Roberto Moscetti

Review: Recent Advances in the Use of Non-Destructive near Infrared Spectroscopy for Intact Olive Fruits:

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.

Postharvest Monitoring of Organic Potato (CV. Anuschka) During Hot-Air Drying Using Vis/Nir Hyperspectral Imaging

BACKGROUND The potential of hyperspectral imaging (500-1010 nm) was evaluated for monitoring of the quality of potato slices (var. Anuschka) of 5, 7 and 9 mm thickness subjected to air drying at 50 °C. The study investigated three different feature selection methods for the prediction of dry basis moisture content and colour of potato slices using partial least squares regression (PLS). RESULTS The feature selection strategies tested include interval PLS regression (iPLS), and differences and ratios between raw reflectance values for each possible pair of wavelengths (R[λ1 ]-R[λ2 ] and R[λ1 ]:R[λ2 ], respectively). Moreover, the combination of spectral and spatial domains was tested. Excellent results were obtained using the iPLS algorithm. However, features from both datasets of raw reflectance differences and ratios represent suitable alternatives for development of low-complex prediction models. Finally, the dry basis moisture content was high accurately predicted by combining spectral data (i.e. R[511 nm]-R[994 nm]) and spatial domain (i.e. relative area shrinkage of slice). CONCLUSIONS Modelling the data acquired during drying through hyperspectral imaging can provide useful information concerning the chemical and physicochemical changes of the product. With all this information, the proposed approach lays the foundations for a more efficient smart dryer that can be designed and its process optimized for drying of potato slices.

Effects of hot-water and steam blanching of sliced potato on polyphenol oxidase activity

The worldwide potato production is considered the fourth-most important food sector due to the increasing use of potatoes as raw materials for high-convenience food. Colour is a crucial quality attribute in fresh-cut potatoes, and enzymatic browning, due to polyphenols oxidase (PPO), is related to unacceptability by consumer. Among additives used to minimize discoloration, sulphites are very common, but affected by health-related hazards and marketing issues. Viable alternatives are thermal treatments, used to inactivate enzymes and improve product shelf-life. In this study, the efficacy of hot-water and steam blanching from 80 to 90°C, used to inactivate PPO in 1-cm potato slices, was evaluated in terms of inactivation kinetic, substrate specificity and transition state parameters. In general, all treatments inactivated PPO and reduced its kinetic efficiency. In detail, results from thermal inactivation kinetic promoted hot-water blanching at 90°C for approx. 2 min as the fastest treatment to obtain colour-stable potato slices.

Detection of pits and pit fragments in fresh cherries using near infrared spectroscopy

Near infrared spectroscopy in the wavelength region from 800 to 2600 nm was evaluated as the basis for a rapid, nondestructive method for the detection of pits and pit fragments in fresh cherries. Partial least squares discriminant analysis following various spectral pretreatments was applied to spectra of cherries with either no pit, a whole pit, a half pit, or a quarter pit to test various classification schemes. An iterative algorithm tested all combinations of pretreatments and parameters as input to the partial least squares discriminant analysis. In addition, a step forward feature selection algorithm was used to identify the most significant wavebands in order to isolate small sets (<10) of spectral bands that represent the entire spectra. The highest accuracy was achieved for a binary model in which the samples were combined into only two classes (no pit versus whole pit + half pit + quarter pit) using all features (reflection at each wavelength) with no false positive error, 4% false negative error, and 98% overall accuracy. Overall accuracy of the same model was reduced only slightly to 96% when employing only the four most significant features. Accuracy declined when models attempted to separate the classes of fragments, with the lowest being 92, 83, 86, and 99% accuracy, respectively, in discriminating no pit, quarter pit, half pit, and whole pit classes separately. The high accuracy achieved under the binary model using only four features indicates that reflection of light at specific near infrared wavelengths is a suitable basis for high-speed, nondestructive detection of pits and pit fragments in cherries.