It was found earlier that moisture content (MC) of intact kernels of grain and nuts could be determined by Near Infra Red (NIR) reflectance spectrometry. However, if the MC values can be determined while the nuts are ...It was found earlier that moisture content (MC) of intact kernels of grain and nuts could be determined by Near Infra Red (NIR) reflectance spectrometry. However, if the MC values can be determined while the nuts are in their shells, it would save lot of labor and money spent in shelling and cleaning the nuts. Grain and nuts absorb low levels of NIR, and when NIR radiation is incident on them, a substantial portion of the radiation is reflected back. Thus, studying the NIR reflectance spectra emanating from in-shell peanuts, an attempt is made for the first time to determine the MC of in-shell peanuts. In-shell peanuts of two different market types, Virginia and Valencia, were conditioned to different moisture levels between 6% and 26% (wet basis), and separated into calibration and validation groups. NIR absorption spectral data from 1000 nm to 2500 nm in 1 nm intervals were collected from both groups. Measurements were obtained on 30 replicates within each moisture level. Reference MC values for each moisture level in these groups were obtained using standard air-oven method. Partial Least Square (PLS) analysis was performed on the calibration data, and prediction models were developed. The Standard Error of Calibration (SEC), and R2 of the calibration models were computed to select the best calibration model. The selected models were used to predict the moisture content of peanuts in the validation sets. Predicted MC values of the validation samples were compared with their standard air-oven moisture values. Goodness of fit was determined based on the lowest Standard Error of Prediction (SEP) and highest R2 value obtained for the prediction models. The model, with reflectance plus normalization spectral data with an SEP of 0.74 for Valencia and 1.57 for Virginia type in-shell peanuts was selected as the best model. The corresponding R2 values were 0.98 for both peanut types. This work establishes the possibility of sensing MC of intact in-shell peanuts by NIR reflectance method, and would be useful for the peanut and allied industries.展开更多
Peanut is a legume crop that belongs to the family of Fabaceae, genus Arachis, and botanically named as Arachis hypogaea. Peanuts are consumed in many forms such as boiled peanuts, peanut oil, peanut butter, roasted p...Peanut is a legume crop that belongs to the family of Fabaceae, genus Arachis, and botanically named as Arachis hypogaea. Peanuts are consumed in many forms such as boiled peanuts, peanut oil, peanut butter, roasted peanuts, and added peanut meal in snack food, energy bars and candies. Peanuts are considered as a vital source of nutrients. Nutrition plays an important role in growth and energy gain of living organisms. Peanuts are rich in calories and contain many nutrients, minerals, antioxidants, and vitamins that are essential for optimum health. All these biomolecules are essential for pumping vital nutrients into the human body for sustaining normal health. This paper presents an overview of the peanut composition in terms of the constituent biomolecules, and their biological functions. This paper also discusses about the relationship between consumption of peanuts and their effect on human metabolism and physiology. It highlights the usefulness of considering peanuts as an essential component in human diet considering its nutritional values.展开更多
Tomato spotted wilt(TSW)is a serious virus disease of peanut in the United States.Breeding for TSWV resistance would be facilitated by the implementation of marker-assisted selection in breeding programs;however,genes...Tomato spotted wilt(TSW)is a serious virus disease of peanut in the United States.Breeding for TSWV resistance would be facilitated by the implementation of marker-assisted selection in breeding programs;however,genes associated with resistance have not been identified.Association mapping is a type of genetic mapping that can exploit relationships between markers and traits in many lineages.The objectives of this study were to examine genetic diversity and population structure in the U.S.peanut mini-core collection using simple sequence repeat(SSR)markers,and to conduct association mapping between SSR markers and TSWV resistance in cultivated peanuts.One hundred and thirty-three SSR markers were used for genotyping 104 accessions.Four subpopulations,generally corresponding to botanical varieties,were classified by population structure analysis.Association mapping analysis indicated that five markers:pP GPseq5D5,GM1135,GM1991,TC23C08,and TC24C06,were consistently associated with TSW resistance by the Q,PCA,Q+K,and PCA+K models.These markers together explained 36.4%of the phenotypic variance.Moreover,pP GPseq5D5 and GM1991 were associated with both visual symptoms of TSWV and ELISA values with a high R^2.The potential of these markers for use in a marker-assisted selection program to breed peanut for resistance to TSWV is discussed.展开更多
NIR spectroscopy was used to measure the moisture concentration of wood pellets. Pellets were conditioned to various moisture levels between 0.63% and 14.16% (wet basis) and the moisture concentration was verified usi...NIR spectroscopy was used to measure the moisture concentration of wood pellets. Pellets were conditioned to various moisture levels between 0.63% and 14.16% (wet basis) and the moisture concentration was verified using a standard oven method. Samples from various moisture levels were separated into two groups, as calibration and validation sets. NIR absorption spectral data from 400 nm to 2500 nm with 0.5 nm intervals were collected using pellets within the calibration and validation sample sets. Spectral wavelength ranges were taken as independent variables and the MC of the pellets as the dependent variable for the analysis. Measurements were obtained on 30 replicates within each moisture level. Partial Least Square (PLS) analysis was performed on both raw and preprocessed spectral data of calibration set to determine the best calibration model based on Standard Error of Calibration (SEC) and coefficient of multiple determinations (R2). The PLS model that yielded the best fit was used to predict the moisture concentration of validation group pellets. Relative Percent Deviation (RPD) and Standard Error of Prediction (SEP) were calculated to validate goodness of fit of the prediction model. Baseline and Multiple Scatter Corrected (MSC) reflectance spectra with 1st derivative model gave the highest RPD value of 4.46 and R2 of 0.95. Also it’s SEP (0.670) and RMSEP (0.782) were less than the other models those had RPD value more than 3.0 with less number of factors. Therefore, this model was selected as the best model for moisture content prediction of wood pellets.展开更多
An electronic method to estimate the moisture content (MC) of dry fruits by measuring the impedance (Z) and phase angle (θ) of a cylindrical parallel-plate capacitor with dry fruit sample between the plates, using a ...An electronic method to estimate the moisture content (MC) of dry fruits by measuring the impedance (Z) and phase angle (θ) of a cylindrical parallel-plate capacitor with dry fruit sample between the plates, using a CI meter (Chari’s Impedance meter) at 1 and 9 MHz is described. Capacitance C was derived from Z and θ, and using the C, θ, and Z values of a set of dried cherries, whose MC values were later determined by the vacuum hot air-oven method, a calibration equation was developed. Using this equation, and their measured C, θ, and Z values, the MC of a group of cherries, not used in the calibration, was predicted. The predicted values were compared with their air-oven values. Similar predictions were done using the same method on dried blueberries. The method worked well with a good R2 value, and showed a low standard error of prediction (SEP) in the measured MC range between 5% and 30% for cherries, and 9% and 22% for blueberries.展开更多
文摘It was found earlier that moisture content (MC) of intact kernels of grain and nuts could be determined by Near Infra Red (NIR) reflectance spectrometry. However, if the MC values can be determined while the nuts are in their shells, it would save lot of labor and money spent in shelling and cleaning the nuts. Grain and nuts absorb low levels of NIR, and when NIR radiation is incident on them, a substantial portion of the radiation is reflected back. Thus, studying the NIR reflectance spectra emanating from in-shell peanuts, an attempt is made for the first time to determine the MC of in-shell peanuts. In-shell peanuts of two different market types, Virginia and Valencia, were conditioned to different moisture levels between 6% and 26% (wet basis), and separated into calibration and validation groups. NIR absorption spectral data from 1000 nm to 2500 nm in 1 nm intervals were collected from both groups. Measurements were obtained on 30 replicates within each moisture level. Reference MC values for each moisture level in these groups were obtained using standard air-oven method. Partial Least Square (PLS) analysis was performed on the calibration data, and prediction models were developed. The Standard Error of Calibration (SEC), and R2 of the calibration models were computed to select the best calibration model. The selected models were used to predict the moisture content of peanuts in the validation sets. Predicted MC values of the validation samples were compared with their standard air-oven moisture values. Goodness of fit was determined based on the lowest Standard Error of Prediction (SEP) and highest R2 value obtained for the prediction models. The model, with reflectance plus normalization spectral data with an SEP of 0.74 for Valencia and 1.57 for Virginia type in-shell peanuts was selected as the best model. The corresponding R2 values were 0.98 for both peanut types. This work establishes the possibility of sensing MC of intact in-shell peanuts by NIR reflectance method, and would be useful for the peanut and allied industries.
文摘Peanut is a legume crop that belongs to the family of Fabaceae, genus Arachis, and botanically named as Arachis hypogaea. Peanuts are consumed in many forms such as boiled peanuts, peanut oil, peanut butter, roasted peanuts, and added peanut meal in snack food, energy bars and candies. Peanuts are considered as a vital source of nutrients. Nutrition plays an important role in growth and energy gain of living organisms. Peanuts are rich in calories and contain many nutrients, minerals, antioxidants, and vitamins that are essential for optimum health. All these biomolecules are essential for pumping vital nutrients into the human body for sustaining normal health. This paper presents an overview of the peanut composition in terms of the constituent biomolecules, and their biological functions. This paper also discusses about the relationship between consumption of peanuts and their effect on human metabolism and physiology. It highlights the usefulness of considering peanuts as an essential component in human diet considering its nutritional values.
基金the Peanut Foundation (04-811-16)the National Peanut Board (RIA16PID456BID1426-CC)+1 种基金Alabama Peanut Producers Associationthe Hatch program of the USDA-NIFA
文摘Tomato spotted wilt(TSW)is a serious virus disease of peanut in the United States.Breeding for TSWV resistance would be facilitated by the implementation of marker-assisted selection in breeding programs;however,genes associated with resistance have not been identified.Association mapping is a type of genetic mapping that can exploit relationships between markers and traits in many lineages.The objectives of this study were to examine genetic diversity and population structure in the U.S.peanut mini-core collection using simple sequence repeat(SSR)markers,and to conduct association mapping between SSR markers and TSWV resistance in cultivated peanuts.One hundred and thirty-three SSR markers were used for genotyping 104 accessions.Four subpopulations,generally corresponding to botanical varieties,were classified by population structure analysis.Association mapping analysis indicated that five markers:pP GPseq5D5,GM1135,GM1991,TC23C08,and TC24C06,were consistently associated with TSW resistance by the Q,PCA,Q+K,and PCA+K models.These markers together explained 36.4%of the phenotypic variance.Moreover,pP GPseq5D5 and GM1991 were associated with both visual symptoms of TSWV and ELISA values with a high R^2.The potential of these markers for use in a marker-assisted selection program to breed peanut for resistance to TSWV is discussed.
文摘NIR spectroscopy was used to measure the moisture concentration of wood pellets. Pellets were conditioned to various moisture levels between 0.63% and 14.16% (wet basis) and the moisture concentration was verified using a standard oven method. Samples from various moisture levels were separated into two groups, as calibration and validation sets. NIR absorption spectral data from 400 nm to 2500 nm with 0.5 nm intervals were collected using pellets within the calibration and validation sample sets. Spectral wavelength ranges were taken as independent variables and the MC of the pellets as the dependent variable for the analysis. Measurements were obtained on 30 replicates within each moisture level. Partial Least Square (PLS) analysis was performed on both raw and preprocessed spectral data of calibration set to determine the best calibration model based on Standard Error of Calibration (SEC) and coefficient of multiple determinations (R2). The PLS model that yielded the best fit was used to predict the moisture concentration of validation group pellets. Relative Percent Deviation (RPD) and Standard Error of Prediction (SEP) were calculated to validate goodness of fit of the prediction model. Baseline and Multiple Scatter Corrected (MSC) reflectance spectra with 1st derivative model gave the highest RPD value of 4.46 and R2 of 0.95. Also it’s SEP (0.670) and RMSEP (0.782) were less than the other models those had RPD value more than 3.0 with less number of factors. Therefore, this model was selected as the best model for moisture content prediction of wood pellets.
文摘An electronic method to estimate the moisture content (MC) of dry fruits by measuring the impedance (Z) and phase angle (θ) of a cylindrical parallel-plate capacitor with dry fruit sample between the plates, using a CI meter (Chari’s Impedance meter) at 1 and 9 MHz is described. Capacitance C was derived from Z and θ, and using the C, θ, and Z values of a set of dried cherries, whose MC values were later determined by the vacuum hot air-oven method, a calibration equation was developed. Using this equation, and their measured C, θ, and Z values, the MC of a group of cherries, not used in the calibration, was predicted. The predicted values were compared with their air-oven values. Similar predictions were done using the same method on dried blueberries. The method worked well with a good R2 value, and showed a low standard error of prediction (SEP) in the measured MC range between 5% and 30% for cherries, and 9% and 22% for blueberries.