PREDICTION OF COOKED RICE TEXTURE QUALITY USING NEAR-INFRARED REFLECTANCE ANALYSIS OF WHOLE-GRAIN MILLED SAMPLES

Rice qualify is based on chemical and physical properties affecting it s appearance, flavor, and texture characteristics. Sensory quality can be assessed by a combination of descriptive sensory and physicochemic al property evaluations. The purpose of the present study was to asses s the potential of near-infrared reflectance spectroscopy (NIRS) and N IRS in combination with other physicochemical measurements for the det ermination of sensory texture attributes in whole-grain milled rice sa mples. Six rice samples representing combinations of variety and growi ng locations received treatments of two degrees of milling and five dr ying conditions to achieve final moisture levels of 12 or 15% (n = 120 ). Quality measurements of the cooked rice included sensory and instru mental texture analyses. Quality measurements of the uncooked rice inc luded amylose and protein (chemical reference), whiteness; transparenc y, and degree of milling (appearance units of milled rice), and NTRS a nalyses. Partial least squares (PLS) regression was used to reveal the relationships between the different types of measurements. The sensor y texture attributes: manual adhesiveness (MADHES), visual adhesivenes s (VADHES), and stickiness to lips (STICKI) were related to deep-mille d samples and positively correlated to amylose, whiteness, and milling degree. The attribute roughness (ROUGH) was related to light-milled s amples and positively correlated to protein and negatively correlated to amylose. The main variation in sensory attributes was a result of a mylose and protein contents of the rices. A noise-compensation value, relative ability of prediction (RAP), was used to express the degree o f prediction (1.0 = best possible prediction). NIRS gave the best pred iction results for the texture attributes: MADHES, VADHES, and STlCKI with an RAP of 0.57, 0.54, and 0.56, respectively. NIRS is best at pre dicting texture characteristics of cooked rice perceived in the visual , tactile, and initial oral phases of sensory evaluation. The calibrat ion of NIRS plus physicochemical variables did not improve the predict ability of sensory texture over NIRS alone. The prediction of sensory texture in rice by NIR needs to be further investigated on a large num ber of samples with different varieties, growing locations, cultivatio n methods, harvesting methods, and processing after harvesting.