Accuracy and application of milk fatty acid estimation with diffuse reflectance near-infrared spectroscopy

Experimental Study on the Transfer of Polychlorinated Biphenyls (PCBs) and Polychlorinated Dibenzo-p-dioxins and Dibenzofurans (PCDD/Fs) into Milk of High-Yielding Cows during Negative and Positive Energy Balance

Dioxin-like polychlorinated biphenyls (dl-PCBs) as well as polychlorinated dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs) are a major concern for food safety, especially in fat-containing foods of animal origin, such as milk. Due to the lipophilic character of PCDD/Fs and PCBs, it is of special interest to explore whether the metabolic state of high-yielding cows influences the transfer rates into milk. Five German Holstein cows were orally exposed to a mixture of 17 PCDD/Fs, 12 dl-PCBs, and 6 non-dioxin-like PCBs (ndl-PCBs) for two dosing periods of 28 days each. The first period covered the negative energy balance (NEB) after calving, while the second period addressed the positive energy balance (PEB) in late lactation. Each dosing period was followed by a depuration period of around 100 days. During the NEB phase, the transfer rates of 14 PCDD/Fs and 7 dl-PCBs quantified were significantly (p ≤ 0.1) higher compared to the PEB phase, indicating an influence of the metabolic state on the transfer. Furthermore, the congener-specific transfer rates (0.3-39%) were in the range of the results from former studies. This indicates that the milk yield of the exposed cows is not the only determining factor for the transfer of these congeners into milk.

Adaptive Spectral Model for abnormality detection based on physiological status monitoring of dairy cows

This research study developed milk spectral data-driven approach, called Adaptive Spectral Model for Abnormality Detection - ASMAD, for detection of physiological abnormalities of individual dairy cows. The algorithm is based on the logic that milk spectra of each individual cow is highly animal-specific, which means it could be used as a respective individual marker for identification. When the algorithm fails to recognize the milk spectra as coming from a certain animal, instead of being treated as a mistake, this outcome is accepted as a deviation of the respective individual marker, and a potential indicator of abnormal physiological state. For the purpose of ASMAD development, near infrared spectra of milk of seven dairy cows have been collected daily during 1-year period. The abnormality detection model is built using supervised recognition method Soft Independent Modeling of Class Analogies - SIMCA, and optimized with respect to spectral pre-processing, choice of the wavelength region and size of the time-window when developing the adaptive model. The sensitivity and specificity of ASMAD were dependent on the animal, and in the ranges 40.00-64.29% and 87.23-98.86%, respectively. Considering significant level of day-to-day spectral variation and multitude of physiological and environmental factors influence on milk constituents and spectra, these results represent a significant potential for creating a health-status monitoring and detection of abnormal physiological states in dairy animals. The adaptive modeling based on the time series of spectral data collected from the individual organism utilized in this work for monitoring physiological status and abnormality detection in dairy cows, has a good potential to be used for similar purposes in other animals and humans.

Real-Time Monitoring of Yogurt Fermentation Process by Aquaphotomics Near-Infrared Spectroscopy

Automated quality control could have a substantial economic impact on the dairy industry. At present, monitoring of yogurt production is performed by sampling for microbiological and physicochemical measurements. In this study, Near-Infrared Spectroscopy (NIRS) is proposed for non-invasive automated control of yogurt production and better understanding of lactic acid bacteria (LAB) fermentation. UHT (ultra-high temperature) sterilized milk was inoculated with Bulgarian yogurt and placed into a quartz cuvette (1 mm pathlength) and test-tubes. Yogurt absorbance spectra (830-2500 nm) were acquired every 15 min, and pH, in the respective test-tubes, was measured every 30 min, during 8 h of fermentation. Spectral data showed substantial baseline and slope changes with acidification. These variations corresponded to respective features of the microbiological growth curve showing water structural changes, protein denaturation, and coagulation of milk. Moving Window Principal Component Analysis (MWPCA) was applied in the spectral range of 954-1880 nm to detect absorbance bands where most variations in the loading curves were caused by LAB fermentation. Characteristic wavelength regions related to the observed physical and multiple chemical changes were identified. The results proved that NIRS is a valuable tool for real-time monitoring and better understanding of the yogurt fermentation process.

Milk as a Complex Multiphase Polydisperse System: Approaches for the Quantitative and Qualitative Analysis

Milk is a product that requires quality control at all stages of production: from the dairy farm, processing at the dairy plant to finished products. Milk is a complex multiphase polydisperse system, whose components not only determine the quality and price of raw milk, but also reflect the physiological state of the herd. Today’s production volumes and rates require simple, fast, cost-effective, and accurate analytical methods, and most manufacturers want to move away from methods that use reagents that increase analysis time and move to rapid analysis methods. The review presents methods for the rapid determination of the main components of milk, examines their advantages and disadvantages. Optical spectroscopy is a fast, non-destructive, precise, and reliable tool for determination of the main constituents and common adulterants in milk. While mid-infrared spectroscopy is a well-established off-line laboratory technique for the routine quality control of milk, near-infrared technologies provide relatively low-cost and robust solutions suitable for on-site and in-line applications on milking farms and dairy production facilities. Other techniques, discussed in this review, including Raman spectroscopy, atomic spectroscopy, molecular fluorescence spectroscopy, are also used for milk analysis but much less extensively. Acoustic methods are also suitable for non-destructive on-line analysis of milk. Acoustic characterization can provide information on fat content, particle size distribution of fat and proteins, changes in the biophysical properties of milk over time, the content of specific proteins and pollutants. The basic principles of ultrasonic techniques, including transmission, pulse-echo, interferometer, and microbalance approaches, are briefly described and milk parameters measured with their help, including frequency ranges and measurement accuracy, are given.

Identification of genetic effects and potential causal polymorphisms of CPM gene impacting milk fatty acid traits in Chinese Holstein

Our previous GWAS revealed 83 significant SNPs and 20 promising candidate genes associated with milk fatty acid traits in dairy cattle. Out of them, the carboxypeptidase M (CPM) gene contains a genome-wide significant SNP, Hapmap49848-BTA-106779, which is strongly associated with myristic acid (C14:0; P = 0.0064). Herein, we aimed to confirm the genetic effects of CPM on milk fatty acids in Chinese Holstein. Seven SNPs were detected by re-sequencing the sequences of entire exons and 3000 bp of up-/downstream flanking regions of the CPM gene, of which three were in 5' flanking region, one in the 3' UTR and three were in the 3' flanking region. Using the Haploview 4.1, we estimated the LD among the identified SNPs and found two haplotype blocks. With the animal model, we performed the SNP- and haplotype-based association analyses, and observed that these SNPs and haplotype blocks mainly had strong genetic associations with medium-chain saturated fatty acids (caproic acid, C6:0; caprylic acid, C8:0; capric acid, C10:0; and lauric acid, C12:0) (P < 0.0001-0.0257). In addition, using the Genomatix software, we predicted that three SNPs in the 5' flanking region of CPM (g.45079507A>G, g.45080228C>A and g.45080335C>G) changed the transcription factor binding sites for PREF (progesterone receptor biding site), ZBRK1 (transcription factor with eight central zinc fingers and an N-terminal KRAB domain), SOX9 (sex-determining region Y-box 9, dimeric binding sites), SOX6 (sex-determining region Y-box 6) and FOXP1-ES (alternative splicing variant of FOXP1, activated in ESCs). Further, the dual-luciferase reporter assay showed these three SNPs altered the transcriptional activity of CPM gene (P ≤ 0.0006). In summary, using the post-GWAS strategy, we first confirmed the significant genetic effects of CPM with milk fatty acids in dairy cattle, and identified three potential causal mutations.

Performance of calibration model with different ratio of sample size to the number of wavelength: Application to hemoglobin determination by NIR spectroscopy

Near infrared spectroscopy is widely used in composition analysis in fields of food, medicines, environment, and so on. The proportion of sample size and the wavelength used is very important for the performance of the calibration model. In this research, we explored the influence of ratio of sample size to the number of wavelength (SWR) on the performance of calibration model, with hemoglobin determination as an example. The results showed that RMSEC increases with the increase of SWR, when SWR is less than 0.5, namely the samples in the calibration set were less than half of the number of wavelengths used in establishing the calibration model, while RMSEP decreases with the increase of SWR. The calibration model was lack of reliability at this range for SWR. RMSEC and RMSEP tend to be stable when SWR value is greater than 0.9. However, in most cases, the samples size was limited, and wavelength selection was commonly used in practical spectroscopy analysis. In order to confirm that the effect of SWR were caused by both sample size and wavelength number, we also studied the performance of calibration model with different WSR. Wavelengths were selected by equidistant combination multiple linear regression (ECMLR) method. The conclusion from results were consistent with the previous part, namely when establishing calibration model, the number of wavelengths used should be less than the twice amount of samples in the calibration set to ensure the validity of the model. We recommend that wavelength selection part was indispensable for small sample size cases. This research can be important evidence and guide for other researches with spectroscopy methods.

Polymorphisms and genetic effects of PRLR, MOGAT1, MINPP1 and CHUK genes on milk fatty acid traits in Chinese Holstein

BACKGROUND

Our initial genome-wide association study (GWAS) identified 20 promising candidate genes for milk fatty acid (FA) traits in a Chinese Holstein population, including PRLR, MOGAT1, MINPP1 and CHUK genes. In this study, we performed whether they had significant genetic effects on milk FA traits in Chinese Holstein.

RESULTS

We re-sequenced the entire exons and 3000 bp of the 5' and 3' flanking regions, and identified 11 single nucleotide polymorphisms (SNPs), containing four in PRLR, two in MOGAT1, two in MINPP1, and three in CHUK. The SNP-based association analyses showed that all the 11 SNPs were significantly associated with at least one milk FA trait (P = 0.0456 ~ < 0.0001), and none of them had association with C11:0, C13:0, C15:0 and C16:0 (P > 0.05). By the linkage disequilibrium (LD) analyses, we found two, one, one, and one haplotype blocks in PRLR, MOGAT1, MINPP1, and CHUK, respectively, and each haplotype block was significantly associated with at least one milk FA trait (P = 0.0456 ~ < 0.0001). Further, g.38949011G > A in PRLR, and g.111599360A > G and g.111601747 T > A in MOGAT1 were predicted to alter the transcription factor binding sites (TFBSs). A missense mutation, g.39115344G > A, could change the PRLR protein structure. The g.20966385C > G of CHUK varied the binding sequences for microRNAs. Therefore, we deduced the five SNPs as the potential functional mutations.

CONCLUSION

In summary, we first detected the genetic effects of PRLR, MOGAT1, MINPP1 and CHUK genes on milk FA traits, and researched the potential functional mutations. These data provided the basis for further investigation on function validation of the four genes in Chinese Holstein.