Fruit juice and wine are important beverages that are consumed all over the world. Due to their constantly increasing demand and high value, fruit juice and wine are one of the most frequent targets of adulteration. Since adulterated foods are proven to have harmful effects on health, several approaches have been utilized for the detection of fruit juice and wine adulteration. Based on the requirement for sample destruction, analytical techniques to assess food authenticity can be classified into 2 main categories, i.e., destructive and non-destructive techniques. This paper provides an overview on the principle of adulteration detection, its application and performance, and the advantages and limitations of various analytical techniques. Destructive approaches, such as physicochemical methods, isotope analysis, elemental analysis, chromatographic techniques, and DNA-based techniques, are reviewed. Furthermore, non-destructive approaches, including spectroscopic-based techniques, nuclear magnetic resonance spectroscopic technique, electronic techniques, and imaging-based techniques, are discussed.

The Polar Qualification System (PQS) was applied on hue spectra fingerprinting to describe color changes in tomato during storage. The cultivar ‘Pitenza’ was harvested at six different maturity stages, and half of the samples were subjected to gaseous 1-methylcyclopropene (1-MCP) treatment. Reference color parameters were recorded with a vision system colorimeter instrument, and the fruit pigment concentration was assessed with the DA-index^®. Additionally, acoustic firmness (Stiffness) was measured. All acquired reference parameters were used to grade fruit in the supply chain. The applied 1-MCP treatments were used to control the ripening of climacteric horticultural produce. Both the DA-index^® and stiffness values, presented as chlorophyll concentration and acoustic firmness, showed significant differences among maturity stages and treated and control samples and in their kinetics during storage. The machine vision parameter PQS-X was significantly affected by 1-MCP treatment (F = 10.18, p < 0.01), while PQS-Y was primarily affected by storage time (F = 18.18, p < 0.01) and maturity stage (F = 11.15, p < 0.01). A significant correlation was achieved for acoustic firmness with normalized color (r > 0.78) and PQS-Y (r > 0.80), as well as for the DA-index^® (r > 0.9). The observed color changes agreed with the reference measurements. The significant statistical effect on the PQS coordinates suggests that hue spectra fingerprinting with this data compression technique is suitable for quality assessment based on color.

The aim of this work was to evaluate how 1-methylcyclopropene (1-MCP) treatment affects appearance of plum fruit. Fruit of ‘Angeleno’ and ‘Topend’ cultivars were treated with 625 ppb gaseous 1-MCP at 1 °C for 24 h after harvest. Samples without treatment, called control, and those subjected to the treatment were stored at 1 °C for 8 weeks (Topend) and 10 weeks (Angeleno). The subgroup of initial samples and those withdrawn from cold storage were also measured after 7 d storage at 20 °C. According to measured parameters of ethylene, CO₂ production, firmness, and total soluble solids content, there was a clear difference between 1-MCP- treated and control samples for both cultivars. Color attributes of hue angle and saturation changed significantly during storage, especially for flesh color measured on fruit cut in half. The comparison revealed that saturation responded more sensitively to changes. Firmness correlated significantly with color attributes, and flesh saturation reached the highest value of Pearson’s correlation of r = 0.608 (p < 0.01) and Spearman’s rank correlation of ρ = 0.636 (p < 0.01). The specific plum color was also evaluated with a normalized blue value, which obtained significant linear correlation with firmness (r = −0.7414, p < 0.001). There was significant difference between cultivars in terms of surface color and its correlation with firmness as Pearson’s correlation obtained r = 0.833 (p < 0.001) for ‘Topend’ and r = 0.556 (p > 0.05) for ‘Angeleno’.