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The present study provides a comparative assessment of non-invasive sensorsas means of estimating the microbial contamination and time-on-shelf (i.e.storage time) of leafy green vegetables, using a novel unified spectra analysisworkflow. Two fresh ready-to-eat green salads were used in the context of thisstudy for the purpose of evaluating the efficiency and practical application ofthe presented workflow: rocket and baby spinach salads. The employed analysisworkflow consisted of robust data normalization, powerful feature selectionbased on random forests regression, and selection of the number of partialleast squares regression coefficients in the training process by estimating theknee-point on the explained variance plot. Training processes were based onmicrobiological and spectral data derived during storage of green salad samplesat isothermal conditions (4, 8 and 12C), whereas testing was performed on dataduring storage under dynamic temperature conditions (simulating real-lifetemperature fluctuations in the food supply chain). Since an increasinginterest in the use of non-invasive sensors in food quality assessment has beenmade evident in recent years, the unified spectra analysis workflow describedherein, by being based on the creation/usage of limited sized featured sets,could be very useful in food-specific low-cost sensor development.

computers and electronics in agriculture

A unified spectra analysis workflow for the assessment of microbial  contamination of ready to eat green salads: Comparative study and application  of non-invasive sensors