Detection of postharvest quality loss in broccoli by means of non-colorimetric reflection spectroscopy and hyperspectral imaging

• There is an overall increase of reflection intensity of broccoli during storage.
• Senescence-dependent changes of spectral signature occur in the red edge region.
• NDVI indicates both storability and nutrient status of broccoli.
• Euclidean distances of hyperspectral image data are suitable for quality detection.

Broccoli is a highly perishable vegetable. In academia, shelf life of broccoli is generally monitored using colorimetric devices based on diffuse reflection. However, actual quality and storability of a single broccoli curd after harvest remains uncertain before changes are noticed by the human eye. The aim of the present study was to evaluate broccoli quality loss during storage based on differences in reflectance spectra and to judge quality stages prior to color changes. Reflection spectra of broccoli harvested throughout one cultivation period in northern Germany (June–September) and stored at 16 °C and 95% relative humidity for four days were analyzed by principal component analysis (PCA). Apart from an overall increase of reflection in the visible range, changes of the spectral signature were limited to the red edge region. Quality and storability of broccoli could sufficiently be rated by the help of the normalized difference vegetation index (NDVI) and the inflection point of the red edge region, respectively. The inflection point was more sensitive to quality changes, while the NDVI was additionally influenced by differences in fertilizer application during cultivation. Analysis of hyperspectral images of broccoli curds showed an increase in both means and variance of Euclidean distances during storage. This applied to the overall VIS spectrum but was most prominent in the range of 530–650 nm. The variation could adequately be demonstrated by the help of composite images, when reflection intensities of three different wavelengths (530, 590, and 650 nm) were placed as RGB image channels. Means of Euclidean distances were highly correlated with means of the colorimetric parameter b∗ (R2 = 0.92). Detected variation within the images was most likely based on different senescence status of single florets. However, classification procedures failed to sort the derived information into image classes because reflection intensity increased rather gradually. Further examinations at lower storage temperatures are required in order to clarify if early quality losses might be sooner detected by Euclidean distances in hyperspectral images compared to CIELAB-readings.