Author:

Abstract:

A healthy lifestyle is becoming more and more important for the modern day consumer and fresh, minimally processed, ready-to-eat vegetables play an essential role. Lamb's lettuce (Valerianella locusta) is a popular greenhouse vegetable mainly thanks to these characteristics. It is used as an ingredient in salad mixtures and as a leafy salad making it an ideal healthy food for the modern day consumer. However, lamb's lettuce plants presented to the market are not always freshly harvested. Producers can store them up to four weeks in their cooling facility, because fresh produce and stored samples are indistinguishable by the human eye. However, the latter have an impaired shelf life potential leading to significant economic losses in distribution and lower consumption quality. Hence, the main objective was to develop a fast and nondestructive methodology to estimate how long a batch of lamb's lettuce has been stored before it is presented to the market. In the first part of this dissertation, lettuce from commercial producers was stored at 1 and 4 °C for 21 d and the effects on metabolite content and respiration rate were studied. After 21 d of storage, there was still a reasonable amount of soluble carbohydrates to sustain the energy metabolism. Although the content of free amino acids increased, these were not used as the main energy source. The respiration rate decreased during storage, which implied a shortage in soluble carbohydrates. Hence, we concluded that if the storage period would be continued, it would lead to a change in the energy metabolism where amino acids would be used as a carbon source. In the second part of the research, visible / near infrared (Vis/NIR) spectroscopy and chlorophyll fluorescence emission ratios were evaluated as a fast and non-destructive method to detect and quantify a prior storage period. Lamb's lettuce from commercial producers was stored at 1 and 4 °C and the Vis/NIR spectra and fluorescence emission ratios were linked to the time in storage by partial least squares regression (PLSR). Preprocessing and variable selection techniques (interval PLS, Variable Importance in Projection scores, Genetic Algorithms PLS and Monte Carlo Uninformative Variable Elimination PLS) were used to improve the performance of the PLSR models. The PLSR model based on Vis/NIR spectra made successful predictions on a possible storage period. This was not possible for PLSR models based on chlorophyll fluorescence emission ratios without a correction for the storage temperature. The unsuccessful predictions based on chlorophyll fluorescence emission ratios could be due to a smaller calibration dataset which contained a lot of variation. The final prediction model based on Vis/NIR spectra used only 10% of the original wavelength variables and had a root mean squared error of cross validation of 3.6 d. This model was tested using 2 external test sets and had a maximum root mean square error of prediction of 3.7 d. Hence, Vis/NIR spectroscopy can be a valuable, rapid and non-destructive method for identifying and quantifying a prior storage period of lamb's lettuce.