Abstract #M1
Section: ADSA Dairy Foods Poster Competition (Graduate)
Session: ADSA Dairy Foods Graduate Competition - POSTER
Format: Poster
Day/Time: Monday 7:30 AM–9:30 AM
Location: Exhibit Hall A
Session: ADSA Dairy Foods Graduate Competition - POSTER
Format: Poster
Day/Time: Monday 7:30 AM–9:30 AM
Location: Exhibit Hall A
# M1
Development and validation of a rapid method for measurement of casein in raw milk using front-face fluorescence spectroscopy and chemometrics.
Yizhou B. Ma*1, Jayendra K. Amamcharla1, 1Food Science Institute, Animal Sciences and Industry, Kansas State University, Manhattan, KS.
Key Words: partial least square regression (PLSR), principal component regression, front-face fluorescence spectroscopy (FFFS)
Development and validation of a rapid method for measurement of casein in raw milk using front-face fluorescence spectroscopy and chemometrics.
Yizhou B. Ma*1, Jayendra K. Amamcharla1, 1Food Science Institute, Animal Sciences and Industry, Kansas State University, Manhattan, KS.
The casein content in raw milk is important for the industry as it influences the cheese yield. The casein content is determined by the difference between true protein and non-casein protein in raw milk. The objective of this study was to develop a rapid quantification method for casein in raw milk using front-face fluorescence spectroscopy (FFFS). To prepare milk samples for calibration, raw skim milk was obtained from Kansas State University’s dairy farm and ultrafiltered to increase the protein concentration. The casein content of retentate and permeate were measured by a reference method. The retentate and permeate were combined at different ratios to make 10 calibration samples with casein content ranging from 0.37 to 3.7%. Sample preparation for the FFFS involved thoroughly mixing 7 mL calibration sample with 0.6 mL acetic acid (10% wt/wt) to precipitate the casein. Sample mixture was vortexed and transferred immediately to a quartz cuvette. Tryptophan emission spectrum of the mixture was immediately measured by a spectrofluorometer with a 1% attenuator (excitation wavelength at 280 nm; emission wavelength range from 300 to 440 nm) at 25°C. The process was repeated twice to obtain a sample size of 20 for the calibration model. Prediction models were developed using principal component regression and partial least square regression (PLSR) and validated with the leave-one-out cross-validation (LOOCV). The principal component regression and PLSR models showed LOOCV correlation coefficients of 0.970 and 0.988, root mean square error (RMSE) of 0.39% and 0.24%, and ratio of prediction to deviation of 4.5 and 4.7, respectively. The developed models were independently validated by 5 raw milk samples collected on different days. Principal component regression and PLSR predictions had a mean difference of 0.12% and 0.11% casein compared with the reference method and RMSE of 0.19% and 0.19%, respectively. The mean bias of 2 prediction models is not significantly different from 0 (P > 0.05). The FFFS method showed potential quantification of casein in raw milk, but validation on a large sample set is further required.
Key Words: partial least square regression (PLSR), principal component regression, front-face fluorescence spectroscopy (FFFS)