Rapid Detection of the Quality of Miso (Japanese Fermented Soybean Paste) Using Visible/Near-Infrared Spectroscopy

Abstract

Miso, the Japanese fermented soybean paste, an essential seasoning for preparing washoku, is gaining increased international recognition. Miso quality is normally evaluated by a panel of sensory assessors, which is a time-consuming and costly procedure. The final quality of miso products depends on the complex interactions of color, taste, flavor, and aroma. This study aimed to use visible/near-infrared (Vis/NIR) spectroscopy combined with a partial least-square regression (PLSR) algorithm to develop a rapid method for predicting the sensory qualities of miso products. The full Vis/NIR spectrum with nine different pretreatment methods was used to build calibration, cross validation and prediction models. The best performance was achieved by a PLSR model with the first derivative pretreatment of the spectra, giving the root mean square error of prediction (RMSEP) and bias values of 25 and 13, respectively, for the validation test. This model effectively classified miso products into different grades. An investigation of particular spectral regions revealed that a similar performance to the best PLSR model was obtained using just the spectra from 400 to 1100 nm with first derivative pretreatment or combined with multiplicative scatter correction (MSC), and standard normal variate (SNV) methods. The study demonstrated that Vis/NIR spectroscopy has the potential for evaluating miso quality with the advantages of being rapid, accurate, low cost, and nondestructive.