The aim of the present study was to determine the effect of dietary cultured wild ginseng root (CWGR) supplementation on goat milk composition and ginsenoside profiles. Sixteen Saanen dairy goats were allocated to two balanced groups based on lactation period, body weight (38.6 ± 3.2 kg), and dairy milk yield (2.85 ± 1.2 kg), and were kept in separate pens. Goats were fed a total mixed ration (TMR) feed (2.3 kg/d, dry matter basis) and 1.5 g of CWGR powder was supplemented in the experimental diet. The total feeding period was 3 weeks, and milk and blood samples were collected on the last three days of the experimental period. There was no effect of CWGR on daily milk yield and milk composition (fat, protein, lactose, and solid-not-fat). However, the CWGR-treatment group had significantly higher plasma IgG and protein contents than the control group (P < 0.05). Significant amounts of ginsenosides were observed in the milk of the CWGR-treatment group, whereas ginsenosides were not detected in the milk of the control group. In conclusion, dietary CWGR was a useful regimen to produce functional goat milk enriched in ginsenosides.

The objective of this study was to develop models for the predict of the milk properties (fat, protein, SNF, lactose, MUN) of unhomogenized milk using the visible and near-infrared (NIR) spectroscopic technique. A total of 180 milk samples were collected from dairy farms. To determine optimal measurement temperature, the temperatures of the milk samples were kept at three levels (5℃, 20℃, and 40℃). A spectrophotometer was used to measure the reflectance spectra of the milk samples. Multilinear-regression (MLR) models with stepwise method were developed for the selection of the optimal wavelength. The preprocessing methods were used to minimize the spectroscopic noise, and the partial-least-square (PLS) models were developed to prediction of the milk properties of the unhomogenized milk. The PLS results showed that there was a good correlation between the predicted and measured milk properties of the samples at 40℃ and at 400∼2,500 nm. The optimal-wavelength range of fat and protein were 1,600∼1,800 nm, and normalization improved the prediction performance. The SNF and lactose were optimized at 1,600∼1,900 nm, and the MUN at 600∼800 nm. The best preprocessing method for SNF, lactose, and MUN turned out to be smoothing, MSC, and second derivative. The Correlation coefficients between the predicted and measured fat, protein, SNF, lactose, and MUN were 0.98, 0.90, 0.82, 0.75, and 0.61, respectively. The study results indicate that the models can be used to assess milk quality.