近红外光谱法测定用于炸各种食物的植物油的降解情况。

Near-infrared spectroscopic determination of degradation in vegetable oils used to fry various foods.

机构信息

Department of Food Science and Technology, University of Nebraska, Lincoln, Nebraska 68583-0919, United States.

出版信息

J Agric Food Chem. 2011 Dec 14;59(23):12286-90. doi: 10.1021/jf202740e. Epub 2011 Nov 9.

DOI:10.1021/jf202740e

PMID:22017673

Abstract

Near-infrared (NIR) spectroscopic methods for measuring degradation products, including total polar materials (TPMs) and free fatty acids (FFAs), in soy-based frying oil used for frying various foods have been successfully developed. Calibration models were developed using forward stepwise multiple linear regression (FSMLR) and partial least-squares (PLS) regression techniques and then tested with an independent set of validation samples. The results show that the quality of oil used for frying different foods can be measured with a single model. First-derivative treatments improved results for TPM measurement. In addition, PLS models gave better prediction results than FSMLR models. For PLS models, the best correlations (r) between the NIR-predicted data and the chemical method data for TPMs and FFAs in oils were 0.995 and 0.981, respectively. For FSMLR models, the best r values for TPMs and FFAs in oils were 0.993 and 0.963, respectively.

摘要

已成功开发出用于测量各种食品油炸用大豆基煎炸油中降解产物（包括总极性物质（TPM）和游离脂肪酸（FFA））的近红外（NIR）光谱方法。使用逐步正向多元线性回归（FSMLR）和偏最小二乘（PLS）回归技术建立校准模型，然后使用独立的验证样本进行测试。结果表明，可以使用单个模型测量用于炸不同食物的油的质量。一阶导数处理可改善 TPM 测量结果。此外，PLS 模型比 FSMLR 模型给出了更好的预测结果。对于 PLS 模型，NIR 预测数据与化学方法数据之间的最佳相关性（r）分别为 0.995 和 0.981，适用于油中的 TPMs 和 FFAs。对于 FSMLR 模型，油中 TPMs 和 FFAs 的最佳 r 值分别为 0.993 和 0.963。