Grassi Silvia, Strani Lorenzo, Casiraghi Ernestina, Alamprese Cristina
Department of Food, Environmental, and Nutritional Sciences, Università degli Studi di Milano, via G. Celoria 2, 20133 Milan, Italy.
Foods. 2019 Sep 12;8(9):405. doi: 10.3390/foods8090405.
Failures in milk coagulation during cheese manufacturing can lead to decreased yield, anomalous behaviour of cheese during storage, significant impact on cheese quality and process wastes. This study proposes a Process Analytical Technology approach based on FT-NIR spectroscopy for milk renneting control during cheese manufacturing. Multivariate Curve Resolution optimized by Alternating Least Squares (MCR-ALS) was used for data analysis and development of Multivariate Statistical Process Control (MSPC) charts. Fifteen renneting batches were set up varying temperature (30, 35, 40 °C), milk pH (6.3, 6.5, 6.7), and fat content (0.1, 2.55, 5 g/100 mL). Three failure batches were also considered. The MCR-ALS models well described the coagulation processes (explained variance ≥99.93%; lack of fit <0.63%; standard deviation of the residuals <0.0067). The three identified MCR-ALS profiles described the main renneting phases. Different shapes and timing of concentration profiles were related to changes in temperature, milk pH, and fat content. The innovative implementation of MSPC charts based on T and Q statistics allowed the detection of coagulation failures from the initial phases of the process.
奶酪制造过程中牛奶凝固失败会导致产量下降、奶酪在储存期间出现异常行为、对奶酪质量产生重大影响以及产生工艺废料。本研究提出了一种基于傅里叶变换近红外光谱（FT-NIR）的过程分析技术方法，用于奶酪制造过程中的牛奶凝乳控制。采用交替最小二乘法优化的多元曲线分辨（MCR-ALS）进行数据分析并开发多元统计过程控制（MSPC）图。设置了15个凝乳批次，改变温度（30、35、40°C）、牛奶pH值（6.3、6.5、6.7）和脂肪含量（0.1、2.55、5 g/100 mL）。还考虑了三个失败批次。MCR-ALS模型很好地描述了凝固过程（解释方差≥99.93%；失拟度<0.63%；残差标准偏差<0.0067）。识别出的三种MCR-ALS谱描述了主要的凝乳阶段。浓度曲线的不同形状和时间与温度、牛奶pH值和脂肪含量的变化有关。基于T和Q统计量的MSPC图的创新应用能够从过程的初始阶段检测出凝固失败。
