title: A novel statistical approach to detect differences in fat and protein test values among mid-infrared spectrophotometers.
abstract: 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.
authors.affiliation: Northeast Dairy Foods Research Center, Department of Food Science, Cornell University, Ithaca, NY 14853.
authors.forename: Michael C
authors.initials: MC
authors.lastname: Adams
authors.affiliation: Northeast Dairy Foods Research Center, Department of Food Science, Cornell University, Ithaca, NY 14853. Electronic address: dmb37@cornell.edu.
authors.forename: David M
authors.initials: DM
authors.lastname: Barbano
mesh_terms: D000818:Animals; D016014:Linear Models; D008892:Milk / Q000737:chemistry* / Q000191:economics; D008894:Milk Proteins / Q000032:analysis*; D013055:Spectrophotometry, Infrared / Q000592:standards / Q000662:veterinary*
chemical_list: D008894:Milk Proteins
keywords: least significant difference; mid-infrared; milk payment testing; statistics