Abstract

Optimised wavelength selection is important to the development of new types of inexpensive and portable near infrared instruments that might be used on fruit in orchards. The use of discrete bandwidth devices, such as light-emitting diodes, requires preselection of a small number of discrete wavelengths. In this work, a kiwifruit data set consisting of 834 absorbance spectra and corresponding fruit dry-matter measurements, an important maturity indicator for kiwifruit, has been subjected to an exhaustive wavelength search to build optimal multiple linear regression models of up to seven wavelengths. Using a standard partial least-squares model as a benchmark, a six-wavelength model has been identified as an optimum, predicting kiwifruit dry matter with r² of 0.88 and root mean square error of prediction (RMSEP) of 1.22%. The sensitivity of the model to shifts in the key wavelengths was also evaluated, revealing that a 1 nm offset or a 0.25 nm random noise component would be enough to increase the RMSEP by around 0.04% in actual dry matter value or 3% in relative percentage terms.

© 2015 The Author(s)

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