Drying kinetics modeling of apple and zucchini slices vacuum impregnated with anthocyanins

Zapata, Luz Marina; Castagnini, Juan Manuel; Quinteros, Carlos Fabio; Veuthey, Mariana Jimenez

doi:10.1590/0103-8478cr20180839

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FOOD TECHNOLOGY • Cienc. Rural 49 (10) • 2019 • https://doi.org/10.1590/0103-8478cr20180839 copy

Drying kinetics modeling of apple and zucchini slices vacuum impregnated with anthocyanins

Modelagem da cinética de secagem de discos de maçã e abobrinha impregnadas a vácuo com antocianinas

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT:

The aim of this research was to study the drying kinetics of apple and zucchini slices enriched with anthocyanins and to evaluate the influence of drying temperature on the anthocyanin content of apple and zucchini snacks. Apple (Granny Smith) and zucchini (Cucurbita pepo) slices were enriched with anthocyanins by vacuum impregnation with blueberry juice. Then, slices were dehydrated at 40, 50 and 60 ºC with 1.0 m/s air flow. Dehydrated samples were referred to as anthocyanin enriched snacks. Diffusion coefficient values improved by increasing the drying temperature, within the 2.81×10^-10 to 5.78×10^-10 m²/s range for apple slices and 2.02×10^-10 to 3.99×10^-10 m²/s for zucchini slices. The activation energy was 31.19 kJ/mol and 80.33 kJ/mol for apple and zucchini slices respectively. Page, Weibull, Logarithmic, Henderson-Pabis and Lewis models best fitted the experimental data. Snacks obtained at 60 °C retained a higher concentration of anthocyanins, reaching values of 592.81±52.55 and 464.62±48.44 mg of cyaniding-3-glucoside equivalent/kg dry matter in apple and zucchini snacks respectively. Combination of vacuum impregnation and hot air drying was a technological alternative for producing snacks with functional properties.

Key words:
drying kinetics; mathematical modeling; functional snacks; anthocyanins

Universidade Federal de Santa Maria Universidade Federal de Santa Maria, Centro de Ciências Rurais , 97105-900 Santa Maria RS Brazil , Tel.: +55 55 3220-8698 , Fax: +55 55 3220-8695 - Santa Maria - RS - Brazil
E-mail: cienciarural@mail.ufsm.br

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[1] E-mail: zapatam@fcal.uner.edu.ar. ^*Corresponding author

Model name	Model equation	References
Newton (Lewis)	$MR = e^{- k.t}$	(DOYMAZ; et al, 2016DOYMAZ, I. et al. Characteristics of thin-layer infrared drying of green bean. Czech Journal of Food Sciences, (s.l.) , v.33, n.No. 1, p.83-90, 2016. Available from: <Available from: https://doi.org/10.17221/423/2014-CJFS >. Accessed: Aug. 13, 2018. doi: 10.17221/423/2014-CJFS. https://doi.org/10.17221/423/2014-CJFS... )
Henderson-Pabis	$MR = n_{1} . e^{- k_{1} .t} + n_{2} . e^{- k_{2}} + n_{3} . e^{- k_{3}}$	(PUENTE-DÍAZ et al., 2012)
Logarithmic	$R^{2} = \frac{\sum_{i}^{N} (M R_{i} - M R_{pre.i}) \sum_{i}^{N} (M R_{i} - M R_{exp. 1})}{{[\sum_{i}^{N} (M R_{i} - M R_{pre.i})^{2}] [\sum_{i}^{N} (M R_{i} - M R_{exp.i})^{2}]}^{\frac{1}{2}}}$	(DOYMAZ; et al, 2016DOYMAZ, I. et al. Characteristics of thin-layer infrared drying of green bean. Czech Journal of Food Sciences, (s.l.) , v.33, n.No. 1, p.83-90, 2016. Available from: <Available from: https://doi.org/10.17221/423/2014-CJFS >. Accessed: Aug. 13, 2018. doi: 10.17221/423/2014-CJFS. https://doi.org/10.17221/423/2014-CJFS... ; VEGA-GÁLVEZ et al., 2012VEGA-GÁLVEZ, Antonio et al. effect of selected pretreatments on convective drying process of blueberries (var. O’Neil). Food and Bioprocess Technology, (s.l.) , v.5, n.7, p.2797-2804, 2012. Available from: <Available from: https://doi.org/10.1007/s11947-011-0656-x >. Accessed: Aug. 1, 2018. doi: 10.1007/s11947-011-0656-x. https://doi.org/10.1007/s11947-011-0656-... )
Page	$SSE = \frac{1}{N} \sum_{i}^{N} (M R_{exp.i} - M R_{pre.i})^{2}$	(DOYMAZ; et al, 2016DOYMAZ, I. et al. Characteristics of thin-layer infrared drying of green bean. Czech Journal of Food Sciences, (s.l.) , v.33, n.No. 1, p.83-90, 2016. Available from: <Available from: https://doi.org/10.17221/423/2014-CJFS >. Accessed: Aug. 13, 2018. doi: 10.17221/423/2014-CJFS. https://doi.org/10.17221/423/2014-CJFS... )
Weibull	$RMSE = [\frac{1}{N} \sum_{i}^{N} (M R_{pre.i} - M R_{exp.i})^{2}]^{1 / 2}$	(CORZO et al., 2008CORZO, O. et al. Weibull distribution for modeling air drying of coroba slices. LWT - Food Science and Technology, (s.l.), v.41, n.10, p. 2023-2028, 2008. Available from: <Available from: https://doi.org/10.1016/j.lwt.2008.01.002 >. Accessed: Aug. 1, 2018. doi: 10.1016/j.lwt.2008.01.002. https://doi.org/10.1016/j.lwt.2008.01.00... )

	Temperature (°C)	D_eff 10^-10 (m²/s)	SSE	R²	RMSE
Apple	40	2.81 ± 0.38 ^aB	1.32	0.85	0.36
	50	3.97 ± 0.46 ^bB	0.66	0.87	0.33
	60	5.78 ± 1.26 ^cB	2.36	0.97	0.40
Zucchini	40	2.02 ± 0.28 ^aA	1.32	0.85	0.36
	50	2.06 ± 0.26 ^aA	0.66	0.87	0.33
	60	3.99 ± 0.44 ^bA	3.26	0.97	0.47

Model	P	A-40 °C	A-50 °C	A-60°C	Z-40 °C	Z-50 °C	Z-60 °C
Newton (Lewis)	k	0.399±0.020 ^aA	0.449±0.235 ^aA	0.690±0.035 ^cA	0.334±0.027 ^aB	0.336±0.025 ^aB	0.529±0.042 ^cB
Henderson-Pabis	k₁	0.298±0.020 ^aA	0.372±0.022 ^bA	0.653±0.038 ^cA	0.337±0.018 ^aA	0.355±0.021 ^aA	0.486±0.022 ^cB
	n₁	1.140±0.049 ^aA	1.099±0.044 ^aA	1.029±0.064 ^aA	0.966±0.049 ^aB	1.027±0.085 ^aA	1.063±0.042 ^aA
	k₂	0.547±0.027 ^aA	0.750±0.031 ^bA	0.523±0.028 ^aA	0.913±0.054 ^aB	0.693±0.036 ^bB	0.042±0.003 ^cB
	n₂	0.265±0.017 ^aA	-0.031±0.002 ^bA	0.301±0.019 ^aA	0.056±0.004 ^aB	0.096±0.007 ^bB	-0.344±0.013 ^cB
	k₃	0.211±0.011 ^aA	0.628±0.031 ^bA	0.521±0.023 ^cA	0.456±0.029 ^aB	0.992±0.052 ^bB	0.534±0.025 ^cA
	n₃	-0.366±0.020 ^aA	-0.143±0.011 ^bA	-0.338±0.019 ^aA	-0.015±0.001^aB	-0.108±0.004 ^bA	0.491±0.029 ^cB
Logarithmic	c	-0.143±0.009 ^aA	-0.091±0.004 ^bA	-0.023±0.001 ^cA	0.013±0.001^aB	0.008±0.001 ^bB	-0.042±0.004 ^cB
	k	0.298±0.010 ^aA	0.372±0.018 ^bA	0.653±0.031 ^cA	0.337±0.002 ^aB	0.355±0.021 ^aA	0.486±0.017 ^cB
	n	1.140±0.080 ^aA	1.099±0.040 ^aA	1.029±0.055 ^aA	0.966±0.059 ^aB	1.027±0.058 ^aA	1.063±0.049 ^aA
Page	c	0.951±0.039 ^aA	0.959±0.067 ^aA	0.988±0.060 ^aA	0.986±0.079 ^aA	1.017±0.050 ^aA	0.987±0.037 ^aA
	k	0.256±0.011 ^aA	0.310±0.023 ^bA	0.627±0.046 ^cA	0.346±0.018 ^aB	0.319±0.023 ^aA	0.433±0.022 ^cB
	n	1.349±0.093 ^aA	1.371±0.088 ^aA	1.130±0.056 ^aA	0.962±0.064 ^aB	1.059±0.038 ^aB	1.219±0.106 ^aB
Weibull	β	1.244±0.084 ^aA	1.276±0.072 ^aA	1.114±0.082 ^aA	0.942±0.039 ^aB	1.082±0.082 ^aB	1.198±0.084 ^aB
	α	2.603±0.183 ^aA	2.254±0.116 ^aA	1.489±0.076 ^cA	2.960±0.157 ^aA	2.997±0.168 ^aB	1.960±0.063 ^cB

Model	Parameter	A-40 °C	A-50 °C	A-60 °C	Z-40 °C	Z-50 °C	Z-60 °C
Newton (Lewis)	R²	0.9782	0.9786	0.9925	0.9949	0.9944	0.9896
	SSE	0.0350	0.0313	0.0107	0.0060	0.0075	0.0162
	RMSE	0.0483	0.0533	0.0258	0.0208	0.0223	0.0318
Henderson-Pabis	R²	0.9929	0.9811	0.9924	0.9939	0.9940	0.9926
	SSE	0.0076	0.0151	0.0074	0.0047	0.0053	0.0079
	RMSE	0.0276	0.0502	0.0260	0.0228	0.0230	0.0268
Logarithmic	R²	0.9945	0.9874	0.9940	0.9954	0.9954	0.9942
	SSE	0.0076	0.0151	0.0074	0.0047	0.0053	0.0079
	RMSE	0.0242	0.0410	0.0231	0.0197	0.0202	0.0238
Page	R²	0.9918	0.9897	0.9941	0.9956	0.9961	0.9964
	SSE	0.0131	0.0123	0.0073	0.0045	0.0045	0.0049
	RMSE	0.0318	0.0370	0.0228	0.0193	0.0185	0.0187
Weibull	R²	0.9893	0.9894	0.9944	0.9957	0.9961	0.9965
	SSE	0.0159	0.0141	0.0074	0.0047	0.0048	0.0051
	RMSE	0.0337	0.0376	0.0223	0.0190	0.0185	0.0184