影响因子：2.329

DOI码：10.1080/00032719.2020.1858092

发表刊物：Analytical Letters

关键字：miso, partial least-square regression (PLSR), quality classification, sensory evaluation, visible/ near-infrared (Vis/NIR) spectroscopy

摘要：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.

合写作者：Xiaofang Liu

第一作者：王硕

论文类型：SCI

通讯作者：Jie Yu Chen

论文编号：2021072709

卷号：54

期号：14

页面范围：2304-2314

ISSN号：0003-2719

是否译文：否

发表时间：2020-12-11