A Green Analytical Method Combined with Chemometrics for Traceability of Tomato Sauce Based on Colloidal and Volatile Fingerprinting

Abstract

Tomato sauce is a world famous food product. Despite standards regulating the production of tomato derivatives, the market suffers frpm fraud such as product adulteration, origin mislabelling and counterfeiting. Methods suitable to discriminate the geographical origin of food samples and identify counterfeits are required. Chemometric approaches offer valuable information: data on tomato sauce is usually obtained through chromatography (HPLC and GC) coupled to mass spectrometry, which requires chemical pretreatment and the use of organic solvents. In this paper, a faster, cheaper, and greener analytical procedure has been developed for the analysis of volatile organic compounds (VOCs) and the colloidal fraction via multivariate statistical analysis. Tomato sauce VOCs were analysed by GC coupled to flame ionisation (GC-FID) and to ion mobility spectrometry (GC-IMS). Instead of using HPLC, the colloidal fraction was analysed by asymmetric flow field-fractionation (AF4), which was applied to this kind of sample for the first time. The GC and AF4 data showed promising perspectives in food-quality control: the AF4 method yielded comparable or better results than GC-IMS and offered complementary information. The ability to work in saline conditions with easy pretreatment and no chemical waste is a significant advantage compared to environmentally heavy techniques. The method presented here should therefore be taken into consideration when designing chemometric approaches which encompass a large number of samples.

Keywords: AF4-multidetection; FFF-chemometrics; Gas Chromatography; asymmetric flow field-flow fractionation AF4; chemometric analysis; food colloids; green analytical methods; ion-mobility spectroscopy; principal component analysis (PCA); volatile compounds (VOC).

Figure 1

Schematics and working mechanism for AF4: (a) schematization of the AF4 channel used for the experiments; and (b) schematization of the two main steps of the Separative Experiment, channel represented as a longitudinal cross section.

Figure 2

The PCA score plots on GC-FID data (without outliers) divided by: (a,b) brand name (i.e., the brand name of the retail product); and (c,d) manufacturers (i.e., the plant where the tomato sauce is manufactured). (a,c): PC1 vs. PC2; (b,d) PC1 vs. PC3.

Figure 3

The PCA score plots (PC1 vs PC2) describing: (a) anufacturer (i.e., the plant where the tomato sauce is manufactured) discriminations for brand 1, and (b) brand (i.e., the brand name of the retail product) discrimination for manufacturer B.

Figure 4

The PCA score plot of the five major brand names of tomato sauce labelled by colour. Letters indicate the corresponding manufacturer (i.e., the plant where the tomato sauce is manufactured).

Figure 5

The PCA score plots on GC-IMS data obtained for tomato sauce divided by: (a) brand names (i.e., the retailer name on the label); and (b) manufacturers (i.e., the plant where the tomato sauce is manufactured).

Figure 6

The GC-IMS 2D plot comparing the volatile content of three pairs of tomato sauce samples: (a) one sample from brand 1 and one sample from brand 6; (b) two samples from brand 3; and (c) two samples from brand 4. Abscissas report the drift times, while ordinates report the retention times. Samples in the left side are at negative values of PC1 in Figure 4. Yellow circles indicate the most discriminative peaks. The peak numbers are referenced to their corresponding molecule attribution in Table 1. Molecules are highlighted only in the sample in which the peak is most evident.

Figure 7

PCA score plots on AF4 data obtained for tomato sauce colloidal fraction divided by: (a) commercial brands (i.e., the retailer name on the label); and (b) manufacturers (i.e., the plant where tomato sauce is manufactured).

Figure 8

The PCA loadings on AF4 data obtained for tomato sauce colloidal fraction of PC1 (black continuous line, range on the left) and PC2 (blue dotted line, range on the right) vs. analysis time. Red dotted lines indicate the separation between three peaks, (I), (II), and (III), corresponding, respectively, to free proteins, small aggregates of proteins, and large colloidal aggregates.

Figure 9

The PCA of FIA, FFIA, and peak area dataset obtained in AF4 for tomato sauce colloidal fraction: (a) score plot (with points coloured by commercial brand); and (b) loading plot.