The influence of color temperature mismatch in star simulator on positioning accuracy and magnitude measurement by star sensor

During the calibrating of star sensor, the calibration accuracy is greatly affected by the mismatch between the color temperature of the light and the to-be-measured star, which further affects the attitude measurement accuracy. This paper studied the near-infrared spectra of stars with different color temperatures, and analyzed the errors on star positioning and magnitude measurement of star sensor due to the color temperature mismatch. The results showed that in the central field of view, the spot centroid deviation caused by spectral mismatch is smaller than that in the edge field of view.And the defocus of the imaging surface also affects the spot centroid deviation. Besides, when calibrating with 6000K color temperature light, the maximum measurement error can reach -1.9126 magnitude.

Infrared Spectroscopy for the Quality Assessment of Habanero Chilli: A Proof-of-Concept Study

Habanero chillies (Capsicum chinense cv Habanero) are a popular species of hot chilli in Australia, with their production steadily increasing. However, there is limited research on this crop due to its relatively low levels of production at present. Rapid methods of assessing fruit quality could be greatly beneficial both for quality assurance purposes and for use in breeding programs or experimental growing trials. Consequently, this work investigated the use of infrared spectroscopy for predicting dry matter content, total phenolic content and capsaicin/dihydrocapsaicin content in 20 Australian Habanero chilli samples. Near-infrared spectra (908–1676 nm) taken from the fresh fruit showed strong potential for the estimation of dry matter content, with an R2cv of 0.65 and standard error of cross-validation (SECV) of 0.50%. A moving-window partial least squares regression model was applied to optimise the spectral window used for dry matter content prediction, with the best-performing window being between 1224 and 1422 nm. However, the near-infrared spectra could not be used to estimate the total phenolic content or capsaicin/dihydrocapsaicin content of the samples. Mid-infrared spectra (4000–400 cm−1) collected from the dried, powdered material showed slightly more promise for the prediction of total phenolics and the ratio of capsaicin-to-dihydrocapsaicin, with an R2cv of 0.45 and SECV of 0.32 for the latter. The results suggest that infrared spectroscopy may be able to determine dry matter content in Habanero chilli with acceptable accuracy, but not the capsaicinoid or total phenolic content.

Single kernel sorting of high and normal oleic acid peanuts using near infrared spectroscopy

Peanuts are known to contain nutrients that deliver cardiovascular and health benefits. One such compound is oleic acid, an omega-9 monounsaturated fatty acid, which occurs naturally in peanuts in the concentration range 40–55% m/m, while some varieties are known to contain oleic acid above 75% m/m. These high oleic peanuts have been shown to have cardiovascular health benefit by lowering lipid levels. Breeders are therefore interested in selecting for peanuts with high oleic acid content in a rapid, non-destructive manner. Near infrared spectra acquired on single peanut kernels was used to classify the kernels as either high oleic content or normal, low oleic content, by means of partial least squares discriminant analysis with an overall error rate in classification of 3.3%.