ABSTRACT
A fast and easy methodology to estimate total polyphenol content in extra virgin olive oil was developed by applying the chemometric multiblock method sequential and orthogonalized partial least squares (SO-PLS) in order to combine front-face emission fluorescence spectra (270 nm excitation wavelength) and absorbance spectra. The hypothesis of this work stated that inner-filter effects in fluorescence spectra that would reduce the estimation performance of a single block model could be overcome by incorporating the absorbance spectral information of the compounds causing them. Different spectral preprocessing algorithms were applied. Double cross-validation with 50 iterations was implemented to improve the robustness of the obtained results. The PLSR model on the single block of fluorescence raw spectra achieved an RMSEP of 177.11 mg·kg-1 as the median value, and the complexity of the model was high, as the median value of latent variables (LVs) was eight. Multiblock SO-PLS models with pretreated fluorescence and absorbance spectra provided better performance, although artefacts could be introduced by transformation. The combination of fluorescence and absorbance raw data decreased the RMSEP median to 134.45 mg·kg-1. Moreover, the complexity of the model was greatly reduced, which contributed to an increase in robustness. The median value of LVs was three for fluorescence data and only one for absorbance data. Validation of the methodology could be addressed by further work considering a higher number of samples and a detailed composition of polyphenols.
ABSTRACT
UNLABELLED: The main objective of this research was to develop an automatic procedure able to classify Rich Lady commercial peaches according to their ripeness stage through multispectral imaging techniques. A classification procedure was applied to the ratio images calculated as red (R, 680 nm) divided by infrared (IR, 800 nm), that is, R/IR images. Four image-based ripeness reference classes (A: unripe to D: overripe) were generated from 380 fruit images (season 1: 2006) by a nonsupervised classification method and evaluated according to reference measurements of the ripeness of the same samples: Magness-Taylor penetrometry firmness, low-mass impact firmness, reflectance at 680 nm (R680, and soluble solids content. The assignment of unknown sample images from those season 1 images (internal validation, n = 380) and of 240 images from the 2nd season (season 2: 2007) to the ripeness reference classes (external validation) was carried out by computing the minimum Euclidean distance (classification distance, C(d)) between each unknown image histogram and the average histogram of each ripeness reference class. For both validation phases, firmness values decreased and R680 increased for increasing alphabetical order of image-based class letter, reflecting the ripening process. Moreover, 70% (season 1) and 80% (season 2) of the samples below bruise susceptibility firmness were classified into class D. PRACTICAL APPLICATION: This work proposes and validates a procedure for assessing peach ripeness through spectral imaging. The control of ripeness in this fruit is crucial for ensuring its quality and the measurement of optimum peach ripeness at harvest and postharvest is a controversial issue, which needs to be balanced between a minimum ripeness, acceptable for the consumer, and a maximum ripeness, to minimize fruit losses during the postharvest process. The proposed method is nondestructive and quick, showing thus, a good perspective for its application in fresh fruit packing lines, either for peach ripeness assessment or for other fruits (providing adequate calibration).
