Galdino Isadora Kaline Camelo Pires de Oliveira, Salles Hévila Oliveira, Dos Santos Karina Maria Olbrich, Veras Germano, Alonso Buriti Flávia Carolina
Centro de Ciências Biológicas e da Saúde, Universidade Estadual da Paraíba, Campina Grande, Paraíba, Brazil.
Embrapa Caprinos e Ovinos, Empresa Brasileira de Pesquisa Agropecuária, Sobral, Ceará, Brazil.
PeerJ. 2020 Feb 12;8:e8619. doi: 10.7717/peerj.8619. eCollection 2020.
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.
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).
The average whey composition values obtained using the referenced methods were in accordance with the consulted literature. The composition did not differ significantly ( > 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 ( cal and pred, 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.
在巴西，过去几年间山羊奶酪的产量和消费量有所增加。此外，人们还要求开发利用奶酪生产过程中提取的乳清的方法。乳清可作为许多产品开发的原料。因此，鉴于食品分析的参考方法执行起来需要时间、训练有素的劳动力和昂贵的试剂，了解其成分至关重要。
采用参考方法和近红外光谱法（NIRS）对冬季和夏季生产的山羊乳清样品进行近似成分分析（水分、总固体、灰分、蛋白质、脂肪和差值法测定的碳水化合物）。光谱数据通过基线校正和Savitzky-Golay导数进行预处理。使用偏最小二乘回归（PLSR）对每个因变量（近似成分参数）的原始数据和预处理数据建立模型。
使用参考方法获得的乳清平均成分值与参考文献一致。夏季和冬季乳清样品的成分差异不显著（>0.05）。PLSR模型通过以下品质因数建立：校准模型和预测模型的决定系数（分别为cal和pred）以及校准和预测的均方根误差（分别为RMSEC和RMSEP）。最佳模型使用原始数据进行脂肪和蛋白质测定，通过NIRS获得的这两个参数的值与其参考方法一致。因此，NIRS可用于测定山羊乳清中的脂肪和蛋白质。
