Moisture Content Of Peanut Kernels Research Articles

A Free-Space-Based Model for Predicting Peanut Moisture Content during Natural Drying

This study aimed to investigate the water dissipation pattern from peanut pods under natural drying conditions after harvest. The Shandong peanut Luhua 22 was used to examine the effects of varying moisture content, bulk density, and porosity on the relative permittivity of the peanut at a signal frequency of 5.8 GHz. The peanut dielectric constant, porosity, and bulk density were used as inputs and peanut kernel moisture as outputs. Support vector regression (SVR), extreme learning machine (ELM), sparrow search algorithm-support vector regression (SSA-SVR), and sparrow search algorithm-extreme learning machine (SSA-ELM) were used to create a prediction model of peanut kernel moisture content. The results show that the water content of peanut kernels decreased in a fast and then slow manner throughout the drying process and that the water content of kernels was stable at 5–8% at the end of drying. The relative permittivity of peanut kernels increased with an increase in the water content and bulk density but decreased with an increase in porosity. The developed SVR, ELM, SSA-SVR, and SSA-ELM water-content prediction models were validated and analyzed in this study, with the model test set coefficients of determination of 0.936, 0.949,0.984, and 0.994, respectively. In comparison to SVR, ELM, and SSA-SVR, the SSA-ELM root mean square error was reduced by 0.0080, 0.0060, and 0.0012, respectively. According to the findings, the ELM neural network model, which is based on the optimization of the SSA, has an improved prediction accuracy. This prediction model provides a theoretical foundation for the variations in peanut seed moisture content during the natural drying process after harvesting peanuts in Shandong, which will be useful for future peanut storage and transportation.

Rapid and Non-destructive measurement of moisture content of peanut (Arachis hypogaea L.) kernel using a near-infrared hyperspectral imaging technique

This study used a rapid and non-destructive way of determining and predicting the moisture content (MC) of peanut kernels using hyperspectral imaging in the near-infrared region (900–1700 nm). Using partial least square regression (PLSR), spectral data from the peanut kernel hyperspectral images were extracted to predict MC. The MC PLSR model displayed good performance with determination coefficient of calibration (R2c), validation (R2v) and prediction (R2p) of 0.9309, 0.9083 and 0.9368, respectively. Also, the root-mean-square error of calibration (RMSEC), cross-validation (RMSEV), and prediction (RMSEP) of 1.6978, 1.9571, and 1.8715, respectively, were achieved. Optimization was done by selecting wavelengths with the highest absolute weighted regression coefficients; on this basis, 20 significant wavelengths were identified for further analysis. These wavelengths were used to build an optimized regression model which resulted in R2c of 0.9357, R2v of 0.9133, and R2p of 0.9445 as well as RMSEC, RMSEV, and RMSEP of 1.6822, 1.8316 and 1.9519, respectively. The optimized model has applied to the peanut kernel hyperspectral images in a pixel-wise manner obtaining peanut kernel moisture content distribution map. Results show promising potential of the hyperspectral imaging system in the near-infrared region combined with partial least square regression (PLSR) for rapid and non- destructive prediction of moisture content of peanut kernels.

Design of a peanut moisture detector based on STM32and MATLAB

The measurement accuracy and repetition rate of the existing peanut moisture detector are poor. To solve this problem, this paper designs a capacitive peanut moisture detector based on the capacitance method. Firstly, the structure of coaxial cylindrical capacitive moisture sensor is optimized. STM32 chip microcomputer is used in the main control chip, and the change in capacitance is transformed to change in frequency by capacitance detection circuit. The temperature and quality of peanut kernel are measured by DS18B20 temperature sensor and weighing sensor respectively. The influence of particle size, moisture content, temperature and volume density on the output differential frequency of capacitance sensor are studied by using the differential frequency method. In the range of moisture content of 4.3%-19.1% and temperature of 10°C-40°C, a mathematical model for the relationship among moisture content, temperature, and differential frequency is established. The prediction test of peanut kernel’s moisture content shows that the measurement error of moisture content is within ±0.5%, indicating that the instrument has high measurement accuracy and repeatability when it is used to detect the moisture content of peanut kernels, and has a wide application prospect.

Application of PLS regression to study relationship between Sensory and textural characteristics of roasted peanut kernels

Introduction: Roasting is one of the processing steps involved in the nut industry to improve the flavor, color, texture and overall acceptability of the product. Ppeanut is a fruit or pod of the order Leguminosae and contains 47–50% oil, 25–30% protein and is an essential source of minerals and vitamins; thus it makes a substantial contribution to human nutrition. Peanuts are readily acceptable as a cheap protein source and popular snack item that can be eaten alone or combined with other foods. Recently, peanuts have gained much attention as functional food and roasted peanuts is one of the most popular snack foods, in which roasting is a key step in the process and directly impacts the quality (crispness, taste, and flavor) and shelf-life of the final product. Understanding of the roasting process is of interest because roasting is a critical processing step not only for peanuts, but many other food products such as coffee, cocoa, grains and other tree nuts. Roasting is a process to develop color, flavor and textural characteristics of product through chemical reactions, therefore proper roasting is critical to flavor and texture development as well as nutritional content of the final product. Materials and methods: In this study, dried Goli peanuts were supplied from a local market in Minodasht, Iran in 2015 and stored at 4°C until processing. The average moisture content of peanut kernels was measured as 5.1 % (d.b.). Kernels were sorted manually to get the uniform sizes for roasting. 100 g peanut kernels were soaked in 500 ml of 25% salt solution for 30 min. After soaking, the salt solution was drained using a strainer and the excess water was removed by a cloth filter. After soaking, the moisture content of soaked peanut kernels increased to 8.27 % (d.b.). For roasting, three temperatures (140, 160 and 180°C) and three times (10, 20 and 30 min) and constant air velocity (1 m/s) were applied. Roasting was performed in a hot air roaster equipped with a controller to adjust the roasting temperature. After roasting, the whole kernels were allowed to cool at room temperature (23 ± 2°C). Roasting process was performed in 3 replications. Instrumental texture measurements (Uniaxial compression test) were carried out at room temperature using a TA-XT Plus Texture Analyzer using cylinder probe (diameter 25 mm) on peanut halves. The textural parameters of peanut halves were expressed as fracture force (initial peak or first fracture force (N)), hardness (highest peak compression force (N)), initial tangent modulus or apparent modulus of elasticity that shows sample rigidity in the linear part of the force-deformation curve (N/m) and compressive energy or area under the curve for the compression that is the work (N×m) required to attain deformation, indicative of internal strength of bonds within product. Sensory attributes including colour, texture, flavor, odour, total acceptance and final acceptance were assessed according to a five-grade hedonic scale (5 points – the best, 1 point – the worst). A completely randomized factorial design was used to evaluate the results and analysis of variance (ANOVA) was carried out to compare the mean values. All significant differences were reported at P ≤ 0.05 levels. Minitab statistical software (Minitab Release 16, Minitab Inc., USA) was used for all statistical analyses in the present research. However, MSTATC (Version 2.10, Michigan State University) was used to determine significant differences. Results & discussion: Roasting is one of the most important steps in peanuts processing that leads to the development of the desired aroma, taste, texture and color of the final product. The results showed that increasing the roasting temperature and time decreased the fracture force (75.19–45.92 N), instrumental hardness (81.74–48.90 N), apparent modulus of elasticity (7.508-5.446 N/s), compressive energy (469.0–199.1 N.s) and improved the sensory characteristics of peanut kernels. During roasting process, moisture content of peanut kernels decreased and they became more crumble and fragile which causes to break easier and moisture reduction helps to create a desirable crisp texture. The results of the consumer test showed that the roasted peanut kernels have good acceptability for color (4.50), texture (4.15), flavor (3.89), odor (4.05), total acceptance and final acceptance (3.97) on roasting temperature and time (160°C and 30 min). The results obtained for VIP coefficients (standard coefficients) in PLS regression for fracture force, instrumental hardness; apparent modulus of elasticity and compressive energy variables indicated that apparent modulus of elasticity could be able to predict the individual sensory attribute of peanut kernels except color. PLS results showed that apparent modulus of elasticity could successfully predict quality sensory characteristics of peanut kernels. Thus, instrumental apparent modulus of elasticity could be replaced with sensory analysis for evaluation the quality of peanut kernels.

Assessing the Utility of Microwave Kernel Moisture Sensing in Peanut Drying

<abstract> <b><sc>Abstract. </sc></b>Presently in the peanut industry, peanut pods (unshelled peanuts) have to be shelled for kernel moisture content determination with the official moisture meter. This makes kernel moisture content determination laborious and limits efficiency during peanut drying. For field testing during the 2013 and 2014 peanut harvest seasons, a microwave moisture sensor was placed in several 45-ft (13.7-m) drying semi-trailers at a buying point in central Georgia to monitor kernel moisture content as the peanuts dried. Laboratory investigation of the accuracy of the portable microwave moisture sensor showed that peanut kernel moisture content can be determined from measurements on unshelled peanuts with a standard error of performance ≤ 0.55% moisture content when compared to the reference oven-drying method. A comparison was also performed between the microwave sensor and the official moisture meter during field testing. Results showed that there was a 2.3% difference in initial kernel moisture content determination and a 0.8% difference in ending kernel moisture content determination between the two. Overall evaluation demonstrated that the accuracy of the microwave moisture sensor and its provision of in-shell kernel moisture content would make it highly effective in the peanut drying process.

Microwave moisture meter for in-shell peanut kernels

A microwave moisture meter built with off-the-shelf components was developed, calibrated and tested in the laboratory and in the field for nondestructive and instantaneous in-shell peanut kernel moisture content determination from dielectric properties measurements on unshelled peanut pod samples. The meter operates at a single frequency of 5.8 GHz and uses free-space transmission measurement principles for determining the dielectric properties of the peanut pods. From these properties, a dielectric-based algorithm provides peanut kernel moisture content from measurements on pods. Field tests at a peanut buying station showed that moisture content of peanut kernels can be determined with a standard error of performance of 0.82% when compared to the official moisture meter which required the pods to be shelled before kernel moisture content can be determined. When performance of both the microwave moisture meter and the official meter were compared to the oven-drying standard method, values of the standard errors of performance were 0.53% for the microwave moisture meter and 0.87% for the official moisture meter.

Rapid and Non-destructive Determination of Moisture Content of Peanut Kernels Using Hyperspectral Imaging Technique

Moisture content (MC) is a fundamental and very important quality indicator of peanut, which has significant influence on the overall quality of peanut in the process of storage. This study aimed to investigate the potential of hyperspectral imaging technique in the spectral range I (400–1000 nm) and spectral range II (1000–2500 nm) for predicting the MC of peanut kernels non-destructively. Hyperspectral images were obtained, and the corresponding spectral data was extracted. The calibration models were built between the extracted spectral data and the measured MC using partial least squares regression (PLSR) analysis. The established PLSR models using the full wavelengths showed good performance with determination coefficient (R 2 p) of 0.908 and 0.906, and root mean square errors by prediction (RMSEP) of 0.063 and 0.063 %, respectively. Optimal wavelengths were then selected based on the regression coefficients of the established PLSR model. The simplified PLSR models established only using identified optimal wavelengths also showed good performance with R 2 p of 0.910 and 0.900 and RMSEP of 0.061 and 0.060 %, respectively. The best PLSR model, established only using six optimal wavelengths (409, 508, 590, 663, 924, and 974 nm) selected from the spectral range I, was used to shift the spectrum of each pixel into its MC value for visualizing the distribution map of MC in peanut kernels. The results demonstrated that hyperspectral imaging technique in tandem with chemometrics analysis has the potential for rapid and non-destructive prediction of MC in peanut kernels.

Infection of Aspergillus Flavus and Physical Quality of Peanuts Collected from Farmers, Local Markets, and Processors

Sixteen peanut samples collected from farmers, collectors, retailers and food processors in Banjarnegara District, Central Java were analyzed for their physical quality and A. flavus infection. On average, the moisture content of peanut kernels was 8.8%, while the damaged kernels (46.7%) and A. flavus infection (45.1%) were considerably high. Six, five and five samples were infected by non-toxic (-), slightly toxic (+), and toxic (++) A. flavus, respectively. Infection of A. flavus positively correlated with damaged kernel (r =0.71). This suggests that high damaged kernels and infection of A. flavus need to be decreased through proper handling and storage practices.

Improving the Accuracy of Electronic Moisture Meters for Runner-Type Peanuts

ABSTRACT Runner-type peanut kernel moisture content (MC) is measured periodically during curing and post harvest processing with electronic moisture meters for marketing and quality control. MC is predicted for 250 g samples of kernels with a mathematical function from measurements of various physical properties. To examine the accuracy of the function used in the Dickey-john GAC2100 for measuring MC of runner-type peanuts, 421 samples were measured with the meter and compared to oven MC data subsequently determined using ASAE Standard S410.1. Peanut moisture content for the peanuts according to the meter averaged 19.1% with a SD of 15.4%. Oven moisture contents had a mean of 17.3% and a SD of 8.5%. Means were significantly different (P = 0.001). A calibration equation was derived from capacitance, conductance, temperature, and test weight data provided by the meter and compared to oven MC data. Moisture contents calculated from the calibration equation had a 17.3% mean and an 8.5% SD equaling values for oven determined moisture contents. Results of the study indicate that the accuracy of the meter in predicting moisture content can be improved considerably utilizing currently collected data and the derived calibration equation developed.

Development of a powder formulation of Aspergillus parasiticus for dry inoculation of peanut kernels

The aim of this study was to develop a method for the dry inoculation of Aspergillus parasiticus on peanut kernels for post-harvest studies. Inoculum powder was prepared by adding sterile kaolin powder to A. parasiticus spores developed on filter papers soaked with spore suspension prepared with Czapek medium (5% agar), which were dried at room temperature. Concentrations were adjusted to 5 x 105 and 1 x 106 spores g-1 of kaolin. Varied amounts of the powder inoculum were then used to inoculate 1 kg of peanut kernels. Inoculation was performed by mixing the powder inoculum with the peanut kernels in plastic bags until the kernels were completely covered. The effectiveness of this inoculation method was compared with the common inoculation methods of immersion in aqueous spore suspensions and contamination through contact with fungal colonies growing on culture medium. Fungal growth assay was performed on PDA saline medium, and near-infrared radiation measurements were used to determine peanut kernel moisture content. The dry inoculation technique was effective in inoculating peanut kernels with A. parasiticus, especially at concentrations of 3 x 106 and 4 x 106 spores g-1 per 1 kg of peanut kernels. In contrast to usual methods of inoculation, the dry inoculation technique does not increase the moisture content of inoculated peanut kernels. Thus, the physical state of the peanut kernel remains intact.

Aspergillus flavus infection and aflatoxin contamination in peanuts stored at wholesale and retail levels in Bandung, Bogor and Jakarta (West Java, Indonesia)

The objective of the study was to obtain information on the percentage of kernels infected by Aspergillus flavus and aflatoxin contents in peanuts stored at wholesale and retail levels (traditional market and supermarket) in three cities (Bandung, Bogor and Jakarta). The moisture content of the kernels were also investigated. The three cities were selected because they are different in terms of their elevations and rainfalls. A total of 105, 101, 87, 99, 104 and 98 peanut kernels were collected in March, June, September, December 2005; in March and June 2006, respectively. The moisture content of peanut kernels either collected at wholesaler or retailer in the three cities (Bandung, Bogor and Jakarta) fluctuated during sampling (between 6 – 8%). The moisture content of kernels either at the wholesaler or retailer in Bogor had the same pattern with those in Jakarta, while those in Bandung had different patterns. The moisture content of kernels either at wholesale or retail levels in the three cities were relatively similar, although the relative humidites and temperatures of the storages at wholesale and retail levels in Bandung were relatively lower than those in Bogor and Jakarta. The percentage of kernels infected by A. flavus in peanuts collected from wholesaler had the same pattern with those collected from retailer. The highest percentage of kernels infected by A. flavus either collected from wholesaler or retailer was in June 2005, while the lowest was in September 2005. The median and the highest range of percentage of kernels infected by A. flavus were in peanuts collected from Bandung, while the lowest were those collected from Bogor. The median and the range of percentage of kernels infected by A. flavus collected from wholesaler were higher than those collected from retailer. The median of aflatoxin B 1 content in peanut kernels either collected from wholesaler or retailer during sampling were relatively similar ( 15 ppb were in samples collected from Bandung, followed by samples from Jakarta (the median ± 20% of 181 samples, the range 10 – 40%), and samples collected from Bogor (the median ± 15% of 180 samples, the range 10 – 40%). The frequency of peanut samples contaminated with aflatoxin B 1 > 15 ppb collected from retailer was higher (the median 26% of 390 samples) than those collected from wholesaler (the median 18% of 175 samples), although the median and the range of percentage of kernels infected by A. flavus collected from wholesaler were higher than those collected from retailer. Keywords : Aspergillus flavus , Aflatoxin, Peanuts, Storage, Wholesale and retail levels.

CONTROL OF AFLATOXIGENIC Aspergillus flavus IN PEANUTS USING NONAFLATOXIGENIC A. flavus, A. niger and Trichoderma harzianum

The effects of nontoxigenic Aspergillus flavus, A. niger and Trichoderma harzianum inoculated into planting media on toxigenic A. flavus infection and its aflatoxin production in peanut kernels at harvest were investigated together with (1) the moisture content of planting media before peanut planting, at the time of inflorescence, and at harvest, (2) the population of aflatoxigenic and nonaflatoxigenic A. flavus, A. niger and T. harzianum in peanut planting media before peanut planting, at the time of inflorescence, and at harvest, (3) the moisture content of peanut kernels at harvest, and (4) toxigenic A. flavus invasion in peanut plant parts (roots, stems, petioles, leaves and flowers) at the time of inflorescence. The fungal isolates were inoculated into planting media at the same time with the planting of peanut seeds. Peanut plants were grown under glasshouse conditions. Treated planting media were inoculated with the combined use of (1) toxigenic and nontoxigenic A. flavus, (2) toxigenic A. flavus and A. niger, and (3) toxigenic A. flavus and T. harzianum. Planting media inoculated only with each fungal isolate and uninoculated planting media were used as controls. Two watering treatments of peanut plants were carried out, i.e. watering until harvest and not watering for 15 days before harvest. The populations of the fungal isolates in the planting media and peanut kernels were determined using dilution method followed by pour plate method; the percentages of toxigenic A. flavus and test fungal colonizations in peanut plant parts were determined using plating method; the moisture content of planting media and peanut kernels were determined using oven method; the aflatoxin content of peanut kernels was determined using Thin Layer Chromatography method. The results indicated that at the time of harvest the decrease in moisture contents of planting media not watered for 15 days before harvest was higher than those watered until harvest. The lowest population of toxigenic A. flavus was in planting media inoculated with the combined use of toxigenic and nontoxigenic A. flavus at the time of inflorescence and at the time of harvest. Toxigenic A. flavus could invade the roots, stems and flowers of peanut plants. The lowest percentage of invasion was on the plant parts which planting media were inoculated with the combined use of toxigenic and nontoxigenic A. flavus. The moisture content of peanut kernels originated from watered plants until harvest were higher than those not watered for 15 days before harvest. The population of toxigenic A. flavus in peanut kernels derived from the plants whose planting media were inoculated with the combined use of toxigenic A. flavus and each test fungi, was lower than those inoculated only with toxigenic A. flavus. It indicated that the test fungi inoculated into planting media could inhibit toxigenic A. flavus infection in peanut kernels. Aflatoxin was only detected in peanut kernels originated from one plant whose planting medium was inoculated only with toxigenic A. flavus and the plant was watered until the time of harvest. Toxigenic A. flavus infection and aflatoxin production were not influenced by planting media which were not watered for 15 days before harvest. Keywords: Biocontrol / Aflatoxigenic / Nonaflatoxigenic / Aspergillus flavus I Aspergillus niger I Trichoderma harzianum I Peanuts

CURING PEANUTS USING CONTINUOUS FLOW DRYERS

A two–year study was conducted to determine the potential for curing peanuts using continuous flow dryers with minimal detrimental effects on quality. A single–pass continuous flow dryer and a recirculating batch dryer were compared with conventional wagon drying systems. The rate of change of peanut kernel moisture content (% w.b./h) was considerably higher in the single pass continuous flow (2.1%/h) and the recirculating batch (1.0%/h) dryers than observed in conventionally cured peanuts (0.41%/h). Peanuts cured using the single–pass continuous flow dryer had unacceptably high levels of split and bald kernels when compared to those cured in wagons. The recirculating batch dryer resulted in significantly higher percent split kernels and skin slippage. However, the reduction in peanut milling quality may be acceptable in order to achieve the faster drying rate.

Measuring the Kinetics of Acetaldehyde, Ethanol, and Ethyl Acetate within Peanut Kernels During High Temperature Drying

The concentration of acetaldehyde, ethanol, and ethyl acetate was measured in NC 9 peanut kernels during drying. Mature (black class, hull scrape method) and immature (orange class) peanuts were dried at air temperatures of 35 and 40C. The concentration of acetaldehyde, ethanol, and ethyl acetate within the kernel increased during drying. The highest concentrations of acetaldehyde, ethanol, and ethyl acetate were observed in immature kernels dried at 40C. The rate of production of acetaldehyde and ethanol appeared to change in response to the same stimuli. The rate of production of ethyl acetate appeared to be proportional to the simultaneous concentration of ethanol. The concentration of ethanol in immature peanuts dried at 40C peaked 30 to 40 h into the drying process, then gradually decreased to near predrying levels. The ratio of acetaldehyde-to-ethanol-to-ethyl acetate during the drying process appeared to be a function of drying air temperature, peanut maturity, and peanut kernel moisture content.