No abstract

The data indicated that the effects on quality and storability were dependent on the method of treatment used, and antagonistic yeast was the best microfungal control because of it did not cause any disorders or negative effects on apple quality during storage.

The objective of this review is to illustrate the state of the art in the use of non-destructive near infrared (NIR) spectroscopy for quality evaluation of intact fruit in the olive industry. First, the most recent studies regarding the application of non-destructive NIR spectroscopy methods for the assessment of external olive quality are reviewed. External defects including mechanical damage, bruising, ground origin and insect infestation, and the consequences of these defects for finished products are reported. Second, research regarding chemical parameters of olive fruits is reviewed; in particular, the use of portable instruments to measure quality parameters such as moisture, oil and phenolic content while the fruit is on the tree, with the goal of monitoring the trends in these parameters during olive development. Finally, research on intact olive authenticity, an important aspect for legal and economic reasons, is reviewed. As most studies cited indicate the feasibility of NIR spectroscopy for non-destructive evaluation of many quality parameters, this review stresses the urgent need for technology transfer to olive facilities to enhance product quality while reducing production costs.

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The study demonstrated the feasibility of the near infrared (NIR) spectroscopy use for hazelnut-cultivar sorting. Hazelnut spectra were acquired from 600 fruit for each cultivar sample, two diffuse reflectance spectra were acquired from opposite sides of the same hazelnut. Spectral data were transformed into absorbance before the computations. A different variety of spectral pretreatments were applied to extract characteristics for the classification. An iterative Linear Discriminant Analysis (LDA) algorithm was used to select a relatively small set of variables to correctly classify samples. The optimal group of features selected for each test was analyzed using Partial Least Squares Discriminant Analysis (PLS-DA). The spectral region most frequently chosen was the 1980-2060 nm range, which corresponds to best differentiation performance for a total minimum error rate lower than 1.00%. This wavelength range is generally associated with stretching and bending of the N-H functional group of amino acids and proteins. The feasibility of using NIR Spectroscopy to distinguish different hazelnut cultivars was demonstrated.