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By focusing our attention on nitrogen components in plants, which are important for cultivation management in data-driven agriculture, we developed a simple, rapid, non-chemical and simultaneous quantification method for proteinic and nitrate nitrogen in a leaf model based on near-infrared (NIR) spectroscopic information obtained using a compact Fourier Transform NIR (FT-NIR) spectrometer. The NIR spectra of wet leaf models impregnated with a protein–nitric acid mixed solution and a dry leaf model obtained by drying filter paper were acquired. For spectral acquisition, a compact MEMS (Micro Electro Mechanical Systems) FT-NIR spectrometer equipped with a diffuse reflectance probe accessory was used. Partial least square regression analysis was performed using the spectral information of the extracted absorption bands based on the determination coefficients between the spectral absorption intensities and the contents of the two-dimensional spectral analysis between NIR and mid-infrared spectral information. Proteinic nitrogen content in the dry leaf model was well predicted using the MEMS FT-NIR spectroscopic method. Additionally, nitrate nitrogen in the dry leaf model was also determined by the provided method, but the necessity of adding the data for a wider range of nitric acid concentrations was experimentally indicated for the prediction of nitrate nitrogen content in the wet leaf model. Consequently, these results experimentally suggest the possibility of the application of the compact MEMS FT-NIR for obtaining the bioinformation of crops at agricultural on-sites. [ABSTRACT FROM AUTHOR]

The infrared spectral characteristics of three different types of disaccharides (trehalose, maltose, and sucrose) and four different types of monosaccharides (glucose, mannose, galactose, and fructose) in aqueous solutions with sodium chloride (NaCl) were determined. The infrared spectra were obtained using the FT-IR/ATR method and the absorption intensities respected the interaction between the saccharide and water with NaCl were determined. This study also focused on not only the glycosidic linkage position and the constituent monosaccharides, but also the concentration of the saccharides and NaCl and found that they have a significant influence on the infrared spectroscopic characterization of the disaccharides in an aqueous solution with NaCl. The absorption intensities representing the interaction between a saccharide and water with NaCl were spectroscopically determined. Additionally, the applications of MIR spectroscopy to obtain information about saccharide–NaCl interactions in foods and biosystems were suggested. We experimentally extracted the spectral characteristics of the monosaccharides and the disaccharides affected by the interactions of saccharides–NaCl–water. [ABSTRACT FROM PUBLISHER]

The appearances of agricultural products are important indices for evaluating the quality of commodities and the characteristics of different varieties. In general, the appearances are evaluated by experts based on visual observations. However, the concern regarding this method is that it lacks objectivity, and it is not quantifiable because it depends greatly on an empirical knowledge. In addition, agricultural products have multiple appearance features; therefore, several of them need to be analyzed simultaneously for correct evaluation of the appearance. In this study, we developed a new image analysis system that can simultaneously evaluate multiple appearance characteristics such as the color, shape and size, of agricultural products in detail. To evaluate the effectiveness of this system, we conducted quality evaluations and cultivar identification on the basis of cluster analysis, multidimensional scaling and discriminant analysis of the appearance characteristics. The results of the cluster analysis revealed that strawberries could be classified on the basis of their appearance characteristics. Furthermore, we were able to visualize the small differences in the appearance of the fruit based on multiple characteristics on a two-dimensional surface by performing multidimensional scaling. The results demonstrate that our system is effective for qualitative evaluations of the appearance of strawberries. The results of the discriminant analysis revealed that the accuracy of strawberry cultivar classification using 14 cultivars was <42%, when only single feature was used. However, the rate increased to 68% after combining the three features. These results indicate that our system exploits the advantage of analyzing multiple appearance characteristics. [ABSTRACT FROM AUTHOR]