FDSC 6000, Graduate Seminar Series: Tuesday, September 2nd, beginning at 3:35 PM
To join the seminar via Zoom, see details below.
Allison Spillane
PhD Candidate
Barbano Lab
Improved Near Infrared Analysis Method for Bovine Milk
Abstract
Our objective was to determine if the use of near infrared (NIR) milk spectra from a combination of modified milks (with an orthogonal design of main component concentrations) and individual farm milks with all-lab mean (n=8 laboratories) reference chemistry would produce NIR partial least squares prediction models that could achieve the validation accuracy of mid infrared milk analysis. Partial least square prediction models were developed for a commercial near infrared milk analyzer to predict the fat, true protein, anhydrous lactose, and total solids content of homogenized and unhomogenized milk using a modeling population of milks that included orthogonal design modified milks and individual farm milks. A commercial mid-infrared milk analyzer with models for testing homogenized milk was used for a validation performance comparison using a common set of validation samples. The unique aspect of the current study used model development samples and validation samples that had all-lab mean reference chemistry (n=8 laboratories) for each milk sample used in model development and validation. Validation performance of all 3 indirect methods of estimation of milk components were compared. Partial least square models were developed for estimation of fat, true protein and total solids concentration in milk using NIR transmission spectra that had analytical accuracy performance on external validation that was equivalent to MIR transmittance analysis of the same milks. The mean difference and standard error of prediction values for fat, protein, and total solids were in compliance with the expected performance accuracy values indicated in standard methods for examination of dairy products. The accuracy of prediction of fat, true protein and total solids on a weight/weight basis was better than previously published NIR models and that improvement was attributed to the design of the population of milks used for the modeling and the quality of the chemical reference method values derived from all lab mean reference chemistry using AOAC performance validated reference chemistry methods.
Come with your curiosity and your questions!
Respect Statement
Diversity in the field of food science – in race, gender, sex, religion, language, ability, veteran status, place of origin, academic specialization, etc. – is an asset to our learning experience. As a result, we hope to provide an inclusive and welcoming space for our speakers to share their expertise. We want to reaffirm our commitment to speaking respectfully and mindfully to members of our Cornell community as well as our guests and note that individuals identifying with historically minoritized groups should not be expected to describe or provide perspective on these groups unless they themselves volunteer to relate their experiences. We value the scholarship of each of our speakers, and we invite our speakers in order to hear their unique contributions to the field.
Join Zoom Meeting
https://cornell.zoom.us/j/99654894844?pwd=uQES4W0xSNbueJYcTow9iagiKLbSe8.1
Meeting ID: 996 5489 4844
Passcode: 829095
One tap mobile
+16465189805,,99654894844# US (New York)
+16468769923,,99654894844# US (New York)
—
Join by SIP
Join instructions