Prediction of key milk biomarkers in dairy cows through milk MIR spectra and international collaborations.

peer reviewedAt the individual cow level, sub-optimum fertility, mastitis, negative energy balance and ketosis are major issues in dairy farming. These problems are widespread on dairy farms and have an important economic impact. The objectives of this study were: 1) to assess the potential of milk Mid Infrared (MIR) spectra to predict key biomarkers of energy deficit (citrate, isocitrate, glucose-6P, free glucose), ketosis (BHB and acetone), mastitis (NAGase and LDH), and fertility (progesterone); 2) to test alternative methodologies to partial least square regression (PLS) to better account for the specific asymmetric distribution of the biomarkers; and 3) to create robust models by merging large data sets from 5 international or national projects. Benefiting from this international collaboration, the data set comprised a total of 9,143 milk samples from 3,758 cows located in 589 herds across 10 countries and represented 7 breeds. The samples were analyzed by reference chemistry for biomarker contents while the MIR analyses were performed on 30 instruments from different models and brands, with spectra harmonized into a common format. Four quantitative methodologies were evaluated to address the strongly skewed distribution of some biomarkers. PLS was used as the reference basis, and compared with a random modification of distribution associated with PLS (Random-downsampling-PLS), an optimized modification of distribution associated with PLS (KennardStone-downsampling-PLS) and Support Vector Machine (SVM). When the ability of MIR to predict biomarkers was too low for quantification, different qualitative methodologies were tested to discriminate low vs high values of biomarkers. For each biomarker, 20% of the herds were randomly removed within all countries to be used as the validation data set. The remaining 80% of herds were used as the calibration data set. In calibration, the 3 alternative methodologies outperform the PLS performances for the majority of biomarkers. However, in the external herd validation, PLS provided the best results for isocitrate, glucose-6P, free glucose and LDH (R2v = 0.48, 0.58, 0.28, and 0.24). For other molecules, PLS-Random-downsampling and PLS-KennardStone-downsampling outperformed PLS in the majority of cases, but the best results were provided by SVM for citrate, BHB, acetone, NAGase and progesterone (R2v = 0.94, 0.58, 0.76, 0.68, and 0.15). Hence, PLS and SVM based on the entire data set provided the best results for normal and skewed distributions, respectively. Complementary to the quantitative methods, the qualitative discriminant models enabled the discrimination of high and low values for BHB, acetone, and NAGase with a global accuracy around 90%, and glucose-6P with an accuracy of 83%. In conclusion, MIR spectra of milk can enable quantitative screening of citrate as a biomarker of energy deficit and discrimination of low and high values of BHB, acetone, and NAGase, as biomarkers of ketosis and mastitis. Finally, progesterone could not be predicted with sufficient accuracy from milk MIR spectra to be further considered. Consequently, MIR spectrometry can bring valuable information regarding the occurrence of energy deficit, ketosis and mastitis in dairy cows, which in turn have major influences on their fertility and survival

An evaluation of the PoLiSh smoothed regression and the Monte Carlo Cross-Validation for the determination of the complexity of a PLS model

A crucial point of the PLS algorithm is the selection of the right number of factors or components (i.e., the determination of the optimal complexity of the system to avoid overfitting). The leave-one-out cross-validation is usually used to determine the optimal complexity of a PLS model, but in practice, it is found that often too many components are retained with this method. In this study, the Monte Carlo Cross-Validation (MCCV) and the PoLiSh smoothed regression are used and compared with the better known adjusted Wold's R criterion.</p

Structural and magnetic behaviour of soft magnetic Finemet-type ribbons

Different kinds of magnetic anisotropies have been induced during the nanocrystallization process of Co- and Ni-rich amorphous ferromagnetic (Finemet) ribbons by the application of a constant stress or an axial magnetic field during the annealing process. Magnetization measurements have evidenced the presence od macroscopic anisotropy in the treated samples. The main goal of this work has been, after a careful DSC study, the structural analysis of the treated ribbons using X-ray Diffraction and Atomic Force Microscopy (AFM), detecting substantial differences in the crystallization state and grain size of the samples depending on the thermal treatment that was carried out. Moreover, AFM measurements revealed in all the treated samples a strong nanocrystallisation of the surface without evidences of amorphous matrix, which contrast with XRD measurements that have shown a high content of amorphous phase in the bulk of the ribbons. Magneto-optical Kerr effect measurements have been performed with the aim to elucidate the complex magnetic behaviour that is expected for the surface of the ribbons, measuring surface hysteresis loops that showed much higher coercive field values than that obtained in the bulk material.Peer reviewe

Standardization of milk mid-infrared spectrometers for the transfer and use of multiple models

An increasing number of models are being developed to provide information from milk Fourier transform mid-infrared (FT-MIR) spectra on fine milk composition, technological properties of milk, or even cows' physiological status. In this context, and to take advantage of these existing models, the purpose of this work was to evaluate whether a spectral standardization method can enable the use of multiple equations within a network of different FT-MIR spectrometers. The piecewise direct standardization method was used, matching “slave” instruments to a common reference, the “master.” The effect of standardization on network reproducibility was assessed on 66 instruments from 3 different brands by comparing the spectral variability of the slaves and the master with and without standardization. With standardization, the global Mahalanobis distance from the slave spectra to the master spectra was reduced on average from 2,655.9 to 14.3, representing a significant reduction of noninformative spectral variability. The transfer of models from instrument to instrument was tested using 3 FT-MIR models predicting (1) the quantity of daily methane emitted by dairy cows, (2) the concentration of polyunsaturated fatty acids in milk, and (3) the fresh cheese yield. The differences, in terms of root mean squared error, between master predictions and slave predictions were reduced after standardization on average from 103 to 17 g/d, from 0.0315 to 0.0045 g/100 mL of milk, and from 2.55 to 0.49 g of curd/100 g of milk, respectively. For all the models, standard deviations of predictions among all the instruments were also reduced by 5.11 times for methane, 5.01 times for polyunsaturated fatty acids, and 7.05 times for fresh cheese yield, showing an improvement of prediction reproducibility within the network. Regarding the results obtained, spectral standardization allows the transfer and use of multiple models on all instruments as well as the improvement of spectral and prediction reproducibility within the network. The method makes the models universal, thereby offering opportunities for data exchange and the creation and use of common robust models at an international level to provide more information to the dairy sector from direct analysis of milk

Nanostructure and magnetic properties of Ni-substituted finemet ribbons

Magnetic anisotropy has been induced during the nanocrystallization process of Ni-rich amorphous ferromagnetic (Finemet) ribbons by means of the application of a constant stress during the annealing process. Magnetization measurements have evidenced the anisotropy of the treated samples. The main goal of this work was the analysis of the treated ribbons using X-ray Diffraction (XRD), Transmission Electronic Microscopy (TEM) and Atomic Force Microscopy (AFM). AFM measurements revealed in all the cases a strong nanocrystallisation of the surface without evidences of amorphous matrix, which contrast with XRD and TEM measurements that have shown a high content of amorphous phase in the bulk of the ribbons. Magneto-optical Kerr effect measurements show much higher coercive field values than in the bulk, indicating a complex magnetic behavior for the surface of the ribbons.Peer reviewe

Magnetic behavior and microstructure of Finemet-type ribbons in both, surface and bulk

Different kinds of magnetic anisotropies have been induced during the nanocrystallization process of Co- and Ni-rich amorphous ferromagnetic (Finemet) ribbons using diverse procedures like the application of a constant stress or an axial magnetic field during the annealing process. Magnetization measurements have evidenced the anisotropy of the treated samples. The main goal of this work has been the structural and microstructural analysis of the treated ribbons using X-ray Diffraction (XRD) and Atomic Force Microscopy (AFM), detecting substantial differences in the crystallization state and grain size of the samples depending on the treatment that was carried out. Moreover, AFM measurements revealed in all the treated samples a strong nanocrystallization of the surface without evidences of amorphous matrix, which contrast with XRD measurements that have shown a high content of amorphous phase in the bulk of the ribbons. Magneto-optical Kerr effect measurements have been performed with the aim to elucidate the complex magnetic behavior that is expected for the surface of the ribbons, measuring surface hysteresis loops that show much higher coercive field values than in the bulk.Peer reviewe

Regression models based on new local strategies for near infrared spectroscopic data

In this work, a comparative study of two novel algorithms to perform sample selection in local regression based on Partial Least Squares Regression (PLS) is presented. These methodologies were applied for Near Infrared Spectroscopy (NIRS) quantification of five major constituents in corn seeds and are compared and contrasted with global PLS calibrations. Validation results show a significant improvement in the prediction quality when local models implemented by the proposed algorithms are applied to large data bases.Fil: Allegrini, Franco. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; ArgentinaFil: Fernández Pierna, J. A.. Walloon Agricultural Research Centre; BélgicaFil: Fragoso, W. D.. Universidade Federal da Paraíba; BrasilFil: Olivieri, Alejandro Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Instituto de Química Rosario. Universidad Nacional de Rosario. Facultad de Ciencias Bioquímicas y Farmacéuticas. Instituto de Química Rosario; ArgentinaFil: Baeten, V.. Walloon Agricultural Research Centre; BélgicaFil: Dardenne, P.. Walloon Agricultural Research Centre; Bélgic