Use of a portable VIS NIR device to predict table olives quality

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Abstract

There is a growing interest today in high-quality and sustainable production especially in the agro-food sector where the use of automated, precise and non-destructive monitoring analytical systems is spreading more and more. In table olives consumption colour and texture are very important quality attributes deriving from ripening, size of the cell wall, middle lamella and fibrous tissues that can be compromised by bruising during harvest operations or postharvest handling. Mechanical damage also accelerates physiological processes, which lead to senescence, spoilage and loss of nutritional value. Nocellara del Belice cultivar is one of the most important table olive varieties in Italy both for the production and the marketed quantities, with an average annual production of 25,000 t. The aim of this study was to evaluate the feasibility of applying vis NIR spectroscopy as a non-destructive technique on Nocellara del Belice table olives to predict colour and firmness during harvest and post-harvest operations. The spectral acquisitions were performed using a portable vis NIR device (600 - 1000 nm). A regression model was considered to evaluate the prediction capacity of vis NIR starting from the observed values of a validation data set. The system gave excellent performance in predicting table olives colour (R2 = 0.96 for “hue”), while the results showed a very low vis NIR ability to predict Nocellara del Belice table olives firmness (R2 = 0.18) which make this device unsuitable for the purpose. The possibility of applying vis NIR spectroscopy in field before harvest or for selection in postharvest operations is very encouraging for colour prediction and seems to be not adequate for firmness or damage evaluation.
Lingua originaleEnglish
pagine (da-a)79-84
Numero di pagine6
RivistaCHEMICAL ENGINEERING TRANSACTIONS
Volume75
Stato di pubblicazionePublished - 2019

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All Science Journal Classification (ASJC) codes

  • Chemical Engineering(all)

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