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Characterization of the Processing Conditions upon Textural Profile Analysis (TPA) Parameters of Processed Cheese Using Near-Infrared Hyperspectral Imaging
Analytical Letters ( IF 2 ) Pub Date : 2019-12-08 , DOI: 10.1080/00032719.2019.1700421
Jiajia Shan 1 , Yituo Zhang 1 , Jing Liang 1 , Xue Wang 1
Affiliation  

Abstract This investigation was undertaken to study the effect of manufactural processing conditions on the textural profile analysis (TPA) parameters and microstructure of processed cheese made by controlling the raw materials, water content, citric acid content, melting temperature, holding time, and emulsifying salts. The effect of manufactural processing conditions on the cheese physical properties was studied and compared. Moreover, hyperspectral images were acquired from the cheese samples. Principal component analysis was performed on the spectra to classify the cheese samples prepared under different processing conditions. A partial least square (PLS) algorithm was used to develop a model for predicting the TPA parameters. To assess the effect of processing conditions on the model performance, a PLS model was developed using the spectra of all of the cheese samples. A high determination correlation was achieved for predicting hardness, gumminess and chewiness with Rcv2 > 0.820 and cohesiveness and springiness with Rcv2 > 0.677. The lowest value of Rcv2 equal to 0.348 was obtained for adhesiveness.

中文翻译:

使用近红外高光谱成像对加工奶酪的纹理轮廓分析 (TPA) 参数进行加工条件表征

摘要 本研究旨在通过控制原料、水分、柠檬酸含量、熔化温度、保温时间和乳化盐,研究制造加工条件对加工奶酪的质构分析 (TPA) 参数和微观结构的影响。 . 研究和比较了生产加工条件对奶酪物理特性的影响。此外,从奶酪样品中获取了高光谱图像。对光谱进行主成分分析以对在不同加工条件下制备的奶酪样品进行分类。偏最小二乘 (PLS) 算法用于开发预测 TPA 参数的模型。为了评估处理条件对模型性能的影响,使用所有奶酪样品的光谱开发了 PLS 模型。Rcv2 > 0.820 可预测硬度、胶粘性和咀嚼性,Rcv2 > 0.677 可实现内聚性和弹性预测相关性。获得的 Rcv2 的最低值等于 0.348 以获得粘附性。
更新日期:2019-12-08
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