Optimization of Milk Sample Cleanup Using Response Surface Methodology

Optimization of Protease Treatment Conditions for Chlorella pyrenoidosa Protein Extraction and Investigation of Its Potential as an Alternative Protein Source

This study aimed to determine enzymes that effectively extract Chlorella pyrenoidosa proteins and optimize the processing conditions using response surface methods. Furthermore, the potential of enzymatically hydrolyzed C. pyrenoidosa protein extract (CPE) as a substitute protein source was investigated. The enzymatic hydrolysis conditions for protein extraction were optimized using single-factor analysis and a response surface methodology-Box-Behnken design. The R2 value of the optimized model was 0.9270, indicating the reliability of the model, and the optimal conditions were as follows: a hydrolysis temperature of 45.56 °C, pH 9.1, and a hydrolysis time of 49.85 min. The amino acid composition of CPE was compared to that of C. pyrenoidosa powder (CP), which was found to have a higher content of essential amino acids (EAA). The electrophoretic profiles of CP and CPE confirmed that CPE has a low molecular weight. Furthermore, CPE showed higher antioxidant activity and phenol content than CP, with ABTS and DPPH radical scavenging abilities of 69.40 ± 1.61% and 19.27 ± 3.16%, respectively. CPE had high EAA content, antioxidant activity, and phenol content, indicating its potential as an alternative protein source. Overall, in this study, we developed an innovative, ecofriendly, and gentle enzymatic hydrolysis strategy for the extraction and refinement of Chlorella proteins.

Instant quantification of sugars in milk tablets using near-infrared spectroscopy and chemometric tools

Milk tablets are a popular dairy product in many Asian countries. This research aimed to develop an instant and rapid method for determining sucrose and lactose contents in milk tablets using near-infrared (NIR) spectroscopy. For the quantitative analysis, a training set composed of laboratory-scale milk samples was generated based on a central composite design (CCD) and used to establish partial least squares (PLS) regression for the predictions of sucrose and lactose contents resulting in R² values of 0.9749 and 0.9987 with the corresponding root mean square error of calibration (RMSEC) values of 1.69 and 0.35. However, the physical difference between the laboratory-scale powder and the final product milk tablet samples resulted in spectral deviations that dramatically affected the predictive performance of the PLS models. Therefore, calibration transfer methods called direct standardization (DS) and piecewise direct standardization (PDS) were used to adjust the NIR spectra from the real milk tablet samples before the quantitative prediction. Using high-performance liquid chromatography (HPLC) as a reference method, the developed NIR-chemometric model could be used to instantly predict the sugar contents in real milk tablets by producing root mean square error of prediction (RMSEP) values for sucrose and lactose of 5.04 and 4.22 with Q² values of 0.7973 and 0.9411, respectively, after the PDS transformation.