Quality monitoring of the food items by
spectroscopy provides information in a large number of
wavelengths including highly correlated and redundant
information. Although increasing the information, the increase in
the number of wavelengths causes the vision set-up to be more
complex and expensive. In this paper, three sparse regression
methods; lasso, elastic-net and fused lasso are employed for
estimation of the chemical and physical characteristics of one
apple cultivar using their high dimensional spectroscopic
measurements. The use of sparse regression reduces the number
of required wavelengths for prediction and thus, simplifies the
required vision set-up. It is shown that, considering a tradeoff
between the number of selected bands and the corresponding
validation performance during the training step can result in a
significant reduction in the number of bands at a small price in
the test performance. Furthermore, appropriate regression
methods for different number of bands and spectrophotometer
design are determined