“Currently, several techniques have been developed to measure fatty acids in milk, including high performance liquid chromatography (HPLC), gas chromatography (GC), near-infrared spectroscopy (NIRS), mid-infrared spectrum (MIRS), etc. [8,9,10]. Chemical methods (e.g., HPLC and GC) provide high measurement accuracy for fatty acid contents of bovine milk, but their pretreatments are multifarious and costly, causing difficulties in realizing the high-throughput measurements [11,12]. Of note, infrared spectroscopy-based measurement methods show advantages of providing rapid and low-cost predictions of milk fatty acid contents [13]; thus, they have become the promising technologies for high-throughput measurements, but they still need to be optimized to improve their prediction accuracy.
The utilization of infrared spectroscopy to predict the milk fatty acid contents in dairy cattle has been reported in many studies. Coppa et al. (2010) established a prediction equation for milk fatty acid contents based on the NIRS from 468 milk samples that predicted the total milk fatty acids, SFA, MUFA, PUFA, C18:1, and conjugated linoleic acid (CLA), with R
2 values greater than 0.88. Soyeurt et al. (2006) developed a fatty acid prediction model using 600 milk samples from 275 cows of 6 breeds to predict C10:0, C12:0, C14:0, C16:0, C16:1cis-9, C18:1, C18:2cis-9, SFA (saturated fatty acids), and MUFA (monounsaturated fatty acids), based on MIRS data, with the cross-validated coefficients of determination (R
2) of 0.62 ~ 0.94. Subsequently, Soyeurt et al. (2011) investigated the MIRS prediction of fatty acids across various cattle breeds, production systems, and countries. They summarized that the usefulness of the built equations providing the best prediction accuracy for animal breeding and milk payment systems was R
2 ≥ 0.75 and 0.95, respectively [
4]. For the Dutch cattle breeds (Dutch Friesian, Meuse-Rhine-Yssel, Groningen White Headed, and Jersey), Maurice-Van Eijndhoven et al. (2020) updated the calibration equations from the European project RobustMilk [
4] using the enlarged datasets and validated their usefulness to predict most milk fatty acids. De Marchi et al. (2011) used 267 milk samples from Brown Swiss cattle to predict fatty acids by MIRS and suggested the implementation of the used prediction models in milk recording schemes on fatty acid contents information for breeding purposes. Fleming et al. (2017) used MIRS to predict fatty acid contents from 373 cows of four breeds and obtained the cross-validation R
2 of 0.60~0.90 for most individual fatty acid models. In addition, the genetic correlations among milk fatty acids predicted by MIRS were also explored in a large-scale milk sampling (
n = 34,141) of New Zealand dairy cattle, where they implied the application of MIRS as the phenotypic proxy for the genetic selection of fatty acid contents [
14]. In the Chinese Holstein population, Du et al. (2020) estimated the heritability of MIRS and several milk production traits, i.e., protein, fat, and lactose percentages, along with their genetic correlations. They found that MIRS heritability ranged from 0 to 0.11 and genetic correlations varied significantly [
15]. In sheep, ewes, and goats, MIRS was also used to predict the fatty acid profiles for the establishment and validation of the predictive models [
16,
17,
18].
“For each milk sample from the 50 mL tubes, 899 raw data points for MIRS values in the complete waveband range of 4000~400 cm−1 were obtained by Bentley spectrometers (Bentley Instruments Inc., Chaska, MN, United States), following the routine methodology (e.g., 30 min preheating and sufficient shaking before operation). Afterwards, additional raw MIRS values, as the measurement replicates, were also obtained using the same milk samples. Finally, two raw MIRS values were transformed by the Fourier method [24] for further pre-processing steps.”
