Melamine has been reported to be responsible for kidney stones and renal failure among infants and children. Con-ventional detection methods, High-Performance Liquid Chromatography (HPLC) and Gas Chromatography (GC), aresensitive enough to detect trace amounts of the contaminant, but they are time consuming, expensive, and labor-intensive. Hyperspectral imaging methods, which combine spectroscopy and imaging, can provide rapid and non-destructive means to assess the quality and safety of agricultural products. In this study, near-infrared hyperspectralreflectance imaging combined with partial least square regression analysis was used to predict melamine particleconcentration in dry milk powder. Melamine particles, with concentration levels ranging from 0.02% to 1% byweight ratio (g/g), were mixed with dry milk powder and used for the experiment. Hyperspectral reflectance imagesin the wavelength range from 992.0nm to 1682.1nm were acquired for the mixtures. Then PLSR models weredeveloped with several preprocessing methods. Optimal wavelength bands were selected from 1454.5nm to 1555.6nm using beta-coefficients from the PLSR model. The best PLSR result for predicting melamine concentration inmilk powder was obtained using a 1st order derivative pretreatment with Rv=0.974, SEP=±0.055%, and F=6.

시중 즉석 면류의 관능적 성질과 back extrusion test 데이터에 대하여 partial least square regression(PLSR)을 실시하였다. 즉석유탕면 8종과 즉석비유탕면 2종에 대한 관능적 속성으로서 경도(A), 탄성(B), 껄끄러운 정도(C), 이에 박히는 정도(D), 굵기감(E)를 검사하였고, 실험 데이터로 힘-변형 곡선 전체를 사용하였다. PLSR의 회귀계수는 힘-변형 곡선의 압착단계, 항복단계, 압출단계로 크게 구분되어 각 관능속성에 대한 특유의 양 또는 음의 효과를 나타냈다. PLSR의 상관계수는 E>D>A>B>C, 오차(root mean square error of prediction expressed in sensory units)는 D>C>E>B>A, 예측능(relative ability of prediction)는 D>C>E>B>A 로 나타나 종합적으로 ‘이에 박히는 정도’가 PLSR의 적용에 가장 우수하게 나타났다. ‘경도’는 예측능은 낮았지만 상관성은 높아서 시료간 순위의 결정에 합당하게 평가되었다.