Optimization Formula of Coffee-Maca Granules as an Aphrodisiac Functional drink using D-Optimal mixture Design and PCA-CA

 

Eka Indra Setyawan¹*, Ni Putu Ari Antari¹, I Gusti Agung Dewantara Putra¹,

Dewa Ayu Swastini¹, Hazrul Hamzah², Oktavia Indrati³

¹Department of Pharmacy, Faculty of Mathematics and Natural Science, Udayana University, Bali, Indonesia.

²Department of Pharmacy, Faculty of Pharmacy, Muhamadiyah University, East Kalimantan, Indonesia.

³Department of Pharmacy, Faculty of Mathematics and Natural Science, Indonesian Islamic University, Yogyakarta, Indonesia.

 

ABSTRACT:

Arabica coffee (Coffea arabica) and maca (Lepidium mayenii) have many health benefits, one of which is as a tonic and aphrodisiac. This study aimed to combine coffee and maca into a functional drink that has health benefits. Formula optimization was carried out by the D-Optimal Mixture Design method using the proportion of coffee, maca, and dextrin as research variables. The research observed were the amount of yield, moisture content, flow rate, compressibility index, and mounting frequency of male rats to see the aphrodisiac effect. Principal Component Analysis-Cluster Analysis (PCA-CA) was used to study the relationship between experimental responses and the correlation between formulas. The results showed that  three components such as coffee, maca, and dextrin gave a positive response in increasing  yield values and compressibility index. Meanwhile, foor experimental responses such as moisture content, flow rate, and aphrodisiac tests only two components namely, coffee and maca, gave a positive response in increasing the response. The optimum formula for coffee-maca granules resulted in the average yield value, moisture content, flow rate, compressibility index and mounting frequency of 29.08 g, 5.09%, 5.98, 0.18%, and 10.67, respectively.

 

 

 

INTRODUCTION: 

Functional drinks are types of food that have been processed scientifically and contain one or more compounds that have physiological functions and are beneficial to health. One example is a drink that enhances the vitality or sexual arousal of men and women (aphrodisiac)^1,2. Some plants are potential to stimulate aphrodisiac effects, including coffee and maca which are served in food and beverages.

 

Coffee become the lifestyle of the Indonesian people, evenly distributed among all ages, young, old, male and female^3,4.

 

Arabica coffee (Coffea arabica) contains caffeine, chlorogenic acid, trigonelin, carbohydrates, fats, amino acids, organic acids, and minerals that are beneficial for health namely, antioxidants, antiviral, hepatoprotective, tonic, and anti-stomach spasms^5–9. Taste of coffee is strongly influenced by the origin area and processing process^10,11. Caffeine in coffee has a role as a stimulant^12,13. Arabica coffee (Coffea arabica) has a lower caffeine content than robusta coffee. Thus, the selection of this type of coffee tends to be safer for people who have a low tolerance for caffeine. Excessive caffeine consumption will cause insomnia, convulsions, heart palpitations, feelings of anxiety, and nervousness^14–17.

 

Maca (Lepidium meyenii) is shaped like a turnip with green shoots and yellow roots. This plant contains lots of vitamins, minerals, and antioxidants, such as glucocyanates and polyphenols¹⁸. This plant is known as an adaptogen, which is a term for an herbal plant that contains natural substances that help the body adapt to certain stress conditions and diseases. Maca has many health benefits including being able to increase energy by stabilizing glucose levels in the blood¹⁹. Increased glucose levels in the blood can stimulate insulin production, which causes fatigue in the body. Maca is able to balance the hormone estrogen. Estrogen is a sex hormone that maintains the menstrual cycle in women so that it can improve fertility and reproductive health and reduce symptoms associated with polycystic ovary syndrome. For men, maca can increase male fertility, increase sexual desire, improve sperm levels and quality^19–22.

 

Formula development requires an effective and efficient method. The formula optimization method that is often used in optimizing a mixture of two or more ingredients in a formula is D-Optimal Mixture Design^23,24. This method is designed to minimize the overall variance of the predicted regression coefficients. Furthermore, the advantage in this experimental design is that it can analyze component and process variables simultaneously^25,26. Principal Component Analysis (PCA) is the most popular chemometrics technique, it is provides the most compact representation of all variation in the data table. PCA is designed to reduce the large complex data set into series of optimized and interpretable size. PCA is trying to find out factors or principal components (PC)²⁷. Cluster Analysis (CA) is one of type unsupervised pattern recognition technique frequently used for classification object. CA is method for dividing group of object into classes so that similar objects fall in the same class²⁸. Previous studies implemented the PCA-CA combination in the optimization process ^28,29. However, there was no research has been found that combines D-Optimal Mixture Design with PCA-CA. Based on  the description above, it is necessary to optimize the formula with a mixture of coffee and maca so that it is able to produce functional drinks that are useful for increasing sexual arousal stamina (aphrodisiac) by implementing both methods into the optimization process.

 

MATERIALS AND METHODS:

Materials:

Coffee bean (Coffea arabica) is harvested from Desa Catur, Kintamani, Bali, Indonesia. Organic maca powder (Lepidium meyenii) is purchased from Armindo Abadi Sentosa, Indonesia. Dextrin is purchased from Bratchem, Indonesia.

 

Methods:

Formulation of coffee-maca granules:

Coffee green bean (Coffea arabica) is sorted and roasted with characteristic medium to dark. Roasted coffee is brewed in 200 mL of hot water (95^oC) using coffee extractor for 10 minutes then filtrates is collected^30,31. Maca (Lepidium meyenii) is brewed in 200 mL water (45^oC) and stirred at 90 rpm for 3 hours then filtrates is collected³². The proportion of coffee, maca, and dextrin is design by D-Optimal Mixture Design (table 1). Granules (concentrates) is made by mixing all components (coffee, maca, dextrin) and concentrated by heating then sieved (100 mesh) to homogenized the granules.

 

Table1. Formulation of coffee-maca granules

 

Granules evaluation:

a)    Yield value:

This study is carried out by weighing total weight of each run.

b)    Moisture content test:

This test is performed using moisture balance. Moisture content measurement was carried out using 1 g of granule sample. The water content specified for granules according to the Indonesian National Standard (SNI 2983-2014) is less than 10%.

c)     Flow rate:

The granules were put into the funnel. The angle produced by the flowing granules was then recorded by measuring the diameter and height of the pile (angle less than 30^o) and the time required to flow was recorded as experimental response.

d)    Compressibility index:

100 g of granules is weighed into a measuring cup and record the volume, the granules are then compressed 500 times using a test apparatus. Record the volume of granules after compression. The compressibility index was calculated by comparing the difference in volume before and after compression with the initial volume of the granules.

 

Aphrodisiac test:

Coffee-maca solution was prepared by dissolving 20 g of each formula and brewed using 30 g of hot water (95^oC). The brewing process is carried out using a short immersion technique.

Table 2. Evaluation of coffee-maca granules

 

The solution (2mL) from each formula according to table 1 was administered orally to male rats. The treatment was carried out at 18.00pm, 1 hour later (at 19.00pm) one female rat was put into each male rat cage. The test was carried out for 7 consecutive days. Observed and recorded the sexual activity of male rats against female rats with the parameters of the number of mounting for one hour. Positive control treatment was carried out by giving Sildenafil citrate 5mg/kgBW³³.

 

RESULT AND DISCUSSION:

The formulation of the coffee-maca granules can be seen in the following table (table 2). The results showed that the amount of yield, moisture content, flow rate, and granule compressibility index was 19.2-48.72g, 4.63-6.96%, 4.02-8.06g/s, and 0.08-0.18%, respectively. Based on the results of the aphrodisiac test, 14 runs gave a mounting frequency of 4-14 times for a hour.

 

The effect of the proportion of the formula for the yield value:

The relationship of these three components to the yield value (figure 1) can be explained through the cubical equation model as follows (equation 1):

 

Yield value =

31.30A + 38.02B + 2265.43C – 60.02AB – 4046.46AC – 4105.86BC + 4554.20ABC + 111.84AB(A-B) + 2153.56AC(A-C) + 2207, 26BC(B-C)     …………...(1)

 

Based on the equation, it can be seen that each component has a positive contribution to increasing the yield value. The biggest contribution is shown by the interaction of the three components with a coefficient value of 4554.20. Based on Anova analysis, the model shows a fairly good significance value (p-value 0.0123), an insignificant lack of fit model (p-value 0.5739) and a good correlation value (R-square value 0.9672). However, this model has values of adjusted R-square, predicted R-square, and adequate precision R-square of 0.8934, 0.4927, and 15.128, respectively. The predicted R-squared of 0.4927 is not as close to the adjusted R-squared of 0.8934 as one might normally expect. This may indicate a large block effect or a possible problem with the model, and/or data.  Things to consider are model reduction, response transformation, outliers, etc³⁴.

 

Figure 1. 3D surface of correlation between components with the yield value

 

The effect of the proportion of the formula for the moisture content

The relationship diagram between each component in the formula with the moisture content can be depicted in figure 2. The relationship of the components to the moisture content can be explained through the cubical equation model as follows (equation 2):

Moisture content =

5.59A + 6.76B – 35.18C – 6.06AB + 74.12AC + 67.53BC – 52.57ABC + 14.44AB(A-B) – 47.69AC(A-C) – 27.66BC(B-C)………………………..…………(2)

 

The equation shows that each component (A and B) contributes positively to increasing the moisture content, while component (C) contributes to the decrease in the moisture content. The largest contribution is shown by the interaction of components (A and C) with a coefficient value of 74.12. Based on the Anova analysis, the model shows a fairly good significance value (p-value 0.0215), an insignificant lack of fit model (p-value 0.7353) and a good correlation value (R-square value 0.9562). The model F-value of 9.69 implies the model is significant. There is only a 2.15% chance that a model F-value this large could occur due to noise. Values of probability > F less than 0.0500 indicate model terms are significant. In this case linear mixture components, AB, AB(A-B) are significant model terms. Values greater than 0.1000 indicate the model terms are not significant. If there are many insignificant model terms (not counting those required to support hierarchy), model reduction may improve the model. The predicted R-squared of 0.6389 is not as close to the adjusted R-squared of 0.8575 as one might normally expect.  This may indicate a large block effect or a possible problem with the model and/or data. Things to consider are model reduction, response transformation, outliers, etc. Adequate precision measures the signal to noise ratio.  A ratio greater than 4 is desirable. Model ratio of 9.305 indicates an adequate signal. This model can be used to navigate the design space²³.

 

The effect of the proportion of the formula for the flow rate:

The relationship of these components to the flow rate (figure 3) can be explained through a quadratic equation model as follows (equation 3):

Flow rate = 5.18A + 7.786B – 1.608C – 8.37AB + 16.35AC + 10.57BC…………………………………(3)

 

 

Figure 2. 3D surface of correlation between components with the moisture content

 

 

Figure 3. 3D surface of correlation between components with the flow rate

 

Figure 4. 3D surface of correlation between components with the compressibility index

 

 

The equation shows that each component (A and B) contributes positively to increasing the flow rate, while component (C) contributes to the decrease in flow rate. The largest contribution is shown by the interaction of components (A and C) with a coefficient value of 16.35. Based on Anova analysis, the model shows a fairly good significance value (p-value 0.0139), an insignificant lack of fit model (p-value 0.3550) and a good correlation value (R-square value 0.7876). The model F-value of 5.93 implies the model is significant. There is only a 1.39% chance that a model F-value this large could occur due to noise. The predicted R-squared of 0.4616 is in reasonable agreement with the adjusted R-squared of 0.6548. Adequate precision measures the signal to noise ratio.  A ratio greater than 4 is desirable. Model ratio of 7.180 indicates an adequate signal. This model can be used to navigate the design space²⁹..

 

The effect of the proportion of the formula for the compressibility index

The relationship of these components to the compressibility index (figure 4) can be explained through the special cubic equation model as follows (equation 4):

 

Compressibility index = 0.14A + 0.13B + 1.03C – 0.21AB – 1.34AC – 1.24BC + 1.28ABC……………(4)

 

The equation shows that each component (A, B, and C) contributes positively to increasing the compressibility index. The largest contribution is shown by the interaction of components (A, B, and C) with a coefficient value of 1.28. Based on the Anova analysis, the model shows a fairly good significance value (p-value 0.0173), an insignificant lack of fit model (p-value 0.9192) and a good correlation value (R-square value 0.8346). The model F-value of 5.89 implies the model is significant. There is only a 1.73% chance that a model F-value" this large could occur due to noise. Values of probability > F less than 0.0500 indicate model terms are significant.  In this case Linear Mixture Component, AB, AC, BC, ABC are significant model terms. The predicted R-squared of 0.3460 is not as close to the adjusted R-squared of 0.6928 as one might normally expect. This may indicate a large block effect or a possible problem with the model and/or data. Things to consider are model reduction, response transformation, outliers, etc. Adequate precision measures the signal to noise ratio. A ratio greater than 4 is desirable. Model ratio of 8.063 indicates an adequate signal. This model can be used to navigate the design space³⁵.

 

The effect of the proportion of the formula for the mounting frequency:

The results of aphrodisiac studies showed that the frequency of riding is in the range of 4 to 13 times. The relationship of these components to the mounting frequency (figure 5) can be explained through the cubic equation model as follows (equation 5):

Mounting frequency = 10,54A + 7,06B - 1181,78C + 19,83AB +2103,05AC +2186,31BC – 2166,85ABC – 2,41AB(A-B) – 1029AC(A-C) – 1134,02BC(B-C)….(5)

 

The equation shows that each component (A and B) contributes positively to increasing the riding frequency of male rats and component C contributes to a decrease in riding frequency. The largest contribution is shown by the interaction of components (B and C) with a coefficient value of 2186.31. Based on the Anova analysis, the model shows a fairly good significance value (p-value 0.0048), an insignificant lack of fit model (p-value 0.1732) and a good correlation value (R-square value 0.9799). The positive control of sildanafil citrate resulted in the mounting frequency of 16 times. This positive response was due to the fact that maca contains vitamins, minerals, and antioxidants, such as glucocyanates and polyphenols. This plant is known as an adaptogen, which is a term for an herbal plant that contains natural substances that help the body adapt to certain stress conditions and diseases.

 

Figure 5. 3D surface of correlation between components with the mounting frequency

 

 

Maca has many health benefits including being able to increase energy, increase fertility, increase sexual desire, improve sperm quality^20–22. In line with caffeine, when consumed in moderation, it can increase energy availability, reduce fatigue, improve motor and cognitive performance, and improve short-term memory³⁶. A negative predicted R-squared implies that the overall mean is a better predictor of your response than the current model. Adequate precision measures the signal to noise ratio. A ratio greater than 4 is desirable. Model ratio of 15.736 indicates an adequate signal. This model can be used to navigate the design space.

 

PCA-CA analysis:

PCA is designed to reduce the large complex data set into series of optimized and interpretable size. PCA is trying to find out factors or principal components (PC). In addition, PCA is also used to study the relationship between experimental responses and variables. PCA is designed to reduce complexity with a big dataset into a series of optimized and interpretable sizes. PCA finds out factors or principle components (PC₁, PC₂,…..PC_n), which are in linear combinations of the original variables describing each object (X₁, X₂,……X_n)³⁷. If there are five variables or responses, there will be five principal components (PCs). There are five parts of PCA, namely scree plot, score plot, loading plot, biplot and outlier plot. Base on the scree plot process (figure 6A), it generated five PCs and there was two PCs (PC₁ and PC₂) that produced more than 90% data variations. This means that only two PCs are needed to represent the research data. In this case, it only needs two responses such as, mounting frequency and yield value. The score plot graph (figure 6B) showed that all runs is divided into four quadrant base on the proximity of the PCs value. If there are observation data that has a close PC value indicates that the run has a similar character. The loading plot shows (figure 6C) the strength of each variable affecting the PC. The angle between vectors describes how these variables correlate with one another. If two vectors form a narrow-angle, it indicates a positive correlation between the two variables, and if the vectors form an angle ≥ 90^o, then they are not correlated or negatively correlated. For example, compressibility index, flow rate, and moisture content have a positive correlation. The biplot graph (figure 6D) is the combination of score plot and loading plot graph. The outlier plot (figure 6E) showed that there were no outlier data of each run. The outlier value is represented by Mahalanobis distance (4.294).

 

Cluster analysis (CA) is a method for dividing a group of objects into classes so that similar objects fall in the same class. CA searches for objects which are close together in the variable space. The distance, d, between two points in n-dimensional space with coordinates (X₁, X₂, X₃, ....., X_n) and (Y₁, Y₂, Y₃, ....., Y_n) is usually taken as the Euclidian distance³⁷. The dendogram (figure 6F) showed that run number 1, 2, 5, 7, 13 have a similar character, and run number 4, 6, 8, 9, 12, 14 also have a similar character. In CA similar object will be clustered together. The main advantage of CA, it provides a numerical values of similarity.

 

Optimization formula of coffee-maca granules:

The optimum formula of coffee-maca granules was obtained with a ratio of coffee:maca:dextrin (12,3:27,7:10 % w/w) (figure 7). The formula will be predicted to produce yield values, moisture content, flow rate, compressibility index, and mounting frequency of 29.74 g, 5.38%, 6.58 g/second, 0.16%, and 12 times, respectively. The desirability value of the resulting model is 0.647 which indicates that the model is strong in predicting the experimental results.

 

Verification of optimum formula:

The results (table 3) showed that there was no significant difference between the response values generated by the prediction model and the experimental results (p-value > 0.05). The aphrodisiac test showed that there was a significant difference between the positive control (sildanafil citrate) and the optimum formula (p-value <0.05).

 

 

Table 3. Verification data optimum formula

 

 

Figure 6.  Chemometrics diagrams of scree plot PCA (A), score plot PCA (B), loading plot PCA (C), biplot PCA (D), outlier plot (E), and dendogram CA (F)

 

Figure 7. Contour plot diagram of optimum formula

 

CONCLUSION:

The results showed that the three components such as coffee, maca, and dextrin gave a positive response in increasing the yield values and compressibility index. Meanwhile, foor experimental responses such as moisture content, flow rate, and aphrodisiac tests only two components, namely coffee and maca, gave a positive response in increasing the response. The optimum formula for coffee-maca granules resulted in the average yield value, moisture content, flow rate, compressibility index and mounting frequency of 29.08 g, 5.09%, 5.98, 0.18%, and 10.67, respectively.

 

CONFLICT OF INTEREST:

The authors have no conflicts of interest regarding this investigation.

 

ACKNOWLEDGMENTS:

The authors would like to thank to Udayana University Hibah Unggulan Program Studi with contract number B/760/UN14.2.8.II/PT.01.03/2021 who has funded this study.

 

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Accepted on 28.08.2022           © RJPT All right reserved

Research J. Pharm. and Tech 2023; 16(3):1463-1470.