Identification of Illicit Conservation Treatments in Fresh Fish by Micro-Raman Spectroscopy and Chemometric Methods

Abstract

In the field of food control for fresh products, the identification of foods subjected to illicit conservation treatments to extend their shelf life is fundamental. Fresh fish products are particularly subjected to this type of fraud due to their high commercial value and the fact that they often have to be transported over a long distance, keeping their organoleptic characteristics unaltered. Treatments of this type involve, e.g., the bleaching of the meat and/or the momentary abatement of the microbial load, while the degradation process continues. It is therefore important to find rapid methods that allow the identification of illicit treatments. The study presented here was performed on 24 sea bass samples divided into four groups: 12 controls (stored on ice in the fridge for 3 or 24 h), and 12 treated with a Cafodos-like solution for 3 or 24 h. Muscle and skin samples were then characterized using micro-Raman spectroscopy. The data were pre-processed by smoothing and taking the first derivative and then PLS-DA models were built to identify short- and long- term effects on the fish's muscle and skin. All the models provided the perfect classification of the samples both in fitting and cross-validation and an analysis of the bands responsible for the effects was also reported. To the best of the authors' knowledge, this is the first time Raman spectroscopy has been applied for the identification of a Cafodos-like illicit treatment, focusing on both fish muscle and skin evaluation. The procedure could pave the way for a future application directly on the market through the use of a portable device.

Keywords: Cafodos-like treatment; PLS-DA; chemometrics; food preservation; fresh sea bass; micro-Raman; muscle and skin.

Figure 1

Study design adopted: paired comparisons were carried out. Two fish samples were analyzed each day: one treated and one control fish sample.

Figure 2

Raman spectra of fish muscle (red) and skin (blue) (a); CV% for each Raman shift calculated for 5 spectral replications on muscle (red) and skin (blue) (b).

Figure 3

Results of PCA on the overall dataset in first derivative: scree plot.

Figure 4

Results of PCA on the overall dataset in first derivative: score plots of PC₂ vs. PC₁ (a) and of PC₄ vs. PC₃ (b). Muscle measurements are indicated in red and skin measurements in blue; empty circles = controls at 3 h (18 muscle and 18 skin samples); full circles = 3 h treated (18 muscle and 18 skin samples); empty triangles = controls at 24 h (18 muscle and 18 skin samples); full triangles = 24 h treated (18 muscle and 18 skin samples).

Figure 5

Results of PLS-DA on muscle. Score plot (a) and plot of the coefficients (b) of short-term treatment and long-term treatment (c,d). The short-term score plot reports the scores of each measurement in the space given by the first two latent variables (LVs) calculated; the long-term score plot reports the score of each measurement on the first LV (LV₁) on the y-axis and the measurements on the x-axis. In the score plots the samples are indicated in blue if they are controls and in red if they are treated samples. The plots of the coefficients report the variables included in the final models on the x-axis and the coefficients on the final model on the y-axis. Positive coefficients correspond to variables with a higher signal in the first derivative of treated samples, while negative coefficients correspond to variables with a lower signal in the same situation.

Figure 6

Results of PLS-DA on skin: score plot (a) and plot of the coefficients (b) of short-term treatment and long-term treatment (c,d). The score plots report the scores of each measurement in the space given by the first two latent variables (LVs) calculated, with the samples indicated in blue if they are controls and in red if they are treated samples. The plots of the coefficients report the variables included in the final models on the x-axis and the coefficients on the final model on the y-axis. Positive coefficients correspond to variables with a higher signal in the first derivative of treated samples, while negative coefficients correspond to variables with a lower signal in the same situation.