Proximate composition determination in goat cheese whey by near infrared spectroscopy (NIRS)

PeerJ

卷:8

Flávia Carolina Alonso Buriti¹  Isadora Kaline Camelo Pires de Oliveira Galdino¹  Germano Veras²  Karina Maria Olbrich dos Santos³  Hévila Oliveira Salles⁴ 

[1] Centro de Ciências Biológicas e da Saúde, Universidade Estadual da Paraíba, Campina Grande, Paraíba, Brazil;

[2] Centro de Ciências e Tecnologia, Universidade Estadual da Paraíba, Campina Grande, Paraíba, Brazil;

[3] Embrapa Agroindústria de Alimentos, Empresa Brasileira de Pesquisa Agropecuária, Rio de Janeiro, Rio de Janeiro, Brazil;

[4] Embrapa Caprinos e Ovinos, Empresa Brasileira de Pesquisa Agropecuária, Sobral, Ceará, Brazil;

关键词: Food analysis;    Dairy;    Chemometric analysis;    By-product upgrading;    Seasonal composition;   

DOI  :  10.7717/peerj.8619

来源: DOAJ

【 摘 要 】

Background In Brazil, over the last few years there has been an increase in the production and consumption of goat cheeses. In addition, there was also a demand to create options to use the whey extracted during the production of cheeses. Whey can be used as an ingredient in the development of many products. Therefore, knowing its composition is a matter of utmost importance, considering that the reference methods of food analysis require time, trained labor and expensive reagents for its execution. Methods Goat whey samples produced in winter and summer were submitted to proximate composition analysis (moisture, total solids, ashes, proteins, fat and carbohydrates by difference) using reference methods and near infrared spectroscopy (NIRS). The spectral data was preprocessed by baseline correction and the Savitzky–Golay derivative. The models were built using Partial Least Square Regression (PLSR) with raw and preprocessed data for each dependent variable (proximate composition parameter). Results The average whey composition values obtained using the referenced methods were in accordance with the consulted literature. The composition did not differ significantly (p > 0.05) between the summer and winter whey samples. The PLSR models were made available using the following figures of merit: coefficients of determination of the calibration and prediction models (R2cal and R2pred, respectively) and the Root Mean Squared Error Calibration and Prediction (RMSEC and RMSEP, respectively). The best models used raw data for fat and protein determinations and the values obtained by NIRS for both parameters were consistent with their referenced methods. Consequently, NIRS can be used to determine fat and protein in goat whey.

【 授权许可】

Unknown