Influence of fatty acid chain length and unsaturation on mid-infrared milk analysis

Kaylegian, K.E.; Lynch, J.M.; Fleming, J.R.; Barbano, D.M.

doi:10.3168/jds.2008-1910

Cited by 46 publications

(77 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The wavelengths for fat, protein, and lactose measurement used in the current study were the traditional optimized IR sample and reference wavelengths reported by Kaylegian et al (2009). Use of well-defined traditional sample and reference wavelengths is the simplest and most controlled situation in IR milk analysis, and that approach was used in the current study.…”

Section: Practical Implicationsmentioning

confidence: 99%

“…measure SCC using a Fossomatic somatic cell counter (AOAC International, 2000; method 978.26). Infrared milk analysis (AOAC International, 2000; method 972.16) for percent (mass/mass) fat, protein, and lactose was done using the standard optimized traditional optimized sample and reference wavelengths for fat, protein, and lactose analysis as described by Kaylegian et al (2009). Precalibration performance of the mid-IR milk analyzer was evaluated and controlled as described by Barbano and Clark (1989) and Lynch et al (2006).…”

Section: Microbiological and Milk Component Testingmentioning

confidence: 99%

“…The principle of mid-IR milk analysis using discrete wavelengths in the IR spectra has been described previously (Barbano and Clark, 1989). Infrared milk analysis assumes that the compounds in the milk sample are only those that came from the cow and the selection of wavelengths (Kaylegian et al, 2009), and calibrations to measure the fat, protein, lactose, and solids concentrations in raw milk are based on that assumption. This is true for both the older filter instruments and the new Fourier transform mid-IR instruments (FTIR)-based milk analyzers.…”

Section: Introductionmentioning

confidence: 99%

“…This is true for both the older filter instruments and the new Fourier transform mid-IR instruments (FTIR)-based milk analyzers. The FTIR instruments can use either a virtual filter approach with a refined version of the traditional wavelengths that were used in the old filter instruments, as described by Kaylegian et al (2009), or partial lest squares (PLS) calibration prediction models. The effect of other compounds that develop in milk due to microbial growth is less predictable when using PLS models than with the virtual filter approach because the wavelengths used in these statistical prediction models will differ between instrument manufacturers and may be different among instrument models within the same manufacturer.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Effect of microbiological testing on subsequent mid-infrared milk component analysis of the same milk sample

Wojciechowski

Melilli

Barbano

2014

Journal of Dairy Science

View full text Add to dashboard Cite

Our objectives were to determine if mixing and sampling of a raw milk sample at 4°C for determination of total bacteria count (TBC) and if incubation at 14°C for 18h and sampling for a preliminary incubation (PI) count influenced the accuracy of subsequent fat, protein, or lactose measurement by mid-infrared (IR) analysis of milk from the same sample container due to either nonrepresentative sampling or the presence of microbial metabolites produced by microbial growth in the milk from the incubation. Milks of 4 fat levels (2.2, 3, 4, and 5%) reflected the range of fat levels encountered in producer milks. If the portion of milk removed from a cold sample was not representative, then the effect on a milk component test would likely be larger as fat content increases. Within the milks at each fat level, 3 treatments were used: (1) 20 vials of the same milk sampled for testing TBC using a BactoScan FC and then used for a milk component test; (2) 20 vials for testing TBC plus PI count followed by component test; and (3) 20 vials to run for IR component test without a prior micro sampling and testing. This was repeated in 3 different weeks using a different batch of milk each week. No large effect on the accuracy of component milk testing [IR fat B (carbon hydrogen stretch) and fat A (carbonyl stretch)] due to the cold milk sample handling and mixing procedures used for TBC was detected, confirming the fact that the physical removal of milk from the vial by the BactoScan FC (Foss Electric, Hillerød, Denmark) was a representative portion of the milk. However, the representativeness of any other sampling procedure (manual or automated) of a cold milk sample before running milk component testing on the same container of milk should be demonstrated and verified periodically as a matter of routine laboratory quality assurance. Running TBC with a BactoScan FC first and then IR milk analysis after had a minimal effect on milk component tests by IR when milk bacteria counts were within pasteurized milk ordinance limits of <100,000 cfu/mL. Running raw milk PI counts (18h of incubation at 13-14°C) with the BactoScan FC before milk component testing by IR milk analysis had an effect on component tests. The effect was largest on fat test results and would decrease the accuracy of milk payment testing on individual producer milks. The effect was most likely due to the absorption of light by bacterial metabolites resulting from microbial growth or other chemical degradation processes occurring in the milk during the PI count incubation, not by the sampling procedure of the BactoScan. The direction of the effect on component test results will vary depending on the bacteria count and the type of bacteria that grew in the milk, and this could be different in every individual producer milk sample.

show abstract

Section: Practical Implicationsmentioning

confidence: 99%

Section: Microbiological and Milk Component Testingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Effect of microbiological testing on subsequent mid-infrared milk component analysis of the same milk sample

Wojciechowski

Melilli

Barbano

2014

Journal of Dairy Science

View full text Add to dashboard Cite

show abstract

“…During precalibration, the instrument's flow system, homogenizer, repeatability on water, zero shift (cuvette condition), linearity, primary slope, repeatability on milk, purging efficiency, and intercorrection factors are inspected . Proper selection of wavelengths for measurement of fat, protein, and lactose by transmission MIR spectrophotometry was discussed by Kaylegian et al (2009a) and a set of optimized wavelengths and intercorrection factors determined. Use of these wavelengths will give excellent analytical performance when a Fourier transform MIR milk analyzer is set up properly.…”

Section: Improving Mir Instrument Performancementioning

confidence: 99%

A novel statistical approach to detect differences in fat and protein test values among mid-infrared spectrophotometers

Adams

Barbano

2015

Journal of Dairy Science

View full text Add to dashboard Cite

Our objective was to develop a statistical approach that could be used to determine whether a handler's fat, protein, or other solids mid-infrared (MIR) spectrophotometer test values were different, on average, from a milk regulatory laboratory's MIR test values when split-sampling test values are not available. To accomplish this objective, the Proc GLM procedure of SAS (SAS Institute Inc., Cary, NC) was used to develop a multiple linear regression model to evaluate 4 mo of MIR producer payment testing data (112 to 167 producers per month) from 2 different MIR instruments. For each of the 4 mo and each of the 2 components (fat or protein), the GLM model was Response=Instrument+Producer+Date+2-Way Interactions+3-Way Interaction. Instrument was significant in determining fat and protein tests for 3 of the 4 mo, and Producer was significant in determining fat and protein tests for all 4 mo. This model was also used to establish fat and protein least significant differences (LSD) between instruments. Fat LSD between instruments ranged from 0.0108 to 0.0144% (α=0.05) for the 4 mo studied, whereas protein LSD between instruments ranged from 0.0046 to 0.0085% (α=0.05). In addition, regression analysis was used to determine the effects of component concentration and date of sampling on fat and protein differences between 2 MIR instruments. This statistical approach could be performed monthly to document a regulatory laboratory's verification that a given handler's instrument has obtained a different test result, on average, from that of the regulatory laboratory's and that an adjustment to producer payment may be required.

show abstract

Prediction accuracies of cheese-making traits using Fourier-transform infrared spectra in goat milk

Stocco

Dadousis²,

Pazzola³

et al. 2023

Food Chemistry

View full text Add to dashboard Cite

Influence of fatty acid chain length and unsaturation on mid-infrared milk analysis

Cited by 46 publications

References 20 publications

Effect of microbiological testing on subsequent mid-infrared milk component analysis of the same milk sample

Effect of microbiological testing on subsequent mid-infrared milk component analysis of the same milk sample

A novel statistical approach to detect differences in fat and protein test values among mid-infrared spectrophotometers

Prediction accuracies of cheese-making traits using Fourier-transform infrared spectra in goat milk

Contact Info

Product

Resources

About