Many plant tissues can be observed thanks to autofluorescence of their cell wall components. Hyperspectral autofluorescence imaging using confocal microscopy is a fast and efficient way of mapping fluorescent compounds in samples with a high spatial resolution. However a huge spectral overlap is observed between molecular species. As a consequence, a new data analysis approach is needed in order to fully exploit the potential of this spectroscopic technique and extract unbiased chemical information about complex biological samples. The objective of this work is to evaluate multi-excitation hyperspectral autofluorescence imaging to identify biological components in wheat grains during their development through their spectral profiles and corresponding contribution maps using Multivariate Curve Resolution - Alternating Least-Squares (MCR-ALS), a signal unmixing algorithm under proper constraints. For this purpose two different scenarios are used: 1) analyzing the total spectral domain of data sets using MCR-ALS under non negativity constraint in both spectral and spatial modes; 2) analyzing a reduced spectral domain of data sets using MCR-ALS under non negativity in both modes and trilinearity constraint in spectral mode. Considering the original instrumental setup and our data analysis approach, we will demonstrate that extracted contribution maps and spectral profiles of constituents can provide complementary information used to identify molecules in complex biological samples. (C) 2019 Elsevier B.V. All rights reserved.