Optimal management of oil content variability in olive mill batches by NIR spectroscopy.
Eva-Cristina CorreaJ M RogerL LleóN Hernández-SánchezP BarreiroB DiezmaPublished in: Scientific reports (2019)
Total oil content (OC) is one of the main parameters used to characterize the whole of olives entering a commercial mill, quantified by the total fresh weight of the lot and the oil concentration (%) assessed in a representative sample on olive paste, by means of chemical extraction. Nuclear magnetic resonance (NMR) and NIR spectroscopy are alternative methods even at individual olives. This work evaluates several strategies to calibrate precise NIR models for the estimation of the total OC. To this end, 278 olives were analysed covering whole season variability in terms of olive fresh-weight and the corresponding OC by chemical extraction in 31 batches. The average spectra from hyperspectral NIR images (1003-2208 nm) were computed for each fruit and the actual OC (g) of those olives determined by NMR (0.09 to 1.29 g with a precision of 0.017 g). According to the results, current batch based assessment of the OC (Soxhlet, %) in mills only reproduces 44% of the underlying heterogeneity, despite being the factory standard. The incorporation of individual NIR spectra (278) to the 31 Soxhlet values of the batches allows a 67% explanation of the OC (%) of olives. When estimating OC (g) gathering individual fresh weight and the estimation of oil concentration in olives, a standard error of prediction of 0.061 g is reached (r2 = 0.93), a precision value that approaches the potential limit according to the NMR reference (0.017 g).
Keyphrases
- magnetic resonance
- photodynamic therapy
- high resolution
- solid state
- drug release
- fluorescence imaging
- fluorescent probe
- body mass index
- physical activity
- fatty acid
- weight loss
- weight gain
- drug delivery
- body weight
- magnetic resonance imaging
- density functional theory
- deep learning
- contrast enhanced
- machine learning
- optical coherence tomography
- computed tomography
- risk assessment
- convolutional neural network
- solid phase extraction
- clinical evaluation