EFFECT OF DRYING KINETICS ON COLOR OF “DEDO DE MOÇA” CHILI PEPPERS (<i>Capsicum baccatum</i>)

Andrade, Ednilton T. de; Figueira, Vitor G.; Teixeira, Luciana P.; Martinazzo, Ana Paula; Araujo, Kátia G. de L.

doi:10.1590/1809-4430-Eng.Agric.v39n5p659-667/2019

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Scientific Paper, Post-Harvest Science and Technology • Eng. agríc. (Online) 39 (5) • Sep-Oct 2019 • https://doi.org/10.1590/1809-4430-Eng.Agric.v39n5p659-667/2019 copy

EFFECT OF DRYING KINETICS ON COLOR OF “DEDO DE MOÇA” CHILI PEPPERS (Capsicum baccatum)

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

This study aimed to assess the drying kinetics of red and yellow chili peppers, “dedo-de-moça” variety, at different temperatures (35, 45, 55, and 70 °C) and model this phenomenon mathematically. We also evaluated the effect of these drying air temperatures on pepper fruit color. Pepper fruits were dried in a forced-air circulation dryer. Ten mathematical models were fitted to the experimental data by nonlinear regression analysis. After drying, pepper samples were analyzed for the following colorimetric parameters: lightness (L*), chroma or saturation (c), and hue angle (h), in order to determine the influence of drying air temperature on fruit coloration. Our findings showed that the higher the air temperature, the faster the drying process. Midilli model fitted well to all temperature data and best represented the drying kinetics of “dedo-de-moça” chili peppers. Moreover, fruit color was affected by the drying air temperatures, in which the higher the drying air temperature, the greater the color variation.

KEYWORDS
dehydration; hue angle; quality; Midilli model

Model name	Model equation	Eq. n°
Page	$MC = {exp(-kt}^{n})$	(1)
Approximation of Diffusion	$MC = aexp(- kt) + (1 - a)exp(- kbt)$	(2)
Two Term	$MC = aexp(- k_{0} t) + bexp(- k_{1} t)$	(3)
Two Term Exponential	$MC = aexp(- kt) + (1 - a)exp(- kat)$	(4)
Henderson and Pabis	$MC = aexp(- kt)$	(5)
Modified Henderson and Pabis	$MC = aexp(- kt) + bexp(- k_{0} t) + cexp(- k_{1} t)$	(6)
Logarithmic	$MC = aexp(- kt) + c$	(7)
Midilli	$MC = aexp(- {kt}^{n}) + bt$	(8)
Thompson	$MC = exp((- a(a^{2} + 4 bt)^{0.5}) / 2b)$	(9)
Verna	$MC = aexp(- kt) + (1 - a)exp(- kt)$	(10)

Yellow
Model	a	b	c	k	k0	k1	N	R² (%)	P (%)	SE (decimal)	χ² (decimal)
(1)				0.3337			0.7943	99.85	0.64	0.0160	0.0003
(2)	0.1853	0.1204		1.6232				99.92	0.67	0.0087	0.0001
(3)	0.4624	0.4624			0.2255	0.2255		98.91	4.00	0.0608	0.0037
(4)	0.1718			1.1776				99.77	1.73	0.0433	0.0019
(5)	0.9248			0.2255				98.91	4.00	0.1052	0.0111
(6)	0.3083	0.3083	0.3083	0.2255	0.2255	0.2255		98.91	4.00	0.0471	0.0022
(7)	0.8964		0.0409	0.2513				99.05	3.35	0.0630	0.0040
(8)	1.0003	-0.0031		0.3390			0.7510	99.99	0.32	0.0042	0.0000
(9)	0.1421	0.0826						91.50	11.21	0.2663	0.0709
(10)	0.1000			0.2480				97.75	6.71	0.1509	0.0228
Red
(1)				0.3329			0.8313	99.57	3.80	0.0666	0.0044
(2)	0.1008	0.0121		19.2378				99.95	1.59	0.0166	0.0003
(3)	0.4684	0.4684			0.2452	0.2452		99.33	3.11	0.0481	0.0023
(4)	0.1011			2.3185				99.67	2.21	0.0587	0.0034
(5)	0.9368			0.2452				99.33	3.11	0.0833	0.0069
(6)	0.3123	0.3123	0.3123	0.2452	0.2452	0.2452		99.33	3.11	0.0373	0.0014
(7)	0.9061		0.0390	0.2693				99.40	2.80	0.0557	0.0031
(8)	0.9849	-0.0058		0.3190			0.7924	99.72	3.39	0.0308	0.0009
(9)	0.2376	0.2343						90.00	25.65	0.3209	0.1030
(10)	0.1000			0.2665				98.32	6.47	0.1315	0.0173

Yellow
Model	a	b	c	k	k0	k1	N	R² (%)	P (%)	SE (decimal)	χ² (decimal)
(1)				0.4827			0.8489	99.84	13.07	0.0447	0.0020
(2)	0.8619	11.0486		0.3499				99.96	7.22	0.0165	0.0003
(3)	0.4723	0.4723			0.3846	0.3846		99.40	7.06	0.0501	0.0025
(4)	0.1479			2.3592				99.93	7.43	0.0301	0.0009
(5)	0.9446			0.3846				99.40	7.06	0.0867	0.0075
(6)	0.3149	0.3149	0.3149	0.3846	0.3846	0.3846		99.40	7.06	0.0388	0.0015
(7)	0.9340		0.0198	0.4103				99.47	13.75	0.0577	0.0033
(8)	0.9982	-0.0046		0.4862			0.7849	99.98	2.80	0.0086	0.0001
(9)	0.1410	0.0437						87.60	93.16	0.3937	0.1550
(10)	0.1000			0.4094				98.97	10.06	0.1134	0.0129
Red
(1)				0.4017			0.9688	99.90	5.45	0.0350	0.0012
(2)	0.9624	82.4723		0.3730				99.93	4.93	0.0206	0.0004
(3)	0.4936	0.4936			0.3825	0.3825		99.89	3.30	0.0211	0.0004
(4)	0.7029			0.4325				99.88	4.77	0.0386	0.0015
(5)	0.9872			0.3825				99.89	3.30	0.0366	0.0013
(6)	0.3291	0.3291	0.3291	0.3825	0.3825	0.3825		99.89	3.30	0.0164	0.0003
(7)	0.9879		-0.0013	0.3811				99.89	2.74	0.0258	0.0007
(8)	0.9969	-0.0019		0.4037			0.9384	99.93	3.43	0.0171	0.0003
(9)	0.1472	0.0484						85.07	98.78	0.4239	0.1797
(10)	0.1000			0.3875				99.87	2.53	0.0398	0.0016

Yellow
Model	a	b	c	k	k0	k1	N	R² (%)	P (%)	SE (decimal)	χ² (decimal)
(1)				0.6131			0.9355	99.66	16.62	0.0567	0.0032
(2)	0.0717	0.0256		21.1809				99.79	15.36	0.0318	0.0010
(3)	0.4887	0.4887			0.5713	0.5713		99.61	12.95	0.0350	0.0012
(4)	0.6152			0.7129				99.56	15.38	0.0646	0.0042
(5)	0.9773			0.5713				99.61	12.95	0.0606	0.0037
(6)	0.3258	0.3258	0.3258	0.5713	0.5713	0.5713		99.61	12.95	0.0271	0.0007
(7)	0.9852		-0.0115	0.5533				99.62	9.82	0.0421	0.0018
(8)	0.9986	-0.0160		0.5905			0.8025	99.95	4.46	0.0130	0.0002
(9)	0.1713	0.0519						81.92	105.99	0.4120	0.1697
(10)	0.1000			0.5851				99.53	11.95	0.0663	0.0044
Red
(1)				0.5304			0.9740	99.85	16.64	0.0396	0.0016
(2)	0.0414	0.0432		11.5156				99.88	16.74	0.0245	0.0006
(3)	0.4950	0.4950			0.5131	0.5131		99.84	14.47	0.0232	0.0005
(4)	0.8951			0.5241				99.83	13.94	0.0419	0.0018
(5)	0.9900			0.5131				99.84	14.47	0.0403	0.0016
(6)	0.3300	0.3300	0.3300	0.5131	0.5131	0.5131		99.84	14.47	0.0180	0.0003
(7)	0.9966		-0.0105	0.4974				99.86	7.23	0.0267	0.0007
(8)	0.9985	-0.0049		0.5342			0.9053	99.95	6.43	0.0128	0.0002
(9)	0.1460	0.0368						82.90	159.30	0.4195	0.1760
(10)	0.1000			0.5179				99.83	13.81	0.0419	0.0018

Yellow
Model	a	b	c	k	k0	k1	N	R² (%)	P (%)	SE (decimal)	χ² (decimal)
(1)				1.0520			1.1509	99.95	6.69	0.0188	0.0004
(2)	0.6095	1.0000		1.0757				99.60	17.63	0.0365	0.0013
(3)	0.5075	0.5075			1.0899	1.0899		99.63	16.85	0.0286	0.0008
(4)	1.0000			1.0757				99.60	17.63	0.0516	0.0027
(5)	1.0150			1.0899				99.63	16.85	0.0495	0.0025
(6)	0.3383	0.3383	0.3383	1.0899	1.0899	1.0899		99.63	16.85	0.0221	0.0005
(7)	1.0626		-0.0581	0.9434				99.95	4.57	0.0116	0.0001
(8)	0.9998	-0.0073		1.0199			1.0985	99.98	3.95	0.0057	0.0000
(9)	0.2082	0.0533						69.14	119.74	0.4585	0.2102
(10)	0.1000			1.0757				99.60	17.63	0.0516	0.0027
Red
(1)				0.9237			1.1678	99.57	12.85	0.0532	0.0028
(2)	70.7046	1.0077		1.5143				99.56	12.94	0.0361	0.0013
(3)	0.5072	0.5072			0.9687	0.9687		99.11	23.32	0.0440	0.0019
(4)	1.6679			1.2451				99.57	13.99	0.0532	0.0028
(5)	1.0144			0.9687				99.11	23.32	0.0762	0.0058
(6)	0.3381	0.3381	0.3381	0.9687	0.9687	0.9687		99.11	23.32	0.0341	0.0012
(7)	1.1489		-0.1523	0.7159				99.83	7.65	0.0235	0.0006
(8)	0.9986	-0.0430		0.7920			0.9756	99.83	8.91	0.0193	0.0004
(9)	0.1748	0.0454						70.57	119.63	0.4381	0.1919
(10)	0.1000			0.9557				99.07	23.98	0.0778	0.0060

Temperature (°C)	L*	a*	b*	C	h
70	42.51 a	32.85 a	26.59 a	42.26 a	38.99 a
55	41.63 ab	31.72 ab	25.57 a	40.77 ab	38.83 a
45	40.10 b	29.72 b	21.19 b	37.19 c	35.49 b
35	38.01 c	33.43 a	19.69 b	38.81 bc	30.52 c
	CV% = 2.72	CV% = 2.96	CV% = 4.39	CV% = 2.94	CV% = 4.51

Associação Brasileira de Engenharia Agrícola SBEA - Associação Brasileira de Engenharia Agrícola, Departamento de Engenharia e Ciências Exatas FCAV/UNESP, Prof. Paulo Donato Castellane, km 5, 14884.900 | Jaboticabal - SP, Tel./Fax: +55 16 3209 7619 - Jaboticabal - SP - Brazil
E-mail: revistasbea@sbea.org.br

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[1] *Corresponding author. Universidade Federal de Lavras/ Lavras - MG, Brasil. E-mail: ednilton@deg.ufla.br