Non-destructive prediction of 'Hass' avocado dry matter via FT-NIR spectroscopy.
BACKGROUND
The inability to consistently guarantee internal quality of horticulture produce is of major importance to the primary producer, marketers and ultimately the consumer. Currently, commercial avocado maturity estimation is based on the destructive assessment of percentage dry matter (%DM), and sometimes percentage oil, both of which are highly correlated with maturity. In this study the utility of Fourier transform (FT) near-infrared spectroscopy (NIRS) was investigated for the first time as a non-invasive technique for estimating %DM of whole intact 'Hass' avocado fruit. Partial least squares regression models were developed from the diffuse reflectance spectra to predict %DM, taking into account effects of intra-seasonal variation and orchard conditions.


RESULTS
It was found that combining three harvests (early, mid and late) from a single farm in the major production district of central Queensland yielded a predictive model for %DM with a coefficient of determination for the validation set of 0.76 and a root mean square error of prediction of 1.53% for DM in the range 19.4-34.2%.


CONCLUSIONS
The results of the study indicate the potential of FT-NIRS in diffuse reflectance mode to non-invasively predict %DM of whole 'Hass' avocado fruit. When the FT-NIRS system was assessed on whole avocados, the results compared favourably against data from other NIRS systems identified in the literature that have been used in research applications on avocados.
Wedding
Brett B
BB
Rapid Assessment Unit, Centre for Tropical Agri-tech Research, and Innovative Food Technologies, Department of Employment, Economic Development and Innovation, Cairns 4870, QLD, Australia. brett.wedding@deed.qld.gov.au
White
Ronald D
RD
Grauf
Steve
S
Wright
Carole
C
Tilse
Bonnie
B
Hofman
Peter
P
Gadek
Paul A
PA
D000383:Agriculture; D005524:Food Technology; D005638:Fruit / Q000737:chemistry* / Q000592:standards; D016018:Least-Squares Analysis; D027421:Persea / Q000737:chemistry*; D011793:Queensland; D012621:Seasons; D017550:Spectroscopy, Fourier Transform Infrared / Q000379:methods*; D019265:Spectroscopy, Near-Infrared / Q000379:methods*