MID-INFRARED DIFFUSE REFLECTANCE SPECTROSCOPY FOR DISCRIMINATE ANALYSIS OF FOOD INGREDIENTS.

Authors: Reeves Iii, James; ZAPF, CHARLES - MCCORMICK

Submitted to: Journal of Agricultural and Food Chemistry
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/18/1998
Publication Date: N/A
Citation: N/A

Interpretive Summary: Spectroscopy is the study of how light interacts with materials. By using light of specific types (i.e., blue versus red) and examining the interaction with different materials it is often possible to determine the composition of the material in question, or to classify the material (discriminate analysis). Mid-infrared is light beyond the ability of humans to see (what we feel as heat). The objective of this work was to determine the potential of using mid-infrared spectra (graphical representation of interaction between the specific light and a given material) to discriminate among food ingredients. For proper discrimination a product, such as wheat flour, needs to be classified as being a wheat flour and not one of the other possible materials. For this work, 106 samples consisting of buttermilk, dehydrated onion, cheese and milk-egg powders, wheat flours, and two powdered seasonings were examined in the mid-infrared. In general, it was found that correct classifications could be achieved. However, trial and error adjustments were often required for the best results.

Technical Abstract: The objective of this research was to determine if mid-infrared diffuse reflectance spectroscopy could be used to discriminate among various food ingredients. One hundred six samples consisting of buttermilk, cheese, dehydrated onion and milk-egg powders, wheat flours, and two powdered seasonings(cheese and ranch) were scanned as neat powders using diffuse reflectance on a Digi-Lab FTS-60 Fourier transform spectrometer equipped with a KBr beamsplitter, ceramic source, TGS detector, dry air purge and custom made sample transport device, and on a Perkin Elmer Model 2000 Fourier transform spectrometer equipped with a KBr beamsplitter, wire coil source, DTGS detector, sealed bench and stationary cell. Discriminate analysis was performed using "Mahalanobis Distance by Principal Component Analysis" using the "Averaged Predicted Distance" or F-test indicator to select factors obtained from a one-out cross validation analysis. Spectra were pretreated using mean centering and multiplicative scatter correction. Results from the two spectrometers demonstrated that discriminate analysis of food ingredient powders based on spectra of neat powders can be successfully carried out. In general, it was found that results using 4 cm-1 resolution spectra were somewhat superior to those based on lower resolution 16 cm-1,but good results were obtained at either resolution. Using one-out cross validation and all samples, correct classifications could be achieved using factor selection based on average predicted distance or F-test. However, when an independent validation set was used (1/3 of total samples),trial and error adjustment of the number of factors was required for the best results.