Effect of Almond Shell Waste on Physicochemical Properties of Polyester-Based Biocomposites.
Polyester-based biocomposites containing INZEA F2® biopolymer and almond shell powder (ASP) at 10 and 25 wt % contents with and without two different compatibilizers, maleinized linseed oil and Joncryl ADR 4400®, were prepared by melt blending in an extruder, followed by injection molding. The effect of fine (125-250 m) and coarse (500-1000 m) milling sizes of ASP was also evaluated. An improvement in elastic modulus was observed with the addition of< both fine and coarse ASP at 25 wt %. The addition of maleinized linseed oil and Joncryl ADR 4400 produced some compatibilizing effect at low filler contents while biocomposites with a higher amount of ASP still presented some gaps at the interface by field emission scanning electron microscopy. Some decrease in thermal stability was shown which was related to the relatively low thermal stability and disintegration of the lignocellulosic filler. The added modifiers provided some enhanced thermal resistance to the final biocomposites. Thermal analysis by differential scanning calorimetry and thermogravimetric analysis suggested the presence of two different polyesters in the polymer matrix, with one of them showing full disintegration after 28 and 90 days for biocomposites containing 25 and 10 wt %, respectively, under composting conditions. The developed biocomposites have been shown to be potential polyester-based matrices for use as compostable materials at high filler contents.
Origin Traceability of Peanut Kernels Based on Multi-Element Fingerprinting Combined With Multivariate Data Analysis.
Background: Multi-elements have been widely used to identify the geographical origins of various agricultural products. The objective of this study was to investigate the feasibility of identifying the geographical origins of peanut kernels at different regional scales by using the multi-element fingerprinting technique. The concentrations of 20 elements (B, Mg, P, K, Ca, etc.) were determined in 135 peanut samples from Jilin Province, Jiangsu Province, and Shandong Province of China. Data obtained were processed by one-way analysis of variance (ANOVA), principal components analysis (PCA), k nearest neighbors (k-NN), linear discriminant analysis (LDA), and support vector machine (SVM). Results: Peanut kernels from different regions had their own element fingerprints. k-NN, LDA, and SVM were all suitable to predict peanut kernels according to their grown provinces with the total correct classification rates of 91.2, 91.1, and 91.1%, respectively. While SVM was the best to identify different grown cities of peanut kernels with the prediction accuracy of 91.3%, compared to 72.2 and 78.3% for k-NN and LDA respectively. Conclusion: It was an effective method to identify producing areas of peanut kernels at different regional scales using multi-element fingerprinting combined with SVM to enhance regional capabilities for quality assurance and control.
Bridging Fe3O4@ Au Nanoflowers and Au@ Ag Nanospheres with Aptamer for Ultrasensitive SERS Detection of Aflatoxin B1.
Aflatoxin B1 (AFB1) as the most toxic mycotoxin in contaminated food can greatly threaten human health, and sensitive and selective detection of AFB1 is thus highly desired. An ultrasensitive surface-enhanced Raman spectroscopy (SERS) aptasensor was developed for AFB1 detection in peanut oil samples. SH-cDNA modified Fe3O4@Au nanoflowers acted as capture probes, SH-Apt modified Au@Ag nanospheres and commercial Cy3-Apt were used as reporter probes. Strong SERS signals of reporter probes were produced due to the recognition of AFB1 aptamer and its complementary strand (SH-cDNA). With the preferred binding of AFB1 aptamer to AFB1, reporter probes were released from capture probes, causing a linear decrease in SERS intensity. Therefore an ultralow detection limit of 0.40 pg·mL-1 in a wide linear range of 0.0001-100 ng·mL-1 was obtained and the sensibility of this SERS aptasensor was higher than that of the Cy3-Apt based SERS aptasensor. In addition, an excellent selectivity in interfering toxins and satisfactory recoveries of 96.6-115% in peanut oil samples were obtained, proving this aptasensor is a promising analytical tool in AFB1 detection.
Identification and analysis of the FAD gene family in walnuts (Juglans regia L.) based on transcriptome data.
Background: Walnut kernels contain a large amount of unsaturated fatty acids, such as linoleic acid and linolenic acid, which are essential fatty acids for humans and have important effects on growth and health. The main function of fatty acid desaturase (FAD), which is widely distributed in organisms, is to remove hydrogen from carbon chains in the biosynthesis of unsaturated fatty acids to generate C=C bonds. Results: By performing a series of bioinformatics analysis, 24 members of the JrFAD gene family were identified from the genome database of walnut, and then compared with the homologous genes from Arabidopsis. Phylogenetic analysis showed that JrFADs were classified into four subfamilies: the SAD desaturase subfamily, Δ7/Δ9 desaturase subfamily, Δ12/ω-3 desaturase subfamily and "front-end" desaturase subfamily. Meanwhile, the expression of fatty acid synthesis genes in walnut kernels at different developmental stages was analysed by transcriptome sequencing, with expression of JrFAD3-1, which encodes an enzyme involved in linolenic acid synthesis, being particularly prominent. The relative expression level of JrFAD3-1 changed dramatically with the kernel development stages and exhibited a Bell-Shaped Curve. A significant positive correlation was observed between the expression of JrFAD3-1 during 70-100 DAF (Days after flowering) and the content of alpha-linolenic acid during 100-130 DAF, with a correlation coefficient of 0.991. Additionally, JrFAD3-1 was proved closely related to homologous genes in Betula pendula and Corylus heterophylla, indicating that the conserved structure of FADs is consistent with classical plant taxonomy. Conclusion: Twenty-four members JrFADs in walnut were identified and classified into four subfamilies. JrFAD3-1 may play significant roles in the biosynthesis of polyunsaturated fatty acids in walnut.
A comparison of ground-based air-blast sprayer and aircraft application of fungicides to manage scab in tall pecan trees.
Pecan scab (caused by Venturia effusa) is a destructive disease of pecan in the southeastern United States. Susceptible cultivars must be sprayed with fungicide every 10 to 21 days to ensure yield and kernel quality. Fungicide is most often applied using large orchard air-blast sprayers. Pecan trees grow tall, and air-blast sprays result in a gradient in spray deposition and consequently of scab. Aerial fungicide application is also practiced. Disease distribution and spray deposition of the two methods have not been compared but will provide information aiding decisions on spray application methods. We compared air-blast, aerial, and air-blast + aerial applications for efficacy controlling scab at five heights in the canopy of 25-m cultivar Schley pecan trees. There was a negative relationship between scab severity and height in control trees, a positive linear relationship with height in air-blast treated trees, and a generally negative linear relationship between scab severity and height in aerially treated trees. Air-blast + aerial treatments resulted in low severity of scab at all heights. Spray deposition on water-sensitive cards indicated a declining gradient with height using an air-blast sprayer, whereas aerial applications resulted in a low deposition at all sample heights. Air-blast sprays tended to result in less good control at heights >12.5 m, and aerially treated trees at ≤7.5 m. The results provide insight into the efficacy and advantages of these methods for applying fungicide to control scab in tall pecan trees; further research is needed to better understand the impact of frequency and timing of these two methods.
Modification of a Predictive Model to Include the Influence of Fat Content on Salmonella Inactivation in Low-Water-Activity Foods.
Low-water-activity (aw) foods (including those containing fat) are often implicated in outbreaks of Salmonella spp. The influence of fat content on survival in foods such as peanut butter remains unclear. Certain Salmonella serovars can survive for long periods in harsh temperatures and low moisture conditions. The objective of this study was to determine the influence of fat content on the survival of Salmonella in low-aw foods and expand an existing secondary inactivation model previously validated for lower-fat foods. Whey protein powder supplemented with peanut oil was equilibrated to five target aw values (aw < 0.60), inoculated with a dried four-strain cocktail of Salmonella, vacuum sealed, and stored at 22, 37, 50, 60, 70, and 80°C for 48 h, 28 days, or 168 days. Survival data were fitted to Weibull, Biphasic-linear, Double Weibull, and Geeraerd-tail models. The Weibull model was chosen for secondary modeling due to its ability to satisfactorily describe the data over most of the conditions under study. The influence of temperature, fat content, and aw on the Weibull model parameters was evaluated using nonlinear least squares regression, and a revised secondary model was developed based on parameter significance. Peanut butter, chia seed powder, toasted oat cereal, and animal crackers within the aw range of the model were used to validate the modified model within its temperature range. Fat content influenced survival in samples held at temperatures ≥50°C, whereas aw influenced survival at 37 and 70°C. The model predictions demonstrated improved % bias and % discrepancy compared with the previous model. Weibull model predictions were accurate and fail-safe in 38 and 58%, respectively, of the food and environmental conditions under study. Predictions were less reliable for peanut butter held at 80°C. This study provides data and a model that can aid in the development of risk mitigation strategies for low-aw foods containing fat.
Effect of Drying Temperatures on the Peanut Quality during Hot Air Drying.
Peanuts are usually with high moisture after harvest and must be dried to prevent mildew. Hot air drying is the most commonly used method for peanut drying. The purpose of this study was to evaluate the drying temperatures on the peanut qualities. In this paper, fresh peanuts were dried with solar radiation (control group) and hot air at 35-60°C until the moisture content of peanut reduced below 10%. The physical (texture, damaged percentage of red testa and breakage percentage of peanut kernel), physiological (germination) and biochemical (the contents of vitamin E and aflatoxin B1; acidity values, iodine values, peroxide values and fatty acid composition of peanut oil; solubility, emulsifying, foaming, water-holding capacity and oil-binding capacity of peanut protein) properties of peanut kernel were determined under different drying conditions (solar radiation, 35°C, 40°C, 45°C, 50°C, 55°C, 60°C). The results showed that hot air temperatures had obvious influences on peanut qualities. The damaged percentage of red testa and breakage percentage of peanut kernel increased remarkably when the drying temperatures were above 45°C. Meanwhile, when drying temperatures were more than 45°C, the acid value and peroxide value of the extracted oil increased significantly. Furthermore, some properties exhibited prominent changes when the temperatures were higher than 50°C, such as hardness, brittleness, germination percentage, and the Vitamin E content of peanut kernel. In addition, the research results revealed that hot air can increase hydrophobicity of peanut protein and affect the functional properties of peanut protein. Therefore, it could be concluded that peanut should be dried by hot air below 45°C for quality maintenance. It also provided reference to choose suitable drying temperatures based on the final use of peanut.
Spray drying and storage of probiotic enriched almond milk: probiotic survival and physicochemical properties.
Background: The combination of the nutritional profile of almond milk with the benefits of probiotic bacteria are an interesting development to cover the demand of sustainable and health-promoting food. Almond milk inoculated with probiotic Lactobacillus plantarum (ATCC 8014) was spray dried. Physicochemical characterization of almond raw material, almond milk and powders were carried out within this study. Besides, samples were characterized in terms of bacterial survival before and after atomization, but also bacterial viability and total fatty acids changes were studied during 8-month storage at 4 and 22 °C. Results: Results showed proper physicochemical properties and an optimal bacterial survival rate, maintaining almost the same values before and after the spray drying operation. A decrease was observed in the cell viability for samples stored at 4 °C, however, the cell count was maintained, above the minimum level suggested (107 living cells) to assure potential probiotic functionality, within 8 months. On the other hand, count cell of powders stored at 22 °C was below the minimum level required, after 6 months. The fatty acids profile was not significantly (p > 0.05) affected by storage time and temperature. Conclusion: A new almond-based-product with probiotics was developed to meet consumer demands. Almond nutrients were properly recovered in almond milk powder and was found to be a good source of K and high in Mg and in monounsaturated fat. The bacteria viability was assured during 8 months of storage at 4 °C and up to 6 months for samples stored at 22 °C.
Advancing Agricultural Production With Machine Learning Analytics: Yield Determinants for California’s Almond Orchards.
Agricultural productivity is subject to various stressors, including abiotic and biotic threats, many of which are exacerbated by a changing climate, thereby affecting long-term sustainability. The productivity of tree crops such as almond orchards, is particularly complex. To understand and mitigate these threats requires a collection of multi-layer large data sets, and advanced analytics is also critical to integrate these highly heterogeneous datasets to generate insights about the key constraints on the yields at tree and field scales. Here we used a machine learning approach to investigate the determinants of almond yield variation in California's almond orchards, based on a unique 10-year dataset of field measurements of light interception and almond yield along with meteorological data. We found that overall the maximum almond yield was highly dependent on light interception, e.g., with each one percent increase in light interception resulting in an increase of 57.9 lbs/acre in the potential yield. Light interception was highest for mature sites with higher long term mean spring incoming solar radiation (SRAD), and lowest for younger orchards when March maximum temperature was lower than 19°C. However, at any given level of light interception, actual yield often falls significantly below full yield potential, driven mostly by tree age, temperature profiles in June and winter, summer mean daily maximum vapor pressure deficit (VPDmax), and SRAD. Utilizing a full random forest model, 82% (±1%) of yield variation could be explained when using a sixfold cross validation, with a RMSE of 480 ± 9 lbs/acre. When excluding light interception from the predictors, overall orchard characteristics (such as age, location, and tree density) and inclusive meteorological variables could still explain 78% of yield variation. The model analysis also showed that warmer winter conditions often limited mature orchards from reaching maximum yield potential and summer VPDmax beyond 40 hPa significantly limited the yield. Our findings through the machine learning approach improved our understanding of the complex interaction between climate, canopy light interception, and almond nut production, and demonstrated a relatively robust predictability of almond yield. This will ultimately benefit data-driven climate adaptation and orchard nutrient management approaches.
Prevalence of Food Sensitization and Food Allergy in Children across Europe.
Background: For adults, prevalence estimates of food sensitization (FS) and food allergy (FA) have been obtained in a standardized manner across Europe. For children, such estimates are lacking. Objectives: To determine prevalence of self-reported FA, FS, probable FA (symptoms plus IgE-sensitization), and challenge-confirmed FA in European school-age children. Methods: Data on self-reported FA were collected through a screening questionnaire sent to a random sample of the general population of 7- to 10-year-old children in eight European centres in phase I of the EuroPrevall study. Data on FS and probable FA were obtained in phase II, comprising an extensive questionnaire on reactions to 24 commonly implicated foods, and serology testing. Food challenge was performed in phase III. Results: Prevalence (95%-CI) of self-reported FA ranged from 6.5% (5.4-7.6) in Athens to 24.6% (22.8-26.5) in Lodz; prevalence of FS from 11.0% (9.7-12.3) in Reykjavik to 28.7% (26.9-30.6) in Zurich; and prevalence of probable FA from 1.9% (0.8-3.5) in Reykjavik to 5.6% (3.6-8.1) in Lodz. In all centres, the majority of food-sensitized subjects had primary (non-cross-reactive) FS. However, FS due to PR-10 cross-reactivity was also common in Central-Northern Europe. Probable FA to milk and egg occurred frequently throughout Europe; to fish and shrimp mainly in the Mediterranean and Reykjavik. Peach, kiwi and peanut were prominent sources of plant FA in most countries, along with notably hazelnut, apple, carrot and celery in Central-Northern Europe, and lentils and walnut in the Mediterranean. Conclusion: There are large geograhical differences in prevalence of FS and FA in school-age children across Europe. Both primary and cross-reactive FS and FA occur frequently.