A near-infrared (NIR) spectroscopic sensing system was constructed on an experimental basis. This system enabled NIR spectra of raw milk to be obtained in an automatic milking system (milking robot system) over a wavelength range of 600 nm to 1050 nm. Calibration models for determining three major milk constituents (fat, protein and lactose), somatic cell count (SCC) and milk urea nitrogen (MUN) of unhomogenized milk were developed, and the precision and accuracy of the models were validated. The coefficient of determination (r2) and standard error of prediction (SEP) of the validation set for fat were 0.95 and 0.25%, respectively. The values of r2 and SEP for lactose were 0.83 and 0.26%, those for protein were 0.72 and 0.15%, those for SCC were 0.68 and 0.28 log SCC/mL, and those for MUN were 0.53 and 1.50 mg/dL, respectively. These results indicate that the NIR spectroscopic system can be used to assess milk quality in real time in an automatic milking system. The system can provide dairy farmers with information on milk quality and physiological condition of an individual cow and, therefore, give them feedback control for optimizing dairy farm management. By using the system, dairy farmers will be able to produce high-quality milk and precision dairy farming will be realized

ear-infrared (NIR) spectroscopy has been used for highly accurate measurement of the chemical composition of rice grain, including such constituents as moisture, protein, and amylose (Iwamoto et al., 1986;Natsuga et al., 1992;Villareal et al., 1994;Delwiche et al., 1995;Delwiche et al., 1996;Kawamura et al., 1997a;Sohn et al., 2004). However, to assess rice grain quality, in addition to chemical composition, various physicochemical properties of the rice must be analyzed. Several studies were previously carried out to estimate quality-related parameters such as fat acidity and the gelatinization characteristics of rice (Onda et al., 1994;Li and Shaw, 1997;Bao et al., 2001;Meadows and Barton, 2002). The physicochemical properties of rice are known to be correlated with its eating quality (Chikubu et al., 1985;Kawamura et al., 1997b;Windham et al., 1997;Barton et al., 2000;Mikami et al., 2000;Champagne et al., 2001;Meullenet et al., 2001).All common existing methods for measuring the physicochemical properties require some specific instruments and chemicals, and are labor-intensive or time-consuming. There is therefore intense interest in the rice breed improvement, culture, and production communities, as well as in the rice industry, for a quick and easy method to measure the physicochemical properties of rice. Although NIR spectros- The authors are Motoyasu Natsuga, ASABE Member Engineer, Associate Professor, Faculty of Agriculture, Yamagata University, Tsuruoka, Japan; and Shuso Kawamura, ASABE Member Engineer, Associate Professor, Agricultural Process Engineering Laboratory, Hokkaido University, Sapporo, Japan. Corresponding author: Motoyasu Natsuga, Faculty of Agriculture, Yamagata University, 1-23 Wakabamachi Tsuruoka, 997-8555 Japan; phone: +81-235-28-2906; e-mail: toko@tds1. tr.yamagata-u.ac.jp. copy is used for measuring rice constituent contents, a method for measuring the physicochemical properties using this technology has not yet been established.The objectives of this study were to: (1) develop VIS/NIR calibration models from brown rice and milled rice spectra to determine physicochemical properties of rice, and (2) validate the accuracy of the calibration models and determine whether VIS/NIR spectroscopy could replace various dedicated analytical methods. MATERIALS AND METHODS RICE SAMPLESFor this study, a total of 61 short-grain brown rice samples (at least 5 kg per sample) were collected from commercial varietal releases all over Japan. The samples consisted of 27 varieties of Japanese non-waxy rice. The brand names of the rice samples were

There has been a need in recent years for a method that will enable dairy farmers to monitor milk quality of individual cow during milking. We constructed a near-infrared (NIR) spectroscopic sensing system for online monitoring of milk quality on an experimental basis. This system enables NIR spectra of unhomogenized milk to be obtained during milking over a wavelength range of 600-1050 nm. We developed calibration models for predicting three major milk constituents (fat, protein and lactose), somatic cell count (SCC) and milk urea nitrogen (MUN) of unhomogenized milk, and we validated the precision and accuracy of the models. The coefficient of determination (r 2 ) and standard error of prediction (SEP) of the validation set were obtained: for fat, r 2 = 0.95, SEP = 0.42%; for protein, r 2 = 0.91, SEP = 0.09%; for lactose, r 2 = 0.94, SEP = 0.05%; for SCC, r 2 = 0.82, SEP = 0.27 log SCC/mL; and for MUN, r 2 = 0.90, SEP = 1.33 mg/dL, respectively. These results indicated that the NIR spectroscopic sensing system developed in this study could be used to monitor milk quality in real-time during milking. The system can provide dairy farmers with information on milk quality and physiological condition of each cow and therefore give them feedback control for producing milk of high quality and for optimizing dairy farm management.

The need has arisen in rice-drying facilities in Japan for an automatic method to measure quality aspects of rice when it arrives at the drying facility. A near-infrared (NIR) transmission instrument was used to obtain NIR spectra of damp rough rice and damp brown rice. Calibration models were developed from the original spectra and reference analysis data to determine moisture and protein content of the samples. A visible light (VIS) segregator was used to determine sound whole kernel of brown rice.The precision and accuracy of the NIR instrument and the VIS segregator were found to be sufficiently high to determine moisture and protein content, and sound whole kernel ratio. An automatic rice-quality inspection system was consequently developed. The system consisted of a rice huller, a rice cleaner, an NIR instrument and a VIS segregator, and it was controlled by a computer. Based on the rice-quality information, this system enabled rough rice transported to a rice-drying facility to be classified into six qualitative grades.

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