Fourier transform infrared spectroscopy (FTIR) spectra were correlated with microbial quality of minimally processed pomegranate (Punica granatum) arils stored at 10 °C using chemometrics. FTIR data processed in three ways i.e. FTIR spectrum, first derivative for FTIR spectrum and peak integrated data of FTIR spectrum was used as independent variables for preparing regression models by partial Least Square Regression (PLS-R) and artificial neural networks (ANN) for predicting the total viable count (TVC) and yeast and mold count (Y&M). Models built with both ANN and PLS-R using FTIR data demonstrated a high correlation value of R² > 0.85. Analysis of PLS-R results suggested the production of alcohols and acids with utilization of sugars during storage. This is a first report demonstrating use of FTIR as a nondestructive rapid method for microbial quality analysis of minimally processed fruits.

使用化学计量学，将傅里叶变换红外光谱（FTIR）光谱与在10°C下储存的最少加工的石榴（Punica granatum）假种皮的微生物质量相关。通过偏最小二乘回归（PLS-R）和人工神经网络（ANN）预测FTIR光谱，FTIR光谱的一阶导数和FTIR光谱的峰积分数据这三种方式处理的FTIR数据作为自变量来创建回归模型总存活数（TVC）和酵母菌和霉菌数（Y＆M）。使用FTIR数据同时使用ANN和PLS-R建立的模型表明R ^2的相关值很高 > 0.85。对PLS-R结果的分析表明，在存储过程中会利用糖来生产醇和酸。这是第一份证明使用FTIR作为无损快速方法进行最低限度加工水果的微生物质量分析的报告。