Abstract
In current work, the influences of high-humidity hot air impingement blanching (HHAIB) time (60, 90, 120, and 150 s), drying temperature (60, 65, 70, and 75 °C), and air velocity (6, 9, and 12 m/s) on drying characteristics and quality attributes of broccoli florets were explored. Extreme learning machine (ELM) was employed to describe the drying behavior of broccoli florets. Results showed that proper HHAIB pretreatment can extensively increase drying rate compared to the control group (unblanched samples). The entire drying process of broccoli florets occurred in the falling period. Besides, proper HHAIB pretreatment can enhance vitamin C preservation, the color quality, and the rehydration capacity of dried broccoli florets. Based on error analysis results, the prediction accuracy of the optimal ELM model with 4-50-1 topology is found to be satisfied for the moisture ratio prediction of broccoli florets during air impingement drying process, with the R2, , and MSE reached to 0.9993, 8.04e−5, and 2.01e−4, respectively.
Disclosure statement
No potential conflict of interests was reported by the author(s).
Nomenclature | ||
HHAIB | = | high-humidity hot air impingement blanching |
SLFN | = | single layer feed-forward neural network |
ELM | = | extreme learning machine |
R2 | = | coefficient of determination |
= | chi-suqare | |
MSE | = | mean square error |
MR | = | moisture ratio |
w.b. | = | wet basis |
CG | = | control group |
AID | = | air impingement drying |
Me | = | equilibrium moisture content |
d.b. | = | dry basis |
Mt | = | moisture content at time t |
M0 | = | initial moisture content |
DR | = | drying rate |
L* | = | lightness |
a* | = | redness/greenness |
b* | = | yellowness/blueness |
ΔE | = | total color difference |
H0 | = | hue angle |
BI | = | browning index |
RR | = | rehydration ratio |
Wr | = | the weight of the sample after rehydration |
Wd | = | the weight after drying |
Notes on contributors
Liu Z. L. and Xiao H. W. designed research; Liu Z. L. and Bai J. W. performed research; Liu Z. L., Yang W. X., Wang L., Deng L. Z., and Yu X. L. analyzed data; Liu Z. L., Zheng Z. A., Gao Z. J., and Xiao H. W. wrote the paper.