Hidaka Y.,Brain Bio |
Kurihara E.,Brain Bio |
Hayashi K.,Brain Bio |
Noda T.,Brain Bio |
And 4 more authors.
Japan Agricultural Research Quarterly | Year: 2011
We have developed a near-infrared (NIR) spectrometer mountable on a head-feeding combine for measuring rice protein in real time while harvesting. The developed sensor employs reflectance optics instead of the more usual transmittance optics because (1) it operates under severe vibration and dust conditions; (2) it performs measurements in high moisture contents, low fluidity of rough rice; and (3) because of low light transmittance due to absorption by husks. The light source was a tungsten halogen lamp, with a diffusion cylinder installed so that uniform light would illuminate the sample. An Si-CCD measured the spectrum from 740 nm to 1140 nm with a post-dispersive grating. We made a calibration curve of brown rice protein from a spectrum of rough rice examined in a laboratory. The calibration curve accuracy was r= 0.87 and SECV (Standard Error of Cross-Validation) =0.47%. In the adopted measurement method, the sensor loaded the rough rice into a wide sample chamber by gravity and analyzed the loaded grain at the bottom using a reflected signal. The developed sensor was able to measure the protein content of brown rice from spectra of rough rice taken under severe conditions, e.g., a high-vibration, high-dust harvesting environment. In addition to the protein content, the rice weight and moisture content could be displayed on the monitoring terminal in real time. The accuracy of the protein content measurements in these field examinations was r=0.65 and SEP (Standard Error of Prediction) =0.22%. The SEP was far better than the SECV of the calibration, but the protein content fell in a narrow range in the field examination. Thus, we concluded that the actual accuracy was the same as the calibration.
Nippon Medical & Chemical Instruments Co. and Soma Optics Ltd. | Date: 2016-03-16
[Problem] A practicable quantum meter capable of knowing the photosynthetic photon flux density at each wavelength in real time is presented. [Solution] Measurement light made incident on a dispersive element 12 by an incident optical system 13 in a spectroscope unit 1 is dispersed by the dispersive element 12, and converted to photoelectric signal on the detector 14. Each photoelectric signal at each wavelength (spectral data) is transmitted to the processing unit 2, which is a general use computer, via interface members 16 and 26. The processing unit 2, to which a photosynthetic photon flux density measurement program 4 and a special device driver 262 have been installed, calculates the distribution of the photosynthetic photon flux density at each wavelength by processing the received spectral data, and displays it on display 24.
Ikehata A.,Japan National Food Research Institute |
Luo X.,Japan National Food Research Institute |
Sashida K.,Soma Optics Ltd |
Park S.,Soma Optics Ltd |
And 2 more authors.
Journal of Near Infrared Spectroscopy | Year: 2014
Feasibility of rapid estimation of haematocrit (Ht) in cattle was studied by near infrared (NIR) spectroscopy in the short wavelength (SW, 700-1050 nm) region. The blood samples, held in standard vacuum blood collection tubes, were measured by a custom-designed, portableNIR spectrometer equipped with a Li-ion battery. Sufficient evidence for estimation of Ht was presented by searching the informative wavelength regions and comparing the root mean square errors of prediction (RMSEP) with moving window partial least square regression (MW-PLSR). The result of the MW-PLSR indicated the importance of the water absorption band with a maximum at 970 nm and the absorption band due to deoxy-haemoglobin with a maximum at 760 nm in estimating Ht. Due to light scattering from blood cells, the optical density of the blood does not have a linear relationship to Ht, especially in the low Ht range. In order to improve the prediction accuracy of Ht, an appropriate transformation was proposed for Ht. We confirmed the usefulness of the SW-NIR in estimatingHt, and hope that this technique will replace the time-consuming conventional centrifuge method. © IM Publications LLP 2014.
Kobori H.,University of Shizuoka |
Inagaki T.,Nagoya University |
Fujimoto T.,Tottori University |
Okura T.,Soma Optics Ltd. |
Tsuchikawa S.,Nagoya University
Holzforschung | Year: 2015
A fast online grading apparatus for sawn lumber based on near-infrared (NIR) spectroscopy has been developed. The method is based on a novel wavelength dispersive NIR spectrophotometer equipped with a diffraction grating linear sensor and high-intensity lighting. It was possible to acquire spectra from the entire surface of Hinoki cypress lumber sections traveling on a conveyor belt at a speed of 120 m min-1. Additionally, predictive models for moisture content (MC) and modulus of elasticity (MOE) under various MC conditions were developed from the NIR spectra with the aid of partial least squares regression (PLSR) analysis. Both the MC and MOE predictive models demonstrated sufficient levels of prediction accuracy for use on high-speed conveyor belts, and the results of various experiments indicate that the developed device could be applied for the online quality certification of sawn lumber in commercial sawmills. © 2015 by De Gruyter.
Okura T.,SOMA OPTICS LTD |
Piao S.,SOMA OPTICS LTD |
Kawano S.,Kagoshima University
Journal of Light and Visual Environment | Year: 2014
Near-infrared spectroscopy (NIRS) is an efficient and non-destructive method for quantitatively analyzing ingredients in a material. However, this method requires an expensive and time-consuming process to establish a calibration model. As a consequence, once the calibration model is established, it usually is used for many other spectrometers. Then, more or less a disparity in the predicted ingredient content of the material is observed. This phenomenon is called the instrumental difference and gives a difficulty in the application of NIRS. The reason is that waveform of near-infrared (NIR) spectrum is influenced by the characteristics of the spectrometer. We examined the process of measurement and found the spectral waveform was distorted by the variation of spectral response within wavelength resolution of the spectrometer. Therefore, the large spectral response variation and low wavelength resolution causes the big waveform distortion and then the big difference of predicted value of contents. Using seven linear array spectrometers, we analyzed this distortion and evaluated the difference of the predicted value. The results coincided with the analysis. By understanding the mechanisms of instrumental difference, we can design a spectrometer with minimal instrumental difference. This result could be applied to any other spectroscopy that acquires spectral waveform by dividing the sample waveform by the reference waveform. © 2014, The Illuminating Engineering Institute of Japan. All rights reserved.