Overnutrition is a significant component of food waste and has a large environmental impact.

Food waste and obesity and overweight conditions are both linked to the unsustainability of current food systems. This article argues that overnutrition should be considered a form of food waste and it provides a first estimation of the quantity of food over-consumed in Italy. This is done by calculating the excess calories consumed by obese and overweight people and converting them into food quantities by comparison with a typical Italian diet. The total quantity of food consumed in excess by Italian citizens due to overnutrition is calculated as 1.553 million tonnes per year, which is comparable to the current national household food waste assessments. The environmental impact arising from production and consumption of this food accounts for 6.15 Mt of CO2-eq per year, as estimated by a Life Cycle Analysis conducted on the 46 food categories which compose the typical Italian diet. Overnutrition in the South-Islands regions of Italy exerts the largest impact (31.6%), followed by the North-West (26.6%), the Centre (22.2%), and the North-East (19.1%).

Impact of 'brown rot' caused by Gnomoniopsis castanea on chestnut fruits during the post-harvest process: critical phases and proposed solutions.

BACKGROUND: The brown rot fungus, Gnomoniopsis castanea, is the main organism responsible for the outbreak of chestnut postharvest decay that is threatening the sustainability of the chestnut market in Europe. Currently, no specific strategy is available to mitigate the impact and remediate the high losses of fruits in postharvest storage. In the present study, the different phases of chestnut handling in a standard facility plant were analyzed by evaluating the amount of fruit rot and infection by G. castanea at each phase. RESULTS: The warm bath (48 °C) was identified as the critical phase, requiring strict parametrization to effectively inactivate G. castanea in fruits. Laboratory tests indicated that maintaining fruits at 50 °C for a maximum of 45 min provided optimal conditions to completely inactivate G. castanea inoculum during postharvest handling. However, the warm bath at 50 °C and over was not effective in inactivating the complex of fungal taxa responsible for contamination and development of molds. Higher temperatures and extended treatment times caused significant losses in fruit quality, as indicated by taste panel evaluation. Upscaling of postharvest facilities is discussed and critically evaluated. CONCLUSION: The warm bath (50 °C for 45 min) is effective in completely inactivating G. castanea in fruits but did not reduce the impacts of the complex of molds responsible for external contamination and mycotoxin production. © 2021 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Chestnut Cultivar Identification through the Data Fusion of Sensory Quality and FT-NIR Spectral Data.

The world production of chestnuts has significantly grown in recent decades. Consumer attitudes, increasingly turned towards healthy foods, show a greater interest in chestnuts due to their health benefits. Consequently, it is important to develop reliable methods for the selection of high-quality products, both from a qualitative and sensory point of view. In this study, Castanea spp. fruits from Italy, namely Sweet chestnut cultivar and the Marrone cultivar, were evaluated by an official panel, and the responses for sensory attributes were used to verify the correlation to the near-infrared spectra. Data fusion strategies have been applied to take advantage of the synergistic effect of the information obtained from NIR and sensory analysis. Large nuts, easy pellicle removal, chestnut aroma, and aromatic intensity render Marrone cv fruits suitable for both the fresh market and candying, i.e., marron glacé. Whereas, sweet chestnut samples, due to their characteristics, have the potential to be used for secondary food products, such as jam, mash chestnut, and flour. The research lays the foundations for a superior data fusion approach for chestnut identification in terms of classification sensitivity and specificity, in which sensory and spectral approaches compensate each other's drawbacks, synergistically contributing to an excellent result.

A Review on High-Power Ultrasound-Assisted Extraction of Olive Oils: Effect on Oil Yield, Quality, Chemical Composition and Consumer Perception.

The objective of this review is to illustrate the state of the art in high-power ultrasound (HPU) application for olive oil extraction with the most recent studies about the effects of HPU treatment on oil yield, quality, chemical composition, as well as on the consumer's perception. All the examined works reported an increase in oil yield and extractability index through the use of HPU, which was ascribed to reduced paste viscosity and cavitation-driven cell disruption. Olive oil legal quality was generally not affected; on the other hand, results regarding oil chemical composition were conflicting with some studies reporting an increase of phenols, tocopherols, and volatile compounds, while others underlined no significant effects to even slight reductions after HPU treatment. Regarding the acceptability of oils extracted through HPU processing, consumer perception is not negatively affected, as long as the marketer effectively delivers information about the positive effects of ultrasound on oil quality and sensory aspect. However, only a few consumers were willing to pay more, and hence the cost of the innovative extraction must be carefully evaluated. Since most of the studies confirm the substantial potential of HPU to reduce processing times, improve process sustainability and produce oils with desired nutritional and sensory quality, this review points out the need for industrial scale-up of such innovative technology.

Stinging Nettles as Potential Food Additive: Effect of Drying Processes on Quality Characteristics of Leaf Powders.

Stinging nettle (Urtica dioica L.) is a ubiquitous, multi-utility, and under-utilized crop with potential health benefits owing to its nutritional and bioactive components. The objective of the work is to produce powders by drying wild stinging nettle leaves as a storable, low-cost functional additive to be used in bakery and ready-to-cook products. Convective drying (CD) and freeze-drying (FD) were applied on unblanched (U) or blanched (B) leaves, which were then milled to nettle powders (NPs). The obtained NPs were evaluated for selected physicochemical (moisture, color), techno-functional (flow indices, hygroscopicity), and phytochemical (pigments, phenols) characteristics as well as mineral contents. Blanching improved mass transfer and reduced the oxidative degradation of pigments during drying, but it caused a loss of total phenols content, antioxidant activity, and potassium content. As for the drying method, CD resulted in better flow properties (i.e., Carr Index and Hausner Ratio), while FD retained better the color, pigments, magnesium content, phenolic, and antioxidant parameters. Overall, the evaluated processing methods resulted in different technological properties that can allow for better evaluation of NPs as a food additive or ingredient. Among the NPs, blanched and freeze-dried powders despite showing inferior technological properties can be recommended as more suitable ingredients targeted f or food enrichment owing to better retention of bio-active components.

Supervised binary classification methods for strawberry ripeness discrimination from bioimpedance data.

Strawberry is one of the most popular fruits in the market. To meet the demanding consumer and market quality standards, there is a strong need for an on-site, accurate and reliable grading system during the whole harvesting process. In this work, a total of 923 strawberry fruit were measured directly on-plant at different ripening stages by means of bioimpedance data, collected at frequencies between 20 Hz and 300 kHz. The fruit batch was then splitted in 2 classes (i.e. ripe and unripe) based on surface color data. Starting from these data, six of the most commonly used supervised machine learning classification techniques, i.e. Logistic Regression (LR), Binary Decision Trees (DT), Naive Bayes Classifiers (NBC), K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Multi-Layer Perceptron Networks (MLP), were employed, optimized, tested and compared in view of their performance in predicting the strawberry fruit ripening stage. Such models were trained to develop a complete feature selection and optimization pipeline, not yet available for bioimpedance data analysis of fruit. The classification results highlighted that, among all the tested methods, MLP networks had the best performances on the test set, with 0.72, 0.82 and 0.73 for the F[Formula: see text], F[Formula: see text] and F[Formula: see text]-score, respectively, and improved the training results, showing good generalization capability, adapting well to new, previously unseen data. Consequently, the MLP models, trained with bioimpedance data, are a promising alternative for real-time estimation of strawberry ripeness directly on-field, which could be a potential application technique for evaluating the harvesting time management for farmers and producers.

Optimisation of Physical and Chemical Treatments to Control Browning Development and Enzymatic Activity on Fresh-cut Apple Slices.

Optimisation of processing time and pre-treatments are crucial factors prior to apple drying to produce a high-quality product. The purpose of the present study was to test the utility of physical (hot-water, HWB and steam blanching, SB) and chemical (1% ascorbic acid, AA; and 1% citric acid, CA) treatments, alone or in combination in reducing surface discolouration as well as oxidative enzyme activity in apple slices (cv. Golden Delicious and Elstar) exposed to air at room temperature for 0, 30 and 60 min. The total colour change (ΔE) for Golden Delicious was equal to 2.38, 2.68, and 4.05 after 0, 30 and 60 min of air exposure, respectively. Dipping in AA solution (1% w/v) was found to be the best treatment to limit surface discolouration of both apple cultivars. The best heat treatments to inhibit polyphenol oxidase/peroxidase enzymes activity were 70 °C HWB for Golden Delicious and 60 °C HWB for Elstar slices, both in combination with a solution of 1% AA and 1% CA. The tested apple cultivars were found to require different treatments at minimum ambient air exposure to obtain the best surface colour condition.

Postharvest monitoring of organic potato (cv. Anuschka) during hot-air drying using visible-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. © 2017 Society of Chemical Industry.

A comparative study on the effectiveness of ozonated water and peracetic acid in the storability of packaged fresh-cut melon.

Ozonated water and peracetic acid were tested as sanitizers to enhance the storability of fresh-cut melon cubes. Sanitizers were also combined with suitable packaging materials (polypropylene and polylactic acid based plastic films). Fresh-cut melon cubes were stored at 4 °C for up to 7 days. Ozonated water and peracetic acid treatments were given by dipping cubes into 0.8 ppm O3 and 100 ppm Tsunami 100™ solutions, respectively, for 3 min. Both sanitizers exhibited efficiency in reducing the total microbial counts on melon cubes (< 2 log CFU g(-1)). Respiratory activity and ethylene production were both affected by the interaction between the sanitizer and the packaging used. Carbon dioxide and oxygen reached 9.89 kPa and 12.20 kPa partial pressures, respectively, using peracetic acid treatment in combination with polypropylene film packaging, consequently developing off-odors starting from day 3. Strong color changes were noted in cubes stored in polylactic acid packaging after 7 days of storage, affecting the sensory quality of the melon cubes. Sensory evaluation (overall visual quality) indicated loss in flavor in the polypropylene packaging. The overall visual quality started to decline on 3rd day because of the development of translucency.Overall, the use of ozone in combination with polypropylene packaging provided the best solution to maintain the quality of melon cubes for up to 5 days of storage at 4 °C.

Near infrared spectroscopy is suitable for the classification of hazelnuts according to Protected Designation of Origin.

BACKGROUND: This study investigates the possibility of using near infrared spectroscopy for the authentication of the 'Nocciola Romana' hazelnut (Corylus avellana L. cvs Tonda Gentile Romana and Nocchione) as a Protected Designation of Origin (PDO) hazelnut from central Italy. Algorithms for the selection of the optimal pretreatments were tested in combination with the following discriminant routines: k-nearest neighbour, soft independent modelling of class analogy, partial least squares discriminant analysis and support vector machine discriminant analysis. RESULTS: The best results were obtained using a support vector machine discriminant analysis routine. Thus, classification performance rates with specificities, sensitivities and accuracies as high as 96.0%, 95.0% and 95.5%, respectively, were achieved. Various pretreatments, such as standard normal variate, mean centring and a Savitzky-Golay filter with seven smoothing points, were used. The optimal wavelengths for classification were mainly correlated with lipids, although some contribution from minor constituents, such as proteins and carbohydrates, was also observed. CONCLUSION: Near infrared spectroscopy could classify hazelnut according to the PDO 'Nocciola Romana' designation. Thus, the experimentation lays the foundations for a rapid, online, authentication system for hazelnut. However, model robustness should be improved taking into account agro-pedo-climatic growing conditions.

Effect of alternative postharvest control treatments on the storability of 'Golden Delicious' apples.

BACKGROUND: Apples are subject to a high degree of fungal diseases, but the use of synthetic fungicides has been questioned because of public safety concerns, social rejection, and the development of resistance in pathogens. Thus, development of new postharvest treatments against apple fungal pathogens is necessary. Most studies have reported their effectiveness, but not all report the effects on the quality and storability of the fruit. In this study, the effects of physical (hot water), chemical (quercetin) and biological (yeast antagonist) microfungal control on the quality of 'Golden Delicious' apple during storage at 2 ± 0.5 °C, and 90 ± 2% of relative humidity, for 2 months were investigated and compared. RESULTS: Heat-treated apples exhibited peel fruit damage (surface browning and internal breakdown disorders) and promoted ripening in the fruit. The quercetin caustic spray caused the development of peel chemical burn in all treated fruit. Both yeast antagonist and quercetin treatments did not affect the apple ripening process but stimulated an increase in ethylene production and in respiratory activity. CONCLUSION: 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.