NIR spectroscopy for the optimization of post harvest apple management

Apple production, with 60 millions of tons processed in a year, has a very important role in the world fruit market. Non destructive, rapid tools are required to assess fruit quality and to monitor changes during post harvest period. The aim of this study was to evaluate the feasibility of NIR spectroscopy in order to optimize post harvest apple management and to follow changes during storage. An automatic desk Vis/NIR system (600-1200nm) was used to classify two apple cultivars (Golden Delicious and Stark Red Delicious) on the basis of Brix degree. Two different ripeness classes (ripe and not ripe) were obtained for each cultivar and stored for about six months (0 °C in controlled atmosphere). A FT-NIR spectrometer (12500-4000 cm-1) was used to evaluate the physico-chemical and nutraceutical characteristics of fruit during long term storage by monthly sampling and analysis. Spectral data were elaborated by PLS regression and LDA classification techniques. Good correlation models were obtained between spectral data and compositional and rheological parameters, indicating that NIR spectroscopy is an effective, non destructive method to assess fruit quality. Classification techniques applied to spectral data showed good ability for the evaluation of storage time. Therefore NIR spectroscopy can be considered a valid tool for the optimization of storage management and for rapid quality assessment.