Cacao quality index for cacao agroecosystems in Bahia, Brazil

ABSTRACT

Brazil is increasingly committed to developing research on the quality of cacao. The objective of this work was to develop a new Cacao Quality Index (CQI) methodology with cacao industry, chocolate flavor, human health and food safety functions, and to show its results in different cacao agroecosystems from the humid region of Bahia. The typical Dystrophic Red-Yellow Argisol, in the cabruca cropping system with 35 shade trees per hectare, reached the highest CQI score. The lowest score obtained from the quality index cacao corresponded to the site characterized by the abrupt Dystrophic Red-Yellow Argisol, in the cabruca cropping system with 60 shade trees per hectare. All cropping sites obtained CQI values between 0.54 and 0.69 and were therefore classified as a regular index. The primary indicators that determined the differences between the highest and lowest CQI score were proteins, total free amino acids, simple carbohydrates (sucrose, fructose and glucose), total acidity, cadmium and barium.

KEYWORDS:

• Theobroma cacao L.
• cacao quality indicators
• food safety
• food properties
• agricultural sustainability
• potentially toxic elements

Introduction

Cacao is the fruit of cacao tree (Theobroma cacao L.), is cultivated over 10 million ha of land area in the tropical countries with world production over 4 million tones.^[1] The cacao beans are raw materials for chocolate, one of the most consumed foods in the world.^[2] Demand for cacao beans is increasing in the word.^[1,3] Farming practices applied by cacao farmers at the beginning of the chocolate supply chain strongly influence several quality parameters of the finished chocolate.^[4] Therefore, the geographic evaluation of the raw material and in its supply chain is one of the tools indispensable to the make cacao sustainable traceable.^[5] For example, cacao bean origin is the most important factor that determining the aroma of all cacao products^[6]; likewise, the differences between cacao varieties and geographical region influence total phenolic contents^[7] and volatile compounds.^[8] Both the mineral^[9] and the biochemical composition^[10] in the dry cacao beans, show differences between soil types and cropping systems.

The research studies have developed standards for aspects of cacao quality that meet industrial criteria as well as international import and export legislation that is aimed for food security.^[11] Some chemical attributes of cacao beans were selected as important quality indicators: pH and total acidity, organic acids (acetic and lactic acids), simple carbohydrates (sucrose, fructose and glucose), lipids, proteins and amino acids, purine alkaloids (theobromine and caffeine) and phenolic substances (catechin, epicatechin), total phenols, minerals elements (nutrients and potentially toxic elements) and mycotoxins.^[11] In this context arose the Cacao Quality Index (CQI) with the purpose of systematize a large number of chemical attributes of cacao beans in functions able to reflect the main needs of the market of this commodity.^[12]

In the initial proposal,^[12] CQI is composed of three functions: for the interest of Cacao Industry (I), for the interest of Flavor of the Chocolate (II), for the interest of Medicine/Human health/Food security (III). The variables were deduced numerically from scientific and analytical methods and, accordingly, were interpreted together in a system of information related to cacao quality.^[12] The objective of this study was to establish a new CQI according to the chemical quality criteria for dry beans, adapting and creating functions with more indicators related to the agricultural, food and environmental realities in the humid region of Bahia. In this region, this study evaluated the CQI for different cropping sites with different soils and cultivation systems of cacao.

Materials and methods

Cacao quality index

Study sites and sampling

To determine CQI were selected twelve study sites located in the humid zone of the cacao region of Bahia, Brazil (Table 1) whose the Thornthwaite climatic classification corresponds to B4r A’, B3r A’, B2r A’, B2r B’, B1r A’, B1r’ A’, B1w A’. These sites (Table 1) were planted with PH-16 cacao clone under different cropping systems, with a range of shade tree densities and across three soil classes: Argisols, Cambisols and Latosols.

Table 1. Cacao agroecosystems participating in the study in the humid region of Bahia, Brazil

Cropping Site	Geographic Coordinates	Soil Classification according to SiBCS^a	Soil Taxonomy^b	Cropping Systems	Average density of shade trees ha^−a	Grafts age (years)
1	13º 40ʹ 30” S, 39º 14ʹ 27” W	cambisolic Dystrophic Yellow Latosol (LAd cam)	Hapludox	CxR^c	150	3
2	13º 44ʹ 38” S, 39º 30ʹ 10” W	typic Dystrophic Red-Yellow Argisol (PVAd)	Hapludult	CxE^d	60	8
3	13º 45ʹ 21” S, 39º 20ʹ 25” W	abrupt Dystrophic Red-Yellow Argisol (PVAd)	Hapludult	Cabruca^e	60	4
4	13º 46ʹ 07.0” S, 39º 17ʹ 52.0”W	typic Dystrophic Yellow Latosol (LAd)	Typic Hapludox	CxR^c	350	3
5	13º 51ʹ 08” S, 39º 17ʹ 54” W	typic Dystrophic Red-Yellow Latosol (LVAd)	Typic Hapludox	CxR^c	400	3
6	14º 31ʹ 14” S, 39º 15ʹ 45” W	cambisolic Eutrophic Red-Yellow Argisol (PVAe cam)	Hapludalf	Cabruca^e	50	5
7	14º 51ʹ 36” S, 39º 14ʹ 42” W	typic Dystrophic Haplic Cambisol (CXd)	Dystropept	Cabruca^e	35	3
8	14º 51ʹ 47” S, 39º 06ʹ 47” W	argisolic Dystrophic Red-Yellow Latosol (LVAd arg)	Hapludox	Cabruca^e	70	9
9	15º 17ʹ 04” S, 39º 28ʹ 43” W	latosolic Dystrophic Yellow Argisol (PAd lat)	Hapludult	Cabruca^e	35	5
10	15º 23ʹ 08” S, 39º 26ʹ 04” W	typic Dystrophic Red-Yellow Argisol (PVAd)	Hapludult	Cabruca^e	35	3
11	15º 23ʹ 15.1” S, 39º 25ʹ 48.6” W	typic Alitic Red-Yellow Argisol (PVA ali)	Hapludult	Cabruca^e	35	3
12	16º 29ʹ 02” S, 39º 23ʹ 56” W	abrupt Cohesive Dystrophic Red-Yellow Argisol (PVAd coe)	Hapludult	CxR^c	400	6

^aBrazilian System of Soil Classification.^[13]

^bSoil Survey Staff.^[14]

^cIntercropping with cacao (Theobroma cacao L.) and rubber tree (Hevea brasiliensis (Willd. Ex Adr de Juss.) Muell. Arg.).

^dCacao with shade of erytrina (Erythrina fusca Lour).

^eCabruca is an ecological system of agroforestry cultivation where cacao trees are grown under native trees of the Atlantic Forest of South of Bahia.^[15]

Each study site with approximately one hectare was subdivided into three collection areas, characterized by the same soil type and cropping site. Each single sample (three for study site) corresponds to 50 mature cacao pods, for the post-harvest processing of fermentation and drying, completely described in the works of Loureiro et al.^[16] and Loureiro et al.^[17] Cacao sampling occurred on November of 2008. This month was chosen because it is representative of the second harvest period (August 2008 to January 2009).

Database

The database used to determine the CQI comes from two previous scientific publications that bring together two main groups of 28 cacao quality indicators: biochemical attributes^[10] and composition of mineral elements.^[9] The cacao beans biochemical attributes pH, total acidity (meq NaOH 100 g⁻¹), acetic acid (mg g⁻¹), lactic acid (mg g⁻¹), sucrose (mg g⁻¹), fructose (mg g⁻¹), glucose (mg g⁻¹), lipids (g kg⁻¹), proteins (g kg⁻¹), amino acids (mg g⁻¹), catechin (mg g⁻¹), epicatechin (mg g⁻¹), total phenols (mg g⁻¹), theobromine (mg g⁻¹), caffeine (mg g⁻¹) were fully described in the work of Araujo et al.^[10]The cacao beans composition of mineral elements nitrogen (g kg⁻¹), phosphorus (g kg⁻¹), potassium (g kg⁻¹), calcium (g kg⁻¹), magnesium (g kg⁻¹), silicon (g kg⁻¹), iron (mg kg⁻¹), zinc (mg kg⁻¹), copper (mg kg⁻¹), manganese (mg kg⁻¹), barium (mg kg⁻¹), cadmium (mg kg⁻¹), lead (mg kg⁻¹) were fully described in the work of Araujo et al.^[9]

Standardized scoring of quality indicators

The quality indicators were standardized for scores ranging from 0 to 1, according to the function of Wymore^[18]:

(1) $v=\frac{1}{1+{\left(\frac{B-L}{x-L}\right)}^{2S\left(B+x-2L\right)}}$(1)

In Equation 1(1) $v=\frac{1}{1+{\left(\frac{B-L}{x-L}\right)}^{2S\left(B+x-2L\right)}}$(1) , v is the standardized score; B is the critical or baseline value of the indicator, whose standard score is 0.5; L is the lower limit or the lowest value of the indicator, which can be equal to zero; S is the slope of the tangent of the curve at the base limit or the critical value of the indicator; x is the original value of the analyzed indicator (attribute). The slope of the tangent (S) was calculated according to Equation 2(2) $S=\frac{log\left(\frac{1}{v}\right)-1}{log\left(\frac{B-L}{x-L}\right)\times 2\left(B+x-2L\right)}$(2) :

(2) $S=\frac{log\left(\frac{1}{v}\right)-1}{log\left(\frac{B-L}{x-L}\right)\times 2\left(B+x-2L\right)}$(2)

To quantify relationships between cacao quality indicators and cacao quality functions, were selected three standardized scoring functions (SSF) types to normalize indicator data (Figure 1). Numerical values for each soil quality indicator were converted into unit less scores ranging from 0 to 1 according to the standardization of equation two (2). As shown in Figure 1, the scoring functions were: (a)”more is better” for scoring curve for positive slopes, (b) ‘Less is better’ for curve for negative slopes, (c) ‘Optimum’ curve when a positive curve is reflected at the upper threshold value.^[12]

Figure 1. Standardized scoring functions (SSF) types to normalize cacao quality indicators for cacao agroecosystems. Theoretical data (TD) with a sample simulation with variable number of observations: (a) More is better for Fructose (mg g⁻¹); (b) Less is better for Cadmium (mg kg⁻¹); and (c) Optimum for copper (mg kg⁻¹). Real data (RD) with 36 sample observations: (d) More is better for Fructose (mg g⁻¹); (e) Less is better for cadmium (mg kg⁻¹); and (f) Optimum for Copper (mg kg⁻¹). More is better: L – lower threshold, at which or below the score is 0, B – baseline, at which score is 0.5, U – upper threshold, at which or above score is 1.0; Optimum: B1 – lower baseline is 0.5, O – Optimum level, at which score is 1.0, B2 – upper baseline is 0.5. Less is better: L – lower threshold, at which or below the score is 1, B – baseline, at which score is 0.5, and U – upper threshold, at which or above score is 0

Mathematical model

This CQI has an additive model, consisting of main functions representing the aspects to be evaluated and the quality indicators associated with them, attributes that have weights in accordance with the criteria adopted in the research.^[19] According to this additive model, each function was established as the following equations:

(3) $Q_{FPn}=I_1\left(W_1\right)+I_2\left(W_2\right)+\cdots +I_n\left(W_n\right)$(3)

(4) $CQI=Q_{PF1}\left(W_{PF1}\right)+Q_{PF2}\left(W_{PF2}\right)+\cdots +Q_{PFn}\left(W_{PFn}\right)$(4)

In Equation 3(3) $Q_{FPn}=I_1\left(W_1\right)+I_2\left(W_2\right)+\cdots +I_n\left(W_n\right)$(3) , Q_FPn is the principal function quality (PF) of the index; I refers to the standardized scores of quality indicators related to each PF; W refers to the weights related to each indicator or principal function. In Equation 4(4) $CQI=Q_{PF1}\left(W_{PF1}\right)+Q_{PF2}\left(W_{PF2}\right)+\cdots +Q_{PFn}\left(W_{PFn}\right)$(4) , CQI is the cacao quality index that integrates all functions. Considering that all the indicators reach the ideal values, the sum of the weights of all the main functions should result in the value 1.0 (one).

CQI functions and indicators

The quality functions for CQI were cacao industry (CIF), chocolate flavor (CFF), human health (HHF) and food safety (FSF) (Tables 2 and 3).

Table 2. Summary of spreadsheets of the cacao quality index of cropping site characterized by typic Dystrophic Red-Yellow Argisol located in humid region of Bahia, Brazil

			Typic Dystrophic Red-Yellow Argisol – 10 PVAd (CQI = 0.69 ± 0.07)
Cacao Quality Index – CQI (Structure)			Sample Observation 1 (CQI = 0.64)						Sample Observation 2 (CQI = 0.67)						Sample Observation 3 (CQI = 0.77)
Function (Weight)	Primary Indicator (Weight)	Secondary Indicator (Weight)	Observed Value	Original Score	Score Indicator	%	Score Function	%	Observed Value	Original Score	Score Indicator	%	Score Function	%	Observed Value	Original Score	Score Indicator	%	Score Function	%
CIF^a -0.2	Lipids (0.40)		370.58	0.05	0.02	3.23	0.12	18.83	348.19	0	0	0.3	0.12	17.58	385.19	0.18	0.07	11.26	0.13	17.09
	pH (0.25)		5.94	1	0.25	41.53			5.85	1	0.25	42.5			5.96	1	0.25	38.11
	Total Acidity (0.25)		13.15	0.99	0.25	41.15			13.04	0.99	0.25	41.9			14.37	0.93	0.23	35.41
	Total Phenols (0.10)		45.99	0.85	0.08	14.1			47.62	0.9	0.09	15.3			62.5	1	0.1	15.23
CFF^b (0.30)	Proteins (0.10)		155.11	0.27	0.03	4.86	0.17	26.35	137.08	0.01	0	0.15	0.15	22.88	179.28	0.98	0.1	12.66	0.23	30.28
	Total Free Amino Acids (0.20)		13.05	0.12	0.02	4.39			8.5	0	0	0.03			15.57	0.64	0.13	16.5
	Total Phenols (0.20)		45.99	0.85	0.17	30.22			47.62	0.9	0.18	35.28			62.5	1	0.2	25.79
	Simple Carbohydrate -0.15	Sucrose (0.20)	0.91	0	0.09	16.88			1.22	0	0.12	23.5			0.95	0	0.11	14.06
		Fructose (0.40)	5.64	0.63					7.02	1					6.45	0.98
		Glucose (0.40)	3.71	0.95					4.77	1					3.41	0.84
	Purine Alkaloids -0.15	Theobromine (0.70)	30.97	0.01	0.05	8.21			28.28	0.03	0.01	2.72			28.89	0.03	0.05	6.16
		Caffeine (0.30)	4.9	1					6.18	0.23					4.85	1
	Total Acidity (0.10)		13.15	0.99	0.1	17.64			13.04	0.99	0.1	19.32			14.37	0.93	0	11.99
	Organic Acids -0.1	Acetic Acid (0.60)	0.93	1	0.1	17.8			1.71	0.95	0.1	19			1.33	1	0	12.85
		Lactic Acid (0.40)	0.48	1					0.72	1					0.96	0.99
HHF^c (0.30)	Phenolic Substances -0.3	Epicatechin (0.70)	4.42	1	0.23	34.5	0.2	30.95	3.62	1	0.25	37.98	0.2	29.97	4.69	1	0.22	32.49	0.21	26.72
		Catechin (0.30)	1.65	0.2					1.99	0.49					1.54	0.13
	Purine Alkaloids -0.2	Theobromine (0.70)	30.97	0.01	0.06	9.32			28.28	0.03	0.02	2.77			28.89	0.03	0.06	9.31
		Caffeine (0.30)	4.9	1					6.18	0.23					4.85	1
	Macronutrients -0.2	N (0.20)	2.48	0.94	0.17	26.47			2.19	0.47	0.16	23.47			2.87	1	0.16	23.83
		P (0.30)	2.79	1					2.83	1					2.41	0.89
		K (0.20)	8.99	1					8.87	1					8.07	1
		Ca (0.10)	2.77	0.99					2.77	0.99					2.7	0.98
		Mg (0.10)	2.18	0.86					2.13	0.79					1.99	0.48
		Si (0.10)	0.4	0					1.2	0.12					0.8	0.01
	Micronutrients -0.2	Fe (0.40)	27.92	1	0.12	18.13			92.02	1	0.14	20.98			33.88	1	0.14	19.82
		Zn (0.40)	29.99	0.5					32.23	0.75					31.63	0.69
		Mn (0.20)	16.25	0					14.27	0					13.79	0
	Potentially Toxic Elements (0.10)	Cd (0.30)	0.4	0.22	0.08	11.57			0.2	0.98	0.1	14.79			0.1	1	0.1	14.55
		Pb (0.30)	0	1					0	1					0	1
		Ba (0.20)	2.5	0.98					2.8	0.97					2.7	0.98
		Cu (0.20)	25.8	1					24.1	1					22.7	0.99
FSF^d -0.2	Cd (0.30)		0.4	0.22	0.07	11.06	0.15	23.87	0.2	0.98	0.29	50.1	0.2	29.56	0.1	1	0.3	45.74	0.2	25.91
	Pb (0.30)		0	1	0.3	39.3			0	1	0.3	30.34			0	1	0.3	30.17
	Ba (0.20)		2.5	0.98	0.2	25.79			2.8	0.97	0.19	19.67			2.7	0.98	0.2	19.65
	Cu (0.20)		25.8	1	0.2	26.19			24.1	1	0.2	20.19			22.7	0.99	0.2	20.01

^aCacao industry function.

^bChocolate flavor function.

^cHuman health function.

^dFood safety function.

Table 3. Summary of spreadsheets of the cacao quality index of cropping site characterized by abrupt Dystrophic Red-Yellow Argisol located in humid region of Bahia, Brazil

									abrupt Dystrophic Red-Yellow Argisol – 3 PVAd (CQI = 0.54 ± 0.07)
Cacao Quality Index – CQI (Structure)			Sample Observation 1 (CQI = 0.47)						Sample Observation 2 (CQI = 0.61)						Sample Observation 3 (CQI = 0.55)
Function (Weight)	Primary Indicator (Weight)	Secondary Indicator (Weight)	Observed Value	Original Score	Score Indicator	%	Score Function	%	Observed Value	Original Score	Score Indicator	%	Score Function	%	Observed Value	Original Score	Score Indicator	%	Score Function	%
CIF^a (0.20)	Lipids (0.40)		342.12	0.00	0.18	0.18	0.09	20.07	403.66	0.59	0.24	36.49	0.13	21.29	364.70	0.03	0.01	2.96	0.07	13.30
	pH (0.25)		5.93	1.00	52.93	52.93			5.77	1.00	0.25	38.60			5.94	1.00	0.25	68.82
	Total Acidity (0.25)		15.00	0.50	26.25	26.25			15.27	0.26	0.06	9.85			16.04	0.01	0.00	0.97
	Total Phenols (0.10)		68.02	0.97	20.64	20.64			68.08	0.97	0.10	15.05			65.76	0.99	0.10	27.25
CFF^b (0.30)	Proteins (0.10)		138.22	0.01	0.29	0.29	0.11	22.56	180.34	0.98	0.10	15.45	0.19	31.43	176.47	0.97	0.10	16.35	0.18	32.45
	Total Free Amino Acids (0.20)		10.56	0.01	0.58	0.58			16.81	0.86	0.17	27.00			16.06	0.74	0.15	25.17
	Total Phenols (0.20)		68.02	0.97	55.08	55.08			68.08	0.97	0.19	30.59			65.76	0.99	0.20	33.52
	Simple Carbohydrate (0.15)	Sucrose (0.20)	1.42	0.16	4.34	4.34			0.76	0.00	0.00	0.00			0.80	0.00	0.00	0.00
		Fructose (0.40)	3.07	0.00					2.59	0.00					2.90	0.00
		Glucose (0.40)	2.62	0.18					0.91	0.00					0.85	0.00
	Purine Alkaloids (0.15)	Theobromine (0.70)	28.12	0.04	13.41	13.41			30.35	0.01	0.05	7.29			30.23	0.01	0.05	7.88
		Caffeine (0.30)	5.47	0.97					5.05	1.00					4.87	1.00
	Total Acidity (0.10)		15.00	0.50	14.01	14.01			15.27	0.26	0.03	4.00			16.04	0.01	0.00	0.24
	Organic Acids (0.10)	Acetic Acid (0.60)	2.25	0.08	12.29	12.29			1.42	1.00	0.10	15.67			1.52	0.99	0.10	16.84
		Lactic Acid (0.40)	1.16	0.97					0.56	1.00					0.76	1.00
HHF^c (0.30)	Phenolic Substances (0.30)	Epicatechin (0.70)	4.36	1.00	37.44	37.44	0.17	36.48	6.58	1.00	0.30	46.53	0.19	31.77	6.27	1.00	0.24	36.43	0.20	35.87
		Catechin (0.30)	1.28	0.05					3.39	1.00					1.80	0.31
	Purine Alkaloids (0.20)	Theobromine (0.70)	28.12	0.04	11.06	11.06			30.35	0.01	0.06	9.61			30.23	0.01	0.06	9.51
		Caffeine (0.30)	5.47	0.97					5.05	1.00					4.87	1.00
	Macronutrients (0.20)	N (0.20)	2.21	0.53	25.49	25.49			2.89	1.00	0.18	27.68			2.82	1.00	0.17	26.48
		P (0.30)	2.47	0.94					2.66	0.99					2.53	0.97
		K (0.20)	7.11	1.00					7.68	1.00					7.64	1.00
		Ca (0.10)	2.56	0.93					2.72	0.99					2.61	0.96
		Mg (0.10)	2.00	0.51					2.11	0.75					2.11	0.76
		Si (0.10)	0.70	0.00					1.30	0.21					1.00	0.03
	Micronutrients (0.20)	Fe (0.40)	41.13	1.00	17.41	17.41			10.71	0.00	0.06	8.84			27.93	1.00	0.13	19.90
		Zn (0.40)	24.72	0.06					28.18	0.29					27.19	0.20
		Mn (0.20)	24.16	0.36					27.61	0.85					27.78	0.87
	Potentially Toxic Elements (0.10)	Cd (0.30)	0.60	0.00	8.59	8.59			0.80	0.00	0.05	7.33			0.50	0.02	0.05	7.69
		Pb (0.30)	0.00	1.00					0.00	1.00					0.00	1.00
		Ba (0.20)	6.50	0.09					6.40	0.10					6.50	0.09
		Cu (0.20)	17.80	0.87					16.70	0.76					18.10	0.89
FSF^d (0.20)	Cd (0.30)		0.60	0.00	0.09	0.09	0.10	20.89	0.80	0.00	0.00	0.00	0.09	15.52	0.50	0.02	0.01	1.77	0.10	18.38
	Pb (0.30)		0.00	1.00	61.02	61.02			0.00	1.00	0.30	63.58			0.00	1.00	0.30	59.76
	Ba (0.20)		6.50	0.09	3.61	3.61			6.40	0.10	0.02	4.33			6.50	0.09	0.02	3.53
	Cu (0.20)		17.80	0.87	35.28	35.28			16.70	0.76	0.15	32.09			18.10	0.89	0.18	35.42

^aCacao industry function.

^bChocolate flavor function.

^cHuman health function.

^dFood safety function.

Critical limits of the CQI indicators

In the following tables are shown theoretical information related to the values and descriptions of biochemical attributes (Table 4) and mineral composition (Table 5) that support the development of cacao quality index. To support the interpretation of scores of mineral elements Mn, Fe, Zn, Cu, Cd, Ba and Pb in dry cacao beans, the information as critical limits, recommended intakes, deficiency and toxicity shown in Table 6.

Table 4. Summary of characteristics of biochemical attributes observed in dry cacao beans

Attribute	Unit	Value or range of values	Sample Descriptions	Literature
pH	Dimensionless	4.5–5.5	For fermented process in cacao beans, most of enzymes requires this range of pH optimum.	Biehl et al.^[Citation20]
		5.0–5.5	Fermented cacao beans with higher flavor potential.	Biehl et al.^[Citation21]
		4.0–4.5	Fermented cacao beans with low flavor potential.	Biehl & Voigt^[Citation22]
Total acidity	mEq NaOH 100 g^−1	12-15	Desired range by the chocolate industry in cacao beans.	Lopez & Passos^[Citation23]
		39	Maximum total fixed acidity.	Bonvehí & Coll^[Citation24]
		35	A nonvolatile fixed acidity.	Bonvehí & Coll^[Citation24]
		8-29	Dry cacao beans from different geographic regions.	Holm et al.^[Citation25]
Acetic acid	mg g^−1	4.19–8.09	Dry cacao beans from different geographic regions.	Jinap & Dimick^[Citation26]
		1.3–11.8	Dry cacao beans from different cropping regions.	Holm et al.^[Citation25]
Lactic acid	mg g^−1	2.1–5.0	Dry cacao beans from different geographic regions.	Jinap & Dimick^[Citation26]
		0.6–11.1	Dry cacao beans from different geographic regions.	Holm et al.^[Citation25]
		5	Optimally fermented cacao samples showed this lactic acid content.	Bonvehí & Coll^[Citation24]
Sucrose	mg g^−1	0.54	Supposed samples of dry cacao beans in ideal condition.	Reineccius et al.^[Citation27]
		9.94	Supposed samples of dry cacao beans in not ideal condition.	Reineccius et al.^[Citation27]
		0.82	Dry beans of Forastero cacao from São Paulo, Brazil.	Brito et al.^[Citation28]
		1.12	Dry beans of Common cacao (Forastero) from Bahia, Brazil.	Loureiro^[Citation29]
Fructose	mg g^−1	3.6	Supposed samples of dry cacao beans in ideal condition.	Reineccius et al.^[Citation27]
		4.9	Supposed samples of dry cacao beans in not ideal condition.	Reineccius et al.^[Citation27]
		1.27	Dry beans of Forastero cacao from São Paulo, Brazil.	Brito et al.^[Citation28]
		5.94	Dry beans of Common cacao (Forastero) from Bahia, Brazil.	Loureiro^[Citation29]
Glucose	mg g^−1	0.43	Supposed samples of dry cacao beans in ideal condition.	Reineccius et al.^[Citation27]
		3.4	Supposed samples of dry cacao beans in not ideal condition.	Reineccius et al.^[Citation27]
		1.93	Dry beans of Common cacao (Forastero) from Bahia, Brazil.	Loureiro^[Citation29]
Lipids	g kg^−1	500	Typical average content approximates reported in dry cacao beans.	Biehl et al.^[Citation30]; Ávila & Dias^[Citation31]; Figueira et al.^[Citation32]; Beckett^[Citation33,Citation34]
		457.5	Dry beans of PH-16 cacao clone.	Cruz^[Citation35]
Proteins	g kg^−1	150-200	Typical range of contents reported in dry cacao beans.	Biehl et al.^[Citation30]
		118	Dry beans of Forastero cacao from São Paulo, Brazil.	Brito et al.^[Citation28]
		137.5	Dry beans of PH-16 cacao clone.	Cruz^[Citation35]
Free Amino Acids	mg g^−1	168.6	Dry beans of Common cacao (Forastero) from Bahia, Brazil.	Loureiro et al.^[Citation29]
		5.0–25.2	Range of contents related to the cacao beans from different cropping regions.	Rohsius et al.^[Citation37]
		13.0	Average content related to the cacao beans from different cropping regions.	Rohsius et al.^[Citation37]
		35.3	Dry beans of Forastero cacao from São Paulo, Brazil.	Brito et al.^[Citation28]
		34.38–50.42	Under-fermented cacao beans, PBC 140 cacao clone, Perak, Malaysia.	Yusep et al.^[Citation38]
Attribute	Unit	Value or range of values	Sample Descriptions	Literature
Theobromine	mg g^−1	8- 21	Not information about the origin of cacao samples.	Schwan & Fleet^[Citation39]
		16.63–29.38	Dry cacao beans from Maylasian	Ramli et al.^[Citation40]
		5.53	Dry cacao of PH-16 cacao clone.	Cruz^[Citation35]
Caffeine	mg g^−1	0.8–2.3	Not information about the origin of cacao samples.	Schwan & Fleet^[Citation39]
		2.52–4.98	Dry cacao beans from Maylasian.	Ramli et al. ^[Citation40]
(-)+catechin	mg g^−1	0.82	Dry beans of PH-16 cacao clone.	Cruz et al. ^[Citation41]
		3.0	Dry beans of Common cacao (Forastero).	Loureiro ^[Citation29]
(-)-catechin	mg g^−1	2.25–3.91	Dry cacao beans from Maylasian.	Ramli et al. ^[Citation40]
(-)-epicatechin	mg g^−1	6.65	PH-16 cacao clone.	Cruz et al. ^[Citation41]
		3.49–5.27	Dry cacao beans from Maylasian.	Ramli et al. ^[Citation40]
		9.24	Dry beans of Common cacao (Forastero).	Loureiro ^[Citation29]
Total phenols	mg g^−1	52.5	PH-16 cacao clone.	Cruz et al. ^[Citation42]
		100	Average value related that varying according to the geographical location.	Oliveira ^[Citation43]
		30-215.5	Different genotypes from different geographic regions.	Loureiro et al. ^[Citation11]
		34.93–60.22	Dry cacao beans from Maylasian.	Ramli et al. ^[Citation40]
		157	Dry beans of Forastero cacao from São Paulo, Brazil.	Brito et al. ^[Citation28]

Table 5. Summary of characteristics of mineral composition observed in dry cacao beans

Attribute	Unit	Value or range of values	Sample Descriptions for dry cacao beans	Literature
N	g kg^−1	20–24	Forastero cacao from Bahia, Brazil.	Santana et al. ^[Citation44]
		33.4	Forastero cacao (Catongo) from Bahia, Brazil.	Malavolta et al. ^[Citation45]
		21.62–23.91	Highlight of productive cacao clones from Brazil.	Muniz et al. ^[Citation46]
P	g kg^−1	3.6–6.9	Forastero cacao from Bahia, Brazil.	Santana et al. ^[Citation44]
		2.1	Forastero cacao (Catongo) from Bahia, Brazil.	Malavolta et al. ^[Citation45]
		2.95–3.91	Highlight of productive cacao clones from brazil.	Muniz et al. ^[Citation46]
		1.96–6.21	Different genotypes from different geographic regions.	Loureiro et al. ^[Citation11]
K	g kg^−1	9.3–16.4	Forastero cacao from Bahia, Brazil.	Santana et al. ^[Citation44]
		8.1	Forastero cacao (Catongo) from Bahia, Brazil.	Malavolta et al. ^[Citation45]
		7.49–11.84	Highlight of productive cacao clones from brazil.	Muniz et al. ^[Citation46]
		4.52–25.58	Different genotypes from different geographic regions.	Loureiro et al. ^[Citation11]
Ca	g kg^−1	1.0–2.1	Forastero cacao from Bahia, Brazil.	Santana et al. ^[Citation44]
		0.46–1.19	Highlight of productive cacao clones from brazil.	Muniz et al. ^[Citation46]
		0.8–2.70	Different genotypes from different geographic regions.	Loureiro et al. ^[Citation11]
Mg	g kg^−1	2.6–6.6	Forastero cacao from Bahia, Brazil.	Santana et al. ^[Citation44]
		1.9	Forastero cacao (Catongo) from Bahia, Brazil.	Malavolta et al. ^[Citation45]
		1.59–3.09	Highlight of productive cacao clones from brazil.	Muniz et al. ^[Citation46]
Si	g kg^−1	0.40–4.70	Two different genotypes from different cropping sites of the same geographic region.	Loureiro et al. ^[Citation11]
Fe	mg kg^−1	80	Forastero cacao (Catongo) from Bahia, Brazil.	Malavolta et al. ^[Citation45]
		22.85–159.10	Highlight of productive cacao clones from brazil.	Muniz et al. ^[Citation46]
		7.57–92.02	Different genotypes from different geographic regions.	Loureiro et al. ^[Citation11]
Mn	mg kg^−1	28	Forastero cacao (Catongo) from Bahia, Brazil.	Malavolta et al. ^[Citation45]
		3.98–10.36	Highlight of productive cacao clones from brazil.	Muniz et al. ^[Citation46]
		11.10–117.01	Different genotypes from different geographic regions.	Loureiro et al. ^[Citation11]
Zn	mg kg^−1	47	Forastero cacao (Catongo) from Bahia, Brazil.	Malavolta et al. ^[Citation45]
		23.66–38.78	Highlight of productive cacao clones from brazil.	Muniz et al. ^[Citation46]
		13.51–156.0	Different genotypes from different geographic regions.	Loureiro et al. ^[Citation11]
Cu	mg kg^−1	16	Forastero cacao (Catongo) from Bahia, Brazil.	Malavolta et al. ^[Citation45]
		14.04–23.55	Highlight of productive cacao clones from brazil.	Muniz et al. ^[Citation46]
		2.48–173	Different genotypes from different geographic regions.	Loureiro et al. ^[Citation11]
Cd	mg kg^−1	0.06–1.50	Different genotypes from different geographic regions.	Loureiro et al. ^[Citation11]
Ba	mg kg^−1	1.90–11.70	Two different genotypes from different cropping sites of the same geographic region.	Loureiro et al. ^[Citation11]
Pb	mg kg^−1	0–4.09	Different genotypes from different geographic regions.	Loureiro et al. ^[Citation11]

Table 6. Information about critical limits, recommended intakes, deficiency and toxicity of mineral elements Mn, Fe, Zn, Cu, Cd, Ba and Pb used for interpretation of the scores these cacao quality indicators

Variable	Critical limits for cacao beans	Recommended Intakes	Deficiency	Toxicity
Mn	Undefined.	2 mg kg^−1 (body weight) day^−1 .[Citation47]	Several diseases in humans and animals.^[Citation48]	Is not been reported in humans, even though certain vegetarian diets could provide up to 20 mg day^−1 of manganese.^[Citation47,Citation49]
Fe	Undefined.	Normative requirements from diets differing in iron bioavailability (15%, 12%, 10%, and 5%), different groups of age ranges and genres (infants and children, adults, male, female, postmenopausal and lactating) range between 3.9 to 65.4 mg kg^−1 (body weight) day^−1.[Citation50]	In human populations is still a challenge to global public health.^[Citation51]	Ingestion of more than 20 mg kg^−1 of iron supplements or medications can cause numerous health changes (Aggett; FNB & IOM)^[Citation52,Citation53], and values above 60 mg kg^−1 can lead to multisystem organ failure, coma, convulsions, and even death.^[Citation54,Citation55]
Zn	Undefined.	Normative requirements for zinc from diets differing in zinc bioavailability (15%, 30%, 50% and 80%), range between 36 to 1200 µg kg^−1 (body weight) day^−1 (FAO & WHO, 2005).^[Citation50] The upper level of zinc intake for an adult man is set at 45 mg day^−1 (690 mmol day^−1) and extrapolated to other groups in relation to basal metabolic rate. For children this extrapolation means an upper limit of intake of 23–28 mg day^−1 (350–430 mmol day^−1), which is close to what has been used in some of the zinc supplementation studies.^[Citation50]	It has been estimated that 2 billion people on the planet are zinc deficient.^[Citation56]	Zinc toxicity in humans is minimal.^[Citation57] However, the toxicity signs are nausea, vomiting, diarrhea, fever, and lethargy and have been observed after ingestion of 4–8 g (60–120 mmol) of zinc.^[Citation50]
Cu	50 mg kg^−1 (EU, 2013).^[Citation58]	10 mg kg^−1(body weight) day^−1.[Citation59]	Clinically evident or frank dietary copper deficiency is relatively uncommon.^[Citation60]	Copper toxicity is rare in the general population.^[Citation59]
Cd	0.60 mg kg^−1 (EU, 2014).^[Citation61]	The Joint FAO/WHO Expert Committee on Food Additives established a provisional tolerable monthly intake of 25 µg kg^−1 (body weight), whereas the EFSA Panel on Contaminants in the Food Chain nominated a tolerable weekly intake of 2.5 µg kg^−1 (body weight) to ensure sufficient protection of all consumers.^[Citation62]	It is not nutrient.	Cadmium toxicity leads to renal and bone problems and reproductive difficulties.^[Citation62]
Ba	Undefined.	Using a no-observed-adverse-effect level in humans of 0.21 mg barium kg^−1 body weight) per day, a tolerable intake value of 0.02 mg kg^−1 (body weight) day^−1 for barium and barium compounds has been recommended.^[Citation63]	It is not nutrient.	The critical end-points in humans for toxicity resulting from exposure to barium and barium compounds appear to be hypertension and renal function.^[Citation63] However, also may cause gastroenteritis (vomiting, diarrhea, abdominal pain), hypopotassemia, cardiac arrhythmias, and skeletal muscle paralysis.^[Citation63]
Pb	10 mg kg^−1 (EU, 2006).^[Citation64]	Lead is considered one of the most toxic metals and is undesirable in every type of food.^[Citation65]	It is not nutrient.	The toxicity of lead has been widely reported, including the numerous symptoms of lead poisoning.^[Citation66]

Performance of the CQI and its functions

The CQI functions consist of simultaneous evaluations after defining the behavior of the indicators (curve type) and distribution of their weights (Tables 2 and 3). The CQI is the sum of the individual performance of each function, and each function is individually the reflection of a certain set of attributes (Tables 2 and 3). For interpretation purposes, the CQI scores have three classifications: ‘good’, when the score is ≥0.70; ‘regular’ when values are between 0.31 and 0.69; and, ‘bad’, when the score is ≤0.30.

Statistical analysis

Data manipulation and statistical procedures used in this study were performed in R Program.^[67]

Results

The baseline values and threshold limits (upper and lower) from different scoring curves (SSF types) showed in Table 7 were established according to published data about dry cacao beans (Tables 2–6) and expert opinion. Table 8 shows the scores of the CQI and its functions (CIF, CFF, HHF and FSF).

Table 7. Baseline values and threshold limits and curve slope of the standardized scoring functions (SSF) for cacao quality indicators in cacao quality index

Quality Indicador	Unit	Function	L	B	U	B1	O	B2	Curve Signal	Curve Slope
pH	Dimensionless	More is better^a	4.00	5.00	6.00				+	2.634
Total Acidity	meq NaOH 100 g^−1	Optimum^b	10.00		17.00	12.00	13.50	15.00	+ and -	1.112
Acetic Acid	mg g^−1	Less is better^c	1.00	2.00	3.00				-	2.781
Lactic Acid	mg g^−1	Less is better^c	0.40	2.20	5.00				-	0.715
Sucrose	mg g^−1	More is better^a	1.00	1.50	2.00				+	5.005
Fructose	mg g^−1	More is better^a	2.00	5.50	9.00				+	0.834
Glucose	mg g^−1	More is better^a	0.50	3.00	5.00				+	1.001
Lipids	g kg^−1	More is better^a	300.00	400.00	500.00				+	0.025
Proteins	g kg^−1	More is better^a	120.00	160.00	200.00				+	0.063
Amino Acids	mg g^−1	More is better^a	5.00	15.00	25.00				+	0.250
Catechin	mg g^−1	More is better^a	0.50	2.00	4.00				+	1.001
Epicatechin	mg g^−1	More is better^a	0.50	2.00	4.00				+	0.834
Total Phenols	mg g^−1	Optimum^b	20.00		100.00	40.00	60.00	80.00	+ and -	0.072
Theobromine	mg g^−1	Less is better^c	5.00	20.00	40.00				-	0.125
Caffeine	mg g^−1	Less is better^c	4.00	6.00	8.00				-	1.001
Nitrogen	g kg^−1	More is better^a	1.50	2.20	3.00				+	2.503
Phosphorus	g kg^−1	More is better^a	1.50	2.20	3.00				+	1.854
Potassium	g kg^−1	More is better^a	4.00	6.00	8.00				+	1.001
Calcium	g kg^−1	More is better^a	1.50	2.30	3.00				+	2.634
Magnesium	g kg^−1	More is better^a	1.00	2.00	3.00				+	1.221
Silicon	g kg^−1	More is better^a	0.50	1.50	3.00				+	1.788
Iron	mg kg^−1	More is better^a	10.00	20.00	30.00				+	0.167
Zinc	mg kg^−1	More is better^a	15.00	30.00	50.00				+	0.125
Copper	mg kg^−1	Optimum^b	5.00		50.00	15.00	28.00	40.00	+ and -	0.173
Manganese	mg kg^−1	More is better^a	10.00	25.00	40.00				+	0.14
Barium	mg kg^−1	Less is better^c	1.00	5.00	8.00				-	0.371
Cadmium	mg kg^−1	Less is better^c	0.10	0.35	0.60				-	7.150
Lead	mg kg^−2	Less is better^c	0.00	0.50	1.00				-	2.503

^aMore is better: L – lower threshold, at which or below the score is 0, B – baseline, at which score is 0.5, U – upper threshold, at which or above score is 1.0

^bOptimum: B1 – lower baseline is 0.5, O – Optimum level, at which score is 1.0, B2 – upper baseline is 0.5.

^cLess is better: L – lower threshold, at which or below the score is 1.0, B – baseline, at which score is 0.5, and U – upper threshold, at which or above score is 0.

Table 8. Summary of scores of the cacao quality index and its functions (cacao in 12 cropping sites characterized by soil types cultivated with PH-16 cacao clone in the humid region of Bahia, Brazil

				Functions
				Cacao Industry (0.20)	Chocolate Flavor (0.30)	Human Health (0.30)	Food Safety (0.20)
Rank	Cropping sites^a	Cacao Quality Index (1.0)	Classification	Average ± Standard Deviation (n = 3)
1	10 PVAd	0.692 ± 0.067	Regular^b	0.1231 ± 0.0072	0.1847 ± 0.042	0.2012 ± 0.0036	0.1831 ± 0.0264
2	1 LAd cam	0.6579 ± 0.0061		0.1073 ± 0.02	0.1695 ± 0.0147	0.1867 ± 0.014	0.1944 ± 0.0069
3	4 LAd	0.6372 ± 0.0379		0.1361 ± 0.0427	0.1667 ± 0.0191	0.1732 ± 0.0059	0.1611 ± 0.0314
4	7 CXd	0.6283 ± 0.0959		0.1305 ± 0.0636	0.1934 ± 0.0374	0.1895 ± 0.02	0.1148 ± 0.0249
5	12 PVAd coe	0.5878 ± 0.0264		0.1154 ± 0.0119	0.1435 ± 0.0127	0.2026 ± 0.0184	0.1264 ± 0.016
6	11 PVA ali	0.5871 ± 0.0716		0.1079 ± 0.0196	0.1785 ± 0.0386	0.1799 ± 0.0104	0.1207 ± 0.0159
7	9 PAd lat	0.5633 ± 0.0269		0.1043 ± 0.015	0.1291 ± 0.0069	0.1902 ± 0.0142	0.1397 ± 0.0095
8	5 LVAd	0.5605 ± 0.0361		0.1012 ± 0.0259	0.148 ± 0.035	0.1579 ± 0.0132	0.1535 ± 0.0272
9	2 PVAd	0.5533 ± 0.0309		0.0922 ± 0.0229	0.1414 ± 0.0296	0.2004 ± 0.0154	0.1193 ± 0.0301
10	6 PVAe cam	0.5513 ± 0.0363		0.0801 ± 0.0238	0.145 ± 0.0359	0.1887 ± 0.0183	0.1376 ± 0.0257
11	8 LVAd arg	0.5428 ± 0.0241		0.0711 ± 0.0027	0.1658 ± 0.0328	0.2024 ± 0.0184	0.1035 ± 0.0022
12	3 PVAd	0.5417 ± 0.0689		0.0989 ± 0.0287	0.1582 ± 0.0455	0.1869 ± 0.0133	0.0977 ± 0.003

^aCropping sites characterized as soil types by Brazilian System of Soil Classification^[13]: cambisolic Dystrophic Yellow Latosol (1 LAd cam); typic Dystrophic Red-Yellow Argisol (2 PVAd); abrupt Dystrophic Red-Yellow Argisol (3 PVAd); typic Dystrophic Yellow Latosol (4 LAd); typic Dystrophic Red-Yellow Latosol (5 LVAd); cambisolic Eutrophic Red-Yellow Argisol (6 PVAe cam); typic Dystrophic Haplic Cambisol (7 CXd); argisolic Dystrophic Red-Yellow Latosol (8 LVAd arg); latosolic Dystrophic Yellow Argisol (9 PAd lat); typic Dystrophic Red-Yellow Argisol (10 PVAd); typic Alitic Red-Yellow Argisol (11 PVA ali); abrupt Cohesive Dystrophic Red-Yellow Argisol (12 PVAd coe).

^bWhen the values are between 0.31 and 0.69.

The cropping sites typic Dystrophic Red-Yellow Argisol (10 PVAd) and abrupt Dystrophic Red-Yellow Argisol (3 PVAd) correspond to the highest and lowest CQI scores obtained in this study (Table 8). The 10 PVAd reached the CQI score of 0.692, and obtained 0.1231 on CIF (0.20), 0.1847 on CFF (0.30), 0.2012 on HHF (0.30) and 0.1831 on FSF (0.20) (Table 8). In turn, the 3 PVAd reached the CQI score of 0.5417, and obtained 0.0989 on the CIF (0.20), 0.1582 on the CFF (0.30), 0.1869 on the HHF (0.30) and 0.0977 on the FSF (0.20) (Table 8). By the SiBCS these soils differ only by the typical character (10 PVAd) and abrupt (3 PVAd). These two cropping sites were also characterized by the cabruca system, differing only in shade tree density per hectare, 35 in the 10 PVAd and 60 in the 3 PVAd (Table 1).

The relative percentages to the weights (potential scores) of the CQI functions in the 10 PVAd were: 60% CIF, 60% CFF, 66% HHF, 90% FSF (Table 8). In 3 PVAd, the relative percentages were: 50% CIF, 53% CFF, 63.33% HHF, 50% FSF (Table 8). None of the cropping sites scored scores that could be “bad” or “good” (Table 8). Summaries of the CQI calculation worksheets for all three sample observations corresponding to sites 10 PVAd and 3 PVAd are shown in Tables 2 and 3, respectively. Tables 2 and 3 show the structure of the CQI and all the information necessary to obtain the final scores. The real value of each of the quality indicators obtained from the chemical analyzes is shown as the observed value because it corresponds to the observation of the sample of the respective cropping site (Tables 2 and 3).

Tables 2 and 3 also show the original scores of the indicators after the definition of standardized scoring functions and the scorching itself by the application of Equations 1(1) $v=\frac{1}{1+{\left(\frac{B-L}{x-L}\right)}^{2S\left(B+x-2L\right)}}$(1) and 2(2) $S=\frac{log\left(\frac{1}{v}\right)-1}{log\left(\frac{B-L}{x-L}\right)\times 2\left(B+x-2L\right)}$(2) described in the methodology of this study. Then, the scores corrected by the weight of each primary indicator and the relative percentage of the contribution of each of them to the score of its respective functions (Tables 2 and 3) are also shown. Functions scores and the relative percentage of contribution in CQI are shown sequence (Tables 2 and 3).

Discussion

The CQI (Table 8) was able to differentiate the studied cacao agroecosystems by the joint interpretation of attributes of cacao quality according to the needs of the cultivation in the cacao region of Bahia, Brazil and its environmental nuances that integrated the theoretical structure of this methodology. The two cropping sites selected for CQI methodological demonstration, 10 PVAd and 3 PVAd, showed the same cropping system (Cabruca) may have a low or high potential for the production of quality cacao beans. But this depends on technical interventions such as shade management, removal of old plants, and replanting and nutritional management.^[68]

The upper threshold set for the lipids indicator (500 g kg⁻¹) (Table 7) is approximately 10% higher than the average content of this attribute of PH-16 cacao beans (366.5 g kg⁻¹).^[10] Lipids content is one of the attributes that most differentiate the cacao genotypes.^[11] It is necessary to check whether the average content of lipids of the PH-16 clone beans is a genetic trait, or is it just an environmental effect. Therefore, some of the observations of cropping sites 10 PVAd (Table 2) and 3 PVAd (Table 3) obtained corrected scores of the lipids indicator equal to zero. The CQI was developed for universal application; however, it is necessary to characterize different genotypes in order to obtain a quality map that allows different interpretations and technical applications.

In Table 3 that characterizes the cropping site with lower performance of the CQI, 3 PVAd, it is possible to verify that the main primary indicators that would interfere with the differences found in relation to the site 10 PVAd (Table 2) were proteins, total free amino acids, simple carbohydrates, total acidity, cadmium and barium. The FSF sample observations for cropping site 3 PVAd (Table 3) reached approximately 50% of the weight of this function. In contrast, the cropping site 10 PVAd (Table 2) reached approximately 100% of the FSF weight. For both cropping sites 3 PVAd and 10 PVAd high levels of potentially toxic elements (PTE).^[9] The FSF was specially created to detect unsatisfactory levels of PTE, Cd, Pb, Ba and Cu in cacao beans (Tables 2 and 3). There is growing worldwide concern about the levels of these PTEs in food, so there are some international regulations that define the permissible limits of these elements.^[11] The discriminant power of the FSF reveals the importance of the method proposed by the CQI for complete studies on the quality of cacao beans and also the usefulness of this index to monitor and differentiate the cacao produced in different cropping systems. This methodology can be applied and/or adapted in regional studies because the critical limits of the CQI indicators for cacao agroecosystems (Tables 4 and 6) were based on the cropping, technological and environmental reality of the cacao processing.

Conclusion

The typic Dystrophic Red-Yellow Argisol, in the cabruca cropping system with 35 shade trees per hectare, reached the highest CQI score. The lowest score obtained from the CQI corresponded to the site characterized by the abrupt Dystrophic Red-Yellow Argisol, in the cabruca cropping system with 60 shade trees per hectare. All cropping sites obtained CQI values between 0.54 and 0.69 and were therefore classified as a regular index according the range values of 0.39 to 0.69 established as criteria of this classification. The primary indicators that determined the differences between the highest and lowest CQI score, corresponding to the two Dystrophics Red-Yellow Argisols, were proteins, total free amino acids, simple carbohydrates (sucrose, fructose and glucose), total acidity, cadmium and barium.

Acknowledgments

This paper is part of the project “Linking soil quality and cacao quality in Bahia, Brazil”. To run fundamental steps of this research, the corresponding author was supported by the Brazilian National Council for Scientific and Technological Development (CNPq) with a Postdoctoral fellowship.