Variation in fat globule size in bovine milk and its prediction using mid-infrared spectroscopy

Abstract

The objectives of this study were to investigate the sources of variation in milk fat globule (MFG) size in bovine milk and its prediction using mid-infrared (MIR) spectroscopy. Mean MFG size was measured in 2,076 milk samples from 399 Ayrshire, Brown Swiss, Holstein, and Jersey cows, and expressed as volume moment mean (D[4,3]) and surface moment mean (D[3,2]). The mid-infrared spectra of the samples and milk performance data were also recorded during routine milk recording and testing. The effects of breed, herd nested within breed, days in milk, season, milking period, age at calving, parity, and individual animal on the variation observed in MFG size were investigated. Breed, herd nested within breed, days in milk, season, and milking period significantly affected mean MFG size. Milk fat globule size was the largest at the beginning of lactation and subsequently decreased. Milk samples with the smallest MFG on average came from Holstein cows, and those with the largest were from Jersey and Brown Swiss cows. Partial least squares regression was used to predict MFG size from MIR spectra of samples with a calibration data set containing 2,034 and 2,032 samples for D[4,3] and D[3,2], respectively. Coefficients of determination of cross validation for D[4,3] and D[3,2] prediction models were 0.51 and 0.54, respectively. The associated ratio of performance deviation values were 1.43 and 1.48 for D[4,3] and D[3,2], respectively. With these models, individual mean MFG size could not be accurately predicted, but results may be sufficient to screen samples for having either small or large MFG on average. Significant but low correlations of D[4,3] and D[3,2] with milk fat yield were estimated (0.16 and 0.21, respectively). Significant and moderate Pearson correlation coefficients for fat percent with D[4,3] and D[3,2] were assessed (0.34 and 0.36, respectively). This correlation was greater between milk fat percentage and predicted MFG size than with measured MFG size with coefficients of 0.47 and 0.49 for D[4,3] and D[3,2], respectively. The MIR prediction equations are potentially overusing the correlation between fat and MFG size and exploiting the strong relationship between the MIR spectra and total milk fat. However, the predictions of MFG size are able to determine variation in mean globule size beyond what would be achieved just by looking at the correlation with fat production.

The effect of physiological state, milk production traits and environmental conditions on milk fat globule size in cow's milk

“…The considerable variation in average MFG size between individual cows is in accordance with previous studies investigating the MFG size distribution within a herd (Logan et al, 2014) or across several herds (Fleming et al, 2017). Logan et al (2014) measured the MFG size distribution within the herd once in spring and twice in autumn over two consecutive years and the average MFG sizes ranged between 2.5 and 5.7 µm.…”

“…A similar effect of parity on MFG size was previously shown in ewes, with first parity animals producing milk with smaller average MFG size (Martini et al, 2004). Conversely, a recent study by Fleming et al (2017) showed no significant impact of parity on MFG size distribution in dairy cows. This extensive study also investigated additive effects of several physiological and environmental variables on MFG size using a linear mixed model.…”

“…Fleming et al . (2017) reported larger average MFG in spring compared to the rest of the year, whereas Logan et al . (2014) showed the opposite with smaller MFG in spring than in autumn.…”

“…A total of 30 cows from the 140 examined in our study were sampled over two consecutive lactation numbers. It is possible that in the study by Fleming et al (2017) the effect of parity was relatively small compared to between-herd and betweenbreed variations, while following the same herd over a one-year period allowed us to capture the independent effect of parity on MFG size.…”

Quantitative Analysis by HPLC and FT-MIR Prediction of Individual Sugars from the Plum Fruit Harvested during Growth and Fruit Development

“…The MIR spectroscopy associated with chemometry has been successfully applied to predict the chemical composition (sugars, organic acids, acidity, dry matter, polyphenols, proteins, humidity, fats, micronutrients, etc.) of various food products, such as: mango juice [9], apple juice [10], or even the whole fruit of apricots [11], or tomatoes [12], wine [13,14], honey [15], leaves and trunk of vine [16], pomegranates [17][18][19], grapes [20], wheat [21], milk [22], lemon [23], black beans [24] and chocolate [25]. The use of FT-MIR for the prediction of sucrose, glucose and fructose was also confirmed by Karoui [26] for plums and by Mureşan [27] for the apple fruit, together with linear discriminant analysis, principal component (PCR) and partial least squares (PLS) regressions.…”