• Journal of the Korean Society of Food Science and Nutrition
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• v.43 no.3
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• pp.425-430
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• 2014

Foods contain various nutrients such as carbohydrates, protein, oil, vitamins, and minerals. Among them, carbohydrates, protein, and oil are the main constituents of foods. Usually, these constituents are analyzed by the Kjeldahl and Soxhlet method and so on. However, these analytical methods are complex, costly, and time-consuming. Thus, this study aimed to rapidly and effectively analyze carbohydrate, protein, and oil contents with near-infrared reflectance spectroscopy (NIRS). A total of 517 food samples were measured within the wavelength range of 400 to 2,500 nm. Exactly 412 food calibration samples and 162 validation samples were used for NIRS equation development and validation, respectively. In the NIRS equation of carbohydrates, the most accurate equation was obtained under 1, 4, 5, 1 (1st derivative, 4 nm gap, 5 points smoothing, and 1 point second smoothing) math treatment conditions using the weighted MSC (multiplicative scatter correction) scatter correction method with MPLS (modified partial least square) regression. In the case of protein and oil, the best equation were obtained under 2, 5, 5, 3 and 1, 1, 1, 1 conditions, respectively, using standard MSC and standard normal variate only scatter correction methods with MPLS regression. Calibrations of these NIRS equations showed a very high coefficient of determination in calibration ($R^2$: carbohydrates, 0.971; protein, 0.974; oil, 0.937) and low standard error of calibration (carbohydrates, 4.066; protein, 1.080; oil, 1.890). Optimal equation conditions were applied to a validation set of 162 samples. Validation results of these NIRS equations showed a very high coefficient of determination in prediction ($r^2$: carbohydrates, 0.987; protein, 0.970; oil, 0.947) and low standard error of prediction (carbohydrates, 2.515; protein, 1.144; oil, 1.370). Therefore, these NIRS equations can be applicable for determination of carbohydrates, proteins, and oil contents in various foods.

• Journal of Korean Society of Forest Science
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• v.70 no.1
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• pp.7-16
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• 1985

To grasp canonical correlations, their related backgrounds in various growth factors of stem, the characteristics of stem by synthetical dispersion analysis, principal component analysis and canonical correlation analysis as optimum method were applied to Larix leptolepis. The results are as follows; 1) There were high or low correlation among all factors (height ($x_1$), clear height ($x_2$), form height ($x_3$), breast height diameter (D. B. H.: $x_4$), mid diameter ($x_5$), crown diameter ($x_6$) and stem volume ($x_7$)) except normal form factor ($x_8$). Especially stem volume showed high correlation with the D.B.H., height, mid diameter (cf. table 1). 3) (1) Canonical correlation coefficients and canonical variate between stem volume and composite variate of various height growth factors ($x_1$, $x_2$ and $x_3$) are ${\gamma}_{u1,v1}=0.82980^{**}$, $\{u_1=1.00000x_7\\v_1=1.08323x_1-0.04299x_2-0.07080x_3$. (2) Those of stem volume and composite variate of various diameter growth factors ($x_4$, $x_5$ and $x_6$) are ${\gamma}_{u1,v1}=0.98198^{**}$, $\{{u_1=1.00000x_7\\v_1=0.86433x_4+0.11996x_5+0.02917x_6$. (3) And canonical correlation between stem volume and composite variate of six factors including various heights and diameters are ${\gamma}_{u1,v1}=0.98700^{**}$, $\{^u_1=1.00000x_7\\v1=0.12948x_1+0.00291x_2+0.03076x_3+0.76707x_4+0.09107x_5+0.02576x_6$. All the cases showed the high canonical correlation. Height in the case of (1), D.B.H. in that of (2), and the D.B.H, and height in that of (3) respectively make an absolute contribution to the canonical correlation. Synthetical characteristics of each qualitative growth are largely affected by each factor. Especially in the case of (3) the influence by the D.B.H. is the most significant in the above six factors (cf. table 2). 3) Canonical correlation coefficient and canonical variate between composite variate of various height growth factors and that of the various diameter factors are ${\gamma}_{u1,v1}=0.78556^{**}$, $\{u_1=1.20569x_1-0.04444x_2-0.21696x_3\\v_1=1.09571x_4-0.14076x_5+0.05285x_6$. As shown in the above facts, only height and D.B.H. affected considerably to the canonical correlation. Thus, it was revealed that the synthetical characteristics of height growth was determined by height and those of the growth in thickness by D.B.H., respectively (cf. table 2). 4) Synthetical characteristics (1st-3rd principal component) derived from eight growth factors of stem, on the basis of 85% accumulated proportion aimed, are as follows; Ist principal component ($z_1$): $Z_1=0.40192x_1+0.23693x_2+0.37047x_3+0.41745x_4+0.41629x_5+0.33454x_60.42798x_7+0.04923x_8$, 2nd principal component ($z_2$): $z_2=-0.09306x_1-0.34707x_2+0.08372x_3-0.03239x_4+0.11152x_5+0.00012x_6+0.02407x_7+0.92185x_8$, 3rd principal component ($z_3$): $Z_3=0.19832x_1+0.68210x_2+0.35824x_3-0.22522x_4-0.20876x_5-0.42373x_6-0.15055x_7+0.26562x_8$. The first principal component ($z_1$) as a "size factor" showed the high information absorption power with 63.26% (proportion), and its principal component score is determined by stem volume, D.B.H., mid diameter and height, which have considerably high factor loading. The second principal component ($z_2$) is the "shape factor" which indicates cubic similarity of the stem and its score is formed under the absolute influence of normal form factor. The third principal component ($z_3$) is the "shape factor" which shows the degree of thickness and length of stem. These three principal components have the satisfactory information absorption power with 88.36% of the accumulated percentage. variance (cf. table 3). 5) Thus the principal component and canonical correlation analyses could be applied to the field of forest measurement, judgement of site qualities, management diagnoses for the forest management and the forest products industries, and the other fields which require the assessment of synthetical characteristics.