Sensory and nutritional attributes of Persian walnut kernel influenced by maturity stage, drying method, and cultivar

External browning mechanism in walnut kernel pellicles under different drying conditions based on the combination of widely-targeted and anthocyanin-targeted metabolomics

There has been limited research on external browning (EB) of walnut. This work discovered 1888 metabolites and 34 anthocyanins in walnut pellicles (WPs) after three drying methods using widely-targeted and anthocyanin-targeted metabolomics. Based on OPLS-DA and correlation analysis, 64 temperature-responsive metabolites (TRMs; 13 anthocyanins and 51 flavonoids) were identified as critical components in relation to EB. Notably, 14 flavonoids exhibited a strong positive correlation (r > 0.9) with the browning index (BI), with upregulation of >60% after browning. Most of the identified anthocyanins were negatively linked with BI because of degradation (>45%), with correlation coefficients ranging from 0.75 to 0.97. Furthermore, anthocyanidin reductase and laccase were the two key enzymes involved in the EB of WPs, with their activities increasing by 10.57-fold and 1.32-fold, respectively, with increasing drying temperature. A metabolic pathway network of the TRM was built to provide insights into the potential mechanisms underlying EB in WPs.

Physical and chemical characteristics of walnut (Juglans regia L.) kernels with different skin lightness

Walnuts undergo rigorous grading before being sold to customers. There are multiple parameters used for the grading, including skin lightness. Walnuts with light skin receive superior grades while walnuts with dark skin are given poor grades or even rejected. However, information on the quality and physicochemical properties of walnuts with varying skin lightness levels is minimal. Therefore, we studied the quality of kernels of varying skin lightness from three common cultivars grown in California, USA (Chandler, Howard, and Tulare). The samples were subjected to size and weight, fat content, free fatty acid, peroxide value, oxidative stability, volatiles, tocopherols, fatty acid profile, and phenol measurements. The dark kernels had significantly lower weight and fat content, higher oxidative stability, and more volatiles than their light counterparts. The dark kernels had higher concentrations of some phenolics but low procyanidin B1 and non-existent epicatechin gallate, compared to the light kernels, indicating that these two phenolics were likely involved in an antioxidant mechanism. Oxidation and depletion of epicatechin gallate likely contributed to the darkening of walnut color.

Comparison of different drying technologies for walnut (Juglans regia L.) pellicles: Changes from phenolic composition, antioxidant activity to potential application

The analysis of the phenolic profile in the walnut pellicle (WP) and its exploitability can help to promote the valorization of the industrial waste from walnut production. Three forms of 33 monomeric phenols in WPs were quantified based on our previously established LC-MS/MS method. The levels of protocatechuic acid and 4-hydroxybenzoic acid in the WPs were the highest, exceeding 400 μg/g. Antioxidant tests revealed that all three phenolic forms of WPs were effective antioxidants (IC50: 2.12-35.05 µg/mL). The findings also revealed that drying temperature had a substantial type-dependent effect on phenolics and their antioxidant ability in WPs. KEGG enrichment analysis found that drying method has the greatest impact on WPs phenols in six metabolic pathways. Besides, 11 active substances in WPs were identified by a compound-targeted activity screening approach, indicating that WPs could be used as a natural antioxidant source in the development of medical and nutraceutical products.

Artificial Neural Network Modeling and Genetic Algorithm Multiobjective Optimization of Process of Drying-Assisted Walnut Breaking

This study combined an artificial neural network (ANN) with a genetic algorithm (GA) to obtain the model and optimal process parameters of drying-assisted walnut breaking. Walnuts were dried at different IR temperatures (40 °C, 45 °C, 50 °C, and 55 °C) and air velocities (1, 2, 3, and 4 m/s) to different moisture contents (10%, 15%, 20%, and 25%) by using air-impingement technology. Subsequently, the dried walnuts were broken in different loading directions (sutural, longitudinal, and vertical). The drying time (DT), specific energy consumption (SEC), high kernel rate (HR), whole kernel rate (WR), and shell-breaking rate (SR) were determined as response variables. An ANN optimized by a GA was applied to simulate the influence of IR temperature, air velocity, moisture content, and loading direction on the five response variables, from which the objective functions of DT, SEC, HR, WR, and SR were developed. A GA was applied for the simultaneous maximization of HR, WR, and SR and minimization of DT and SEC to determine the optimized process parameters. The ANN model had a satisfactory prediction ability, with the coefficients of determination of 0.996, 0.998, 0.990, 0.991, and 0.993 for DT, SEC, HR, WR, and SR, respectively. The optimized process parameters were found to be 54.9 °C of IR temperature, 3.66 m/s of air velocity, 10.9% of moisture content, and vertical loading direction. The model combining an ANN and a GA was proven to be an effective method for predicting and optimizing the process parameters of walnut breaking. The predicted values under optimized process parameters fitted the experimental data well, with a low relative error value of 2.51-3.96%. This study can help improve the quality of walnut breaking, processing efficiency, and energy conservation. The ANN modeling and GA multiobjective optimization method developed in this study provide references for the process optimization of walnut and other similar commodities.

Intelligent grading method for walnut kernels based on deep learning and physiological indicators

Walnut grading is an important step before the product enters the market. However, traditional walnut grading primarily relies on manual assessment of physiological features, which is difficult to implement efficiently. Furthermore, walnut kernel grading is, at present, relatively unsophisticated. Therefore, this study proposes a novel deep-learning model based on a spatial attention mechanism and SE-network structure to grade walnut kernels using machine vision to ensure accuracy and improve assessment efficiency. In this experiment, we found through the literature that both the lightness (L* value) and malondialdehyde (MDA) contens of walnut kernels were correlated with the oxidation phenomenon in walnuts. Subsequently, we clustered four partitionings using the L* values. We then used the MDA values to verify the rationality of these partitionings. Finally, four network models were used for comparison and training: VGG19, EfficientNetB7, ResNet152V2, and spatial attention and spatial enhancement network combined with ResNet152V2 (ResNet152V2-SA-SE). We found that the ResNet152V2-SA-SE model exhibited the best performance, with a maximum test set accuracy of 92.2%. The test set accuracy was improved by 6.2, 63.2, and 74.1% compared with that of ResNet152V2, EfficientNetB7, and VGG19, respectively. Our testing demonstrated that combining spatial attention and spatial enhancement methods improved the recognition of target locations and intrinsic information, while decreasing the attention given to non-target regions. Experiments have demonstrated that combining spatial attention mechanisms with SE networks increases focus on recognizing target locations and intrinsic information, while decreasing focus on non-target regions. Finally, by comparing different learning rates, regularization methods, and batch sizes of the model, we found that the training performance of the model was optimal with a learning rate of 0.001, a batch size of 128, and no regularization methods. In conclusion, this study demonstrated that the ResNet152V2-SA-SE network model was effective in the detection and evaluation of the walnut kernels.

Diversity in Fruit Morphology and Nutritional Composition of Juglans mandshurica Maxim in Northeast China

Although Manchurian walnut (Juglans mandshurica Maxim) is widely distributed in northeast China, very few studies had been reported on its diversity among different populations. We surveyed 12 J. mandshurica populations in their native habitats across the northeast region of China and profiled 13 fruit morphological traits. We found a large degree of variations for these traits, especially for fruit weight (coefficient of variation, or CV of 22.00%), nut weight (CV of 19.42%), and kernel weight (CV of 19.89%). Statistical analysis showed that a large portion of the total variation can be attributed to within-population variation (66.64%), followed by random error (20.96%). We also comprehensively quantified the nutritional composition including fatty acids, amino acids, vitamins, and micronutrients. Similar to fruit morphological traits, we found large variation for most kernel components, which mostly can be explained by within-population variation. Further correlation analysis revealed the dependence of some morphological and nutritional traits on key geographical and ecological factors such as latitude, accumulated temperature, and day length. For instance, a significant positive correlation was found between fruit dimensions and equivalent latitude and precipitation, indicating that such factors should be considered for breeding. Taken together, our data provided a rich dataset for characterizing the variation among J. mandshurica populations and a foundation for selective breeding.