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ARS Home » Pacific West Area » Albany, California » Western Regional Research Center » Healthy Processed Foods Research » Research » Publications at this Location » Publication #364039
Research Project: New Sustainable Processing Technologies to Produce Healthy, Value-Added Foods from Specialty Crops

Location: Healthy Processed Foods Research

Title: Prediction of drying rate of nectarines (Prunus persica var. nucipersica) from real-time ambient weather factors during direct sun drying

Author
item Milczarek, Rebecca
item RAMIREZ-GUTIERREZ, DIANA - Purdue University
item ILELEJI, KLEIN - Purdue University

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 6/12/2019
Publication Date: 9/23/2019
Citation: Milczarek, R.R., Ramirez-Gutierrez, D.M., Ileleji, K.E. 2019. Prediction of drying rate of nectarines (Prunus persica var. nucipersica) from real-time ambient weather factors during direct sun drying [abstract]. International Congress on Engineering and Food (ICEF), September 23-26, 2019, Melbourne, Australia.

Interpretive Summary:

Technical Abstract: Sun drying of fruits and vegetables is an ancient food preservation technique, and it is still in widespread use, especially in developing countries. Sun drying is inexpensive and makes direct use of renewable energy, but it suffers from a lack of predictability and user control. To address these shortcomings and enable design of modern sun drying equipment, we sought to predict the drying rate of sliced nectarines (Prunus persica var. nucipersica) based on easily-measured, real-time weather factors. Four basic and 5 derived weather factors were continuously measured during 3 drying runs, conducted under ambient conditions in Albany, California, USA. Nectarine cultivar and weather conditions varied for each run, lending robustness to the modeling. Weight change of the samples was tracked and used to determine drying rate. Partial least squares regression modeling was used to determine the factors’ influences on drying rate during daylight hours; a variable importance in projection (VIP) score cutoff of 1.0 was used to determine the most important factors. It was found that solar radiation, Temperature-Humidity-Sun-Wind index, evapotranspiration, and relative humidity had the strongest influence on drying rate, with VIP scores of 1.31, 1.30, 1.24, and 1.06, respectively. The prediction error sum of squares was minimized with a 5-factor model, with a prediction R2 value of 0.63. Such a model would only be implementable if a full weather station (including a solar pyranometer) is available; if only a simple hygrometer is available, a model with prediction R2 value of 0.24 can still be achieved.