| Summary: | The study proposed to develop a rapid method for classifying corn according to drying temperature, to develop methods for predicting corn quality characteristics relating to wet-milling, dry-milling and alkaline cooking, and to improve a high performance liquid chromatography (HPLC) technique for measuring fumonisin B1 (FB1) in extruded corn. To evaluate the ability of Near Infrared Spectroscopy (NIRS) to classify dried corn according to drying temperature, whole kernel corn samples were dried under different temperatures. Spectra were collected from the visible and near infrared regions. Discriminant analysis based on Mahalanobis distances was applied to classify the samples. The results indicated that NIRS was a promising technique for classifying corn by drying temperature, as correct classification rates of 84.3% were achieved. Corn characteristics, including starch yield, tangential abrasive dehulling device (TADD) index, dry matter loss (DML), and nixtamal moisture content, were measured using NIRS. In establishing calibration models, Partial Least Squares (PLS) and Multiple Linear Regression (MLR) were applied. The results revealed that NIRS has the ability to predict starch yield with a high correlation coefficient of validation (r-value = 0.898). A high drying temperature lowered the ability of NIRS to predict. For TADD index evaluation, the study suggested that NIRS has potential to predict this parameter, and drying temperature had no affect on its ability. In the assessment of the ability of NIRS to predict corn characteristics for alkaline cooking, the study showed that the ability of NIRS to predict DML was poor, but the technique could successfully predict nixtamal moisture content. The drying temperature had a slight effect on the NIRS prediction. Use of an enzyme to extract FB1 from extruded corn prior to HPLC determination and the use of 6-aminoquinolyl-n-hydroxysuccinimidyl carbamate (AQC) for derivatizing FB1 were investigated. The results indicated that using an amylase enzyme significantly ...
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