M.L. Amodio, F. Piazzolla, F. Colantuono, G.Colelli Reference: The use of rapid FT-NIR methods to predict soluble solids, pH, titratable acidity and phenols of clingstone peaches (`Baby Gold 9´) M.L. Amodio, F. Piazzolla, F. Colantuono, G.Colelli Dip.to SAFE, Università di Foggia, Via Napoli 25, 71122 Foggia, Italy (g.colelli@unifg.it) Introduction Traditional analytic methods applied to the measurement of peach and nectarines maturity and quality index are destructive and have proved to be slow because involving a considerable amount of manual work, primarily for sample preparation. Therefore non-destructive analytical techniques, including spectroscopy, can be a valid support for monitoring the quality of peach fruits. Objetives The aim of this study was to evaluate the ability of FT-NIR technique applied on whole fruits and puree to predict soluble solids, titratable acidity, pH and total phenolic compounds of clingstone peaches. Even in the case of puree, the destructive analysis based on FT-NIR reading would be much faster and easy to perform, if compared to the chemical reference method. Materials and Methods 50 fruits ‘Baby Gold 9’ were used for the analysis soon after harvest. For each side of the intact fruit 3 spectra were acquired using a multipurpose analyzer (MPA, Bruker) spectrometer, working in the range of 4000-12500 cm-1. Then a wedge corresponding to the region of acquisition, was taken peeled and homogenized; the puree was put in a glass Petri dish and a single spectrum was acquired. On the same puree SSC, pH, TA were determined, while a portion was stored at -80°C until determination of phenol contents of the flesh. In addition, the skins were stored at -80°C for determination of phenols. The spectral data were analyzed using the OPUS software (version 7.2, Bruker Optics). The spectra were subjected to different pre-treatments, vector normalization (VN), multiplicative scatter correction (MSC), first (FD) and second derivative (SD), and their combination. Then a PLS regression was performed to built prediction model for each quality attribute. Results On intact clingstone peaches using the SNV transformation good results were obtained for SSC and skin phenols with R2 in cross validation of 84.9 and 68.1, respectively. For pH prediction R2 of 52.1 and RMSECV of 0.084 were obtained, whereas TA and flesh phenols could not be predicted with good accuracy. For purees, best results were observed for soluble solids with coefficient of determination of 84.1 and RMSECV of 0.454, while less lower accuracy were found for flesh phenols, followed by acidity and pH, with R2 of 52.9, 44.1 and 33.2, respectively. Conclusions The results suggested that this nondestructive technique can be a valid support to predict internal content of SSC, acidity and skin phenol content, normally evaluated with destructive and time consuming analysis, showing that no improvement was obtained with the homogenization of the sample. Further studies on method optimization and extension to other cultivars are encouraged.