Folate vitamers in the Australian green plum: Through growth and ripening and across locations

The green plum is a native fruit of Australia that grows on the tree Buchanania obovata. This study aimed to confirm the high level of folate in green plums by analyzing a large number of ripe samples from multiple locations and to understand how folate vitamers change as the fruit grows through maturity stages. This study analyzed green plums for five vitamers of folate, H4folate, 5-CH3-H4folate, 5-CHO-H4folate, 10-CHO-PteGlu, and PteGlu (folic acid) using a stable isotope dilution assay on a liquid chromatograph mass spectrometer (LC-MS). Green plums were tested from four locations, two harvests and five maturity stages. Another 11 ripe samples, each from different tree clumps from one location, were also tested as were ripe red-colored green plums. The results show the 5-CH3-H4folate in green plum increases and accumulates in the fruit through development, ripening and senescence. The ripe green plums contain between 82.4 ± 5.5 and 149.4 ± 10.7 μg/100 g Fresh Weight (FW). The red-colored green plums are even higher in folate, with total folate measured as 192.5 ± 7.0 and 293.7 ± 27.4 μg/100 g FW, and further analysis of them is suggested. There is some variation in amounts of folate between fruit from different locations and sets of trees, but all ripe green plums tested are considered good dietary sources of folate.

Moisture variation analysis of the green plum during the drying process based on low-field nuclear magnetic resonance

Green plums were dried at 50, 60, 70, and 80 ℃ to study the dynamic changes of internal moisture during the drying process. Low-field nuclear magnetic resonance (LF-NMR) was used to study the dynamic changes across the T₂ relaxation spectrum, while magnetic resonance imaging (MRI) provided visualization of the plums throughout the process. The results indicate a negative linear relationship between the lost moisture of the plums (p < 0.05) as drying time increased. Relaxation times T₂₁ , T_22, and T₂₃ , and the peak areas of A₂₁ and A₂₃ decreased significantly during the drying process. The MRI results also show that the brightness of the images decreased as the drying time increased, indicating that the higher the temperature, the greater the water loss inside the plums. Color measurements demonstrated that the high temperature dried plums had better sensory quality. Correlation analysis implies a strong positive relationship between A₂₃ and A_total and water content, with coefficients of 0.958 and 0.936, respectively. Principal component analysis results show that the drying temperature has a significant effect on the sample's internal moisture release. LF-NMR is a fast, convenient, and feasible technique for monitoring the moisture variation of green plums during the drying process. PRACTICAL APPLICATION: Low-field nuclear magnetic resonance (LF-NMR) was used to study the moisture dynamic changes of green plums across the T₂ relaxation spectrum, while magnetic resonance imaging (MRI) provided visualization of plums throughout the process. The drying temperature has a significant effect on the green plum's internal moisture release and may affect the quality of the plums. LF-NMR might be a complementary technique in monitoring the moisture variation of green plums during the drying process.

Research on the Prediction of Green Plum Acidity Based on Improved XGBoost

The acidity of green plum has an important influence on the fruit’s deep processing. Traditional physical and chemical analysis methods for green plum acidity detection are destructive, time-consuming, and unable to achieve online detection. In response, a rapid and non-destructive detection method based on hyperspectral imaging technology was studied in this paper. Research on prediction performance comparisons between supervised learning methods and unsupervised learning methods is currently popular. To further improve the accuracy of component prediction, a new hyperspectral imaging system was developed, and the kernel principle component analysis—linear discriminant analysis—extreme gradient boosting algorithm (KPCA-LDA-XGB) model was proposed to predict the acidity of green plum. The KPCA-LDA-XGB model is a supervised learning model combined with the extreme gradient boosting algorithm (XGBoost), kernel principal component analysis (KPCA), and linear discriminant analysis (LDA). The experimental results proved that the KPCA-LDA-XGB model offers good acidity predictions for green plum, with a correlation coefficient (R) of 0.829 and a root mean squared error (RMSE) of 0.107 for the prediction set. Compared with the basic XGBoost model, the KPCA-LDA-XGB model showed a 79.4% increase in R and a 31.2% decrease in RMSE. The use of linear, radial basis function (RBF), and polynomial (Poly) kernel functions were also compared and analyzed in this paper to further optimize the KPCA-LDA-XGB model.

The Nutritional Potential of the Native Australian Green Plum (Buchanania obovata) Compared to Other Anacardiaceae Fruit and Nuts

The native Australian green plum (Buchanania obovata) is a small fruit that grows in the northern parts of the Northern Territory and Western Australia. The fruit belongs to the family Anacardiaceae, which includes the other agriculturally important fruit mangoes, pistachios and cashew nuts. The green plum is a favored species of fruit for the Aboriginal communities and an important bush food in the Northern Territory. To date, only minimal scientific studies have been performed on the green plum as a food. This review is about plant foods in the family Anacardiaceae and the key nutritional compounds that occur in these fruit and nuts. It looks at the more traditional nutrient profiles, some key health metabolites, allergens and anti-nutrients that occur, and the role these foods play in the health of populations. This provides a guide for future studies of the green plum to show what nutritional and anti-nutritional properties and compounds should be analyzed and if there are areas where future studies should focus. This review includes an update on studies and analysis of the green plum and how its nutritional properties give it potential as a food for diet diversification in Australia.

Defect Classification of Green Plums Based on Deep Learning

The green plum is rich in amino acids, lipids, inorganic salts, vitamins, and trace elements. It has high nutritional value and medicinal value and is very popular among Chinese people. However, green plums are susceptible to collisions and pests during growth, picking, storage, and transportation, causing surface defects, affecting the quality of green plums and their products and reducing their economic value. In China, defect detection and grading of green plum products are still performed manually. Traditional manual classification has low accuracy and high cost, which is far from meeting the production needs of green plum products. In order to improve the economic value of green plums and their products and improve the automation and intelligence level of the product production process, this study adopted deep learning methods based on a convolutional neural network and cost-effective computer vision technology to achieve efficient classification of green plum defects. First, a camera and LEDs were used to collect 1240 green plum images of RGB, and the green plum experimental classification standard was formulated and divided into five categories, namely, rot, spot, scar, crack, and normal. Images were randomly divided into a training set and test set, and the number of images of the training set was expanded. Then, the stochastic weight averaging (SWA) optimizer and w-softmax loss function were used to improve the VGG network, which was trained and tested to generate a green plum defect detection network model. The average recognition accuracy of green plum defects was 93.8%, the test time for each picture was 84.69 ms, the recognition rate of decay defect was 99.25%, and the recognition rate of normal green plum was 95.65%. The results were compared with the source VGG network, resnet18 network, and green lemon network. The results show that for the classification of green plum defects, the recognition accuracy of the green plum defect detection network increased by 9.8% and 16.6%, and the test speed is increased by 1.87 and 6.21 ms, respectively, which has certain advantages.

Buchanania obovata: Functionality and Phytochemical Profiling of the Australian Native Green Plum

The green plum is the fruit of Buchanania obovata Engl. and is an Australian Indigenous bush food. Very little study has been done on the green plum, so this is an initial screening study of the functional properties and phytochemical profile found in the flesh and seed. The flesh was shown to have antimicrobial properties effective against gram negative (Escherichia coli 9001&mdash;NCTC) and gram positive (Staphylococcus aureus 6571&mdash;NCTC) bacteria. Scanning electron microscopy analysis shows that the antimicrobial activity causes cell wall disintegration and cytoplasmic leakage in both bacteria. Antioxidant 2,2-diphenyl-1-picrylhydrazyl (DPPH) testing shows the flesh has high radical scavenging activity (106.3 &plusmn; 28.6 &mu;M Trolox equivalant/g Dry Weight in methanol). The flesh and seed contain a range of polyphenols including gallic acid, ellagic acid, p-coumaric acid, kaempferol, quercetin and trans-ferulic acid that may be responsible for this activity. The seed is eaten as a bush food and contains a delphinidin-based anthocyanin. The green plum has potential as a functional ingredient in food products for its antimicrobial and antioxidant activity, and further investigation into its bioactivity, chemical composition and potential applications in different food products is warranted.