Detection of peach soluble solids based on near-infrared spectroscopy with High Order Spatial Interaction network

BACKGROUND

Due to the scalability of deep learning technology, researchers have applied it to the non-destructive testing of peach internal quality. In addition, the soluble solids content (SSC) is an important internal quality indicator that determines the quality of peaches. Peaches with high SSC have a sweeter taste and better texture, making them popular in the market. Therefore, SSC is an important indicator for measuring peach internal quality and making harvesting decisions.

RESULTS

This article presents the High Order Spatial Interaction Network (HOSINet), which combines the Position Attention Module (PAM) and Channel Attention Module (CAM). Additionally, a feature wavelength selection algorithm similar to the Group-based Clustering Subspace Representation (GCSR-C) is used to establish the Position and Channel Attention Module-High Order Spatial Interaction (PC-HOSI) model for peach SSC prediction. The accuracy of this model is compared with traditional machine learning and traditional deep learning models. Finally, the permutation algorithm is combined with deep learning models to visually evaluate the importance of feature wavelengths. Increasing the order of the PC-HOSI model enhances its ability to learn spatial correlations in the dataset, thus improving its predictive performance.

CONCLUSION

The optimal model, PC-HOSI model, performed well with an order of 3 (PC-HOSI-3), with a root mean square error of 0.421 °Brix and a coefficient of determination of 0.864. Compared with traditional machine learning and deep learning algorithms, the coefficient of determination for the prediction set was improved by 0.07 and 0.39, respectively. The permutation algorithm also provided interpretability analysis for the predictions of the deep learning model, offering insights into the importance of spectral bands. These results contribute to the accurate prediction of SSC in peaches and support research on interpretability of neural network models for prediction. © 2024 Society of Chemical Industry.

Evaluation of Blue Honeysuckle Berries (Lonicera caerulea L.) Dried at Different Temperatures: Basic Quality, Sensory Attributes, Bioactive Compounds, and In Vitro Antioxidant Activity

This study aims to comprehensively investigate the effects of hot-air dehydration on the quality of blue honeysuckle berries (Lonicera caerulea L.). The results demonstrated that drying with hot air at 40-65 °C for 7-72 h resulted in blue honeysuckle berries with a moisture content of 0.21-1.10 g H2O/g dry weight. Generally, low to medium temperatures (40-55 °C) showed a better effect on the quality than high temperatures (60-65 °C). Specifically, drying at 40 °C exclusively resulted in better retention of cuticular wax, the best sensory appearance, and the highest total phenolic content. Drying at 45 °C and 50 °C resulted in the highest antioxidant capacity and the optimal sensory flavor. Drying at 55 °C led to the highest soluble solid/acid ratio, ascorbic acid concentration, total flavonoid, and total anthocyanin. The work introduces an innovative raw berry product and provides a comprehensive practical and theoretical framework for convective dehydration of blue honeysuckle berries.

Rootstock Affects the Fruit Quality of 'Early Bigi' Sweet Cherries

The cherry rootstock influences the performance of the scion cultivar. It has an effect on cherry fruit quality, tree growth, yield and yield efficiency and floral and foliar nutrition. In this work, the influence of Saint Lucie 64 and Maxma 60 rootstocks on the fruit quality traits of cv. Early Bigi was evaluated. For this, several parameters, namely fruit weight (FW) and size (FS), soluble solids content (SSC), pH, titratable acidity (TA), flesh firmness (FF), epidermis rupture force (ERF), color and sensory profile, were assessed. Results showed that the fruits from trees on Saint Lucie 64 presented higher FF and ERF values and, consequently, better texture. On the other hand, fruits from trees on Maxma 60 showed sweeter cherries (higher SSC). Moreover, these trees presented the darkest cherries (lower values of L*, a*, b*, C* and hue°) and the highest SSC. Therefore, although the trees on Saint Lucie 64 produced firmer cherries, it was those with the Maxma 60 rootstock that produced sweeter and darker fruits. In conclusion, both scion–rootstock combinations proved to be good options for the region of Resende.

Preliminary Insights in Sensory Profile of Sweet Cherries

Sweet cherry (Prunus avium L.) is a fruit appreciated by consumers for its well-known physical and sensory characteristics and its health benefits. Being an extremely perishable fruit, it is important to know the unique attributes of the cultivars to develop cultivation or postharvest strategies that can enhance their quality. This study aimed to understand the influence of physicochemical characteristics of two sweet cherry cultivars, Burlat and Van, on the food quality perception. Several parameters (weight, dimensions, soluble solids content (SSC), pH, titratable acidity (TA), colour, and texture) were measured and correlated with sensory data. Results showed that cv. Van presented heavier and firmer fruits with high sugar content. In turn, cv. Burlat showed higher pH, lower TA, and presented redder and brightest fruits. The principal component analysis revealed an evident separation between cultivars. Van cherries stood out for their sensory parameters and were classified as more acidic, bitter, and astringent, and presented a firmer texture. Contrarily, Burlat cherries were distinguished as being more flavourful, succulent, sweeter, and more uniform in terms of visual and colour parameters. The results of the sensory analysis suggested that perceived quality does not always depend on and/or recognize the quality parameters inherent to the physicochemical characteristics of each cultivar.

Molecular and Genetic Bases of Fruit Firmness Variation in Blueberry—A Review

Blueberry (Vaccinium spp.) has been recognized worldwide as a valuable source of health-promoting compounds, becoming a crop with some of the fastest rising consumer demand trends. Fruit firmness is a key target for blueberry breeding as it directly affects fruit quality, consumer preference, transportability, shelf life, and the ability of cultivars to be machine harvested. Fruit softening naturally occurs during berry development, maturation, and postharvest ripening. However, some genotypes are better at retaining firmness than others, and some are crispy, which is a putatively extra-firmness phenotype that provides a distinct eating experience. In this review, we summarized important studies addressing the firmness trait in blueberry, focusing on physiological and molecular changes affecting this trait at the onset of ripening and also the genetic basis of firmness variation across individuals. New insights into these topics were also achieved by using previously available data and historical records from the blueberry breeding program at the University of Florida. The complex quantitative nature of firmness in an autopolyploid species such as blueberry imposes additional challenges for the implementation of molecular techniques in breeding. However, we highlighted some recent genomics-based studies and the potential of a QTL (Quantitative Trait Locus) mapping analysis and genome editing protocols such as CRISPR/Cas9 to further assist and accelerate the breeding process for this important trait.

Influence of cultivar and of conventional and organic agricultural practices on phenolic and sensory profile of blackberries (Rubus fruticosus)

BACKGROUND

Consumer demand for organic products is increasing because of their claimed health benefits. Blackberries are a rich source of polyphenols, with high antioxidant activity; nevertheless, the impact of organic versus conventional agricultural practices on its phytochemical composition is unknown. 'Loch Ness' and 'Chester Thornless' were selected as blackberry cultivars for this study because of their desired sensory and technological properties, which make them more suitable for export.

RESULTS

'Loch Ness' variety presented a higher amounts of polyphenols and higher antioxidant activity when compared to the 'Chester Thornless' variety. The impact of agricultural practices on the phytochemical composition of the two varieties was contradictory. Under organic agricultural practices, levels of polyphenols increased for 'Loch Ness' and decreased for 'Chester Thornless', whereas the soluble solids content increased in both varieties. These changes in composition were correlated with changes observed in the blackberries' sensory profile.

CONCLUSION

The effect of agricultural practices on the blackberries' chemical and sensory profile was dependent on the variety and cannot be generalized. © 2018 Society of Chemical Industry.