Moisture determination of single soybean seeds by near-infrared transmittance

Abstract

A calibration set of 140 soybeans from seven different varieties, ranging in moisture from 5 to 22%, wet basis, was used to calibrate a spectrophotometer for moisture prediction of single soybean seeds. Near-infrared absorbance (A) of individual soybean seeds was measured over the spectral region from 800 to 1100 nm by 0.5 nm. The axis of the soybean seed parallel to the incident light beam was measured as an estimate of optical path length. Three mathematical techniques were used to develop calibration equations: linear correlation to a difference in absorbance (AA), stepwise multiple linear regression (MLR), and partial least squares (PLS). A validation set contained 100 soybeans from five different varieties, ranging in moisture from 5 to 20%, wet basis. The standard error of prediction (SEP) for equations using absorbance data only was 0.88% for AA, 0.82% for MLR, and 0.81% for PLS. The SEP for equations using path length and absorbance data was 0.73% for AA, 0.69% for MLR, and 0.65% for PLS.

Measurement of Single Soybean Seed Attributes by Near-Infrared Technologies. A Comparative Study

“…The first NIRS study analyzing single soybeans determined moisture by transmittance (standard error of prediction (SEP) = 0.65− 0.69%). 5 Armstrong later obtained better cross-validation results with the USDA light tube reflectance instrument (SECV = 0.32%), which scans the bean while tumbling. 6 Armstrong also developed PLS protein calibrations (SECV = 0.99%), with results on the same order as those of Tajuddin et al 7 (SEP = 1.32%), who used a transmittance instrument and large-diameter soybeans (>6 mm).…”

A Review of Optical Nondestructive Visual and Near-Infrared Methods for Food Quality and Safety

“…The moisture content of whole soybean in the range of 5 to 22% can be determined with great accuracy using nearinfrared transmittance (800 to 1100 nm) and difference in absorbance, stepwise multiple linear regression, or partial least-square modeling [78].…”

Hyperspectral ratio feature selection: agricultural product inspection example

“…Hyperspectral (HS) data has been shown to be useful for the detection of objects such as military vehicles [1][2][3] and mines [3,4], for land use applications [5], and for many USDA product inspection applications [6][7][8][9][10][11][12][13][14][15][16][17][18]. HS data has been shown to be successful in detecting skin tumors on chicken carcasses [6][7][8], fecal and ingesta contaminants on chicken carcasses [9], aflatoxin levels in corn kernels [10,11,13,14], and internal-damage in almonds [12][13][14], and in determining wheat grain quality [15,16], moisture in soybean seeds [17], and sugar content in potatoes [18].…”

“…HS data has been shown to be successful in detecting skin tumors on chicken carcasses [6][7][8], fecal and ingesta contaminants on chicken carcasses [9], aflatoxin levels in corn kernels [10,11,13,14], and internal-damage in almonds [12][13][14], and in determining wheat grain quality [15,16], moisture in soybean seeds [17], and sugar content in potatoes [18]. These uses occur, since HS data provides spectral information that uniquely characterizes and identifies the chemical, moisture, and physical properties of the constituent parts of an input object, scene region, or an agricultural product.…”