Detection and quantification of bacterial spoilage in milk and pork meat using MALDI-TOF-MS and multivariate analysis.
Microbiological safety is one of the cornerstones of quality control in the food industry. Identification and quantification of spoilage bacteria in pasteurized milk and meat in the food industry currently relies on accurate and sensitive yet time-consuming techniques which give retrospective values for microbial contamination. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS), a proven technique in the field of protein and peptide identification and quantification, may be a valuable alternative approach for the rapid assessment of microbial spoilage. In this work we therefore developed MALDI-TOF-MS as a novel analytical approach for the assessment of food that when combined with chemometrics allows for the detection and quantification of milk and pork meat spoilage bacteria. To develop this approach, natural spoilage of pasteurized milk and raw pork meat samples incubated at 15 °C and at room temperature, respectively, was conducted. Samples were collected for MALDI-TOF-MS analysis (which took 4 min per sample) at regular time intervals throughout the spoilage process, with concurrent calculation and documentation of reference total viable counts using traditional microbiological methods (these took 2 days). Multivariate statistical techniques such as principal component discriminant function analysis, canonical correlation analysis, partial least-squares (PLS) regression, and kernel PLS (KPLS) were used to analyze the data. The results from MALDI-TOF-MS combined with PLS or KPLS gave excellent bacterial quantification results for both milk and meat spoilage, and typical root mean squared errors for prediction in test spectra were between 0.53 and 0.79 log unit. Overall these novel findings strongly indicate that MALDI-TOF-MS when combined with chemometric approaches would be a useful adjunct for routine use in the milk and meat industry as a fast and accurate viable bacterial detection and quantification method.
School of Chemistry and Manchester Institute of Biotechnology, The University of Manchester, Manchester, United Kingdom.
Nicoletta
N
Nicolaou
Yun
Y
Xu
Royston
R
Goodacre
D000818:Animals; D001419:Bacteria / Q000302:isolation & purification*; D005516:Food Microbiology / Q000379:methods*; D059022:Food Safety / Q000379:methods; D016018:Least-Squares Analysis; D008460:Meat / Q000382:microbiology*; D050296:Microbial Viability; D008892:Milk / Q000382:microbiology*; D015999:Multivariate Analysis; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods*; D013552:Swine