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Near-infrared (NIR) spectroscopy has become a very popular technique for the non-invasive assessment of intact fruit. This work presents an application of a low-cost commercially available NIR spectrometer for the estimation of ripeness of Chilean wine grapes. Two configurations for the spectra acquisition were used (diffuse transmittance and interactance), using a custom-designed contact probe. Samples of Chardonnay, Carménère and Cabernet Sauvignon, collected over the 2002 harvest and pre-harvest seasons, were analysed for total soluble solids content ( • Brix). Partial least squares calibration models, obtained from several preprocessing techniques (smoothing, multiplicative signal correction, standard normal variate, etc), were compared. Also, two spectral regions were used, one without the red part of the visible spectrum (just the short-wave (SW-NIR) region) and the other including it. Performance of different models was assessed in terms of root mean square of cross-validation, root mean square of prediction (RMSEP) and R 2 for a validation set of samples. RMSEPs of 1.06 with R 2 = 0.942 indicate that it is possible to estimate wine grape ripeness ( • Brix value), by using a CCD portable spectrometer. The red grape models performed better than the white grape models.

This paper describes the data pre-processing and reduction methods together with SLOpe Detection And Ranging (SLODAR) analysis and wind profiling techniques for the Gemini South Multi-Conjugate Adaptive Optics System (GeMS).The wavefront gradient measurements of the five GeMS Shack-Hartmann sensors, each pointing to a laser guide star, are combined with the deformable mirror (DM) commands sent to three DMs optically conjugated at 0, 4.5 and 9 km in order to reconstruct pseudo-open loop slopes.These pseudo-open loop slopes are then used to reconstruct atmospheric turbulence profiles, based on the SLODAR and wind-profiling methods. We introduce the SLODAR method, and how it has been adapted to work in a closed-loop, multi-laser guide star system. We show that our method allows characterizing the turbulence of up to 16 layers for altitudes spanning from 0 to 19 km. The data pre-processing and reduction methods are described, and results obtained from observations made in 2011 are presented. The wind profiling analysis is shown to be a powerful technique not only for characterizing the turbulence intensity, wind direction and speed, but also as it can provide a verification tool for SLODAR results. Finally, problems such as the fratricide effect in multiple laser systems due to Rayleigh scattering, centroid gain variations, and limitations of the method are also addressed.

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Abstract:Modern adaptive optics (AO) systems for large telescopes require tomographic techniques to reconstruct the phase aberrations induced by the turbulent atmosphere along a line of sight to a target which is angularly separated from the guide sources that are used to sample the atmosphere. Multi-object adaptive optics (MOAO) is one such technique.Here, we present a method which uses an artificial neural network (ANN) to reconstruct the target phase given off-axis references sources. We compare our ANN method with a standard least squares type matrix multiplication method and to the learn and apply method developed for the CANARY MOAO instrument. The ANN is trained with a large range of possible turbulent layer positions and therefore does not require any input of the optical turbulence profile. It is therefore less susceptible to changing conditions than some existing methods. We also exploit the non-linear response of the ANN to make it more robust to noisy centroid measurements than other linear techniques. Assémat, E. Gendron, and F. Hammer, "The FALCON concept: multi-object adaptive optics and atmospheric tomography for integral field spectroscopy -principles and performance on an 8-m telescope," MNRAS 376, 287-312 (2007). 5. Morris, T., Hubert, Z., Myers, R., Gendron, E., Longmore, A., Rousset, G., Talbot, G., Fusco, T., Dipper, N., Vidal, F., Henry, D., Gratadour, D., Butterley, T., Chemla, F., Guzman, D., Laporte, P., Younger, E., Kellerer, A., Harrison, M., Marteaud, M., Geng, D., Basden, A., Guesalaga, A., Dunlop, C., Todd, S., Robert, C., Dee, K., Dickson, C., Vedrenne, N., Greenaway, A., Stobie, B., Dalgarno, H., and Skvarc, J., "CANARY: The NGS/LGS MOAO demonstrator for EAGLE," 1st AO4ELT conference p. 08003 (2010). 2527-2538 (2001). 18. J. W. Wild, E. J. Kibblewhite, and R. Vuilleumier, "Sparse matrix wave-front estimators for adaptive-optics systems for large ground-based telescopes," Opt. Lett. 20(9), 955 -957 (1995). 19. E. Thiébaut and M. Tallon, "Fast minimum variance wavefront reconstruction for extremely large telescopes," J.