Rapid and Non-Destructive Techniques for the Discrimination of Ripening Stages in Candonga Strawberries

Assessment of Changes in Sensory Characteristics of Strawberries during 5-Day Storage through Correlation between Human Senses and Electronic Senses

In the last decade, significant efforts have been made to predict sensory characteristics using electronic senses, such as the electronic nose (e-nose) and the electronic tongue (e-tongue), and discuss their relationship to the eating quality evaluated by human panels. This study was conducted (1) to characterize the aroma and taste profiles of strawberries over a 5-day storage period (4 °C) using both electronic senses and human panels and (2) to correlate the electronic sense data with human panel data. A total of 10 sensory attributes of strawberries, including 7 aroma and 3 taste attributes, were analyzed by a descriptive sensory panel (n = 16) over the five days. Although the human panel did not find significant differences in the intensities of the strawberry attributes over the five days, the intensity ratings showed an increasing or decreasing trend over the storage period. However, the e-nose and the e-tongue discriminated each of the storage days of the strawberry samples. Furthermore, the partial least square regression coefficients of determination (R2) indicated that the e-nose and the e-tongue were highly predictive in their evaluation of the intensities of all the descriptive sensory attributes. Lastly, the concentrations of furaneol, one of the key volatiles imparting a distinct ripe strawberry aroma, were determined using an e-nose to correlate with the intensities of aroma attributes evaluated by the panel. A significant positive Pearson's correlation coefficient was found with the intensities of overripe aroma. The findings indicate the potential of electronic senses to determine sensory characteristics and their excellent capability to predict the eating quality of strawberries.

Rapid Determination of Different Ripening Stages of Occidental Pears (Pyrus communis L.) by Volatile Organic Compounds Using Proton-Transfer-Reaction Mass Spectrometry (PTR-MS)

Determination of Occidental pear (Pyrus communis) ripening is difficult because the appearance of Occidental pears does not change significantly during the ripening process. Occidental pears at different ripening stages release different volatile organic compounds (VOCs), which can be used to determine fruit ripeness non-destructively and rapidly. In this study, VOCs were detected using proton-transfer-reaction mass spectrometry (PTR-MS). Notably, data were acquired within 1 min. Occidental pears harvested at five separate times were divided into three ripening stages: unripe, ripe, and overripe. The results showed that the composition of VOCs differed depending on the ripening stage. In particular, the concentrations of esters and terpenes significantly increased during the overripe stage. Three ripening stages were clearly discriminated by heatmap clustering and principal component analysis (PCA). This study provided a rapid and non-destructive method to evaluate the ripening stages of Occidental pears. The result can help fruit farmers to decide the optimum harvest time and hence reduce their economic losses.

The Visibility of Changes in the Antioxidant Compound Profiles of Strawberry and Raspberry Fruits Subjected to Different Storage Conditions Using ATR-FTIR and Chemometrics

Strawberry cultivars Portola and Enduro, as well as raspberry cultivars Enrosadira and Kwazi, were evaluated for their antioxidant potential after treatment with gaseous ozone and different refrigeration storage conditions. Their antioxidant capacity was investigated with ABTS and DPPH methods, and the chemical composition was determined by measuring the total phenolic (TPC) and flavonoid (TFC) compounds. The classification of different samples of berry puree was influenced significantly by both the cultivars and the refrigeration storage method. Moreover, FTIR spectroscopy coupled with chemometrics was used as an alternative technique to conventional methods to determine the chemical composition of strawberries and raspberries. The chemometric discrimination of samples was achieved using principal component analysis (PCA), hierarchical clustering analysis (HCA) and linear discriminant analysis (LDA) modelling procedures performed on the FTIR preprocessed spectral data for the fingerprint region (1800-500 cm-1). The fingerprint range between 1500 and 500 cm-1, corresponding to deformation vibrations from polysaccharides, pectin and organic acid content, had a significant impact on the grouping of samples. The results obtained by PCA-LDA scores revealed a clear separation between four classes of samples and demonstrated a high overall classification rate of 97.5% in differentiating between the raspberry and strawberry cultivars.

Nano and Technological Frontiers as a Sustainable Platform for Postharvest Preservation of Berry Fruits

Berries are highly perishable and susceptible to spoilage, resulting in significant food and economic losses. The use of chemicals in traditional postharvest protection techniques can harm both human health and the environment. Consequently, there is an increasing interest in creating environmentally friendly solutions for postharvest protection. This article discusses various approaches, including the use of "green" chemical compounds such as ozone and peracetic acid, biocontrol agents, physical treatments, and modern technologies such as the use of nanostructures and molecular tools. The potential of these alternatives is evaluated in terms of their effect on microbial growth, nutritional value, and physicochemical and sensorial properties of the berries. Moreover, the development of nanotechnology, molecular biology, and artificial intelligence offers a wide range of opportunities to develop formulations using nanostructures, improving the functionality of the coatings by enhancing their physicochemical and antimicrobial properties and providing protection to bioactive compounds. Some challenges remain for their implementation into the food industry such as scale-up and regulatory policies. However, the use of sustainable postharvest protection methods can help to reduce the negative impacts of chemical treatments and improve the availability of safe and quality berries.

Advances Postharvest Preservation Technology

Fruits and vegetables are important sources of nutrients such as vitamins, minerals, and bioactive compounds, which provide many health benefits [...].

Influence of Plant-Based Biostimulant (BORTAN) on Qualitative and Aromatic Traits of Rocket Salad (Diplotaxis tenuifolia L.)

In this study, the influence of a new plant-based biostimulant (Bortan) on physiological and aromatic traits of rocket (Diplotaxis tenuifolia L. var. Pamela) was monitored by evaluating physico-chemical parameters (fresh and dry weight, leaf color and chlorophyll content) and biochemical traits (total phenolic compound (TP), total flavonoids (TF), ascorbic acid (AA) and antioxidant activity (AOX). Volatile profiles were also analyzed by headspace solid-phase microextraction coupled to gas chromatography-mass spectrometry, allowing the detection of 32 volatiles belonging to 5 chemical classes. Compared to the control, Bortan application enhanced leaf pigment content, including chlorophyll a, b and carotenoids (+10%, +16% and +28%, respectively) and increased TP (+34%), TF (+26%), AA (+19%) amonts and AOX value (+16%). Principal component analysis revealed a significant discrimination between the two samples. Specifically, treated samples were mainly associated with "green-leaf" volatiles, namely hexanal and 2-hexenal, 3-hexenal and 1-penten-3-one, while control rocket was directly correlated with several alcohols and to all isothiocyanates, associated with the sulfur-like odor of rocket. These findings can add further support, both for farmers and the agro-food industry, in choosing PBs as a new and sustainable practice in complementing enhanced yields with premium-quality produce. To confirm these preliminary data, further experiments are needed by enlarging the sample size, testing different concentrations of Bortan and/or using other food crops.

Emerging Postharvest Technologies to Enhance the Shelf-Life of Fruit and Vegetables: An Overview

Quality losses in fresh produce throughout the postharvest phase are often due to the inappropriate use of preservation technologies. In the last few decades, besides the traditional approaches, advanced postharvest physical and chemical treatments (active packaging, dipping, vacuum impregnation, conventional heating, pulsed electric field, high hydrostatic pressure, and cold plasma) and biocontrol techniques have been implemented to preserve the nutritional value and safety of fresh produce. The application of these methodologies after harvesting is useful when addressing quality loss due to the long duration when transporting products to distant markets. Among the emerging technologies and contactless and non-destructive techniques for quality monitoring (image analysis, electronic noses, and near-infrared spectroscopy) present numerous advantages over the traditional, destructive methods. The present review paper has grouped original studies within the topic of advanced postharvest technologies, to preserve quality and reduce losses and waste in fresh produce. Moreover, the effectiveness and advantages of some contactless and non-destructive methodologies for monitoring the quality of fruit and vegetables will also be discussed and compared to the traditional methods.

The Changes in Bell Pepper Flesh as a Result of Lacto-Fermentation Evaluated Using Image Features and Machine Learning

Food processing allows for maintaining the quality of perishable products and extending their shelf life. Nondestructive procedures combining image analysis and machine learning can be used to control the quality of processed foods. This study was aimed at developing an innovative approach to distinguishing fresh and lacto-fermented red bell pepper samples involving selected image textures and machine learning algorithms. Before processing, the pieces of fresh pepper and samples subjected to spontaneous lacto-fermentation were imaged using a digital camera. The texture parameters were extracted from images converted to different color channels L, a, b, R, G, B, X, Y, and Z. The textures after selection were used to build models for the classification of fresh and lacto-fermented samples using algorithms from the groups of Lazy, Functions, Trees, Bayes, Meta, and Rules. The highest average accuracy of classification reached 99% for the models developed based on sets of selected textures for color space Lab using the IBk (instance-based K-nearest learner) algorithm from the group of Lazy, color space RGB using SMO (sequential minimal optimization) from Functions, and color space XYZ and color channel X using IBk (Lazy) and SMO (Functions). The results confirmed the differences in image features of fresh and lacto-fermented red bell pepper and revealed the effectiveness of models built based on textures using machine learning algorithms for the evaluation of the changes in the pepper flesh structure caused by processing.