Yaman Hulya, Aykas Didem P, Jiménez-Flores Rafael, Rodriguez-Saona Luis E
Department of Food Science and Technology, The Ohio State University, 2015 Fyffe Road, Columbus 43210; Department of Food Processing, Bolu Abant Izzet Baysal University, Bolu, Turkey 14100.
Department of Food Science and Technology, The Ohio State University, 2015 Fyffe Road, Columbus 43210; Department of Food Engineering, Faculty of Engineering, Adnan Menderes University, Aydin, 09100, Turkey.
J Dairy Sci. 2022 Jan;105(1):40-55. doi: 10.3168/jds.2021-20313. Epub 2021 Oct 23.
Monitoring the ripening process by prevalent analytic methods is laborious, expensive, and time consuming. Our objective was to develop a rapid and simple method based on vibrational spectroscopic techniques to understand the biochemical changes occurring during the ripening process of Turkish white cheese and to generate predictive algorithms for the determination of the content of key cheese quality and ripening indicator compounds. Turkish white cheese samples were produced in a pilot plant scale and ripened for 100 d, and samples were analyzed at 20 d intervals during storage. The collected spectra (Fourier-transform infrared, Raman, and near-infrared) correlated with major composition characteristics (fat, protein, and moisture) and primary products of the ripening process and analyzed by pattern recognition to generate prediction (partial least squares regression) and classification (soft independent analysis of class analogy) models. The soft independent analysis of class analogy models classified cheese samples based on the unique biochemical changes taking place during the ripening process. partial least squares regression models showed good correlation (R = 0.87 to 0.98) between the predicted values by vibrational spectroscopy and the reference values, giving low standard errors of prediction (0.01 to 0.57). Portable and handheld vibrational spectroscopy units can be used as a rapid, simple, and in situ technique for monitoring the quality of cheese during aging and provide real-time tools for addressing deviations in manufacturing.
采用常规分析方法监测奶酪成熟过程既费力、昂贵又耗时。我们的目标是开发一种基于振动光谱技术的快速简便方法，以了解土耳其白奶酪成熟过程中发生的生化变化，并生成预测算法，用于测定关键奶酪品质和成熟指标化合物的含量。土耳其白奶酪样品在中试规模下生产，并成熟100天，在储存期间每隔20天对样品进行分析。收集的光谱（傅里叶变换红外光谱、拉曼光谱和近红外光谱）与主要成分特征（脂肪、蛋白质和水分）以及成熟过程的主要产物相关，并通过模式识别进行分析，以生成预测（偏最小二乘回归）和分类（类相关软独立分析）模型。类相关软独立分析模型根据成熟过程中发生的独特生化变化对奶酪样品进行分类。偏最小二乘回归模型显示，振动光谱预测值与参考值之间具有良好的相关性（R = 0.87至0.98），预测标准误差较低（0.01至0.57）。便携式和手持式振动光谱仪可作为一种快速、简单的原位技术，用于监测奶酪陈化过程中的质量，并为解决生产偏差提供实时工具。
