The potential of imaging spectroscopy for noncontact sensing of thermal treatments experienced on Japanese kamaboko was investigated. Samples were thermally treated at 100, 120, 140, 160, 180 and 200°C to core temperatures of 45, 50, 55, 60, 65, 70, 75 and 80°C and then promptly cooled and imaged in the short-wave near infrared spectral range of 900-2500 nm. Partial least square (PLS) regression models were developed using the whole spectral range as well as using the most important wavelengths to predict the core temperature (T C ) and thermal history (TH) yielding a reasonable level of accuracy of (R 2 P = 0.86 and RMSEP = 3.9°C) and (R 2 P = 0.83 and RMSEP = 0.29 min), respectively. Moreover, a stepwise linear discriminant analysis (LDA) model was developed for identifying samples whose core temperatures reached a threshold of 65°C. The LDA model yielded overall classification accuracy of 93.75% in both calibration and validation sets. The resulting discrimination function was then applied in a pixel-wise manner to produce understandable classification maps to exhibit the difference among samples with high accuracy.