EFFECT OF DRYING CONDITIONS ON TRITICALE SEED GERMINATION AND RICE WEEVIL INFESTATION
The combination of high protein content and a soft seed coat makes the wheat-rye hybrid Triticale (Triticosecale) vulnerable to attack by rice weevils. Drying triticale grain to moisture contents safe for storage can prevent infestation by rice weevils, but if grain is being stored for seed, high drying temperatures can affect seed germination. Grain can be effectively dried at low temperatures, but low-temperature drying is difficult in hot, humid regions such as the Gulf Coast. This study investigated the effects of drying temperatures from 35°C to 45°C on triticale seed germination and found no statistical differences between the germination rates of the seed at any of the drying temperatures and the germination rates of controls. Final moisture contents after drying ranged from 7.9% wet basis (w.b.) at 45°C for 48 h to 9.7% w.b. at 35°C for 24 h. New generations of rice weevils appeared in the control samples maintained at 23°C and 13.8% or 13.9% w.b. but were not observed in grain dried at any of the drying treatments. The results of this study showed that triticale seed can be dried to moisture contents that effectively control rice weevil infestation in hot, humid climates without significantly affecting germination rate.
Determination of Degree of Infestation of Triticale Seed Using NIR Spectroscopy
Insect infestation of triticale (Triticosecale) seed causes extraordinary storage losses as a consequence of vulnerability of triticale seed to insect infestation and its soft coat. Rice weevil, Sitophilus Oryzae (L.), is a common insect that causes infestation in Florida, which was the focus of this research. The objective of this research was to develop a method to determine the degrees of infestation (DI) in the seed at two growth stages by measuring their spectral reflectance. To achieve this goal, triticale samples with eleven DI (0, 6, 11… and 62%) were prepared with two growth stages of larvae 2nd instar, and adult outside seed with three to four replications. The reflectance was measured from 400 nm to 2500 nm. The DI results were compared to manual inspection in order to evaluate the accuracy. The data was analyzed using stepwise multiple linear regression (SMLR). The result showed that the DI for larvae 2nd instar stage could be detected using an average reflectance in 400 - 410 nm, with an R 2 of 0.87 using the SMLR method. The adult outside stage also resulted in a good prediction using the SMLR method, where it yielded four wavelengths (400, 783, 791, and 967 nm) that provided an acceptable result with an R 2 of 0.87 for the adult outside stage. The prediction of early growth stages was more challenging than for late growth stages, due to the smaller size of larvae 2 nd instar stage compared to the adult stage. Overall, NIR spectroscopy was proved to be useful to detect insect infestation in triticale seed.
Insect species and infestation level determination in stored wheat using near-infrared spectroscopy
Paliwal, J., Wang, W., Symons, S.J. and Karunakaran, C. 2004. Insect species and infestation level determination in stored wheat using near-infrared spectroscopy. Canadian Biosystems Engineering/Le genie des biosystemes au Canada 46: 7.17 7.24. Near-infrared spectroscopy (NIRS) technique was evaluated for detection of four different life stages (i.e. eggs, larvae, pupae, and adults) of Sitophilus oryzae (rice weevil) in artificially infested bulk samples of Canada Western Red Spring (CWRS) wheat at infestation levels from 0 through 50% at 5% infestation intervals. Sound wheat kernels were also infested with Rhyzopertha dominica (lesser grain borer) to create infestation levels of 0, 5, 10, 20, 25, 50, 75, and 100% and the spectral data from their pupae were compared to the pupal stage of rice weevil at corresponding levels of infestation. To distinguish wheat kernels infested with pupae of lesser grain borer from those infested with rice weevil, Principal Component Analysis (PCA) was employed and insect differentiation was done by examining score plots of spectral data. Original and difference spectra of infested wheat kernels and sound wheat kernels were tested. It was observed that differentiation of insect species becomes easier as infestation levels increase for difference spectra. Calibration models for quantitative determination of infestation levels were developed using Partial Least Square Regression (PLSR). Multiplicative Scatter Correction (MSC) and Standard Normal Variant (SNV) followed by detrending were found equally effective in removing irrelevant spectral information. Prediction performed well for high infestation levels but lower classification accuracies were obtained at low infestation levels.