期刊
JOURNAL OF FOOD SCIENCE AND TECHNOLOGY-MYSORE
卷 52, 期 3, 页码 1649-1655出版社
SPRINGER INDIA
DOI: 10.1007/s13197-013-1147-z
关键词
Ricotta cheese; Fat; Protein; Moisture; Near infrared; PLSR
资金
- Fundacao Araucaria-Fundacao de Amparo a Pesquisa do Parana [232/2007-10788]
Near-infrared reflectance spectroscopy (NIRS) with partial least squares regression (PLSR) was used to determine levels of fat, protein and moisture in ricotta cheese without complex sample preparation. Spectra of 19 conventional and low-fat ricotta samples from different manufacturers were collected in duplicate, with 33 of the 38 spectra used as a calibration set and the remaining 5 spectra used as an external validation set. The best results were obtained by processing the spectral region between 1,100 and 2,500 nm. Multivariate models with six latent variables (LVs) showed good prediction capability for fat and protein determinations, with average relative errors (Er) of 6.37 % and 5.95 %, respectively. For moisture, a more robust model was obtained with 4 LVs, showing better prevision capacity and Er of 1.91 %.
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