Determining the end-date of long-ripening cheese maturation using NIR hyperspectral image modelling: A feasibility study
Hasitha Priyashantha, Annika Höjer, Karin Hallin Saedén, Åse Lundh, Monika Johansson, Gun Bernes, Paul Geladi, Mårten Hetta
Abstract
Near-infrared (874–1734 nm) hyperspectral (NIR-HS) imaging, coupled with chemometric tools, was used to explore the relationship between spectroscopic data and cheese maturation. A predictive tool to determine the end-date of cheese maturation (E-index, in days) was developed using a set of 425 NIR-HS images acquired during industrial-scale cheese production. The NIR-HS images were obtained by scanning the cheeses at 14, 16, 18 and 20 months of ripening, before a final sensorial assessment in which all cheeses were approved by 20 months. Regression modelling by partial least squares (PLS) was used to explore the relationship between average spectra and E-index. The best PLS model achieved 69.6% accuracy in the prediction of E-index when standard normal variate (SNV) correction and mean centring pre-processing were applied. Thus, NIR-HS image modelling can be useful as a complementary tool to optimise the logistics/efficiency of cheese ripening facilities by rapid and non-destructive prediction of the end-date of ripening for individual cheeses. However, the commercial application will require future improvements in the predictive capacity of the model, e.g. for larger datasets and repetitive scans of cheeses on random occasions.