Rapid and quantitative detection of the microbial spoilage of meat by fourier transform infrared spectroscopy and machine learning

Abstract

Fourier transform infrared (FT-IR) spectroscopy is a rapid, noninvasive technique with considerable potential for application in the food and related industries. We show here that this technique can be used directly on the surface of food to produce biochemically interpretable "fingerprints." Spoilage in meat is the result of decomposition and the formation of metabolites caused by the growth and enzymatic activity of microorganisms. FT-IR was exploited to measure biochemical changes within the meat substrate, enhancing and accelerating the detection of microbial spoilage. Chicken breasts were purchased from a national retailer, comminuted for 10 s, and left to spoil at room temperature for 24 h. Every hour, FT-IR measurements were taken directly from the meat surface using attenuated total reflectance, and the total viable counts were obtained by classical plating methods. Quantitative interpretation of FT-IR spectra was possible using partial least-squares regression and allowed accurate estimates of bacterial loads to be calculated directly from the meat surface in 60 s. Genetic programming was used to derive rules showing that at levels of 10(7) bacteria.g(-1) the main biochemical indicator of spoilage was the onset of proteolysis. Thus, using FT-IR we were able to acquire a metabolic snapshot and quantify, noninvasively, the microbial loads of food samples accurately and rapidly in 60 s, directly from the sample surface. We believe this approach will aid in the Hazard Analysis Critical Control Point process for the assessment of the microbiological safety of food at the production, processing, manufacturing, packaging, and storage levels.

FIG. 1.

All 150 HATR absorbance spectra from both fresh and spoiled meats from experiment 1. These illustrate the reproducibility of both HATR FT-IR spectroscopy and the preparation method employed throughout the series of experiments. The amide I and amide II vibrations from proteins and the CH_x vibrations from fatty acids are indicated. The box indicates where the most variance in these spectra occur and hence where spoilage signals are likely to be seen.

FIG. 2.

Typical FT-IR absorbance spectra from pre- and postspoilage chicken. Also shown is the Pearson correlation coefficient (R) between the FT-IR absorbances (in experiments 1 and 2) and the log₁₀(TVC). The asterisks indicate peaks that are attributable to amide I (1,640 cm⁻¹), amide II (1,550 cm⁻¹), and amine (1,240 and 1,088 cm⁻¹) vibrations.

FIG. 3.

Estimates from PLS versus the true log₁₀(TVC). The data points are the averages of the six measurements, and the error bars show standard deviations. The RMS errors in these measurements are 0.15, 0.23, and 0.27 log units for the calibration, cross validation, and independent test sets, respectively.

FIG. 4.

Typical GP tree evolved to discriminate between chicken carrying <10⁷ and ≥10⁷ bacterial counts. The use of the logistic function Pr{Y} = 1/(1 + exp^{(−Model Expression)}), defines a maximum-likelihood (Pr{Y}) decision boundary for an output being either false (0) or true (1).

FIG. 5.

Frequency plot of the number of times an input was used in 10 independent GPs, evolved to discriminate between chicken carrying <10⁷ and ≥10⁷ bacterial counts (encoded as 0 and 1, respectively).

FIG. 6.

Plots of selected IR vibrations versus time for experiment 2. The selected vibrations are amide I (C=O vibration at 1,640 cm⁻¹) and amide II (N—H deformation at 1,550 cm⁻¹) bands and C-N stretching from amines at 1,240 and 1,088 cm⁻¹. Note the increase in the absorbance of 1,088 cm⁻¹ at 17 h, which corresponds to the point at which the onset of spoilage occurs (Table 1).