Alternative Approaches for Accelerated Shelf Life Testing (ASLT) of a Shelf-Stable Food Product: A Case Study of Dried Shrimp

Alternative approaches for shelf life evaluation of food based on accelerated shelf life testing (ASLT) assisted with principal component analysis-based machine learning (ML-ASLT), and hyperspectral imaging integrated with ML-ASLT (HSI-ML-ASLT) were developed and compared with the conventional ASLT (C-ASLT). Dried shrimp was stored at 15, 30, and 40 °C and monitored for changes in sensory characteristics including odor, color and overall acceptability during the storage. Physicochemical properties of the dried shrimp were monitored to describe the changes in sensory attributes. Volatile compounds of initial and unacceptable dried shrimp were analyzed using gas chromatograph-mass spectrometer (GC–MS). The samples were also analyzed using HSI in the wavelength range of 400–1000 nm. PCA-based machine learning was applied with ASLT and HSI-ASLT to determine quality degradation rate constant and shelf life. The ML-ASLT gave the predicted shelf life of 9.87, 6.13, and 3.34 wk., while HSI-ML-ASLT gave the shelf life of 9.71, 6.51 and 3.31 wk. for dried shrimp stored at 15, 30, and 40 °C, respectively, which were more accurate than C-ASLT (10.29, 6.80 and 3.49 wk), when compared to the actual shelf life (10, 6 and 3 wk). By simultaneously evaluating diverse quality attributes, the PCA-based machine learning model enabled the ASLT approach to practically predict dried shrimp shelf life. Moreover, HSI-ML-ASLT provided a rapid, simple, and chemical-free technique to unravel food shelf life assessment.