Near-infrared spectroscopy has become a common quality assessment tool for fishmeal products during the last two decades. However, to date it has not been used for active online quality monitoring during fishmeal processing. Our aim was to investigate whether NIR spectroscopy, in combination with multivariate chemometrics, could actively predict the changes in the main chemical quality parameters of pelagic fishmeal and oil during processing, with an emphasis on lipid quality changes. Results indicated that partial least square regression (PLSR) models from the NIR data effectively predicted proximate composition changes during processing (with coefficients of determination of an independent test set at 𝑅2𝐶𝑉RCV2 = 0.9938, RMSECV = 2.41 for water; 𝑅2𝐶𝑉RCV2 = 0.9773, RMSECV = 3.94 for lipids; and 𝑅2𝐶𝑉RCV2 = 0.9356, RMSECV = 5.58 for FFDM) and were successful in distinguishing between fatty acids according to their level of saturation (SFA (𝑅2𝐶𝑉=0.9928, 𝑅𝑀𝑆𝐸𝐶𝑉=0.24)RCV2=0.9928, RMSECV=0.24), MUFA (𝑅2𝐶𝑉=0.8291, 𝑅𝑀𝑆𝐸𝐶𝑉=1.49)RCV2=0.8291, RMSECV=1.49), PUFA (𝑅2𝐶𝑉=0.8588, 𝑅𝑀𝑆𝐸𝐶𝑉=2.11)RCV2=0.8588, RMSECV=2.11)). This technique also allowed the prediction of phospholipids (PL 𝑅2𝐶𝑉=0.8617, 𝑅𝑀𝑆𝐸𝐶𝑉=0.11RCV2=0.8617, RMSECV=0.11, and DHA (𝑅2𝐶𝑉=0.8785, 𝑅𝑀𝑆𝐸𝐶𝑉=0.89) RCV2=0.8785, RMSECV=0.89) and EPA content 𝑅2𝐶𝑉=0.8689, 𝑅𝑀𝑆𝐸𝐶𝑉=0.62)RCV2=0.8689, RMSECV=0.62) throughout processing. NIR spectroscopy in combination with chemometrics is, thus, a powerful quality assessment tool that can be applied for active online quality monitoring and processing control during fishmeal and oil processing.
MatísVerðmætasköpun, matvælaöryggi og lýðheilsa