KEYWORDS: Near infrared, Data modeling, Calibration, Reflectivity, Absorbance, Absorption, Statistical analysis, Reflectance spectroscopy, Chemical analysis, Near infrared spectroscopy
Near Infrared (NIR) Reflectance spectroscopy has established itself as an important tool in quantifying water and oil present in various food materials. It is rapid and nondestructive, easier to use, and does not require processing the samples with corrosive chemicals that would render them non-edible. Earlier, the samples had to be ground into powder form before making any measurements. With the development of new soft ware packages, NIR techniques could now be used in the analysis of intact grain and nuts. While most of the commercial instruments presently available work well with small grain size materials such as wheat and corn, the method present here is suitable for large kernel size products such as shelled or in-shell peanuts. Absorbance spectra were collected from 400 nm to 2500 nm using a NIR instrument. Average values of total oil contents (TOC) of peanut samples were determined by standard extraction methods, and fatty acids were determined using gas chromatography. Partial least square (PLS) analysis was performed on the calibration set of absorption spectra, and models were developed for prediction of total oil and fatty acids. The best model was selected based on the coefficient of determination (R2), Standard error of prediction (SEP) and residual percent deviation (RPD) values. Peanut samples analyzed showed RPD values greater than 5.0 for both absorbance and reflectance models and thus could be used for quality control and analysis. Ability to rapidly and nondestructively measure the TOC, and analyze the fatty acid composition, will be immensely useful in peanut varietal improvement as well as in the grading process of grain and nuts.
A Custom made NIR spectroscope was used to determine the moisture content of in-shell peanuts
of two different market type peanuts namely Virginia and Valencia. Peanuts were conditioned to
different moisture levels between 6 and 26 % (wet basis). Samples from the different moisture
levels were separated into two groups one for calibration and the other for validation. NIR
absorption spectral data from 1000 nm to 2500 nm were collected on the peanuts from the
calibration and validation groups. Measurements were obtained on 30 replicates within each
moisture level. Reference moisture data were developed using standard air-oven method on
calibration set samples. Partial Least Square (PLS) analysis was performed on the calibration set
with certain pretreatments on the measured data and models were developed using the reference
moisture data. The Standard Error of Calibration (SEC) and R2 of the calibration models were
computed to select the best calibration model for each of the two peanut types. Both Valencia and
Virginia types gave R2 of 0.99 for the pretreated as well as for the raw spectral data. The selected
models were used to predict the moisture content of peanuts in the validation sample set.
Predicted moisture contents of the validation samples were compared with their air-oven moisture
values determined similarly as for the calibration samples. 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.
In this study percentage of total kernel mass within a given mass of in-shell peanuts was determined
nondestructively using a low-cost RF impedance meter. Peanut samples were divided into two groups, one the
calibration and the other the validation group. Each group contained 50 samples of about 100 g of peanuts.
Capacitance (C), phase angle (θ) and impedance (Z) measurements on in-shell peanut samples were made at
frequencies 1 MHz, 5 MHz and 9 MHz. Ten measurements on each sample set were made, to minimize the errors
due to the orientation of the peanuts as they settle between the electrodes of the impedance meter, by emptying and
refilling the samples after each measurement. After completing the measurements on each set, the peanuts from that
set were shelled, kernels were separated and weighed. Multi linear regression (MLR) calibration equation was
developed by correlating the percentage of the kernel mass in a given peanut sample set with the measured
capacitance, impedance and phase angle values. This equation was used to predict the kernel mass ratio of the
samples from the validation group. The fitness of the MLR equation was verified using Standard Error of Prediction (SEP) and Root Mean Square Error of Prediction (RMSEP). Also, the predictability of total kernel mass ratio was calculated by comparing the mass ratio predicted using MLR model with the actual mass ratio determined using the conventional standard method of visual determination.
Moisture and oil contents are important quality factors often measured and monitored in the processing and storage
of food products such as corn and peanuts. For estimating these parameters for peanuts nondestructively a parallel-plate
capacitance sensor was used in conjunction with an impedance analyzer. Impedance, phase angle and dissipation factor
were measured for the parallel-plate system, holding the in-shell peanut samples between its plates, at frequencies
ranging between 1MHz and 30 MHz in intervals of 0.5 MHz. The acquired signals were analyzed with discrete wavelet
analysis. The signals were decomposed to 6 levels using Daubechies mother wavelet. The decomposition coefficients
of the sixth level were passed onto a stepwise variable selection routine to select significant variables. A linear
regression was developed using only the significant variables to predict the moisture and oil content of peanut pods (inshell
peanuts) from the impedance measurements. The wavelet analysis yielded similar R2 values with fewer variables
as compared to multiple linear and partial least squares regressions. The estimated values were found to be in good
agreement with the standard values for the samples tested. Ability to estimate the moisture and oil contents in peanuts
without shelling them will be of considerable help to the peanut industry.
Moisture content (MC) is an important quality factor that is measured and monitored, at various stages of processing and storage, in the food industry. There are some commercial instruments available that use near infrared (NIR) radiation measurements to determine the moisture content of a variety of grain products, such as wheat and corn, with out the need of any sample grinding or preparation. However, to measure the MC of peanuts with these instruments the peanut kernels have to be chopped into smaller pieces and filled into the measuring cell. This is cumbersome, time consuming and destructive. An NIR reflectance method is presented here by which the average MC of about 100 g of whole kernels could be determined rapidly and nondestructively. The MC range of the peanut kernels tested was between 8% and 26%. Initially, NIR reflectance measurements were made at 1 nm intervals in the wave length range of 1000 nm to 1800 nm and the data was modeled using partial least squares regression (PLSR). The predicted values of the samples tested in the above range were compared with the values determined by the standard air-oven method. The predicted values agreed well with the air-oven values with an R2 value of 0.96 and a standard error of prediction (SEP) of 0.83. Using the PLSR beta coefficients, five key wavelengths were identified and using multiple linear regression (MLR) method MC predictions were made. The R2 and SEP values of the MLR model were 0.84 and 1.62, respectively. Both methods performed satisfactorily and being rapid, nondestructive, and non-contact, may be suitable for continuous monitoring of MC of grain and peanuts as they move on conveyor belts during their processing.
Moisture content (MC) in peanuts is measured at various stages of their processing and storage in the peanut industry.
A method was developed earlier that would estimate the MC of a small sample of in-shell peanuts (peanut pods) held
between two circular parallel-plates, from the measured values of capacitance and phase angle at two frequencies 1 and 5
MHz. These values were used in an empirical equation, developed using the capacitance and phase angle values of
samples of known MC levels, to obtain the average MC values of peanut samples with moisture contents in the range of
7 to 18%. In the present work, two rectangular parallel-plates were mounted inside a vertical cylinder made of acrylic
material and filled with about 100 g of in-shell peanuts and their average mc was determined from a similar empirical
equation. The calculated MC values were compared with those obtained by the standard air-oven method. For over
85% of the samples tested in the moisture range between 6% and 22% the MC values were found to be within 1% of the
air-oven values. Ability to determine the average MC of slightly larger quantities of in-shell peanuts without shelling
and cleaning them, as being done presently, will save time, labor and sampling material for the peanut industry.
The design and performance of an electrical instrument that would be useful in estimating the moisture content (mc) of agricultural products such as grain and nuts nondestructively and rapidly is described here. The instrument, here after called the impedance meter, determines the capacitance and phase angle of a sample of the produce (about 100 g), filling the space between two parallel-plate electrodes, at two frequencies 1 and 5 MHz. The measured values were used in a semi-empirical equation to obtain the mc of the sample. In this paper, capacitance and phase angle were determined for in-shell peanuts in the moisture range between 6 and 25% by the impedance meter, and their moisture contents were calculated. The calculated values were compared with the mc values obtained by the standard air-oven method. The estimated values were in good agreement with the standard values. This method is applicable to produce such as corn, wheat and pecans also.
A method to determine the moisture content from the complex impedance measurements of a parallel-plate capacitor with a single shelled or in-shell peanut between the plates, at two frequencies 1 and 5 MHz, is described here. Capacitance (C), phase angle (θ) and dissipation factor (D) of the parallel-plate system at the two frequencies were measured. Using these values in a derived empirical equation, the moisture content (mc) of the peanuts was estimated to an accuracy of within 1% of the standard air-oven value. The moisture range of the peanuts tested was between 5 and 20%. The method is rapid and nondestructive and was found earlier to be applicable in certain types of grain such as corn. The study establishes a basis for the development of a practical instrument that could be useful in the grain and peanut industry.
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