Department of Food Engineering, Faculty of Engineering, Adnan Menderes University, Aydin 09100, Turkey.
Department of Food Engineering, Faculty of Agriculture, Bursa Uludağ University, Bursa, Turkey.
Food Chem. 2023 Nov 30;427:136727. doi: 10.1016/j.foodchem.2023.136727. Epub 2023 Jun 25.
We aimed to develop portable Fourier transform infrared (FT-IR) spectroscopy-based prediction algorithms for the key quality characteristics (soluble solids, water activity, pH, sucrose, glucose, fructose, fructose/glucose, hydroxymethylfurfural) of various types of molasses, establish their legitimacy, and create a model to separate them based on their botanical origin. Samples labeled as carob (n = 27), grape (n = 24), Juniper (n = 13), and mulberry (n = 12) were purchased from different local markets in Turkey. Labeling issues were revealed in five carob and seven grape molasses, and those samples classified as non-authentic by the FT-IR algorithms were corroborated by reference analysis. Partial least squares regression models generated to predict the key quality traits of Turkish molasses demonstrated excellent correlation with reference analysis (R ≥ 0.96) and low standard error of prediction (SEP ≤ 2.88). The FT-IR sensor provided a feasible approach for molasses testing to assess its quality through manufacturing and storage, also provided a powerful tool to -ensure proper product labeling.
我们旨在开发基于傅里叶变换红外(FT-IR)光谱的便携式预测算法,用于预测各种类型糖蜜的关键质量特性(可溶性固形物、水分活度、pH 值、蔗糖、葡萄糖、果糖、果糖/葡萄糖、羟甲基糠醛),验证其合法性,并建立一种基于植物来源对其进行分类的模型。从土耳其的不同当地市场购买了标签为角豆(n=27)、葡萄(n=24)、杜松(n=13)和桑椹(n=12)的样品。在五份角豆糖蜜和七份葡萄糖蜜中发现了标签问题,并且通过 FT-IR 算法将这些被归类为非正宗的样品通过参考分析得到了证实。为预测土耳其糖蜜的关键质量特性而生成的偏最小二乘回归模型与参考分析具有极好的相关性(R≥0.96),预测标准误差(SEP≤2.88)较低。FT-IR 传感器为糖蜜测试提供了一种可行的方法,可通过制造和储存来评估其质量,也为确保产品正确标签提供了有力工具。