HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Google Scholar Articles by Al-Qadiri, H. M. Articles by Rasco, B. A. PubMed PubMed Citation Articles by Al-Qadiri, H. M. Articles by Rasco, B. A.

Monitoring Quality Loss of Pasteurized Skim Milk Using Visible and Short Wavelength Near-Infrared Spectroscopy and Multivariate Analysis

H. M. Al-Qadiri^*,1, M. Lin, M. A. Al-Holy, A. G. Cavinato and B. A. Rasco^#

^* Department of Nutrition and Food Technology, Faculty of Agriculture, University of Jordan, Amman 11942, Jordan
Food Science Program, University of Missouri, Columbia 65211-5160
Department of Clinical Nutrition and Dietetics, Faculty of Allied Health Sciences, Hashemite University, Zarqa 13115, Jordan
Chemistry and Biochemistry Program, Eastern Oregon University, One University Blvd., La Grande 97850-2899
^# Department of Food Science and Human Nutrition, Box 646376, Washington State University, Pullman 99164-6376

¹ Corresponding author: h.qadiri{at}ju.edu.jo

Visible and short wavelength near-infrared diffuse reflectance spectroscopy (600 to 1,100 nm) was evaluated as a technique for detecting and monitoring spoilage of pasteurized skim milk at 3 storage temperatures (6, 21, and 37°C) over 3 to 30 h (control, t = 0 h; n = 3). Spectra, total aerobic plate count, and pH were obtained, with a total of 60 spectra acquired per sample. Multivariate statistical procedures, including principal component analysis, soft independent modeling of class analogy, and partial least squares calibration models were developed for predicting the degree of milk spoilage. Principal component analysis showed apparent clustering and segregation of milk samples that were stored at different time intervals. Milk samples that were stored for 30 h or less at different temperatures were noticeably separated from control and distinctly clustered. Soft independent modeling of class analogy analysis could correctly classify 88 to 93% of spectra of incubated samples from control at 30 h. A partial least squares model with 5 latent variables correlating spectral features with bacterial counts and pH yielded a correlation coefficient (R = 0.99 and 0.99) and a standard error of prediction (0.34 log₁₀ cfu/mL and 0.031 pH unit), respectively. It may be feasible to use short wavelength near-infrared spectroscopy to detect and monitor milk spoilage rapidly and noninvasively by correlating changes in spectral features with the level of bacterial proliferation and milk spoilage.

Key Words: short wavelength near-infrared • milk spoilage • pasteurization • spectroscopy