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Utilisation of front-face fluorescence spectroscopy for the determination of some selected chemical parameters in soft cheeses

机译:利用正面荧光光谱法测定软干酪中的某些选定化学参数

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摘要

This study evaluated the usefulness of front-face fluorescence spectroscopy for predicting some selected chemical parameters in soft cheeses in external (E) and central (C) zones. Fat, dry matter (DM), pH, total nitrogen (TN) and water-soluble nitrogen (WSN) contents were determined by reference methods. Tryptophan (excitation: 290 nm; emission: 305—450 nm), riboflavin (excitation: 380 nm; emission: 400-640 nm) and vitamin A fluorescence spectra (emission: 410 nm; excitation: 270-350 nm) were recorded on the investigated cheeses. Three separate prediction models were developed from the tryptophan, riboflavin and vitamin A spectra using partial least squares (PLS) regression and the leave-one-out cross-validation technique. The results showed that fat, DM, fat in dry matter and WSN were the best predicted with the vitamin A fluorescence spectra models providing the highest values of the determination coefficient (R~2) of 0.88, 0.86, 0.86 and 0.84, respectively The prediction of the pH was also successful using riboflavin fluorescence spectra (R~2 = 0.85). The WSN/TN ratio can also be predicted from the three intrinsic probes, but with much lower precision.
机译:这项研究评估了正面荧光光谱法对预测外部(E)和中央(C)区域软干酪中某些选定化学参数的有用性。脂肪,干物质(DM),pH,总氮(TN)和水溶性氮(WSN)含量通过参考方法测定。记录了色氨酸(激发:290 nm;发射:305-450 nm),核黄素(激发:380 nm;发射:400-640 nm)和维生素A荧光光谱(发射:410 nm;激发:270-350 nm)。被调查的奶酪。使用偏最小二乘(PLS)回归和留一法交叉验证技术,从色氨酸,核黄素和维生素A光谱中开发了三个单独的预测模型。结果表明,维生素A荧光光谱模型对脂肪,干物质,干物质中的脂肪和WSN的预测最佳,其测定系数(R〜2)的最大值分别为0.88、0.86、0.86和0.84。使用核黄素荧光光谱也成功测定了pH值(R〜2 = 0.85)。 WSN / TN比率也可以通过三种内在探针进行预测,但精度要低得多。

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