Next Article in Journal
Comparative Analyses of Grain Quality in Response to High Temperature during the Grain-Filling Stage between Wxa and Wxb under Indica and Japonica Backgrounds
Previous Article in Journal
Exogenous Gallic Acid Confers Salt Tolerance in Rice Seedlings: Modulation of Ion Homeostasis, Osmoregulation, Antioxidant Defense, and Methylglyoxal Detoxification Systems
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agronomy
Volume 13
Issue 1
10.3390/agronomy13010015
agronomy-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Spatial Distribution of Cocoa Quality: Relationship between Physicochemical, Functional and Sensory Attributes of Clones from Southern Colombia

by
Vivian Ramón
1,
Héctor Eduardo Hernández
1,2,3,
Paola Polania
1 and
Juan Carlos Suárez
2,3,*
1
Programa de Maestría en Sistemas Sostenibles de Producción, Facultad de Ingeniería, Universidad de la Amazonia, Florencia 180001, Colombia
2
Programa de Ingeniería Agroecológica, Facultad de Ingeniería, Universidad de la Amazonia, Florencia 180001, Colombia
3
Centro de Investigaciones Amazónicas CIMAZ Macagual César Augusto Estrada González, Grupo de Investigaciones Agroecosistemas y Conservación en Bosques Amazónicos-GAIA, Florencia 180001, Colombia
*
Author to whom correspondence should be addressed.
Agronomy 2023, 13(1), 15; https://doi.org/10.3390/agronomy13010015
Submission received: 20 October 2022 / Revised: 27 November 2022 / Accepted: 5 December 2022 / Published: 21 December 2022
Browse Figures
Versions Notes

Abstract

:
Cocoa quality is affected by genetic variability and conditions in the region of origin that impact the physicochemical, functional and sensory characteristics of the cocoa bean. For the purpose of this study, the spatial distribution was analyzed using a cocoa quality indicator that related physicochemical, functional variables (phenolic and antioxidant content) and sensory attributes (organoleptic qualities and sensory notes) of cocoa samples from different cocoa clones in the municipalities of Mesetas and Vista Hermosa in the department of Meta, Colombia. In 83 sampling plots distributed in 22 farms with agroforestry cocoa arrangements, 50 pods of the main clone were selected in each plot. Subsequently, the cocoa bean samples were subjected to fermentation and drying processes using a uniform process, then each of the samples was subjected to physicochemical, functional and sensory properties. A medium quality level was found in the cocoa beans that depended on genetic variability, whose properties ranged between protein levels of 10.312%, sugars of 2.7–3.7 °Brix, and fat contents of 51.5–52.2%, with 178.4–235.2 mg GAE g−1 in total phenol content and 1208.1–3464.1 μmol AAE g−1 in antioxidant capacity. Clones TSH-565 and FEAR-5 showed respectively higher and lower grain quality. Within the sensory profile, complementary aromatic notes such as floral, fruity, nutty, malty, with high correlations between acidity and fresh fruit, astringency and cocoa, and astringency and bitterness, were found. At the clone level, ICS-95 and TSH-565 presented the best sensory attributes (floral, sweet, acidity, fresh fruit). According to the physicochemical, functional and sensory variables of the cocoa beans, it was found that only 13% of the samples presented values higher than 0.8 in the cocoa quality index, with notes of banana, melon, peach, plum, and fresh fruit related to clones such as TSH-565 and ICS-95, whose notes are considered high-quality; these cocoa plantations are located in the Palestina village in the municipality of Vistahermosa and in the Las Mercedes village in the municipality of Mesetas.

1. Introduction

Cocoa cultivation is considered one of the main products in the world economy, reaching a production of 4.9 million tons on 9.9 million hectares by 2021 [1]. Currently, its commercialization depends on different attributes among which quality stands out [2], which is considered a challenge for the world chocolate industry [3]. Cocoa bean quality, among other factors, is affected by both genetic variability and conditions in the region of origin [4,5,6,7,8,9], as well as by postharvest and processing practices [10].
During cocoa production, one of the most important processes affecting quality is fermentation, at which time significant biochemical changes occur [11]. Then the fermented bean goes to the drying stage, which is necessary for the termination of the biochemical processes previously initiated, such as oxidation reactions [12] and decreases in acidity, astringency, and bitterness, among others, which define the flavor and aroma of cocoa [13,14]. Finally, for processing, roasting is carried out, where, together with the reduction of sugars, peptides and free amino acids are subjected to the Maillard reaction, during which process the typical cocoa aroma is finally defined [15]. Likewise, cocoa quality is related to spatial variations in altitude, soil and climate [16], as well as to the characteristics of cocoa clones [17,18]. Martínez [15] specifies that the commercialization of the product is related to variability at the clone level, as well as in the chemical and sensory changes that affect the final product, and this depends on the specific climatic conditions where the cocoa is grown.
Currently, cocoa planting in Colombia is increasing in several regions of the country, due to the economic opportunity represented by its high demand worldwide [19]. In addition to the above, the national policy of the Colombian national government has considered cocoa as the “Crop for Peace” because of the public policy of illicit crop substitution, giving it social importance [20]. The department of Meta is one of them; this has generated high availability of cocoa bean production in the municipalities of Mesetas and Vista Hermosa [21].
Due to the cocoa production boom, production is showing a high variability in physicochemical and functional variables (phenolic and antioxidant content) and sensory attributes (organoleptic qualities and sensory notes) [22]. This is due to the lack of reference parameters in relation to sensory attributes based on the genetic diversity of cocoa clones implemented in crops. This is a great opportunity for the national cocoa industry, since niche markets have developed worldwide that specialize in fine aroma cocoa; this possibly translates into more economic income for cocoa families due to differentiated prices at the time of marketing. Consequently, the objective of this study was to determine the effect of clone and spatial distribution on cocoa quality in the municipalities of Mesetas and Vista Hermosa, in the department of Meta, based on a quality indicator that compiled the spatial variability in terms of management, fermentation, and type of clone, translated into different physicochemical and functional variables (phenolic and antioxidant content) and sensory attributes (organoleptic qualities and sensory notes). It is expected to find variability at the level of cocoa quality (physicochemical, functional properties and sensory attributes) among cocoa clones, as well as at the spatial level — important information that will identify the main production niches of high-quality cocoa beans from the sensory map in the municipalities of Mesetas and Vista Hermosa in the department of Meta.

2. Materials and Methods

2.1. Description of the Study Area and Identified Cocoa Clones

The study was conducted in rural areas of the municipalities of Mesetas and Vista Hermosa, located in the department of Meta, southern Colombia. These municipalities are part of the Ariari sub-region, where cocoa production in the department is located (Figure 1). Eighty-three plots were randomly selected under agroforestry arrangements with cocoa from 22 farms belonging to the Association of Cocoa Growers of the Department of Meta CACAOMET. On each farm, cocoa lots in production with an area greater than 1.5 ha were identified, where the sampling plot was established with an area of 1000 m2 (50 m × 20 m) in the central part of each lot. A total of 13 cocoa clones were located, among which were the universal ones such as CCN-51 (Collection Castro Naranjal), ICS-51, ICS-95 (Imperial College Selection), TSH-565 (Trinidad Selection Hybrid) and regional ones described by Perea et al. [23] as FEAR-5 (Fedecacao Arauquita Arauca, Trinidadian Hybrid), FSA-11, FSA-12, FSA-13 (Fedecacao Saravena Arauca, Trinidadian Hybrid), FTA-2 (Fedecacao Tame Arauca, Trinidadian Hybrid). As well, MON-1 (regional hybrid) and hybrids with different pod coloration at maturity (yellow, red and green) were located. For the present study, clones CCN-51, ICS-95, TSH-565, FEAR-5, FSA-11, FSA-12 were selected, since they were the most frequent in the field; the other clones were excluded due to the low availability of replicates.

2.2. Postharvest Sample Handling

The samples collected in each plot were taken by clone from the random selection of 50 pods at optimum maturity and free of diseases. Manual pulping was performed to obtain the cocoa seeds, sliding the fingers along the central vein, in order to avoid germination and immediately cause the death of the embryo. For each sample, 1 kg of seeds covered with mucilage was extracted, separated in nylon bags duly labeled, and then deposited in a white wooden box located in an open space, covered with fique sacking. The samples were fermented for seven days, with turning every 24 h using a wooden shovel. The fermented grains (1 kg) were subjected to drying in a wooden raisin-type canopy, where they were turned every 3 h with the use of a wooden rake, in order to avoid breakage of the grain during handling and the appearance of fungi that would diminish its sensory quality [24]. This process was carried out until a brown color was obtained [25] and a grain moisture level of 7.5% was calculated according to NTC 1252 [26]. From these dry seeds, 250 g were taken and subjected to a roasting chamber (Roasty DelaniÒ) at an initial drum temperature of 180 °C until the first crack was achieved [27]. Hulling was performed to obtain nibs, then a mill with a worm screw and rotating disk by manual action (Corona) was used to obtain cocoa powder with a particle size of approximately 1 mm, from which the authors determined titratable acidity, moisture, ash, ethereal extract, crude protein and soluble and insoluble dietary fiber. Cocoa paste with particle diameter between 2 and 10 microns (μm) was used to determine pH, phenolic and antioxidant content. Liquor samples of 20 μm were used to evaluate the sensory profile.

2.3. Determination of Physicochemical and Functional Properties of Cocoa Clones

Measurements were performed in triplicate for each variable in each cocoa sample (clone) collected from each plot. The pH was determined using an automatic potentiometric titrator, model HI 931 (Hanna Instrument, Woonsocket, RI, USA), following the methodology described by Nazaruddin et al. [28]. Titratable acidity was measured according to AOAC 939.05 [29]. Percent moisture by weight loss was calculated according to NTC 1252 [26]. The ash percentage was calculated according to that described by AOAC 942.05 [30]. Ether extract was measured according to AOAC 920.39 [30]. The percentage of fiber in the Neutral Detergent or Insoluble Dietary Fiber (IDF) and the fiber in Acid Detergent or Soluble Dietary Fiber (SDF) were calculated based on the method proposed by Van Soest et al. [31]. Crude protein content was determined according to the AOAC method 970.22 [32]. The total phenol content was measured following the procedure described by Wollgast [33], and the results were expressed as gallic acid equivalents over grams of the sample on a dry basis (mg g−1 GAE). The antioxidant content was determined by the antioxidant-reducing power of Iron FRAP, and the results were expressed as μmol ascorbic acid mg−1 of dry weight of the extract (AAE, [34]).

2.4. Sensory Evaluation of Cocoa Clones

The sensory evaluation of each of the samples was carried out following the protocol described in NTC-429 [35], where the organoleptic qualities were considered as basic (acidity, astringency, bitter and sweet), specific (cocoa, floral, fruity and nutty), and acquired (green) [36,37], as described in GTC-165 [38] and NTC-3929 [39]. Based on the ratings given by the sensory panel, the order of perception, intensity, residual flavor, and persistence was determined. Then, the total impression was evaluated according to NTC-3929 [39] using the international scale for the evaluation of sensory profiles from 0 to 10 points, where zero (0) means the absence of the evaluated criterion and ten (10) means a very high intensity, following the Braudeau methodology [40].

2.5. Data Analysis

With each of the data obtained, descriptive statistics analysis was performed (means and frequencies of the variables) to subsequently perform an analysis of the variance from a Linear Mixed Model (MLM) using the LSD Fisher test (p < 0.05). The fixed factor within the MLM was each cocoa clone and the random factor was each cocoa sample collected in each plot. The assumptions of normality and homogeneity of the variances were evaluated by graphical inspection of the residuals. Principal Component Analysis (PCA) was performed to determine the relationship between variables, as well as between cocoa clones for physicochemical, functional and sensory variables. Differences between clones for physicochemical, functional and sensory variables were determined using the MonteCarlo test. The relationship between the matrices was then explored using a co-inertia analysis [41], which is used as an ordination technique that correlates the first ordination axes of two matrices. Multivariate analyses were performed in R software, version 4.2.0 [42], using the Ade4 statistical package [43]. After carrying out the different analyses, an indicator was generated that had the capacity to express the quality of the cocoa (QI). This indicator, whose range is 0 (minimum) to 1 (maximum), shows the cocoa quality from the different physicochemical, functional and sensory variables. It was based on converting the values of each variable into scores with a range of values from 0 to 1, using a standardized scoring function, under the criteria: I. More is better, suitable for standardizing the scores of cocoa properties (indicators) in cocoa quality, associated with values close to one (1) and II. Less is worse, those properties whose values were close to zero (0).
Then, the quality index of the cocoa used was calculated in two stages:
qFpn = I1 × W1 + … + In × Wn
QI = q FP1 × (W1) + q FP2 × (W2) + … + q FPn (Wn)
where qFPn are the main functions, In are the standard scores for the quality indicators associated with each main function, and Wn are the weights associated with each indicator or each main function.
Based on the cocoa quality indicator (QI), the spatial distribution was examined in order to identify the areas where the highest quality cocoa is produced. To this end, the distribution of the data was examined, global trends were sought, and spatial autocorrelation and directional variation of the data were examined. A point shape was created from geographic coordinate data collected in each of the plots where cocoa samples were collected, validating the location in the study area within the GCS Magna geographic coordinate system. This location describes the behavior of the variable in the same direction as the geomorphology of the area, avoiding the conclusion that the plots are poorly located. The Geostatistical Analyst tool was used within the ArcGis program, version 10.2.2, whose principle was based on an interpolation process (Interpolated Surface) from the sets of sampling points; the result was the creation of a continuous surface based on spatial autocorrelation and is part of the Spatial Analysis, in general [44,45].

3. Results

3.1. Physicochemical and Functional Properties of Cocoa Clones

Some physicochemical variables (ash, acidity, and crude protein, among others) were different among cocoa clones; however, others (pH, SDF, IDF and Phenol) did not show this difference (p > 0.05, Table 1). At the variable level, sugar content and FRAP showed a high variation. Among the clones, based on each of the physicochemical variables, TSH-565 and FEAR-5 presented higher and lower quality. When analyzing the relationship between fiber content (IDF and SDF), it was found that IDF was higher for all cocoa clones evaluated (Table 1). The highest IDF content was for ICS-95, whereas CCN-51 and TSH-565 were higher in SDF (Table 1). At the functional level, the total phenolic content profile found in the cocoa beans was high (178.4–235.2 mg GAE g−1); in terms of the FRAP values, it was found that cocoa clone TSH-565 provided the highest activity (1208.1–3464.1 μmol AAE g−1) (Table 1).
The first two axes of the PCA for physicochemical and functional properties (phenolic and antioxidant content) explained 61.2% of the variability in the data (Figure 2). Axis 1 (40.8%) clearly divided the cocoa clones by the highest amount of total phenolic content, soluble and insoluble dietary fiber and pH; in the case of axis 2 (20.4%), variables such as crude protein, ether extract, and moisture, among others (Figure 2), were the most discriminating variables of cocoa clone quality. The MonteCarlo test showed that the physicochemical and functional properties (phenolic and antioxidant content) differed significantly (9.3% of the variability explained, p < 0.001) among the cocoa clones analyzed in our study. Considering the information provided by the PCA, FRAP presented a positive correlation with IDF and pH (Figure 2), as well as ash, with total acidity (p < 0.05). However, SDS correlated negatively with the total phenolic content, pH and IDF (p < 0.05) (Figure 2).

3.2. Sensory Attributes of Cocoa Clones

When analyzing the effect of the clones in relation to the sensory characteristics, a basic profile was found for most clones, with sensory characteristics related to acidity, bitterness and astringency (Table 2). Based on the organoleptic qualities and due to obtaining the lowest ratings from the tasting panel, the FSA-11 clone was found to be of lower quality, contrary to that presented by ICS-95 and TSH-565, which were related to positive sensory attributes such as floral, sweetness, acidity, and fresh fruit (Table 2). As for the results obtained with PCA, 81.5% of the variability was explained by sensory characteristics (Figure 3a,b). Axis 1 (47.5%) separated the cocoa clones with the characteristics floral, sweet, and nutty, among others, while axis 2 (34%) separated the cocoa clones by characteristics such as cocoa, astringency, and bitterness, among others. The variability, explained by the cocoa clones, was 10.3% (MonteCarlo test p < 0.001). As for the notes found during the sensory panel, they were related to caramel flavor, spice, and fresh and dried fruits, as well as floral odors and some defects such as herbs, smoke, dairy, wood, and tobacco, which are the result of the fermentation process (Figure 3c,d).
Figure 4 shows the results of the co-inertia analysis between the matrix of physicochemical variables and sensory attributes (organoleptic qualities and sensory notes), where there is a significant relationship between these matrices. For example, physicochemical variables and organoleptic qualities had a relationship of 9.1% (p < 0.001), and between physicochemical variables and sensory notes, there was a relationship of 8.2% (p < 0.001). However, these two matrices (organoleptic qualities and sensory notes) presented a greater co-interference reaching 23.8% (p < 0.001). When analyzing the relationship between the variables of each of the matrices, for example, a relationship was found between antioxidant activity and astringency, as well as brix and bitterness (Figure 4a). As for the relationship between the physicochemical variables and the notes, it was found that the total polyphenol content was related to tobacco notes and the crude protein content to the spicy notes (Figure 4b).
When analyzing the behavior of cocoa quality at the level of distribution according to quality, it was found that only 13% (Figure 5) of the samples analyzed had QI values above 0.8, with notes of banana, melon, peach, plum, fresh fruit, which are considered high-quality. These samples were located spatially in the village of Palestina in the municipality of Vista Hermosa (Figure 5) and in the village of Las Mercedes in the municipality of Mesetas (Figure 5). This shows that a low proportion of the samples analyzed (10 of 83 samples) have exceptional characteristics in terms of physicochemical and functional properties (phenolic and antioxidant content) and sensory attributes (organoleptic qualities and sensory notes).

4. Discussion

4.1. Physicochemical Properties, Phenolic and Antioxidant Contents

It was found in our study that cocoa quality depended on the clone, handling in the fermentation process given by each producer, and the spatial distribution, results that support what Calvo et al. [22] and Del Aguila [46] report: that the physicochemical composition of cocoa beans depends on several factors, such as the type of cocoa, the geographical origin, the degree of maturity, and the quality of fermentation and drying. In this sense, our results indicate that the cocoa samples showed overfermentation caused by poor fermentation, which is supported by the pH values obtained. High pH values are indicative of overfermentation, while values below five indicate poor fermentation [24,47,48]. PH is a crucial parameter in the quality of cocoa used in the manufacture of chocolates [49]. The average pH found in this study, as a result of the fermentation process, was 5.8, which, as described, is within an adequate range. Vera et al. [50] found a pH of 6.87 for CCN-51 and 6.76 for IMC-67, while Calderón [51] reported values between 3.4 and 4.6 in the tests, explained by the permeability of acetic acid, which penetrates the embryo and lowers the pH [52]. According to Lemus et al. [53] and Juran et al. [54], all these differences could be attributed to the genetic variability of the material in the area and the application of different processing methods. Consequently, low pH can contribute to increased formation of free fatty acids [27], which can further lead to fat oxidation [55].
The acidity found in the present study (17.2%) corresponds to the value of the sample expressed as acetic acid. In agreement with the values described in studies conducted in different areas, Del Águila [46] found variability in terms of acetic acid content with an average of 1.1%. Likewise, Alvarez et al. [48] report values of 0.5% in the localities of Miranda, Venezuela. These results are much lower than those found in the present study, probably due to drying processes [56] or temperature management in the roasting process [57]. Krysiak et al. [58] indicated that cocoa beans of the Ivory Coast variety, under selected roasting conditions, are characterized by higher titratable acidity compared to raw cocoa beans. This increase in the titratable acidity of the beans may contribute to the acceleration of the oxidation rate, as indicated by Tańska and Rotkiewicz [55]. Ash content values (3.0%) were within the range (2.7 to 4.1%) found in different studies [50,59,60]. This variable is sensitive to spatial variations, as reported by Perea et al. [61] and Alvarez et al. [62], who found differences in the ash value.
As for the crude protein (CP) content found, it conforms to the parameters set by the NTC 793 standard [63] for cocoa beans in Colombia (10–15% CP). Studies by Martinez [15] report variability in the CP content in different sampled sites; for example, in Huila (fifth largest cocoa producing department in Colombia), 11.39% CP was found, lower than that identified for the same clone, TSH-565, in Arauca (13.26% CP) (the second largest cocoa-producing department in Colombia). Nevertheless, in the present study, the variability of the CP content was low. However, it has been described that these variations can be generated by bean composition due to origin, environmental conditions during growth and growing conditions [15,64]. On the other hand, regarding moisture, authors such as Vera et al. [50], Álvarez et al. [62] and Braudeau [40] have recommended that the value should be between 6 and 7%, this being the adequate value to avoid mold attacks. This variable is a key quality factor for the preservation, packaging, transportation and storage of beans [65], and, according to the results obtained, it is within the parameter mentioned in the Colombian Standard NTC 1252 [26] (7%) for processed cocoa.
The average fat content in our study was 52.2%, a value that is within the range reported for fat content in seeds, which is 40–50% [66,67,68,69]. For example, a fat content of 41.4% has been reported for CCN-51 [50], lower than what was found in this study. Likewise, data contrasting to those of Vera et al. [50] have been reported for CCN-51 mentioned by Martínez [15], who, in an area of Santander (major cocoa producing department in Colombia), found 59.6%. In turn, this author reports a variability for different cocoa clones in different areas of Colombia of between 49.5 and 60.9%. TSH-565 was the clone with the highest fat content, similar to the results of this study. In general, the fat content found is within the ranges reported by other studies [15,37,70].
When analyzing the relationship between fiber content (FDI and SDS) in this study, it was found that FDI was higher for all cocoa clones evaluated, a trend that has also been reported by different authors [71,72]. The presence of high contents of each of the fibers is related to the content of cellulose, hemicellulose and lignin for FDI and cellulose and lignin for FDS [73]. The phenolic profile found in the cocoa beans was high compared to that reported by Efraim [74] in dry beans (149.5 mg GAE g−1). These possible differences are mainly due to genetic variability, the ripening stage of the pods and the fermentation process. Regarding the FRAP values, when comparing with the results reported by Zúñiga [75]—who reported, on average, 6089.17 μmol AAE g−1—our results were much lower (2544.8 μmol AAE g−1). However, they are superior when we compare them with studies such as those conducted by Sangronis et al. [76] and Quiroz-Reyes et al. [77], who reported contents of 350 μmol AAE g−1 and 473.13 μmol AAE g−1, respectively.

4.2. Sensory Attributes

When analyzing the sensory profile of the different cocoa clones grown in the municipalities of Mesetas and Vista Hermosa, complementary aromatic notes were identified, such as floral, fruity, nutty, malty and others, which, according to Ardhana and Fleet [78], allow for the denomination of fine aroma cocoa. Therefore, the particular characteristics found in the present study correspond to both spatial variability and the cocoa varieties evaluated [79,80], which may have influenced the intensity and interaction of flavor components. In general, the sensory profile varied according to the clones studied for the department of Meta, which depend on the origin. In this regard, Amoa-Awua [81] mentions variations in the sensory profile according to the main types of cocoa—Forastero (ordinary grade), Criollo (fine grade) and Híbrido Trinitario (fine grade)— which show large variations in aroma and final flavor. Thus, the clone influences both the quality and intensity of the chocolate flavor, probably based on the amounts of precursors and enzyme activity and, therefore, aroma and flavor formation [5].
The results obtained in several studies show that cocoa varieties influence chocolate quality [82,83,84]. Similarly, Martínez [15] mentions that the high genetic variability of Colombian cocoa may play an important role in the variability of the sensory profiles found and their grouping by region. This sensory profile depends in turn on an adequate postharvest process that is related to cocoa, nutty and acid flavors, with a low value of astringency and bitterness [24]. It has also been observed that there is a positive correlation between the level of fermentation and the expression of floral, fruity and sweet aromas [83]. However, when there is a process where fermentation is insufficient, there are raw/green flavors [85], as well as higher acid and bitter flavors [10,86] that overshadow the other sensory profiles and decrease the organoleptic quality of the grain. Reduced bitter taste, reduced astringency, loss of acidity and peanut flavor, and increased nutty and panela or malty flavor are characteristics that may be associated with environmental factors [8,87,88]. Likewise, postharvest practices such as harvesting unripe pods can generate a staining sensation in the mouth, which is unpleasant to the palate of consumers. This coincides with the results from Vera et al. [50] and Alvis et al. [89], who relate this defect to a limited percentage of sugars, which affects the quality of fermentation.
Results obtained by Alvarez et al. [48] determined significant differences for cocoa flavor and astringency descriptors similar to the results obtained in the present study. Similarly, Sukha et al. [80] found a low (p < 0.05) significant statistical difference for “Cocoa” flavor among Trinitario type cocoa clones in a germplasm bank. When analyzing the relationships between sensory attributes, Alvarez et al. [48] found that acidity shows a positive correlation with cocoa flavor, astringency and bitterness. Similarly, the same authors found a positive relationship between floral flavor and malty panela flavor. This means that floral flavor is a function of fruit and panela malt flavor, results very similar to those found in the present study. Another important feature highlighted by Alvarez et al. [48] is the observation of an inverse or negative relationship between herbal and nutty flavor. This means that the higher the herbal attribute, the lower the nutty flavor, which could be explained by the relationship that exists between the herbal and the bad or low fermentation of the grain, since it is characterized by a green or fresh grass flavor, contrasting with what could be the development of the nutty flavor [90,91].
At the clone level, CCN-51, FSA-11 and FSA-12 were found to be associated with very astringent flavor characteristics, possibly due to high concentrations of polyphenols [48]. These concentrations of polyphenols, when reacting with sugar and amino acids, contribute to the flavor, aroma and color of cocoa beans, and alkaloids contribute to bitterness [92], which can probably impact the clone. To reduce astringency levels, proper management must be performed during the aerobic phases of fermentation, since oxygen-mediated reactions occur in that process, such as the oxidation of protein–polyphenol complexes formed in the anaerobic phase [93]. Clones FSA-11 and FSA-12, mostly from the Arauca region, are recommended by FEDECACAO for cultivation in the Humid Tropical Forest (HTF) agroecological region [94], with notes of sweet fruits, caramel, spices and nuts, a sustained chocolate flavor and a reference for fruity cocoa [23]. Variation in cocoa flavor and aroma intensity, such as floral, nutty and caramel types, has been demonstrated in other studies [85].
A comparison of the studies conducted by Machado et al. [10] in the department of Huila finds that PCA explained 85.6% of the variability, very similar to that obtained in the present research. The study conducted by the authors in the department of Huila reported a profile with those basic flavors—such as astringency, bitterness, acidity, salty and green—acquired; they observed, in smaller proportion, specific flavors, such as cocoa, fruity, floral and nutty. While, in the sensory analysis conducted for the department of Meta, high concentrations of astringent flavor were found, the above is probably due to an incomplete fermentation process, since high values of astringency, bitterness, acidity and green were found [10,95].
In general, each clone presented a different sensory profile. According to Reineccius [96], varietal differences are mainly due to quantitative (rather than qualitative) differences given by aroma precursors, which are inversely proportional to polyphenol content. They are also due to sugar content and enzymatic breakdown of polysaccharides, which are an important source of precursors of the final flavor and aroma of chocolate. As mentioned by different authors [10,95,96], in addition to finding an incidence of cocoa clone variety affecting quality, there are also other processes, such as fermentation, that affect aroma precursors. The basis of sensory quality depends on the way the pod harvest is developed, since the state of maturation determines the content of sugars and polyphenols, which are an important source of precursors of the final flavor and aroma of chocolate. From the results obtained, it was demonstrated that, in addition to the clone, edaphoclimatic conditions, as well as crop management, affect the sensory profile and quality of chocolate [5,97,98,99]. Therefore, it is important to establish areas with special characteristics for the production of high-quality cocoa or the so-called “terroir”, as established for other types of foods, such as coffee, wines, and, thus managing to obtain quality certificates [100,101].

5. Conclusions

A medium quality level was found, depending on genetic variability, whose properties ranged at protein levels of 10.3–12%, sugar levels of 2.7–3.7 °Brix, and fat levels of 51.5–52.2%, with 178.4–235.2 mg GAE g−1 and 1208.1–3464.1 μmol AAE g−1 in antioxidant capacity. TSH-565 and FEAR-5 presented higher and lower grain quality, respectively. Within the sensory profile, complementary aromatic notes, such as floral, fruity, nutty, and malty, with high correlations between acidity and fresh fruit, astringency and cocoa, and astringency and bitterness, were found. At the clone level, ICS-95 and TSH-565 presented the best sensory attributes (floral, sweet, acidity, fresh fruit). According to the physicochemical and functional variables (phenolic and antioxidant content) and sensory attributes (organoleptic qualities and sensory notes) of the cocoa beans, it was found that only 13% presented values higher than 0.8 (QI). These samples presented notes of banana, melon, peach, plum, and fresh fruits related to clones such as TSH-565 and ICS-95, whose notes are considered of high quality and are spatially located in the Palestina village in the municipality of Vistahermosa and in the Las Mercedes village in the municipality of Mesetas, southern Colombia. Our results identified areas with higher-quality cocoa related to THS-565, which could be potential areas to generate differential markets. Likewise, our methodology may be replicable to identify areas with exceptional characteristics related to high cocoa quality.

Author Contributions

Conceptualization, V.R., H.E.H. and J.C.S.; data curation, V.R., H.E.H. and J.C.S.; formal analysis, V.R., H.E.H. and J.C.S.; investigation, V.R., H.E.H. and J.C.S.; methodology, V.R., H.E.H. and J.C.S.; funding acquisition, H.E.H. and J.C.S.; project administration, H.E.H. and J.C.S.; resources, H.E.H. and J.C.S.; supervision, J.C.S.; writing—original draft, V.R., P.P., H.E.H. and J.C.S.; writing—review and editing, V.R., P.P., H.E.H. and J.C.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Data are available from the authors upon request.

Acknowledgments

The authors would like to thank the following collaborators: Ministry of National Education (MEN) and the rural households in the department of Meta that supported the investigation.

Conflicts of Interest

The authors have declared that no competing interest exist.

References

  1. ICCO. ICCO Quarterly Bulletin of Cocoa Statistics. The International Cocoa Organization (ICCO) Cocoa Producing and Cocoa Consuming Countries. Supply & Demand. Available online: https://www.icco.org/statistics/ (accessed on 26 July 2022).
  2. Fibrianto, K.; Fajrul Azhar, L.O.M.; Widyotomo, S.; Harijono, H. Effect of cocoa bean origin and conching time on the physicochemical and microstructural properties of Indonesian dark chocolate. Braz. J. Food Technol. 2021, 24, 1–14. [Google Scholar] [CrossRef]
  3. Perez, M.; Lopez-Yerena, A.; Vallverdú-Queralt, A.; Vallverd U-Queralt, A. Traceability, authenticity and sustainability of cocoa and chocolate products: A challenge for the chocolate industry. Crit. Rev. Food Sci. Nutr. 2021, 62, 475–489. [Google Scholar] [CrossRef]
  4. Frauendorfer, F.; Schieberle, P. Changes in Key Aroma Compounds of Criollo Cocoa Beans During Roasting. J. Agric. Food Chem. 2008, 56, 10244–10251. [Google Scholar] [CrossRef]
  5. Luna, F.; Crouzillat, D.; Cirou, L.; Bucheli, P. Chemical Composition and Flavor of Ecuadorian Cocoa Liquor. J. Agric. Food Chem. 2002, 50, 3527–3532. [Google Scholar] [CrossRef]
  6. González, Y.; Pérez, E.; Palomino, C. Factores que inciden en la calidad sensorial del chocolate. Actual Nutr. 2012, 13, 314–331. [Google Scholar]
  7. Pino Alea, J.A.; Llera, L.; Roncal, E. Caracterización del perfil de compuestos volátiles de licores de cacao cubanos. Cienc. Tecnol. Aliment. 2011, 21, 41–49. [Google Scholar]
  8. Brito-Vega, H.; Salaya-Domínguez, J.M.; Gómez-Méndez, E.; Gómez-Vázquez, A.; Antele-Gómez, J.B. Physico-chemical properties of soil and pods (Theobroma cacao L.) in cocoa agroforestry systems. J. Agron. 2018, 17, 48–55. [Google Scholar] [CrossRef] [Green Version]
  9. Sánchez Vargas, A.D.P.; Castellanos Domínguez, O.F.; Domínguez Martínez, K.P. Mejoramiento de la poscosecha del cacao a partir del roadmapping. Rev. Ing. Investig. 2008, 28, 150–158. [Google Scholar]
  10. Cuellar, L.M.; Espinosa, C.M.O.; Sánchez, Y.K.A.; Cruz, L.G.; Salazar, J.C.S. Organoleptic quality assessment of Theobroma cacao L. in cocoa farms in northern Huila, Colombia. Acta Agron. 2018, 67, 46–52. [Google Scholar] [CrossRef]
  11. Fernández, R.D.R.; Gallo, F.W.M.; Cedeño, Á.M.G.; Galeas, M.M.P.; Quinteros, H.N.M.; Ferrín, L.M.C.; Álvarez, A.E.B.; Morante, P.E.N. Efecto del Tipo y Tiempo de Fermentación en la Calidad Física y Química del Cacao (Theobroma cacao L.) Tipo Nacional. Available online: https://dialnet.unirioja.es/descarga/articulo/4149700.pdf (accessed on 18 October 2022).
  12. Alean, J.; Chejne, F.; Rojano, B. Degradation of polyphenols during the cocoa drying process. J. Food Eng. 2016, 189, 99–105. [Google Scholar] [CrossRef]
  13. Rodriguez-Campos, J.; Escalona-Buendía, H.B.; Orozco-Avila, I.; Lugo-Cervantes, E.; Jaramillo-Flores, M.E. Dynamics of volatile and non-volatile compounds in cocoa (Theobroma cacao L.) during fermentation and drying processes using principal components analysis. Food Res. Int. 2011, 44, 250–258. [Google Scholar] [CrossRef]
  14. Cubillos, G.C.; Merizalde, G.; Correa, E. Manual de Beneficio del Cacao: Para Técnicos, Profesionales del Sector Agropecuario y Productores (No. 16057); Gobernación de Antioquia: Medellin, Colombia, 2008. [Google Scholar]
  15. Martinez, N. Evaluación de Componentes Físicos, Químicos, Organolépticos y de Rendimiento de Clones Universales y Regionales de Cacao (Theobroma cacao L.) En Las Zonas Productoras de Santander, Arauca y Huila. Master’s Thesis, Universidad Nacional de Colombia, Bogotá, Columbia, 2016. [Google Scholar]
  16. Magalhães da Veiga Moreira, I.; Gabriela da Cruz Pedrozo Miguel, M.; Lacerda Ramos, C.; Ferreira Duarte, W.; Efraim, P.; Freitas Schwan, R. Influence of Cocoa Hybrids on Volatile Compounds of Fermented Beans, Microbial Diversity during Fermentation and Sensory Characteristics and Acceptance of Chocolates. J. Food Qual. 2016, 39, 839–849. [Google Scholar] [CrossRef]
  17. Marcucci, M.C.; Gonç, A.; Gonç, C.P.; Sawaya, A.C.H.F.; Alonso, R.C.B.; de Oliveira, M.M.; Barbin, D.F. Quality Parameters, Caffeine and Theobromine Contents and Antioxidant Activity of Artisan and Commercial Chocolate from Brazil. Open Access Libr. J. 2021, 8, 1–18. [Google Scholar] [CrossRef]
  18. Kumar, M.; Kumar, P.; Tewari, J.C.; Pandey, C.B. Changes in Soil Fertility under Multipurpose Tree Species in Thar Desert of Rajasthan. Available online: https://www.researchgate.net/publication/323445344 (accessed on 22 March 2022).
  19. Castro-Nunez, A.; Charry, A.; Castro-Llanos, F.; Sylvester, J.; Bax, V. Reducing deforestation through value chain interventions in countries emerging from conflict: The case of the Colombian cocoa sector. Appl. Geogr. 2020, 123, 102280. [Google Scholar] [CrossRef]
  20. Benjamin, T.; Burniske, G.; Croft, M.; Fenton, M.C. An Analysis of the Supply Chain of Cacao in Colombia View Project. 2018. Available online: https://cgspace.cgiar.org/bitstream/handle/10568/96636/Cacao_Final_Report.pdf?sequence=1 (accessed on 18 October 2022).
  21. Hernández-Núñez, H.E.; Gutiérrez-Montes, I.; Bernal-Núñez, A.P.; Gutiérrez-García, G.A.; Suárez, J.C.; Casanoves, F.; Flora, C.B. Cacao cultivation as a livelihood strategy: Contributions to the well-being of Colombian rural households. Agric. Hum. Values 2022, 39, 201–216. [Google Scholar] [CrossRef]
  22. Calvo, A.M.; Botina, B.L.; García, M.C.; Cardona, W.A.; Montenegro, A.C.; Criollo, J. Dynamics of cocoa fermentation and its effect on quality. Sci. Rep. 2021, 11, 16746. [Google Scholar] [CrossRef]
  23. Perea, A.; Martínez, N.; Aranzazu, F.; Cadena, T. Características de calidad del cacao de Colombia. Catálogo de 26 Cultivares; Universidad Industrial de Santander—Fedecacao: Bucaramanga, Colombia, 2013; p. 107. [Google Scholar]
  24. Zambrano, A.; Gómez, Á.; Ramos, G.; Romero, C.; Lacruz, C.; Rivas, E. Characterization of physical quality parameters in criollo, trinitario and forastero cocoa beans during the drying process. Inst. Nac. Investig. Agrícolas 2010, 60, 389–396. [Google Scholar]
  25. Ortiz de Bertorelli, L.; Rovedas, L.G.; Graziani de Fariñas, L. Influencia de varios factores sobre índices físicos del grano de cacao en fermentación. Agron. Trop. 2009, 59, 81–88. [Google Scholar]
  26. ICONTEC. Norma Técnica Colombiana Cacao en Grano 1252. NTC 1252; ICONTEC: Bogotá, Colombia, 2012. [Google Scholar]
  27. Zyzelewicz, D.; Budryn, G.; Krysiak, W.; Oracz, J.; Nebesny, E.; Bojczuk, M. Influence of roasting conditions on fatty acid composition and oxidative changes of cocoa butter extracted from cocoa bean of Forastero variety cultivated in Togo. Food Res. Int. 2014, 63, 328–343. [Google Scholar] [CrossRef]
  28. Nazaruddin, R.; Seng, L.K.; Hassan, O.; Said, M. Effect of pulp preconditioning on the content of polyphenols in cocoa beans (Theobroma Cacao) during fermentation. Ind. Crops Prod. 2006, 24, 87–94. [Google Scholar] [CrossRef]
  29. AOAC. Official Methods of Analysis of AOAC International (939.05); Association of Official Analytical Chemists International: Rockville, MD, USA, 1995. [Google Scholar]
  30. AOAC. Official Methods of Analysis of AOAC International (31.5.13); Association of Official Analytical Chemists International: Rockville, MD, USA, 1990. [Google Scholar]
  31. Van Soest, P.J.; Robertson, J.B.; Lewis, B.A. Methods for Dietary Fiber, Neutral Detergent Fiber and Nonstarch Polysaccharides in Relation to Animal Nutrition. J. Dairy Sci. 1991, 74, 3583–3597. [Google Scholar] [CrossRef]
  32. AOAC. Official Methods of Analysis of AOAC International, (970.22), 19th ed.; Association of Official Analytical Chemists International: Rockville, MD, USA, 2012. [Google Scholar]
  33. Wollgast, J. The Contents and Effects of Polyphenols in Chocolate: Qualitative and Quantitative Analyses of Polyphenols in Chocolate and Chocolate Raw Products as Well as Evaluation of Potential Implications of Chocolate Consumption in Human Health. Ph.D. Thesis, University of Giessen, Giessen, Germany, 2004. [Google Scholar]
  34. Benzie, I.F.F.; Strain, J.J. The Ferric Reducing Ability of Plasma (FRAP) as a Measure of “Antioxidant Power”: The FRAP Assay. Anal. Biochem. 1996, 239, 70–76. [Google Scholar] [CrossRef] [Green Version]
  35. ICONTEC. Análisis Sensorial. Guía General Para la Selección y Seguimiento de Evaluadores. Parte 1 Evaluadores Seleccionados; ICONTEC: Bogotá, Columbia, 1997. [Google Scholar]
  36. Ramos, G.; González, N.; Zambrano, A. Olores y sabores de cacaos (Theobroma cacao L.) venezolanos obtenidos usando un panel de catación entrenado. Rev. Cient. UDO Agríc. 2013, 13, 114–117. [Google Scholar]
  37. Khan, N.; Motilal, L.A.; Sukha, D.A.; Bekele, F.L.; Iwaro, A.D.; Bidaisee, G.G.; Umaharan, P.; Grierson, L.H.; Zhang, D. Variability of butterfat content in cacao (Theobroma cacao L.): Combination and correlation with other seed-derived traits at the International Cocoa Genebank, Trinidad. Plant Genet. Resour. 2008, 6, 175–186. [Google Scholar] [CrossRef] [Green Version]
  38. ICONTEC. Análisis Sensorial. Metodología. Guía General. GTC 165; ICONTEC: Bogotá, Columbia, 2014; p. 26. [Google Scholar]
  39. ICONTEC. Análisis Sensorial. Metodología. Métodos del Perfil del Sabor. Norma Técnica Colombiana NTC 3929; ICONTEC: Bogotá, Columbia, 2009. [Google Scholar]
  40. Braudeau, J. El Cacao: Tecnicas Agricolas y Producciones Tropicales; Blume: Barcelona, Spain, 1970; p. 299. [Google Scholar]
  41. Dray, S.; Chessel, D.; Thioulouse, J. Co-inertia analysis and the linking of ecological data tables. Ecology 2003, 84, 3078–3089. [Google Scholar] [CrossRef] [Green Version]
  42. R Development Core Team. R: The R Project for Statistical Computing. Available online: https://www.r-project.org/ (accessed on 29 July 2022).
  43. Thioulouse, J.; Dray, S.; Dufour, A.B.; Siberchicot, A.; Jombart, T.; Pavoine, S. Description of Species-Environment Relationships. In Multivariate Analysis of Ecological Data with ade4; Springer: New York, NY, USA, 2018; pp. 141–166. [Google Scholar]
  44. Kotsaki, E.; Reynolds, A.G.; Brown, R.; Jollineau, M.; Lee, H.S.; Aubie, E. Proximal Sensing and Relationships to Soil and Vine Water Status, Yield, and Berry Composition in Ontario Vineyards. Am. J. Enol. Vitic. 2020, 71, 114–131. [Google Scholar] [CrossRef]
  45. Radovic, D.I.; Lazarević, K.B.; Trifković, J.; Andrić, F.; Tešić, Ž.L.; Anđelković, I.; Nedic, N.M.; Stanimirovic, Z.; Stevanovic, J.; Curcic, B.P.M.; et al. Gis techology in regional recognition of the distribution pattern of multifloral honey: The chemical traits in Serbia. Arch. Biol. Sci. 2014, 66, 935–946. [Google Scholar] [CrossRef] [Green Version]
  46. Del Aguila, E. Determinación de Cadmio y Plomo en Granos de Cacao, Frescos, Secos y en Licor de Cacao (Theobroma cacao). Available online: http://repositorio.unas.edu.pe/handle/UNAS/1269 (accessed on 19 October 2022).
  47. Portillo, E.; Labarca, M.; Grazziani, L.; Cros, E.; Assemat, S.; Davrieux, F.; Boulager, R. Influencia de las condiciones del tratamiento poscosecha sobre la temperatura y acidez en granos de cacao Criollo (Theobroma cacao L.). Rev. Fac. Agron. 2011, 28, 646–660. [Google Scholar]
  48. Álvarez, R.; Portillo, E.; Portillo, A.; Villasmil, R. Evaluación de las propiedades sensoriales del licor de cacao (Theobroma cacao L.) obtenido en forma artesanal e industrial. Rev. Agrollanía 2018, 15, 1690–8066. [Google Scholar]
  49. Del Valle González-Canache, A.; Orlando-Álvarez, C.; Durand-Cos, I.; Utria-Borges, C. Evaluación de diferentes tipos de fermentadores y frecuencia de remoción en la calidad del grano de Theobroma cacao L. Hombre Cienc. Tecnol. 2014, 18, 36–45. [Google Scholar]
  50. Vera Chang, J.F.; Vallejo Torres, C.; Párraga Morán, D.E.; Macías Véliz, J.; Ramos Remache, R.; Morales Rodríguez, W. Atributos físicos-químicos y sensoriales de las almendras de quince clones de cacao nacional (Theobroma cacao L.) en el Ecuador. Cienc. Tecnol. 2015, 7, 21–34. [Google Scholar] [CrossRef]
  51. Calderón, D. Caracterización y Evaluación de Accesiones de Cacao Amazónico con Énfasis en su Comportamiento Sanitario Productivo. Available online: http://repositorio.iniap.gob.ec/bitstream/41000/3959/7/iniaptpC146c.PDF (accessed on 8 August 2022).
  52. Wood, G. Cacao, Trad. por Marino, Primera edición en español, Compañía Editorial Continental S.A., México D.F. Available online: https://scirp.org/reference/referencespapers.aspx?referenceid=3098065 (accessed on 8 August 2022).
  53. Lemus, M.; Graziani de Farinas, L.; Ortiz de Bertorelli, L.; Trujillo de Leal, Y.A. Efecto del mezclado de cacaos tipos Criollo y Forastero de la localidad de Cumboto sobre algunas características físicas de los granos durante la fermentacion. Agron. Trop. 2002, 52, 45–58. [Google Scholar]
  54. Juran, J.M.; Bingham, R.S.; Gryna, F.M. Manual de Control de la Calidad, 2nd ed.; Reverte: Barcelona, Spain, 2005; p. 1534. [Google Scholar]
  55. Tanska, M.; Rotkiewicz, D. Quality of fat from oilseeds used to produce selected kinds of bread. ZYWNOSC Nauka Technol. JAKOSC 2011, 18, 62–74. [Google Scholar]
  56. Nogales, J.; Graziani de Fariñas, L.; Ortiz de Bertorelli, L. Cambios físicos y químicos durante el secado al sol del grano de cacao fermentado en dos diseños de cajones de madera. Agron. Trop. 2006, 56, 5–20. [Google Scholar]
  57. García-Alamilla, P.; Salgado-Cervantes, M.A.; Barel, M.; Berthomieu, G.; Rodríguez-Jimenes, G.C.; García-Alvarado, M.A. Moisture, acidity and temperature evolution during cacao drying. J. Food Eng. 2007, 79, 1159–1165. [Google Scholar] [CrossRef]
  58. Krysiak, W.; Iciek, J.; Motyl-Patelska, L. Wpływ warunków prażenia na wybrane właściwości fizykochemiczne ziarna kakaowego. Inż. Chem. Proces 2003, 24, 9–23. [Google Scholar]
  59. Guerron, V. Elaboracion de Pasta a Partir de Mezclas de Cacao Nacional (Theobroma cacao L.) y CCN-51 Producidos en Quevedo. Ph.D. Thesis, Universidad Tecnica Estatal de Quevedo, Quevedo, Ecuador, 2009; pp. 38–40. [Google Scholar]
  60. Steinkamp, J.A.; Lehnert, B.E.; Lehnert, N.M. Discrimination of damaged/dead cells by propidium iodide uptake in immunofluorescently labeled populations analyzed by phase-sensitive flow cytometry. J. Immunol. Methods 1999, 226, 59–70. [Google Scholar] [CrossRef]
  61. Perea, J.A.; Ramirez, O.L.; Villamizar, A.R. Caracterización fisicoquimica de materiales regionales de cacao colombiano. Biotecnol. Sect. Agropecu Agroind. 2011, 9, 35–42. [Google Scholar]
  62. Álvarez, C.; Pérez, E.; Lares, M.C. Caracterización física y química de almendras de cacao fermentadas, secas y tostadas cultivadas en la región de Cuyagua, estado Aragua. Agron. Trop. 2007, 57, 249–256. [Google Scholar]
  63. ICONTEC. Chocolate de Mesa. NTC 793; ICONTEC: Bogotá, Columbia, 2008. [Google Scholar]
  64. Graziani de Fariñas, L.; Ortiz de Bertorelli, L.; Parra, P. Características químicas de la semilla de diferentes tipos de cacao de la localidad de cumboto, aragua. Agron. Trop. 2003, 53, 133–144. [Google Scholar]
  65. Bekele, F.; Butler, D.R. Proposed short list of cocoa descriptors for characterization. In Working Procedures for Cocoa Germplasm Evaluation and Selection. Proceedings of the CFC/ICCO/IPGRI Project Workshop, Montpellier, France, 1–6 February, 1998; International Plant Genetic Resources Institute (IPGRI): Rome, Italy, 2000. [Google Scholar]
  66. Clough, Y.; Dwi Putra, D.; Pitopang, R.; Tscharntke, T. Local and landscape factors determine functional bird diversity in Indonesian cacao agroforestry. Biol. Conserv. 2009, 142, 1032–1041. [Google Scholar] [CrossRef]
  67. Jahurul, M.H.A.; Zaidul, I.S.M.; Norulaini, N.A.N.; Sahena, F.; Jinap, S.; Azmir, J.; Sharif, K.M.; Omar, A.M. Cocoa butter fats and possibilities of substitution in food products concerning cocoa varieties, alternative sources, extraction methods, composition, and characteristics. J. Food Eng. 2013, 117, 467–476. [Google Scholar] [CrossRef]
  68. Enriquez, G. Cacao Organico. Guia para Productores Ecuatorianos; Instituto Nacional de Investigaciones Agropecuarias: Quito, Ecuador, 2004; p. 54. [Google Scholar]
  69. Vera, J. Origen del Cacao, Botanica y Clasificacion del Cacao en Manual del Cultivo de Cacao. Segunda Edicion, Manual Numero 25; Estacion Experimental Tropical Pichilingue, INIAP: Quito, Ecuador, 1993; pp. 8–16. [Google Scholar]
  70. Bertazzo, A.; Comai, S.; Brunato, I.; Zancato, M.; Costa, C.V.L. The content of protein and non-protein (free and protein-bound) tryptophan in Theobroma cacao beans. Food Chem. 2011, 124, 93–96. [Google Scholar] [CrossRef]
  71. Sandesh, K.; Shishir, R.K.; Vaman Rao, C. Optimization and Comparison of Induction Heating and LPG Assisted Acid Pretreatment of Cocoa Pod for ABE Fermentation. Available online: https://www.sciencedirect.com/science/article/pii/S0016236119318538 (accessed on 20 July 2022).
  72. Serra Bonvehí, J.; Aragay Benería, M. Composition of Dietary fibre in Cocoa Husk. Z. Lebensm. Forsch. A 1998, 207, 105–109. [Google Scholar] [CrossRef]
  73. Baena, L.; Cardona, N.G. Obtención y Caracterización de Fibra Dietaría a Partir de Cascarilla de las Semillas Tostadas de Theobroma cacao L. de Una Industria Chocolatera Colombiana. Bachelor’s Thesis, Universidad Tecnológica de Pereira, Pereira, Colombia, 2012. [Google Scholar]
  74. Efraim, P. Estudo para Minimizar as Perdas de Flavonóides Durante a Fermentação de Sementes de Cacau para Produção de Chocolate. Master’s Thesis, Faculdade de Engenharia de Alimentos, Universidade Estadual de Campinas, Campinas, Brazil, 2004. [Google Scholar]
  75. Zuñiga, G. Propiedades Bromatológicas y Funcionales de Diferentes Accesiones de Theobroma Cacao L. del Departamento del Huila; Universidad de la Amazonia: Florencia, Colombia, 2019. [Google Scholar]
  76. Sangronis, E.; Soto, M.J.; Valero, Y.; Buscema, I. Husk of Venezuelan cocoa as raw material of infusions. Arch. Latinoam. Nutr. 2014, 64, 123–130. [Google Scholar]
  77. Quiroz-Reyes, C.N.; Aguilar-Méndez, M.A.; Ramírez-Ortíz, M.E.; Ronquillo-De Jesús, E. Comparative study of ultrasound and maceration techniques for the extraction of polyphenols from cocoa beans (Theobroma cacao L.). Rev. Mex.Ing. Quim. 2013, 12, 11–18. [Google Scholar]
  78. Ardhana, M.M.; Fleet, G.H. The microbial ecology of cocoa bean fermentations in Indonesia. Int. J. Food Microbiol. 2003, 86, 87–99. [Google Scholar] [CrossRef]
  79. Cros, E. Factores que afectan el desarrollo del sabor a cacao bases bioquimicas del perfil aromatico. In Memoria. Taller Internacional Calidad Integral del Cacao: Teoría y Práctica; INIAP/EET-P: Quevedo, Ecuador, 2004. [Google Scholar]
  80. Sukha, D.A.; Butler, D.R.; Umaharan, P.; Boult, E. The use of an optimised organoleptic assessment protocol to describe and quantify different flavour attributes of cocoa liquors made from Ghana and Trinitario beans. Eur. Food Res. Technol. 2008, 226, 405–413. [Google Scholar] [CrossRef]
  81. Amoa-Awua, W.K. Methods of Cocoa Fermentation and Drying. In Cocoa and Coffee Fermentations; CRC Press: Boca Raton, FL, USA, 2020; pp. 90–147. [Google Scholar]
  82. Batista, N.N.; Ramos, C.L.; Dias, D.R.; Pinheiro, A.C.M.; Schwan, R.F. The impact of yeast starter cultures on the microbial communities and volatile compounds in cocoa fermentation and the resulting sensory attributes of chocolate. J. Food Sci. Technol. 2016, 53, 1101–1110. [Google Scholar] [CrossRef] [Green Version]
  83. Menezes, A.G.T.; Batista, N.N.; Ramos, C.L.; e Silva, A.R.D.A.; Efraim, P.; Pinheiro, A.C.M.; Schwan, R.F. Investigation of chocolate produced from four different Brazilian varieties of cocoa (Theobroma cacao L.) inoculated with Saccharomyces cerevisiae. Food Res. Int. 2016, 81, 83–90. [Google Scholar] [CrossRef]
  84. Ramos, C.L.; Dias, D.R.; Miguel, M.G.D.C.P.; Schwan, R.F. Impact of different cocoa hybrids (Theobroma cacao L.) and S. cerevisiae UFLA CA11 inoculation on microbial communities and volatile compounds of cocoa fermentation. Food Res. Int. 2014, 64, 908–918. [Google Scholar] [CrossRef] [PubMed]
  85. Afoakwa, E.; Quao, J.; Takrama, F.; Budu, A.; Saalia, F. Changes in Total Polyphenols, O-diphenols and Anthocyanin Concentrations during Fermentation of Pulp Pre-Conditioned Cocoa (Theobroma cacao) Beans. 2012. Available online: http://ugspace.ug.edu.gh/handle/123456789/26792 (accessed on 8 August 2022).
  86. Cedeño, P. Determinación de Perfiles Organolépticos en Ocho Grupos de Cacao Mediante la Degustación de Licor de Cacao y Chocolates Oscuros Elaborados Artesanalmente. Bachelor’s Thesis, Escuela Superior Agronómica de Manabí, Facultad de Ingeniería Agroindustrial, Calceta, Ecuador, 2010. [Google Scholar]
  87. Cros, E.; Mermet, G.; Jeanjean, N.; Georges, G. Relation Précurseurs—Développement de l’arôme Cacao; Cocoa Producers’ Alliance: Lagos, Nigeria, 1993; pp. 723–726. [Google Scholar]
  88. Cros, E. Factores condicionantes de la calidad del cacao. In Memorias del Primer Congreso Venezolano del Cacao y su Industria Maracay, Estado Aragua; Fundacite: Mérida, Venezuela, 2000; pp. 16–32. [Google Scholar]
  89. Alvis, A.; Pérez, L.; Arrazola, G. Determinación de las propiedades de textura de tabletas de chocolate mediante técnicas instrumentales. Inf. Tecnol. 2011, 22, 11–18. [Google Scholar] [CrossRef]
  90. Augusto, P.P.C.; Bolini, H.M.A. The role of conching in chocolate flavor development: A review. Compr. Rev. Food Sci. Food Saf. 2022, 21, 3274–3296. [Google Scholar] [CrossRef] [PubMed]
  91. Spada, F.P.; de Alencar, S.M.; Purgatto, E. Comprehensive chocolate aroma characterization in beverages containing jackfruit seed flours and cocoa powder. Future Foods 2022, 6, 100158. [Google Scholar] [CrossRef]
  92. Gutiérrez-Ríos, H.G.; Suárez-Quiroz, M.L.; Hernández-Estrada, Z.J.; Castellanos-Onorio, O.P.; Alonso-Villegas, R.; Rayas-Duarte, P.; Cano-Sarmiento, C.; Figueroa-Hernández, C.Y.; González-Rios, O. Yeasts as Producers of Flavor Precursors during Cocoa Bean Fermentation and Their Relevance as Starter Cultures: A Review. Fermentation 2022, 8, 331. [Google Scholar] [CrossRef]
  93. Sioriki, E.; Tuenter, E.; Van de Walle, D.; Lemarcq, V.; Cazin, C.S.; Nolan, S.P.; Pieters, L.; Dewettinck, K. The effect of cocoa alkalization on the non-volatile and volatile mood-enhancing compounds. Food Chem. 2022, 381, 132082. [Google Scholar] [CrossRef] [PubMed]
  94. Pinzón, J. Guía Técnica para el Cultivo del Cacao, 5th ed.; FEDECACAO: Bogotá, Columbia, 2012; p. 192. [Google Scholar]
  95. Quintero, R.M.L.; Díaz Morales, K.M. El mercado mundial del cacao. Agroalimentaria 2004, 9, 47–59. [Google Scholar]
  96. Reineccius, G.A. Flavor Technology. Second Edition. In Flavour Chemistry and Technology; CRC Press: Boca Raton, FL, USA, 2006. [Google Scholar]
  97. Liu, M.; Liu, J.; He, C.; Song, H.; Liu, Y.; Zhang, Y.; Wang, Y.; Guo, J.; Yang, H.; Su, X. Characterization and comparison of key aroma-active compounds of cocoa liquors from five different areas. Int. J. Food Prop. 2017, 20, 2396–2408. [Google Scholar] [CrossRef] [Green Version]
  98. Torres-Moreno, M.; Tarrega, A.; Costell, E.; Blanch, C. Dark chocolate acceptability: Influence of cocoa origin and processing conditions. J. Sci. Food Agric. 2012, 92, 404–411. [Google Scholar] [CrossRef]
  99. Cambrai, A.; Marcic, C.; Morville, S.; Houer, P.S.; Bindler, F.; Marchioni, E. Differentiation of Chocolates According to the Cocoa’s Geographical Origin Using Chemometrics. J. Agric. Food Chem. 2010, 58, 1478–1483. [Google Scholar] [CrossRef]
  100. Sukha, D.A.; Butler, D.R.; Comissiong, E.A.; Umaharan, P. The impact of processing location and growing environment on flavor in cocoa (Theobroma cacao L.)—Implications for “terroir” and certification—Processing location study. Acta Hortic. 2014, 1047, 255–262. [Google Scholar] [CrossRef]
  101. Bertoldi, D.; Barbero, A.; Camin, F.; Caligiani, A.; Larcher, R. Multielemental fingerprinting and geographic traceability of Theobroma cacao beans and cocoa products. Food Control 2016, 65, 46–53. [Google Scholar] [CrossRef]
Agronomy 13 00015 g001 550
Figure 1. Spatial distribution of cocoa quality study area.
Figure 1. Spatial distribution of cocoa quality study area.
Agronomy 13 00015 g001
Agronomy 13 00015 g002 550
Figure 2. Principal component analysis for the physicochemical variables in relation to the clones evaluated in the department of Meta (Colombia). The color of each variable shows the level of contribution in each component, where green and red are higher to lower contribution.
Figure 2. Principal component analysis for the physicochemical variables in relation to the clones evaluated in the department of Meta (Colombia). The color of each variable shows the level of contribution in each component, where green and red are higher to lower contribution.
Agronomy 13 00015 g002
Agronomy 13 00015 g003 550
Figure 3. Principal component analysis of the matrix of sensory profile and cocoa notes according to cocoa clones from the department of Meta, Colombia. (a) organoleptic qualities, (b) distribution of cocoa clones in relation to organoleptic qualities, (c) sensory notes, (d) distribution of cocoa clones in relation to sensory notes. The color of each variable shows the level of contribution in each component, where green and red is higher to lower contribution.
Figure 3. Principal component analysis of the matrix of sensory profile and cocoa notes according to cocoa clones from the department of Meta, Colombia. (a) organoleptic qualities, (b) distribution of cocoa clones in relation to organoleptic qualities, (c) sensory notes, (d) distribution of cocoa clones in relation to sensory notes. The color of each variable shows the level of contribution in each component, where green and red is higher to lower contribution.
Agronomy 13 00015 g003
Agronomy 13 00015 g004 550
Figure 4. Co-inertia analysis of the relationship between the matrix of physicochemical variables and sensory attributes (organoleptic qualities and sensory notes) of cocoa samples from the department of Meta, Colombia. (a) relationship between the matrix of physicochemical variables and the matrix of sensory attributes of beans, (b) relationship between the matrix of physicochemical variables and positive-negative cocoa notes of cocoa samples, (c) relationship between the matrix of sensory profile and positive-negative cocoa notes of cocoa samples.
Figure 4. Co-inertia analysis of the relationship between the matrix of physicochemical variables and sensory attributes (organoleptic qualities and sensory notes) of cocoa samples from the department of Meta, Colombia. (a) relationship between the matrix of physicochemical variables and the matrix of sensory attributes of beans, (b) relationship between the matrix of physicochemical variables and positive-negative cocoa notes of cocoa samples, (c) relationship between the matrix of sensory profile and positive-negative cocoa notes of cocoa samples.
Agronomy 13 00015 g004
Agronomy 13 00015 g005 550
Figure 5. Map of the sensory distribution of cocoa in the municipalities of Mesetas (a) and Vista Hermosa (b). Blue to red indicates highest to lowest cocoa bean quality.
Figure 5. Map of the sensory distribution of cocoa in the municipalities of Mesetas (a) and Vista Hermosa (b). Blue to red indicates highest to lowest cocoa bean quality.
Agronomy 13 00015 g005
Table
Table 1. Mean and standard error of physicochemical and functional properties of different cocoa clones from the department of Meta, Colombia.
Table 1. Mean and standard error of physicochemical and functional properties of different cocoa clones from the department of Meta, Colombia.
VariableCCN-51FEAR-5FSA-11FSA-12ICS-95TSH-565General Averagep-Value
pH5.74 ± 0.05 5.66 ± 0.06 5.7 ± 0.1 5.79 ± 0.04 5.84 ± 0.03 5.77 ± 0.04 5.78 ± 0.02NS
Titratable acidity16.29 ± 0.32b18.72 ± 0.96a15.58 ± 0.74b20.02 ± 0.63a18.93 ± 0.42a16.54 ± 0.53b17.82 ± 0.24<0.0001
Ash2.95 ± 0.08b3.39 ± 0.34a2.82 ± 0.18b2.91 ± 0.04b3.22 ± 0.08a2.8 ± 0.12b3.04 ± 0.050.0081
Crude Protein10.34 ± 0.18b11.52 ± 0.47a11.96 ± 0.35a10.98 ± 0.41b10.54 ± 0.12b10.46 ± 0.29b10.65 ± 0.10.0029
°Brix2.69 ± 0.04b2.76 ± 0.1b3.68 ± 0.26a3.4 ± 0.15a2.91 ± 0.06b3.01 ± 0.1b2.95 ± 0.04<0.0001
Moisture2.67 ± 0.08a2.12 ± 0.11b2.29 ± 0.26b2.68 ± 0.08a2.75 ± 0.08a2.75 ± 0.16a2.66 ± 0.050.0404
Ether extract52.22 ± 0.09a51.53 ± 0.18b51.81 ± 0.28b52.24 ± 0.08a52.26 ± 0.08a52.33 ± 0.18a52.2 ± 0.050.0274
Insoluble D. Fiber46.2 ± 0.3 45.24 ± 0.62 45.73 ± 0.64 45.87 ± 0.26 45.5 ± 0.19 46.24 ± 0.29 45.84 ± 0.13NS
Soluble D. Fiber54.8 ± 0.52 53.4 ± 0.69 53.64 ± 1.02 54.97 ± 0.55 55.57 ± 0.31 55.1 ± 0.44 55.02 ± 0.21NS
Total polyphenol210.4 ± 1.82 209.52 ± 3.11 214.77 ± 3.99 207.59 ± 2.09 210.09 ± 1.24 210.82 ± 1.47 210.18 ± 0.78NS
FRAP2671 ± 48.1a2235.5 ± 126.5b2395.1 ± 126.8b2501.8 ± 82.8b2468.3 ± 40.6b2693.2 ± 85.4a2544.8 ± 28.10.0009
a, b: Different letters within each column indicate statistical differences according to Fisher’s LSD means test (p < 0.05). NS: not significant.
Table
Table 2. Mean and standard error of organoleptic qualities of cocoa clones from the department of Meta, Colombia.
Table 2. Mean and standard error of organoleptic qualities of cocoa clones from the department of Meta, Colombia.
Organoleptic QualitiesCCN-51FEAR-5FSA-11FSA-12ICS-95TSH-565General Averagep-Value
Cocoa3.9 ± 0.2a4.0 ± 0.0a4.0 ± 0.5a4.3 ± 0.2a3.3 ± 0.1b3.3 ± 0.2b3.7 ± 0.10.0004
Acidity4.1 ± 0.3 3.7 ± 0.7 2.0 ± 0.0 3.6 ± 0.4 3.6 ± 0.2 3.7 ± 0.4 3.7 ± 0.1NS
Astringency3.7 ± 0.2a2.7 ± 0.2b4.0 ± 0.5a4.0 ± 0.3a2.9 ± 0.1b2.7 ± 0.1b3.3 ± 0.1<0.0001
Bitterness4.0 ± 0.2a3.7 ± 0.3a4.7 ± 0.7a4.4 ± 0.4a3.0 ± 0.1b3.0 ± 0.2b3.5 ± 0.1<0.0001
Fresh fruit4.0 ± 0.3 3.7 ± 0.7 2.0 ± 0.0 3.7 ± 0.5 3.6 ± 0.2 3.8 ± 0.4 3.7 ± 0.1NS
Browned Fruit3.1 ± 0.2a1.7 ± 0.2b2.7 ± 0.3a3.1 ± 0.3a3.1 ± 0.1a3.0 ± 0.2a3.0 ± 0.10.0135
Floral4.0 ± 0.3a3.7 ± 0.7a2.0 ± 0.5b3.6 ± 0.5a4.3 ± 0.2a4.4 ± 0.3a4.0 ± 0.10.0117
Nutty3.4 ± 0.2a2.3 ± 0.3b2.3 ± 0.2b3.0 ± 0.3a3.2 ± 0.1a3.0 ± 0.2a3.1 ± 0.10.0097
Sweet4.2 ± 0.3a4.3 ± 0.3a2.0 ± 0.5b3.6 ± 0.5a4.5 ± 0.2a4.7 ± 0.2a4.2 ± 0.10.0008
Green0.3 ± 0.1a0.7 ± 0.3a0.1 ± 0.1b0.3 ± 0.2a0.6 ± 0.1a0.4 ± 0.2a0.5 ± 0.10.0378
a, b: Different letters within each column indicate statistical differences according to Fisher’s LSD means test (p < 0.05). NS: not significant.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Ramón, V.; Hernández, H.E.; Polania, P.; Suárez, J.C. Spatial Distribution of Cocoa Quality: Relationship between Physicochemical, Functional and Sensory Attributes of Clones from Southern Colombia. Agronomy 2023, 13, 15. https://doi.org/10.3390/agronomy13010015

AMA Style

Ramón V, Hernández HE, Polania P, Suárez JC. Spatial Distribution of Cocoa Quality: Relationship between Physicochemical, Functional and Sensory Attributes of Clones from Southern Colombia. Agronomy. 2023; 13(1):15. https://doi.org/10.3390/agronomy13010015

Chicago/Turabian Style

Ramón, Vivian, Héctor Eduardo Hernández, Paola Polania, and Juan Carlos Suárez. 2023. "Spatial Distribution of Cocoa Quality: Relationship between Physicochemical, Functional and Sensory Attributes of Clones from Southern Colombia" Agronomy 13, no. 1: 15. https://doi.org/10.3390/agronomy13010015

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop