CAJ | 학술논문

固态发酵是镇江香醋生产的重要环节之一，直接决定着成品醋的风味和品质。但目前固态发酵的生产控制主要依赖人工经验，难以有效保障镇江香醋的品质。该文分析了总酸（total acid content，TAC）、pH值、含水率在不同阶段的变化规律；采用高光谱图像技术结合联合区间偏最小二乘法（synergy interval partial least squares，siPLS）快速预测固态发酵基质（醋醅）的TAC、pH值和含水率，其最佳模型的相关系数R分别为0.8316、0.9455和0.8503；同时利用主成分分析和逐步多元线性回归模型（stepwise multiple linear regression，SMLR）对醋醅高光谱图像进行分析，研究了总酸在醋醅中的分布情况，以此来快速判断醋醅发酵的均匀性。研究表明，利用高光谱图像技术快速预测醋醅的理化参数及其分布的方法是可行的，结果可为镇江香醋固态发酵的工艺控制提供基础数据和技术手段。
고태발효시진강향작생산적중요배절지일，직접결정착성품작적풍미화품질。단목전고태발효적생산공제주요의뢰인공경험，난이유효보장진강향작적품질。해문분석료총산（total acid content，TAC）、pH치、함수솔재불동계단적변화규률；채용고광보도상기술결합연합구간편최소이승법（synergy interval partial least squares，siPLS）쾌속예측고태발효기질（작배）적TAC、pH치화함수솔，기최가모형적상관계수R분별위0.8316、0.9455화0.8503；동시이용주성분분석화축보다원선성회귀모형（stepwise multiple linear regression，SMLR）대작배고광보도상진행분석，연구료총산재작배중적분포정황，이차래쾌속판단작배발효적균균성。연구표명，이용고광보도상기술쾌속예측작배적이화삼수급기분포적방법시가행적，결과가위진강향작고태발효적공예공제제공기출수거화기술수단。
In China and Southeast Asian countries, the solid-state fermentation (SSF) process is maintained empirically, especially in fed-batch fermentation by layers. In this study, the feasibility of determination of the total acid content (TAC), pH value and moisture content of Zhenjiang balsamic vinegar during SSF process were investigated. Hyperspectral imaging technology (HSIT) was combined with an appropriate multivariate analysis method. A synergy interval partial leastsquare (siPLS) was used to select the efficient spectral subintervals and wavelengths by k-fold cross-validation during the development of model. The performance of the final model was evaluated by use of the root mean square error of cross-validation (RMSECV) and correlation coefficient (Rc) for the calibration set, and verified by use of the root mean square error of prediction (RMSEP) and correlation coefficient (Rp) for the validation set. The changes of TAC, pH value and moisture were obtained by normal standard methods. TAC was constantly increased during fermentation process. The change of pH value was from 3.6 to 3.99, which was appropriate for the growth of the main microbes and can inhibit the growth of other unnecessary bacteria in the fermentation process. The moisture was increased in all stages during SSF process. Besides, the experimental results showed that the optimum siPLS model for TAC was achieved by use of 7 PLS factors, when 4 spectral subintervals were selected by siPLS. The predicted precision of the best model obtained was: RMSECV=0.625, Rc=0.8316, RMSEP=0.773, and Rp=0.7965. The pH value was achieved using siPLS with 6 PLS factors. The predicted precision of the best model obtained was: RMSECV=0.0465, Rc=0.9455, RMSEP=0.0482, and Rp=0.9321. Besides, the moisture content was achieved using siPLS with 4 PLS factors. The predicted precision of the best model obtained was: RMSECV=0.2104, Rc=0.8503, RMSEP=0.2459, and Rp=0.8277. Finally, the superior performance of the siPLS model was demonstrated by comparison with two other PLS models. The overall results indicated that HSIT can be successfully used for the measurement of TAC, pH value and moisture content in SSF, and the use of siPLS algorithm is the best means of calibration for modelling. Besides, in order to get the distribution map of TAC, principal component analysis (PCA) and stepwise linear regression (SMLR) were used to calculate the TAC in each pixel points. The distribution map of TAC in vinegar culture was obtained. The homogeneity of SSF also could be determined by hyperspectral imaging.