraction temperature and time play a critical role in the extraction of phenolic compounds. While extraction temperatures exceeding 180 °C can lead to the degradation of certain phenolic compounds, total phenolic contents (TPC) may still increase due to enhanced solubilization. The solid-to-liquid (S/L) ratio in the HDTE method has a minimal direct impact on extraction yield, TPC, antioxidant properties (AP), or total chlorogenic acid (T-CQA). However, it facilitates efficient emulsification of the solvent (i.e., water) with the sample, thus improving the overall extraction performance. Additionally, extending the extraction time at an appropriate temperature allows for the release of compounds while minimizing heat-induced degradation [61,66].

At temperatures above 140 °C, the Maillard reaction occurs, producing Maillard-derived products (MRPs) such as melanoidins, which exhibit antioxidant properties [66]. This reaction enhances antioxidant activity and helps protect phenolic compounds from degradation. Consequently, a positive correlation is observed between extraction yield, total phenolic content (TPC), and antioxidant properties (AP). However, total chlorogenic acid (T-CQA) negatively correlates with all four responses. Our results unveil that T-CQA content decreases as the extraction temperature exceeds 150 °C, primarily due to thermal degradation.

Additionally, water becomes less polar at higher temperatures, reducing the solubility of polar phenolic acids like CQAs, thereby lowering their extraction efficiency. A separate optimization process using RSM-CCD is recommended to address this dynamic phenomenon. While yield, TPC, and AP are positively correlated and can be optimized collectively, T-CQA requires specific conditions for maximum recovery. The optimal extraction conditions for maximizing T-CQA are a temperature of 143.20 °C, an extraction time of 10.41 min, and an S/L ratio of 1:10.

The results confirm that CSE is a bioactive substance with significant potential for use in various commercial applications. This functionality is attributed to its ability to reduce substrate binding affinity at active sites when inhibitors, particularly phenolic compounds in the extract, bind antagonistically. Due to its richness in bioactive phenolics, including chlorogenic acids, gallic acid, ellagic acid, ferulic acid, oxy-resveratrol, resveratrol, quercetin, catechin, and caffeine, HDT-CSE exhibits excellent potential to inhibit the digestive enzyme α-glucosidase. Enzyme inhibition assays validated its bioactivity, showing an IC50 of 5.00 mg/mL for hyaluronidase, which supports its potential use in anti-wrinkle skincare products, and an IC50 of 9.00 mg/mL for α-glucosidase, demonstrating its suitability as a functional ingredient for regulating blood sugar levels and supporting diabetes management.

Repurposing CS into functional food or beverages offers a sustainable solution to promote carbon neutrality. As a by-product of the coffee roasting process, CS can be valorized to reduce organic waste in landfills and reused as a raw material for manufacturing new products, conserving both resources and energy [67,68]. This approach enhances the efficient use of existing materials, thereby reducing the overall carbon footprint. For instance, CS contains approximately 50 ± 12% carbon; if one ton of CS is burned, it would release around 1.835 tons of CO₂ into the atmosphere. Incorporating CS into functional beverages also supports the development of a circular economy by reducing reliance on resource-intensive and carbon-emitting production processes. This strategy promotes long-term sustainability by improving resource efficiency and reducing carbon emissions.

5. Conclusions

Coffee silverskin (CS), a widely available waste product worldwide, is a potential source of sustainable chlorogenic acids, phenolic compounds, and antioxidants. Therefore, coffee silverskin extract (CSE) is a valuable industrial ingredient source of antioxidative materials. Subcritical water extraction (SWE) is an effective and environmentally friendly technique for the high extraction yield of TPC, AP, and T-CQA. Coffee silverskin can be a low-cost substrate for the bioproduction of high-value health-promoting products such as phenolic compounds, phenolic acids, caffeine, and other bioactive compounds. The inhibition of hyaluronidase was lower with chlorogenic acid alone compared to chlorogenic acid with other phenolic compounds. Chlorogenic acid may have a synergistic effect with other phenolic acids and phenolic compounds [69]; thus, suggesting that separation or purification of the chlorogenic acid contents in CSE is not required.

To the best of our knowledge, through this study, the total phenolic contents, the radical scavenging activities, the total caffeoylquinic acid contents, the anti-hyaluronidase activities, and anti-α-glucosidase activities in CSE from the SWE method and CS brewing make these extracts suitable for other applications such as in soft drinks or in direct consumption as tea products. New research should focus on applying CS and CSE in drinks for future studies. CSE has many emerging applications, such as for use in healthy drinks, and studies should focus on the bioactivity of the product, the nutritional facts of the products, the safety of the product, and the shelf-life of the product. CS could be made into infusion tea or mixed with medicinal herbs for more health benefits.